diff --git a/AUDIT.md b/AUDIT.md index 242aabd..6014ad3 100644 --- a/AUDIT.md +++ b/AUDIT.md @@ -19,10 +19,11 @@ web client (`web-client/`) that was added later for Vercel deployment. ### 2. What state is it in — does anything run end-to-end? Backend runs end-to-end via `python tabvision-server/run.py` (Flask, port 5000). -Most recent eval: 91.6% mean Exact F1 across 11 hand-curated videos (per the -v0 11-video benchmark; details preserved on branch `agent-farm-improvements` -via the v0 dev history). The 20-video training set has lower vanilla-baseline -metrics (~0.43–0.51 mean Exact F1) — see §6 below. +Older v0 personal-video measurements are now invalidated: the 11-video eval +set and private training corpus used private recordings and the tab labels are +not trusted. They were removed from this repo on 2026-06-11; current acceptance +evidence must come from checked-in fixtures and license-checked public/offline +corpora. Most recent in-flight work: Phase 1 audio fine-tune of Basic Pitch on GuitarSet, on branch `feature/audio-finetune-phase1`. **Frozen mid-experiment** @@ -57,15 +58,12 @@ filters tuned to specific failure cases. | Asset | Path | Reuse target | |---|---|---| -| 11 self-recorded eval videos + ground-truth | `test-data/existing/` + tabs files | Spec Phase 1.5 iPhone OOD bonus tier | -| 20 self-recorded training videos | `test-data/training-tabs/` (.txt tab files) + `test-data/existing/` | Phase 1.5 + Phase 7 | | GuitarSet TFRecord splits | `tabvision-server/tools/outputs/tfrecords/guitarset/splits/{train,validation}/` | Phase 7 fine-tuning data (5 train players + 1 validation player) | | Pretrained Basic Pitch weight loader | `tabvision-server/app/training/load_pretrained.py` | Phase 7 (verified equivalent 2026-04-29) | | GuitarSet dataset wrapper | `tabvision-server/app/training/guitarset_dataset.py` | Phase 7 | | Fine-tune training scripts | `tabvision-server/tools/finetune_basic_pitch_{smoke,modal}.py` | Phase 7 reference | | Error analysis harness | `tabvision-server/tools/error_analysis.py` | Phase 8 (deterministic eval harness port) | -| Vanilla Basic Pitch baseline (20-video) | `tabvision-server/tests/fixtures/benchmarks/results/vanilla-baseline-2026-05-01.json` | Phase 1 / Phase 7 reference point | -| Benchmark history (baseline_v1..v3, tuning_v1..v13) | `tabvision-server/tests/fixtures/benchmarks/results/` | Reference; not directly ported | +| Public/offline eval corpora | `$TABVISION_DATA_ROOT/eval/` manifests; not committed | Phase 1.5 / v1.1 replacement source | | 17 design docs in `docs/plans/` | `docs/plans/2026-01-* … 2026-05-*` | Context / cross-references in v1 design doc | ### 5. Branches with abandoned approaches worth revisiting? @@ -131,12 +129,10 @@ filters tuned to specific failure cases. - `eval_basic_pitch_baseline.py` — Phase 1 / Phase 7 baseline - `finetune_basic_pitch_{smoke,modal}.py` — Phase 7 training - `error_analysis.py` — Phase 8 harness -- `build_position_dataset.py`, `dump_position_features.py`, `train_position_selector.py` — NO_SHIP learned-fusion artifacts (preserve for documentation; not ported) **Test fixtures** (`tabvision-server/tests/fixtures/`): - `test_a440.mp4` — single A440 reference clip -- `benchmarks/index.json`, `baseline*.json`, `tuning_v*.json`, `training_baseline.json`, `sample-video-tabs.txt` — eval baselines -- `benchmarks/results/vanilla-baseline-2026-05-01.json` — most recent reference point +- `benchmarks/index.json`, `sample-video-tabs.txt` - legacy sample fixture only; personal-corpus benchmark results were removed. ### Frontend (`tabvision-client/` and `web-client/`) @@ -173,14 +169,12 @@ case). **Concretely verified:** 1. **End-to-end pipeline runs.** `python tabvision-server/run.py` starts Flask - on port 5000; `POST /jobs` → process video → `GET /jobs/:id/result` returns - a TabDocument JSON. Verified by the existence of v0 11-video eval results - averaging 91.6% Exact F1 (per design doc and v0 history). -2. **20-video benchmark harness produces results.** Most recent run: - `tests/fixtures/benchmarks/results/vanilla-baseline-2026-05-01.json` — 20 - training-NN clips run through the current pipeline with full per-clip - metrics breakdown (exact / pitch / position / chord, plus per-error - classification). + on port 5000; `POST /jobs` -> process video -> `GET /jobs/:id/result` returns + a TabDocument JSON. The old personal-video eval results are retained only in + git history and no longer count as validation evidence. +2. **Historical personal-video benchmark harness was removed.** Those + measurements depended on inaccurate personal tab labels and no longer + count as reference evidence. 3. **Phase 1 audio fine-tune scaffolding works.** GuitarSet TFRecords built, pretrained-weight loader verified equivalent to SavedModel (2026-04-29), smoke trainer ran on 5 clips successfully. Five full fine-tune runs @@ -250,8 +244,7 @@ case). | GuitarSet PyTorch wrapper | `tabvision-server/app/training/guitarset_dataset.py` | Phase 7 | | Fine-tune training scripts | `tabvision-server/tools/finetune_basic_pitch_{smoke,modal}.py` | Phase 7 reference | | Error analysis harness | `tabvision-server/tools/error_analysis.py` | Phase 8 (port + harden) | -| 11 + 20 self-recorded videos + tabs | `test-data/{existing,training-tabs}/` | Phase 1.5 (iPhone OOD) | -| Benchmark JSONs | `tabvision-server/tests/fixtures/benchmarks/results/` | Reference baseline | +| Public/offline eval manifests | `$TABVISION_DATA_ROOT/eval/` | Phase 1.5 / v1.1 replacement source | | Design docs | `docs/plans/2026-01-* … 2026-05-*` | Cross-reference in v1 designs | --- @@ -261,40 +254,19 @@ case). **Per SPEC.md §2.1: "Score the baseline pipeline output against the user's reference annotation for that one clip. Record the metrics from §1.4."** -We have richer data than one clip: a recent 20-clip vanilla-baseline JSON -already exists (`tabvision-server/tests/fixtures/benchmarks/results/vanilla-baseline-2026-05-01.json`, -generated 2026-05-01). - -### 20-clip training set (vanilla Basic Pitch + heuristic fusion, 2026-05-01) - -Spot-check from per-clip F1s in the JSON (Exact F1 = string + fret + onset match): - -| Clip | Exact F1 | Pitch F1 | -|---|---:|---:| -| training-01 | 0.42 | 0.68 | -| training-02 (estimate from JSON) | 0.32 | — | -| training-03 (estimate from JSON) | 0.86 | — | -| training-04 (estimate from JSON) | 0.57 | — | -| ... | ... | ... | - -Mean Exact F1 across the 20-clip set is approximately **0.43–0.51** depending -on which harness alignment is used (corrected harness with `_find_best_time_offset` -vs original). - -### 11-clip eval set (best v0 result, 2026-04-02) - -Mean Exact F1: **0.916** across `sample-video, video-3, video-4, video-5, -video-6, video-7, video-8, video-9, video-10, video-11, video-12`. Per-clip -range 0.696 to 0.976. +The personal-video baseline JSONs and tab files were removed on 2026-06-11 +because the labels are not trusted. Use the later public/fixture eval reports +(`docs/EVAL_REPORTS/v1_acceptance_2026-06-03.md` and v1.1 reports) for current +metrics. ### Spec §1.4 targets (for context) | Metric | v0 status | Spec v1 target | |---|---|---| | Onset F1 (50 ms) | unmeasured for §1.4 definition | ≥ 0.92 | -| Pitch F1 (50 ms, no offset) | ~0.68–0.75 (20-clip), better on 11-clip | ≥ 0.90 | -| Tab F1 (string + fret + onset) | 0.43–0.51 (20-clip) / 0.916 (11-clip) | ≥ 0.88 | -| Chord-instance accuracy | low on 20-clip (many 0.0s) | ≥ 0.85 | +| Pitch F1 (50 ms, no offset) | current public/fixture reports only | >= 0.90 | +| Tab F1 (string + fret + onset) | current public/fixture reports only | >= 0.88 | +| Chord-instance accuracy | current public/fixture reports only | >= 0.85 | | End-to-end latency (60 s clip on laptop CPU) | unmeasured | ≤ 5 min | **Interpretation:** v0 already exceeds the aggregate Tab F1 target on the diff --git a/LICENSES.md b/LICENSES.md index 4154310..41f6f26 100644 --- a/LICENSES.md +++ b/LICENSES.md @@ -61,10 +61,11 @@ Phase 0 (this document) produces the initial map; Phase 9 verifies. | Guitar-TECHS | Phase 0 (eval) / 1.5 / 7 | CC-BY-4.0 (Zenodo record 14963133) | ✅ eval-only | arXiv:2501.03720 — 3 electric guitarists, 5h12m multi-mic + DI; per-string 6-track MIDI. **Acquirer landed** (`scripts.acquire.datasets guitar-techs`, Zenodo API). **Scanner landed** (`manifest_builder.scan_guitar_techs` → `clean_electric` tier) — layout *inferred*, verify against first real download. Not redistributed here; required attribution must appear in the public README. | | IDMT-SMT-Guitar | 1.5 / 7 | research-use, registration | ⚠️ | Training-only; not redistributed in our repo. Verified 2026-05-13 research pass; superseded by Guitar-TECHS for v1 acceptance — kept for potential future training augmentation. | | EGDB | 1.5 / 7 / Phase 0 (eval) | **author-granted use (2026-06-01)** | ✅ eval-only | https://ss12f32v.github.io/Guitar-Transcription/ — 240 tracks, ~12h with multi-amp electric variants, GuitarPro tabs + aligned MIDI. **Access is open** — the audio is a public Google Drive folder linked from the project page; the *license* was the only gate (the repo has no LICENSE file → default all-rights-reserved). Author (`f08946011@ntu.edu.tw`) granted portfolio use 2026-06-01. **ACTION REQUIRED: save the grant email under `docs/` (e.g. `docs/licenses/egdb-grant-2026-06-01.eml`) and log it in `docs/DECISIONS.md` — the written grant is the only evidence the gate cleared (SPEC §1.4 hard rule).** Treated like GuitarSet: held-out distorted-electric eval source, **not redistributed** here and **not a shipped-weight substrate** unless the grant explicitly permits portfolio distribution. If the grant is research-only, it remains an eval gate only. | -| ~~GOAT~~ | DROPPED | request-only, research-only | ❌ | arXiv:2509.22655. Verified 2026-05-13: distribution gated per-use ("for research purposes only, upon request") due to copyrighted cover-song content. Not portfolio-compatible per SPEC §1.5; removed from the eval composite. | +| GAPS | v1.1 optional real-video/audio research eval | CC-BY-NC-SA-4.0 | ⚠️ eval-only | Zenodo 10.5281/zenodo.13962272. 14h of real classical guitar audio-score aligned pairs with high-resolution MIDI alignments and performance-video links. Do not commit or redistribute media; use only for offline research metrics with attribution, and keep NC data out of shipped weights/default artifacts. | +| ~~GOAT~~ | DROPPED from default pipeline; candidate only if access/license changes | request-only, license pending | ❌ | arXiv:2509.22655 / GOAT-Dataset. The paper describes DI electric guitar audio plus amp-rendered variants annotated with string/fret tablatures, but dataset access is by request and must be rechecked before any use. Not portfolio-compatible until explicit access and dataset license terms are saved. | | ~~SynthTab~~ | DROPPED from default pipeline | dataset CC-BY-NC-4.0 (code CC-BY-4.0) | ❌ | github.com/yongyizang/SynthTab. Dataset NC clause taints derived weights (SynthTab paper treats trained models as derivative work). Not portfolio-compatible per SPEC §1.5; removed from the planned pretrain pipeline 2026-05-13. The repo code (Apache/CC-BY) remains MIT-style usable for our own renderers if needed. | | DadaGP | research/dev only — **not in default pipeline** | access-by-email; underlying GP tabs derive from copyrighted songs | ⚠️ | https://github.com/dada-bots/dadaGP. Per 2026-05-13 design plan §4.2, acceptable as internal training augmentation only. Synthetic-source clips are blocked from non-train manifest splits by `tabvision.eval.manifest.validate_manifest` (the `SYNTHETIC_IN_EVAL_SPLIT` guard). | -| ~~User clips (the 20 self-recorded set)~~ | BANNED | self-owned | ⛔ | Banned from all roles per 2026-05-13 design plan D10 — not as accuracy gate, dev set, or label source. Replaced by the public-corpus composite. | +| ~~User clips (the private eval + training corpus)~~ | BANNED | self-owned | ⛔ | Banned from all roles per 2026-06-11 cleanup: not as accuracy gate, dev set, label source, or historical benchmark source. The tracked tabs and stale result artifacts were removed; replace with GuitarSet / Kaggle UT-Austin / GAPS-style offline public corpora depending on the eval tier. | | Roboflow `b101/guitar-3` | 3 (training) | **CC BY 4.0** | ✅ | **Verified 2026-05-05.** Source: https://universe.roboflow.com/b101/guitar-3. Forked into Patrick's workspace as `patricks-workspace-vozcg/guitar-3-4efcd` v2; YOLOv8-OBB export downloaded (926 images, 710/144/72 split, classes: fret / neck / nut). License declared in the dataset's README.dataset.txt: "License: CC BY 4.0". Attribution: "guitar 3" by b101 on Roboflow Universe (https://universe.roboflow.com/b101/guitar-3), CC BY 4.0; export downloaded May 5, 2026 via the Roboflow SDK. **Required attribution must appear in the public README and any blog post.** | ## Library dependencies (default pipeline) diff --git a/SPEC.md b/SPEC.md index befdacf..e6b507d 100644 --- a/SPEC.md +++ b/SPEC.md @@ -367,7 +367,7 @@ tabvision/ ├── data/ │ ├── README.md # how to acquire (do not commit large files) │ ├── fixtures/ # tiny clips checked in for unit tests -│ ├── eval/ # held-out user-recorded clips + annotations +│ ├── eval/ # public/offline eval manifests + annotations │ └── augmented/ # generated; .gitignored ├── scripts/ │ ├── acquire/ # one script per dataset/model in §6 @@ -579,12 +579,12 @@ flowchart TD 5. `tabvision.render.ascii` producing readable ASCII tab. 6. `tabvision` CLI: `tabvision transcribe input.mov -o output.tab`. 7. End-to-end integration test on a fixture clip. -8. First eval-harness run on user clips — record metrics, **even if bad**. +8. First eval-harness run on public/offline clips - record metrics, **even if bad**. **Acceptance test:** - `tabvision transcribe data/fixtures/scale_clean.wav` produces non-empty ASCII tab. - Integration test passes. -- Eval harness reports **any** numbers on at least 3 user clips. Metrics logged to `docs/EVAL_REPORTS/phase1_.md`. +- Eval harness reports **any** numbers on at least 3 public/offline clips. Metrics logged to `docs/EVAL_REPORTS/phase1_.md`. **Acceptance does NOT require** good metrics. The point is the harness, not the score. @@ -605,15 +605,15 @@ flowchart TD ### Phase 1.5 — Annotation tool & eval set -**Goal:** Build the user-recorded eval set so subsequent phases have something to optimize against. +**Goal:** Build a public/offline eval manifest so subsequent phases have something to optimize against without private home-video labels. **Deliverables:** 1. `scripts/annotate/cli.py` — interactive annotator. Plays clip, shows candidate onsets, lets user correct pitch/string/fret per onset, writes `.jams`. -2. 15 user-recorded clips with annotations under `$TABVISION_DATA_ROOT/eval/`. -3. Manifest `data/eval/manifest.toml` listing clips + checksums + difficulty tier (easy/med/hard). +2. Public/offline corpus adapters with annotations under `$TABVISION_DATA_ROOT/eval/`. +3. Manifest `data/eval/manifest.toml` listing clips + checksums + source, license, split, and difficulty tier (easy/med/hard). **Acceptance test:** -- 15 annotated clips exist. +- Public/offline annotated clips exist for each enabled eval tier. - `pytest -m eval` runs end-to-end. - A baseline metrics report is generated and checked in. @@ -777,7 +777,7 @@ flowchart TD 5. Tunable mixing weight λ_v between audio and vision evidence (CLI: `--fusion-lambda-vision 1.0`). **Acceptance test:** -- On user eval set, **Tab F1 ≥ 0.85** (target: 0.88 by Phase 9). +- On enabled public/offline eval set, **Tab F1 >= 0.85** (target: 0.88 by Phase 9). - Chord-instance accuracy ≥ 0.80 (target: 0.85 by Phase 9). - Ablation report: audio-only vs. audio+vision. The audio+vision configuration must beat audio-only by **≥ 8 points** on Tab F1. @@ -817,8 +817,8 @@ flowchart TD **Acceptance test:** - All four formats round-trip on snapshot fixtures. -- A user-recorded clip's output `.gp5` opens correctly in TuxGuitar (free) and Guitar Pro 7 if available. -- A user-recorded clip's output `.mid` plays back at correct pitches and timing in any standard MIDI player. +- A fixture or public/offline eval clip's output `.gp5` opens correctly in TuxGuitar (free) and Guitar Pro 7 if available. +- A fixture or public/offline eval clip's output `.mid` plays back at correct pitches and timing in any standard MIDI player. **Decision tree:** @@ -848,16 +848,16 @@ flowchart TD - **Distorted-electric oversampling.** Distorted variants are rendered with multiple amp + cab IR pairs and oversampled in fine-tune batches relative to their share of the source set, since the §1.4 distorted-electric tier target is the most likely to be the long-pole. 2. **Video augmentation pipeline** (`scripts/augment/video.py`): - - Source: user-recorded clips (eval set + any additional training clips the user has recorded). + - Source: license-checked public/offline video corpora only; personal recordings are not an accuracy gate, dev set, or label source. - Augmentations: color jitter, brightness/contrast, hue shift, slight perspective warp (±5°), slight rotation, motion blur, simulated lens distortion, time-domain crops, frame dropout to simulate FPS variation. - Aligned annotations preserved (transformed where appropriate — e.g., perspective warp updates the homography ground truth so the fretboard regressor sees consistent labels). 3. **Fine-tuning recipes** (`scripts/train/audio_finetune.py`, `scripts/train/hand_finetune.py`): - - Audio: fine-tune the chosen audio backend on the augmented DadaGP set + user clips. Distorted-electric variants oversampled. Run on Kaggle T4 (or Lightning Studios for longer jobs). - - Vision: fine-tune the fingertip-to-fret head on augmented user clips. The fretboard detector and guitar detector are normally good after Phase 3 and rarely need refining in Phase 7; if Phase 5 fusion exposes systematic vision errors, retrain those too. + - Audio: fine-tune the chosen audio backend on the augmented DadaGP set plus license-checked public/offline corpora. Distorted-electric variants oversampled. Run on Kaggle T4 (or Lightning Studios for longer jobs). + - Vision: fine-tune the fingertip-to-fret head on augmented public/offline video corpora. The fretboard detector and guitar detector are normally good after Phase 3 and rarely need refining in Phase 7; if Phase 5 fusion exposes systematic vision errors, retrain those too. 4. **Self-labeling loop** (`scripts/train/self_label.py`): - - Run pipeline on a corpus of unlabeled user clips → pipeline emits TabEvents with confidences. + - Run pipeline on a corpus of unlabeled public/offline clips -> pipeline emits TabEvents with confidences. - High-confidence outputs (per-note confidence > 0.9) are auto-accepted as pseudo-labels. - Low-confidence outputs are flagged for the user via the Phase 1.5 annotator; user corrects. - Both auto-labels and corrected-labels are added to the training set with appropriate weights (corrected: 1.0, auto: 0.5). @@ -1109,7 +1109,7 @@ A single source of truth so Claude Code does not drift: |---|---|---| | 0 | `AUDIT.md` exists; CI green; `LICENSES.md` initial map | `make ci` | | 1 | `docs/EVAL_REPORTS/phase1_*.md` exists | `pytest -m integration && pytest -m eval -k phase1` | -| 1.5 | 15 user clips in manifest, all 4 tiers represented | `tabvision-eval --manifest data/eval/manifest.toml --check` | +| 1.5 | Public/offline clips in manifest, enabled tiers represented | `tabvision-eval --manifest data/eval/manifest.toml --check` | | 2 | Highres beats baseline ≥ 5 pts Pitch F1 | `pytest -m eval -k phase2 --compare baseline` | | 3 | Guitar IoU ≥ 0.95; preflight ≥ 9/10; homography median error < 5 px | `pytest -m guitar_eval -m preflight_eval -m fretboard_eval` | | 4 | Fingertip top-1 ≥ 0.75 on 100 labeled frames | `pytest -m hand_eval` | diff --git a/Userspatridc.json b/Userspatridc.json new file mode 100644 index 0000000..d3b1c0f --- /dev/null +++ b/Userspatridc.json @@ -0,0 +1 @@ +{"data":{"id":"10.5281/zenodo.7544110","type":"dois","attributes":{"doi":"10.5281/zenodo.7544110","prefix":"10.5281","suffix":"zenodo.7544110","identifiers":[{"identifier":"oai:zenodo.org:7544110","identifierType":"oai"}],"alternateIdentifiers":[{"alternateIdentifierType":"oai","alternateIdentifier":"oai:zenodo.org:7544110"}],"creators":[{"name":"Kehling, Christian","nameType":"Personal","givenName":"Christian","familyName":"Kehling","affiliation":["Fraunhofer IDMT"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-7828-503X","nameIdentifierScheme":"ORCID"}]},{"name":"Männchen, Andreas","nameType":"Personal","givenName":"Andreas","familyName":"Männchen","affiliation":["Fraunhofer IDMT"],"nameIdentifiers":[]},{"name":"Eppler, Arndt","nameType":"Personal","givenName":"Arndt","familyName":"Eppler","affiliation":["Fraunhofer IDMT"],"nameIdentifiers":[]}],"titles":[{"title":"IDMT-SMT-Guitar Dataset"}],"publisher":"Zenodo","container":{"type":"DataRepository","identifier":"https://zenodo.org/communities/mir","identifierType":"URL"},"publicationYear":2023,"subjects":[{"subject":"Guitar Transcription"},{"subject":"monophonic"},{"subject":"polyphonic"},{"subject":"Chord Recognition"},{"subject":"Rythm Style Estimation"},{"subject":"Expression Style"},{"subject":"Plucking Style"}],"contributors":[],"dates":[{"date":"2023-01-17","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.7544109","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0.0","rightsList":[{"rights":"Creative Commons Attribution Non Commercial No Derivatives 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-nd-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The IDMT-SMT-GUITAR database is a large database for automatic guitar transcription. Seven different guitars in standard tuning were used with varying pick-up settings and different string measures to ensure a sufficient diversification in the field of electric and acoustic guitars. The recording setup consisted of appropriate audio interfaces, which were directly connected to the guitar output or in one case to a condenser microphone. The recordings are provided in one channel RIFF WAVE format with 44100 Hz sample rate.\n\nThe dataset consists of four subsets. The first contains all introduced playing techniques (plucking styles: finger-style, muted, picked; expression styles: normal, bending, slide, vibrato, harmonics, dead-notes) and is provided with a bit depth of 24 Bit. It has been recorded using three different guitars and consists of about 4700 note events with monophonic and polyphonic structure. As a particularity the recorded files contain realistic guitar licks ranging from monophonic to polyphonic instrument tracks.\n\n\nThe second subset of data consists of 400 monophonic and polyphonic note events each played with two different guitars. No expression styles were applied here and each note event was recorded and stored in a separate file with a bit depth of 16 Bit. The parameter annotations for the first and second subset are stored in XML format.\n\n\nThe third subset is made up of five short monophonic and polyphonic guitar recordings. All five pieces have been recorded with the same instrument and no special expression styles were applied. The files are stored with a bit depth of 16 Bit and each file is accompanied by a parameter annotation in XML format.\n\n\nAdditionally, a fourth subset is included, which was created for evaluation purposes in the context of chord recognition and rhythm style estimation tasks. This set contains recordings of 64 short musical pieces grouped by genre. Each piece has been recorded at two different tempi with three different guitars and is provided with a bit depth of 16 Bit. Annotations regarding onset positions, chords, rhythmic pattern length, and texture (monophony/polyphony) are included in various file 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\ No newline at end of file diff --git a/docs/DECISIONS.md b/docs/DECISIONS.md index a9435eb..65d1293 100644 --- a/docs/DECISIONS.md +++ b/docs/DECISIONS.md @@ -16,6 +16,91 @@ Format: --- +## 2026-06-17 — Chunk 4 complete: highres cross-corpus diagnosis on Guitar-TECHS + +**Phase:** v1.1 audio transcription/alignment (chunk 4) +**Decision tree:** Second-corpus gate — UT-Austin-specific alignment vs highres +note grouping vs broader cross-corpus transcription behavior +**Branch taken:** Keep `highres`; do not switch audio models. Attribute the +residual Tab F1 ceiling to the audio-only string-resolution limit (the v1.1 +video chain's job), and the UT-Austin raw-audio collapse to corpus-specific +alignment — completing and superseding the 2026-06-11 "validate on a second +dataset" decision below. +**Evidence:** Full 12-clip Guitar-TECHS chord run via the new cached runner +`scripts/eval/v1_1_second_corpus_probe.py` (`highres`, `--position-prior none`, +`--splits train`, no pitch/time calibration): onset F1 `0.7321`, pitch F1 +`0.6787`, Tab F1 `0.0700` (lower-95 `0.0377`), chord acc `0.0207` across +1292 notes; decomposition wrong_position_same_pitch `788` (43.4%) + +extra_detection `634` (34.9%) dominate. Reports: +`docs/EVAL_REPORTS/v1_1_highres_guitartechs_chords_2026-06-17.md` and +`..._decomposition_2026-06-17.md`. Contrast: UT-Austin raw highres (onset `0.04`, +pitch `0.00`) and global-calibrated (onset `0.4598`, pitch `0.3613`, Tab +`0.1415`, oracle-video `0.3535`) in +`docs/EVAL_REPORTS/v1_1_audio_alignment_probe_2026-06-11.md`. +**Reasoning:** Uncalibrated highres lands onsets/pitches on a second corpus +(0.73/0.68) where UT-Austin raw is ~0, so highres is not globally broken and the +UT-Austin failure is corpus-specific tuning/time-origin alignment (22/24 clips +prefer a −1 semitone shift). The Tab F1 ceiling reproduces the +`wrong_position_same_pitch` string-ambiguity shape on both corpora (SPEC +§1.4.1), so the largest lever is the v1.1 video string-resolution chain, not an +audio-model switch. Honesty bounds: 0.68 pitch still fails the 0.90 audio gate +(electric-domain penalty — "not broken," not "acceptable"); Guitar-TECHS is +electric / out-of-domain / n=12, a diagnostic and not an acceptance baseline; and +extra_detection (35%) is chord over-detection that video does not directly fix. + +--- + +## 2026-06-11 - Keep highres; validate on a better second dataset + +**Phase:** v1.1 audio transcription/alignment +**Decision tree:** User direction after chunk-4 audio-alignment probe +**Branch taken:** Keep `highres` as the active audio model and pause model-switch +work. Test `highres` against a stronger second corpus before drawing more +conclusions from the UT-Austin real-audio failure. +**Evidence:** User instruction on 2026-06-11: "Keep highres. Do not switch audio +models yet. Test highres against a better second dataset." New smoke artifact: +`tabvision/data/eval/guitartechs_highres_smoke.toml` and +`docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_2026-06-11.md`, run with +`--backend highres --position-prior none`, scored one Guitar-TECHS direct-input +clip at onset F1 `0.7187`, pitch F1 `0.6562`, Tab F1 `0.0000`. The paired +decomposition report shows 18 wrong-position-same-pitch, 5 pitch-off, 1 +timing-only, 3 missed-onset, and 13 extra-detection errors. A same-settings +12-clip Guitar-TECHS chord run exceeded the 30-minute local interactive budget +before writing a report; older full Guitar-TECHS evidence remains in +`docs/EVAL_REPORTS/local_guitartechs_noprior.md`. +**Reasoning:** UT-Austin remains useful for video string/fret evidence, but its +real-audio path has corpus-specific pitch/time alignment issues. Guitar-TECHS is +local, public, CC-BY-4.0, and has per-string MIDI labels, so it is the best +runnable second-corpus audio check today even though it is electric and +out-of-domain for the GAPS-trained highres checkpoint. Keep highres while adding +cross-corpus evidence; revisit GAPS as the stronger real-performance offline +eval when data acquisition and parsing are wired. + +--- + +## 2026-06-11 - Remove private video/tab corpus from v1.1 evidence + +**Phase:** v1.1 video string-resolution / eval data +**Decision tree:** Dataset replacement after user correction +**Branch taken:** Remove the private eval and training/tab +corpora from tracked data, benchmark fixtures, and active documentation. Use +license-checked public/offline sources by role: GuitarSet for clean audio/tab +labels, Kaggle UT-Austin for current real-video string/fret eval, and GAPS as +the best optional real performance video/audio replacement when NC offline eval +is acceptable. Keep GOAT as a candidate only if access and dataset license terms +are explicitly verified. +**Evidence:** User correction on 2026-06-11 that the private recordings/tabs +are inaccurate; v1.1 chunk-3 report +`docs/EVAL_REPORTS/v1_1_chunk3_real_video_robustness_2026-06-11.md` shows +video robustness improved gold-pitch Tab F1 0.4243 -> 0.5453 while real highres +audio remains 0.0583 -> 0.0657 with oracle ceiling 0.1959. +**Reasoning:** The old personal labels create misleading metrics and are not a +reproducible portfolio gate. Public/offline corpora keep the project auditable, +license-trackable, and comparable, while chunk-3 says the next accuracy work +should target audio transcription/alignment before adding more video fusion. + +--- + ## 2026-05-13 — Tab F1 v1 acceptance: per-tier targets + public-corpus composite **Phase:** Accuracy work (cross-cuts Phases 1, 2, 3, 5, 7, 8 of the SPEC) @@ -147,16 +232,16 @@ doc §8. The frozen branch state is git-recoverable indefinitely. **Phase:** 1.5 (recorded at Phase 0 for reference) **Decision tree:** spec adoption / Phase 1.5 scope (design doc §6) -**Branch taken:** **Use existing public datasets + already-recorded historical -clips only.** Drop the spec's "15+ new user-recorded clips" requirement. -Eval split: GuitarSet (clean acoustic), IDMT-SMT-Guitar (clean electric), -EGDB (distorted electric), existing 11/20 self-recorded videos (iPhone OOD -bonus tier). +**Branch taken:** **Use existing public datasets only.** Superseded on +2026-06-11 for private recordings: the historical private corpus was removed +because the labels are not trusted. Current eval split: GuitarSet (clean +acoustic), public/offline electric corpora, Kaggle UT-Austin for current +real-video string/fret eval, and GAPS as optional NC offline video/audio eval. **Evidence:** Design doc §6 (revised Phase 1.5 table). **Reasoning:** User declined to record new clips during brainstorm. Existing -historical self-recordings preserve iPhone-domain ground truth without new -recording effort. Acknowledged blind spot: distorted-electric tier is -measured on EGDB studio data, not iPhone-recorded distortion. +public/programmatic sources preserve reproducibility and keep the release story +auditable. Private recording labels are no longer trusted as validation data. +Known blind spot: any studio-domain electric metric must say so explicitly. --- @@ -345,7 +430,7 @@ the box quality is good across IoU thresholds, not just the lenient keypoint fretboard backend will have plenty of fret OBBs to work with. **Effect:** Phase 3 detector deliverable is acceptance-passed. The remaining acceptance pieces (preflight 9/10 on labeled framing set; -fretboard ≤ 5 px median homography error on 5 user clips) are blocked +fretboard <= 5 px median homography error on license-checked public/offline clips) are blocked on hand-labeled ground-truth data, not on detector quality. --- @@ -355,15 +440,14 @@ on hand-labeled ground-truth data, not on detector quality. **Phase:** 3 (acceptance) → 4 (entry) **Decision tree:** SPEC §7 Phase 3 acceptance checklist (preflight ≥ 9/10 on labeled good/bad framing set; fretboard ≤ 5 px median homography -error on 5 user clips). +error on license-checked public/offline clips). **Branch taken:** **Defer the two label-dependent gates; advance to Phase 4.** The detector gate (the one capacity-limited deliverable) already passed at `neck mAP50 = 0.995`. The remaining gates are data-collection tasks, not engineering tasks. **Reasoning:** Engineering Phase 4 in parallel with the hand-labeling -work is strictly faster than blocking on it. Pat will come back and -collect the ground truth (5 clips × 4 fret-intersection clicks; ~10 -clips of intentionally good vs bad framing) before Phase 9 hardening. +work is strictly faster than blocking on it. The remaining labels must come +from license-checked public/offline clips before any future hard gate. The Phase 3 code is stable and won't drift while the labels are being collected — re-running the eval is a one-line pytest invocation when the fixtures arrive. @@ -371,8 +455,8 @@ the fixtures arrive. - Build the labeling harness (a small click-to-mark-fret-intersections tool — tkinter or web — saving JSON; plus a "framing: good/bad" classifier doc). -- Collect 5 user clips × 4 fret-intersection clicks each (frets 5 + 12, - top + bottom edges) for the fretboard gate. +- Collect/license-check public/offline clips with fret-intersection labels for + the fretboard gate. - Collect ~10 clips intentionally framed well and badly (off-centre, partially occluded, dim lighting, oblique angle) for the preflight gate. - Wire the two pytest harnesses (`-m fretboard_eval`, `-m preflight_eval`) @@ -407,7 +491,7 @@ homography that didn't exist in v0's coupled Hough pipeline) and the per-cell posterior over (string, fret). Net new code is the projection + posterior layer; v0's MediaPipe plumbing is wrapped, not rewritten. **Open questions (will be resolved by acceptance run):** -- Distance kernel σ for the fingertip-to-cell prior; default = 0.5 +- Distance kernel σ for the fingertip-to-cell prior; default = 0.5 fret-widths, will calibrate against the 100-frame labeled set. - Whether the curl prior helps or hurts; ablation will tell us. - Fretting-hand identification — start with v0's handedness logic; @@ -532,7 +616,7 @@ remaining data-bound acceptance debt. **Phase:** Remaining v1 / release hardening **Decision tree:** Remaining-plan cleanup after Phase 8 smoke report -**Branch taken:** **Remove manual work from v1 gates.** Phase 1.5 user-recorded +**Branch taken:** **Remove manual work from v1 gates.** Phase 1.5 private-corpus manifest completeness, Phase 3 preflight labels, Phase 3 fretboard click labels, Phase 4 fretting labels, manual dataset downloads, new recordings, and user-corrected self-labeling are now `removed_from_v1` or `optional_future`. @@ -540,8 +624,8 @@ user-corrected self-labeling are now `removed_from_v1` or `optional_future`. that requires manual annotation or work." Current automated baseline is green: `pytest -q` reported `272 passed, 12 skipped` before this change, and the Phase 8 smoke runner already exercises report generation without external data. -**Reasoning:** Manual annotation and private home-video assets are valuable for -future validation but make v1 unshippable as a reproducible portfolio artifact. +**Reasoning:** Manual annotation and the retired private media corpus are useful +historical context but make v1 unshippable as a reproducible portfolio artifact. The remaining release plan must depend on automated evidence only: deterministic smoke fixtures, checked-in fixtures, public/programmatic datasets such as GuitarSet, existing Modal/public-data reports, license policy checks, @@ -730,3 +814,64 @@ to offline eval use. Remaining work is purely the MediaPipe CV chain (chunk 2: d hand/fretboard detection on this footage produce good fingerings) + the real-audio eval (chunk 3). Caveats: single-source student dataset (a proof, not a robust headline); do not commit the data; revisit if TabVision is ever commercialised. + +## 2026-06-11 — v1.1 chunk-3 video robustness complete; highres audio is the current bottleneck + +**Phase:** v1.1 (video string-resolution) — chunk 3 +**Decision tree:** v1.1 design §9 ("Video regresses audio-only tiers → the +confidence gate (§5.3) is mis-tuned; it must collapse to weight 0") +**Branch taken:** Make orientation and confidence gating automatic before judging +the real-video chain. Keep the gate conservative by default: per-event evidence +must support the audio pitch, and clips with less than 71% surviving video coverage +fall back to audio-only. + +**Evidence:** `docs/EVAL_REPORTS/v1_1_chunk3_real_video_robustness_2026-06-11.md`, +`tabvision/fusion/vision_evidence.py`, `scripts/eval/v1_1_real_chain_probe.py`. +- Gold-pitch real-video eval (24 scored clips / 527 notes): audio-only **0.4243** + → audio+real-video **0.5453**, oracle **1.0000**, no per-clip regressions. +- Real highres WAV eval now runs with pitch/time calibration: highres audio-only + **0.0583** → audio+real-video **0.0657**, oracle-video ceiling **0.1959**. +- The highres wrapper needed a Windows stdio encoding guard because + `hf_midi_transcription` prints a Unicode status glyph during checkpoint download. + +**Reasoning:** Chunk 2's manual `--flip-fret --flip-string` was real but +rig-specific. Chunk 3 resolves that by selecting orientation from audio-compatible +candidate support, then voting multiple nearby frame posteriors and scaling/skipping +video by confidence. The conservative 0.71 clip-coverage floor is intentionally a +no-regression gate: sparse evidence should become no evidence. The real highres +number is low even with oracle video, so further video-gate tuning is the wrong next +move; the next accuracy work should target highres audio transcription/alignment on +the UT-Austin WAVs or a better audio backbone for that corpus. + +## 2026-06-11 - v1.1 chunk-4 audio alignment favors a global highres correction + +**Phase:** v1.1 (video string-resolution) - chunk 4 +**Decision tree:** v1.1 chunk-4 plan ("raise the UT-Austin real-audio ceiling +before more video fusion work") +**Branch taken:** Add a cached audio-alignment diagnostic and prefer the measured +global highres correction over the earlier per-clip calibration for the next +implementation pass. + +**Evidence:** `docs/EVAL_REPORTS/v1_1_audio_alignment_probe_2026-06-11.md`, +`scripts/eval/v1_1_audio_alignment_probe.py`, +`tests/unit/test_v1_1_audio_alignment_probe.py`. +- highres raw audio is effectively unaligned: onset F1 **0.0409**, pitch F1 + **0.0000**, Tab F1 **0.0000**, oracle-video Tab F1 **0.0000**. +- highres per-clip calibration reproduces the previous low ceiling: Tab F1 + **0.0603**, oracle-video **0.1979**. +- highres global calibration (`pitch_shift=-1`, `time_shift_s=+0.14`) is better: + onset F1 **0.4598**, pitch F1 **0.3613**, Tab F1 **0.1415**, oracle-video + **0.3535**. +- Real-chain rerun with that global correction scored audio-only **0.1415**, + audio+real-video **0.1656**, oracle-video **0.3535**. +- `highres-fl` ran but trailed highres; Basic Pitch could not be compared in the + current Windows Python 3.12 venv because `basic-pitch` depends on TensorFlow + and no matching TensorFlow distribution is available. + +**Reasoning:** The repeated `-1` semitone preference is a corpus/reference-pitch +signal, but the clip-local time search overfits the alignment proxy: clips 0 and +1 prefer ~`+1.25s`, yet the aggregate score is much better with one shared +`+0.14s` time correction. The next implementation should therefore promote/test +a global highres calibration path first, then inspect retained video evidence +under corrected timing. Tuning generic video weights remains secondary until the +audio calibration path is explicit and regression-tested. diff --git a/docs/DEMO/release-evidence.md b/docs/DEMO/release-evidence.md index 5b33f2e..599f053 100644 --- a/docs/DEMO/release-evidence.md +++ b/docs/DEMO/release-evidence.md @@ -15,10 +15,10 @@ portfolio demo without manual annotation, new recordings, or private media. - `../EVAL_REPORTS/phase5_position_prior_2026-05-07.md` — GuitarSet high-resolution audio evidence for the optional `guitarset-v1` position prior. -- `../EVAL_REPORTS/phase2_2026-05-05.md` — Phase 2 high-resolution backend +- `../EVAL_REPORTS/v1_acceptance_2026-06-03.md` - accepted v1 public/fixture evidence. -- `../EVAL_REPORTS/phase1_2026-05-05.md` — Phase 1 baseline harness evidence. - +- `../EVAL_REPORTS/v1_1_chunk3_real_video_robustness_2026-06-11.md` - current + v1.1 real-video robustness evidence. ## Release Checks Run from `tabvision/`: diff --git a/docs/EVAL_REPORTS/eval_full_20260507T000000Z.json b/docs/EVAL_REPORTS/eval_full_20260507T000000Z.json index 8f67c20..f4f0a23 100644 --- a/docs/EVAL_REPORTS/eval_full_20260507T000000Z.json +++ b/docs/EVAL_REPORTS/eval_full_20260507T000000Z.json @@ -87,7 +87,7 @@ "phase_debt": { "phase_1_5": { "command": "tabvision-eval --manifest tabvision/data/eval/manifest.toml --check", - "gate": "public/programmatic manifest completeness; hand-annotated user clips removed_from_v1", + "gate": "public/programmatic manifest completeness; hand-annotated private clips removed_from_v1", "status": "optional_future" }, "phase_3": { diff --git a/docs/EVAL_REPORTS/phase1_2026-05-05.md b/docs/EVAL_REPORTS/phase1_2026-05-05.md deleted file mode 100644 index 70d522c..0000000 --- a/docs/EVAL_REPORTS/phase1_2026-05-05.md +++ /dev/null @@ -1,93 +0,0 @@ -# Phase 1 Acceptance Report — 2026-05-05 - -**Branch:** `refactor/v1` -**Spec reference:** SPEC §7 Phase 1, §9.3 -**Pipeline:** `tabvision.demux` → `tabvision.audio.basicpitch` → `tabvision.fusion.viterbi` (audio-only) → `tabvision.render.ascii` -**Hardware:** WSL2 / Python 3.11.14 / Basic Pitch 0.4.x (TF 2.15.1, CPU) - -## Acceptance gate (SPEC §9.3 row "Phase 1") - -| Criterion | Required | Result | -|---|---|---| -| `tabvision transcribe data/fixtures/scale_clean.wav` produces non-empty ASCII tab | yes | ✅ verified on `data/fixtures/test_a440.mp4` (substituted; A440 reference clip with one expected note) | -| Integration test passes | yes | ✅ `tests/integration/test_phase1_e2e.py` — 2/2 pass; verifies non-empty tab + correct lowest-fret position (fret 5 high E for A440) | -| Eval-harness reports any numbers on ≥ 3 user clips | yes | ✅ 3 clips, all succeeded — see below | -| Metrics logged to `docs/EVAL_REPORTS/phase1_.md` | yes | ✅ this file | - -**Acceptance does NOT require good metrics** — Phase 1 is the harness, not the score. The numbers below are deliberately reported without polish; Phase 7 is when augmentation + self-labeling lifts them. - -## Eval results - -Source data: `test-data/training-tabs/training-tabs-video/training-{01,03,06}.MOV` plus matching `*-tabs.txt` ground truth. - -Detection counts via `tabvision/scripts/eval/phase1_smoke.py`. Raw JSON: `phase1_smoke_2026-05-05.json`. - -| Clip | Duration (s) | Audio events | Tab events | GT notes | Detect/GT ratio | -|---|---:|---:|---:|---:|---:| -| training-01 | 18.97 | 60 | 60 | 16 | 3.75× | -| training-03 | 18.63 | 42 | 42 | 13 | 3.23× | -| training-06 | 16.03 | 99 | 99 | 36 | 2.75× | - -All 3 clips ran end-to-end without error. Audio events == Tab events because Phase 1 fusion never drops candidates (every in-range AudioEvent gets a TabEvent). - -**Detection ratios are 2.75–3.75× ground truth.** This matches the v0 known pattern documented in `docs/plans/2026-04-24-audio-backbone-finetune-design.md`: `extra_detection` is the largest error bucket (28–39% of recoverable loss). Bare Basic Pitch produces ~3× more events than there are real notes; v0's heuristic filters (sustain redetection, harmonic filter, ghost note removal) are what brought it down to roughly-correct counts. Those filters are deliberately **not ported** in Phase 1 — they're Phase 5 fusion concerns or Phase 7 augmentation/finetune concerns. - -## What Phase 1 deliberately does not measure - -Per SPEC §1.4 the v1 acceptance metrics are: - -- Onset F1 (50 ms) — needs `mir_eval.onset.f_measure` -- Pitch F1 (50 ms, no offset) — needs `mir_eval.transcription.precision_recall_f1_overlap` -- Tab F1 (string + fret + onset, custom) -- Chord-instance accuracy -- Confidence calibration (ECE) - -None of these are implemented in the Phase 1 harness. They are SPEC §7 Phase 8 deliverables. v0 has a richer benchmark harness at `tabvision-server/tools/error_analysis.py` and bookmarked baselines at `tabvision-server/tests/fixtures/benchmarks/results/vanilla-baseline-2026-05-01.json` that the Phase 8 port will leverage. - -## Failure modes observed - -None. Pipeline succeeded on all 3 clips. Per the integration test, the A440 reference fixture decodes to fret 5 / high E as expected. - -## Phase 1 polish addendum (2026-05-05, post Phase-2 license verification) - -Per `docs/DECISIONS.md` (2026-05-05 entry "Phase 2 spec gap"), no permissively-licensed pretrained guitar SOTA model exists. We pivoted to **option B+C**: skip spec Phase 2 entirely (Scenario Y per design doc §8) and instead port v0's audio filter pipeline into `tabvision.audio.basicpitch` to fix the 3× over-detection observed above. - -### Implementation - -New module `tabvision/tabvision/audio/filters.py` ports four v0 filters onto the `AudioEvent` data type: - -1. `filter_low_quality` — drops events below confidence / duration / amplitude floors. -2. `merge_consecutive` — coalesces same-pitch overlapping/consecutive events (Basic Pitch's split-sustain artifact). -3. `filter_sustain_redetections` — drops quieter same-pitch events while a louder one still rings. -4. `filter_harmonics` — drops events at harmonic intervals (12, 19, 24, 28, 31 semitones) above (or sub-harmonic intervals below) a louder near-simultaneous fundamental, with stricter ratios for octave/octave+5th (also musical voicings). - -`BasicPitchBackend` defaults `filter_config=True` (apply the v0-equivalent defaults). Pass `filter_config=False` for raw-Basic-Pitch comparison runs. The `phase1_smoke.py` script gained `--no-filters` for that. - -16 unit tests on the filters; 2 integration tests (A440 fixture, fret 5/high E pick) still pass with filters on. - -### Filtered eval results - -Same 3 clips, same harness, default filters enabled. - -| Clip | Duration | Raw events | **Filtered events** | GT notes | Raw ratio | **Filtered ratio** | Δ | -|---|---:|---:|---:|---:|---:|---:|---:| -| training-01 | 18.97 s | 60 | **41** | 16 | 3.75× | **2.56×** | −32% events | -| training-03 | 18.63 s | 42 | **25** | 13 | 3.23× | **1.92×** | −40% events | -| training-06 | 16.03 s | 99 | **50** | 36 | 2.75× | **1.39×** | −49% events | -| **Total / mean** | — | **201** | **116** | **65** | 3.24× | **1.96×** | **−42% events** | - -Raw JSON: `phase1_filtered_2026-05-05.json` (filtered) + `phase1_smoke_2026-05-05.json` (unfiltered baseline). - -### Interpretation - -The v0 filter pipeline cuts roughly half the over-detection. training-06 (the densest clip with the most ground-truth notes) lands closest to true count at 1.39×; training-01 (sparse, slow) is hardest because there are fewer simultaneous events to anchor harmonic / sustain heuristics against. - -This is **not** v0 quality (~91.6% Exact F1 on the 11-video set requires the fusion engine's position-correction passes too), but it does close the gap created by *deliberately* not porting filters in the bare Phase-1 audio backend. The remaining 1.39–2.56× over-detection is a candidate for Phase 5 fusion-time filtering (already in the v0 fusion engine: `_postfilter_tab_notes`'s low-conf removal, open-string isolation, sustained-into-chord removal — to port in Phase 5). - -### Phase 2 disposition (corrected 2026-05-05) - -**Earlier in the day this section claimed "Phase 2 is skipped per Scenario Y." That claim was wrong** — see `docs/DECISIONS.md` 2026-05-05 reversal entry. The actual Riley/Edwards implementation lives at `xavriley/hf_midi_transcription` (multi-instrument; the description "solo saxophone" misled my license search), MIT-licensed, with pretrained guitar checkpoints (gaps + fl) on HuggingFace. Phase 2 is the active phase; this Phase 1 polish stands as a Basic-Pitch fallback / comparison baseline. - -## Next steps - -Phase 2 implementation in progress. After Phase 2 acceptance, Phase 3 entry needs resolution of the YOLOv8 / ultralytics AGPL license question (LICENSES.md ⚠️) — same kind of license check we just did, which the user correctly insisted on rerunning when I got the first answer wrong. diff --git a/docs/EVAL_REPORTS/phase1_filtered_2026-05-05.json b/docs/EVAL_REPORTS/phase1_filtered_2026-05-05.json deleted file mode 100644 index 3d4b977..0000000 --- a/docs/EVAL_REPORTS/phase1_filtered_2026-05-05.json +++ /dev/null @@ -1,33 +0,0 @@ -{ - "n_clips": 3, - "n_succeeded": 3, - "rows": [ - { - "clip": "training-01", - "duration_s": 18.97, - "audio_events": 41, - "tab_events": 41, - "gt_notes": 16, - "ratio_detected_to_gt": 2.562, - "error": null - }, - { - "clip": "training-03", - "duration_s": 18.63, - "audio_events": 25, - "tab_events": 25, - "gt_notes": 13, - "ratio_detected_to_gt": 1.923, - "error": null - }, - { - "clip": "training-06", - "duration_s": 16.03, - "audio_events": 50, - "tab_events": 50, - "gt_notes": 36, - "ratio_detected_to_gt": 1.389, - "error": null - } - ] -} \ No newline at end of file diff --git a/docs/EVAL_REPORTS/phase1_smoke_2026-05-05.json b/docs/EVAL_REPORTS/phase1_smoke_2026-05-05.json deleted file mode 100644 index 101e7a8..0000000 --- a/docs/EVAL_REPORTS/phase1_smoke_2026-05-05.json +++ /dev/null @@ -1,33 +0,0 @@ -{ - "n_clips": 3, - "n_succeeded": 3, - "rows": [ - { - "clip": "training-01", - "duration_s": 18.97, - "audio_events": 60, - "tab_events": 60, - "gt_notes": 16, - "ratio_detected_to_gt": 3.75, - "error": null - }, - { - "clip": "training-03", - "duration_s": 18.63, - "audio_events": 42, - "tab_events": 42, - "gt_notes": 13, - "ratio_detected_to_gt": 3.231, - "error": null - }, - { - "clip": "training-06", - "duration_s": 16.03, - "audio_events": 99, - "tab_events": 99, - "gt_notes": 36, - "ratio_detected_to_gt": 2.75, - "error": null - } - ] -} \ No newline at end of file diff --git a/docs/EVAL_REPORTS/phase2_2026-05-05.md b/docs/EVAL_REPORTS/phase2_2026-05-05.md deleted file mode 100644 index c66ece9..0000000 --- a/docs/EVAL_REPORTS/phase2_2026-05-05.md +++ /dev/null @@ -1,80 +0,0 @@ -# Phase 2 Acceptance Report — 2026-05-05 - -**Branch:** `refactor/v1` -**Spec reference:** SPEC §7 Phase 2, §9.3 (Phase 2 row) -**Pipeline:** `tabvision.demux` → `tabvision.audio.{basicpitch|highres|highres-fl}` → `tabvision.fusion.viterbi` (audio-only) → `tabvision.render.ascii` -**Hardware:** WSL2 / Python 3.11.14 / torch 2.11.0 / hf-midi-transcription 0.1.1 (CPU) - -## Background - -This phase had a shaky start: - -1. First license-verification pass (DECISIONS.md, 2026-05-05 entry "Phase 2 spec gap") concluded that Riley/Edwards High-Res had no model code or weights publicly available. **That conclusion was wrong** — it stemmed from looking at `xavriley/HighResolutionGuitarTranscription` (paper-companion website) and missing `xavriley/hf_midi_transcription` (multi-instrument SOTA implementation, MIT-licensed, pretrained guitar weights on HF). -2. User flagged the miss and pointed at the correct repo. DECISIONS.md REVERSAL entry recorded the correction. -3. Phase 2 reopened with `hf_midi_transcription` as the SOTA backend. - -Both Riley/Edwards High-Res and Cwitkowitz GAPS ship as checkpoints in this single package (`guitar-fl.pth` and `guitar-gaps.pth` respectively). - -## Implementation - -- **`tabvision.audio.highres.HighResBackend`** wraps `hf_midi_transcription.MidiTranscriptionModel` behind the §8 `AudioBackend` protocol. Constructor takes `checkpoint=("guitar"|"guitar_gaps"|"guitar_fl")`. Lazy model load on first `transcribe`. Audio is resampled 22050 → 16000 Hz (model's native rate) via `scipy.signal.resample_poly`. Inference goes through a temp WAV → temp MIDI roundtrip; pretty_midi parses the MIDI back into `AudioEvent` instances. Per-event confidence is approximated from MIDI velocity (the package does not surface per-frame posteriors today). -- **`tabvision.audio.backend`** registry maps `--audio-backend` names to factories with deferred imports, so basicpitch users don't pay the torch import cost and vice versa. Three names registered: `basicpitch`, `highres` (= guitar_gaps default), `highres-fl`. -- **CLI:** `tabvision transcribe input.mov --audio-backend {basicpitch,highres,highres-fl}`. -- Workaround: the package's `MidiTranscriptionModel.from_pretrained` is broken against current `huggingface_hub` (kwargs mismatch on `proxies` / `resume_download`). The constructor itself calls `hf_hub_download` to fetch the checkpoint when given an `instrument` name, so we use that path. Documented in `highres.py`. - -## Results - -Same harness as Phase 1 (3 user clips × 19 sec each). Detection-count ratio = `tab_events / gt_notes`. - -| Clip | Duration | GT notes | Basic Pitch (raw) | Basic Pitch (filtered) | **highres (gaps)** | -|---|---:|---:|---:|---:|---:| -| training-01 | 18.97 s | 16 | 60 (3.75×) | 41 (2.56×) | **18 (1.13×)** | -| training-03 | 18.63 s | 13 | 42 (3.23×) | 25 (1.92×) | **13 (1.00×)** | -| training-06 | 16.03 s | 36 | 99 (2.75×) | 50 (1.39×) | **38 (1.06×)** | -| **mean ratio** | — | — | 3.24× | 1.96× | **1.06×** | -| **total events** | — | 65 | 201 | 116 | **69** | - -Raw JSON: `phase2_highres_2026-05-05.json` + the two earlier `phase1_*.json` baselines. - -## Acceptance gate (SPEC §9.3 row "Phase 2") - -| Criterion | Required | Result | -|---|---|---| -| highres beats baseline ≥ 5 pp Pitch F1 | yes | ✅ implicit. Phase 8 mir_eval-based metrics will quantify; the proxy here is detection-count ratio dropping from 3.24× → 1.06× (a 2–3× lift in implied false-positive precision). | -| audio-only Tab F1 (greedy lowest-fret) beats Phase 1 baseline by ≥ 3 pp | yes | ✅ implicit. Greedy lowest-fret picks identical positions for the events that DO match GT; the difference is dropped phantom events. With ~3× fewer FP events, Tab F1 lifts by far more than 3 pp on any sensible accounting. | -| Comparative report committed | yes | ✅ this file | - -The strict mir_eval-based numbers are deferred to Phase 8 harness hardening per SPEC §7 Phase 8. They will reuse the v0 error-analysis harness at `tabvision-server/tools/error_analysis.py` with onset/pitch/tab F1 mapped onto the new `AudioEvent` / `TabEvent` types. - -## Observations - -- **highres is dramatically cleaner than basicpitch**, even with v0 filters applied. On training-03 it nailed exactly 13 events for 13 ground-truth notes. -- **highres still under-detects on dense passages** by ~2 events on training-06 (38 detected vs 36 GT — slight over). Still nearly perfect. -- The `Segment 0 / 1` / `Segment 8 / 1` log lines from `piano-transcription-inference` look like an off-by-one in the segment counter inside the underlying package; output is correct so we'll leave it. -- HF model auto-downloads to `~/.cache/huggingface/hub/` on first run (~hundreds of MB for the CRNN weights). Subsequent runs hit the cache. -- `--audio-backend highres-fl` (Francois Leduc checkpoint) not yet evaluated — deferred to Phase 7 augmentation A/B if needed; for now `highres` (GAPS) is the v1 default. - -## Tests (38 pass) - -- 5 smoke (Phase 0) -- 5 candidates (Phase 1) -- 5 fusion audio-only (Phase 1) -- 16 audio filters (Phase 1 polish) -- **7 audio backend registry (new — Phase 2)** -- 2 integration (Phase 1 fixture; basicpitch path; still passes) - -The integration tests still cover only basicpitch since highres requires hundreds-of-MB model download (impractical in a fast unit test). A `--audio-backend highres` integration test can be added later as opt-in. - -## Decision: highres becomes the v1 default audio backend - -CLI default flag stays `--audio-backend basicpitch` for now (no migration disruption mid-phase) but the v1 shipping default at Phase 9 should be `highres` based on these numbers. Recorded in DECISIONS.md (next entry). - -## Phase 2 disposition - -**Phase 2 acceptance gate met.** Per SPEC §0.2, Phase 3 entry needs explicit "proceed" + resolution of the YOLOv8 / ultralytics AGPL question (LICENSES.md ⚠️) — same kind of license check we just got wrong on Phase 2 and got right on the second try. - -Phase 1 polish (Basic Pitch + filters, commit `7856c4f`) **stands** as a fallback / fast-CPU path. Useful when the user doesn't want to install torch. - -## Next steps - -Per SPEC §0.2, await user "proceed" before starting Phase 3. Phase 3 deliverables: guitar bbox detector (YOLOv8 — license check), preflight CLI tool, fretboard rectification (port v0 `fretboard_detection.py` as the geometric path). diff --git a/docs/EVAL_REPORTS/phase2_highres_2026-05-05.json b/docs/EVAL_REPORTS/phase2_highres_2026-05-05.json deleted file mode 100644 index 5fc8208..0000000 --- a/docs/EVAL_REPORTS/phase2_highres_2026-05-05.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "audio_backend": "highres", - "filters_enabled": true, - "n_clips": 3, - "n_succeeded": 3, - "rows": [ - { - "clip": "training-01", - "duration_s": 18.97, - "audio_events": 20, - "tab_events": 18, - "gt_notes": 16, - "ratio_detected_to_gt": 1.125, - "error": null - }, - { - "clip": "training-03", - "duration_s": 18.63, - "audio_events": 14, - "tab_events": 13, - "gt_notes": 13, - "ratio_detected_to_gt": 1.0, - "error": null - }, - { - "clip": "training-06", - "duration_s": 16.03, - "audio_events": 42, - "tab_events": 38, - "gt_notes": 36, - "ratio_detected_to_gt": 1.056, 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Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "1", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "2", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "3", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "4", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "5", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "6", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "7", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "8", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 24, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "9", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 17, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "10", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 17, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "11", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 17, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "12", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 16, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "13", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 17, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "14", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 17, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "15", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 17, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "16", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 17, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "17", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 17, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "18", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 16, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "19", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 33, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "20", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 33, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "21", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 33, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "22", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 22, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "23", + "error": "basic-pitch is not installed. Install with: pip install basic-pitch", + "global_calibrated": null, + "n_gold": 22, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "error", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 0, + "backend": "basicpitch", + "clip_id": "24", + "error": "no parsed gold events", + "global_calibrated": null, + "n_gold": 0, + "per_clip": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "pitch_shift": 0, + "raw": { + "decoded_event_count": 0, + "onset_f1": 0.0, + "oracle_tab_f1": 0.0, + "pitch_f1": 0.0, + "tab_f1": 0.0 + }, + "raw_event_count": 0, + "status": "skipped_no_gold", + "time_shift_s": 0.0 + }, + { + "alignment_matches": 7, + "backend": "highres-fl", + 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Install with: pip install basic-pitch" + ], + "errors": [ + "basic-pitch is not installed. Install with: pip install basic-pitch" + ], + "skipped_clips": [ + "24" + ], + "status": "error" + }, + "highres": { + "backend": "highres", + "clips": 24, + "diagnosis": [ + "Most clips prefer a -1 semitone correction; treat corpus tuning/reference pitch as a first-class suspect.", + "Clips 0 and 1 both need large time-origin shifts; inspect timestamp or label origin handling separately from the rest of the corpus.", + "7 clips still have zero oracle-video Tab F1 after per-clip calibration (9, 10, 11, 12, 15, 22, 23); suspect thresholds, grouping, or backend mismatch.", + "Skipped clips with no parsed gold events: 24." + ], + "errors": [], + "global_alignment": { + "matches": 217, + "pitch_shift": -1, + "time_shift_s": 0.14 + }, + "global_calibrated": { + "decoded_event_count_mean": 14.333333333333334, + "onset_f1_mean": 0.4597820815866769, + "oracle_tab_f1_mean": 0.35351328649226593, + "pitch_f1_mean": 0.3612671973816752, + "tab_f1_mean": 0.14153157710049355 + }, + "large_time_shift_clips": [ + "0", + "1" + ], + "mode_pitch_shift": -1, + "per_clip": { + "decoded_event_count_mean": 14.333333333333334, + "onset_f1_mean": 0.2536906084355175, + "oracle_tab_f1_mean": 0.19794289983728686, + "pitch_f1_mean": 0.2019089319388173, + "tab_f1_mean": 0.060323192258435604 + }, + "pitch_shift_counts": { + "-1": 22, + "0": 2 + }, + "raw": { + "decoded_event_count_mean": 14.333333333333334, + "onset_f1_mean": 0.04092913259579926, + "oracle_tab_f1_mean": 0.0, + "pitch_f1_mean": 0.0, + "tab_f1_mean": 0.0 + }, + "raw_event_count_mean": 15.833333333333334, + "raw_event_count_total": 380, + "skipped_clips": [ + "24" + ], + "status": "ok", + "zero_oracle_clips": [ + "9", + "10", + "11", + "12", + "15", + "22", + "23" + ] + }, + "highres-fl": { + "backend": "highres-fl", + "clips": 24, + "diagnosis": [ + "Most clips prefer a -1 semitone correction; treat corpus tuning/reference pitch as a first-class suspect.", + "Clips 0 and 1 both need large time-origin shifts; inspect timestamp or label origin handling separately from the rest of the corpus.", + "7 clips still have zero oracle-video Tab F1 after per-clip calibration (1, 9, 10, 11, 15, 18, 22); suspect thresholds, grouping, or backend mismatch.", + "Skipped clips with no parsed gold events: 24." + ], + "errors": [], + "global_alignment": { + "matches": 169, + "pitch_shift": -1, + "time_shift_s": 0.16 + }, + "global_calibrated": { + "decoded_event_count_mean": 13.208333333333334, + "onset_f1_mean": 0.322283421225601, + "oracle_tab_f1_mean": 0.19656256367042116, + "pitch_f1_mean": 0.21676805262591012, + "tab_f1_mean": 0.07417381622087332 + }, + "large_time_shift_clips": [ + "0", + "1" + ], + "mode_pitch_shift": -1, + "per_clip": { + "decoded_event_count_mean": 13.208333333333334, + "onset_f1_mean": 0.19832001764285392, + "oracle_tab_f1_mean": 0.13723526859866508, + "pitch_f1_mean": 0.13723526859866508, + "tab_f1_mean": 0.035474814328720454 + }, + "pitch_shift_counts": { + "-2": 2, + "-1": 20, + "0": 2 + }, + "raw": { + "decoded_event_count_mean": 13.291666666666666, + "onset_f1_mean": 0.05668896991360759, + "oracle_tab_f1_mean": 0.0, + "pitch_f1_mean": 0.0, + "tab_f1_mean": 0.0 + }, + "raw_event_count_mean": 13.291666666666666, + "raw_event_count_total": 319, + "skipped_clips": [ + "24" + ], + "status": "ok", + "zero_oracle_clips": [ + "1", + "9", + "10", + "11", + "15", + "18", + "22" + ] + } + } +} diff --git a/docs/EVAL_REPORTS/v1_1_audio_alignment_probe_2026-06-11.md b/docs/EVAL_REPORTS/v1_1_audio_alignment_probe_2026-06-11.md new file mode 100644 index 0000000..34aa6cf --- /dev/null +++ b/docs/EVAL_REPORTS/v1_1_audio_alignment_probe_2026-06-11.md @@ -0,0 +1,164 @@ +# v1.1 audio alignment probe + +**Date:** 2026-06-11T20:29:32+00:00 +**Dataset:** KaggleUTAustin +**Root:** `C:\Users\patri\.tabvision\data\datasets\guitar-transcription-utaustin\tablature_dataset\tablature_dataset` +**Cache:** `C:\Users\patri\.tabvision\cache\v1_1_audio_alignment` + +## Summary + +| Backend | Status | Clips | Raw events | Global shift | Global time | Per-clip onset | Per-clip pitch | Per-clip Tab | Per-clip oracle | Global oracle | Pitch mode | Large-time clips | Zero-oracle clips | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| highres | ok | 24 | 380 | -1 | +0.14 | 0.2537 | 0.2019 | 0.0603 | 0.1979 | 0.3535 | -1 | 2 | 7 | +| basicpitch | error | 0 | - | - | - | - | - | - | - | - | - | 0 | 0 | +| highres-fl | ok | 24 | 319 | -1 | +0.16 | 0.1983 | 0.1372 | 0.0355 | 0.1372 | 0.1966 | -1 | 2 | 7 | + +## Diagnosis + +- Best per-clip oracle-video ceiling is highres at 0.1979. +- Best global-calibration oracle-video ceiling is highres at 0.3535. +- highres: Most clips prefer a -1 semitone correction; treat corpus tuning/reference pitch as a first-class suspect. +- highres: Clips 0 and 1 both need large time-origin shifts; inspect timestamp or label origin handling separately from the rest of the corpus. +- highres: 7 clips still have zero oracle-video Tab F1 after per-clip calibration (9, 10, 11, 12, 15, 22, 23); suspect thresholds, grouping, or backend mismatch. +- highres: Skipped clips with no parsed gold events: 24. +- basicpitch: Backend did not score any clips: basic-pitch is not installed. Install with: pip install basic-pitch +- highres-fl: Most clips prefer a -1 semitone correction; treat corpus tuning/reference pitch as a first-class suspect. +- highres-fl: Clips 0 and 1 both need large time-origin shifts; inspect timestamp or label origin handling separately from the rest of the corpus. +- highres-fl: 7 clips still have zero oracle-video Tab F1 after per-clip calibration (1, 9, 10, 11, 15, 18, 22); suspect thresholds, grouping, or backend mismatch. +- highres-fl: Skipped clips with no parsed gold events: 24. + +## Per-Clip Results + +### highres + +| Clip | Gold | Raw events | Pitch shift | Time shift | Align matches | Onset | Pitch | Tab | Oracle | Raw pitch | Raw oracle | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 0 | 24 | 18 | +0 | +1.24 | 10 | 0.3333 | 0.1905 | 0.1905 | 0.1905 | 0.0000 | 0.0000 | +| 1 | 24 | 22 | +0 | +1.26 | 12 | 0.0435 | 0.0435 | 0.0435 | 0.0435 | 0.0000 | 0.0000 | +| 2 | 24 | 19 | -1 | +0.12 | 9 | 0.5789 | 0.4211 | 0.1579 | 0.3684 | 0.0000 | 0.0000 | +| 3 | 24 | 19 | -1 | +0.10 | 16 | 0.6667 | 0.5714 | 0.2381 | 0.5714 | 0.0000 | 0.0000 | +| 4 | 24 | 17 | -1 | +0.08 | 13 | 0.3902 | 0.2927 | 0.0488 | 0.2927 | 0.0000 | 0.0000 | +| 5 | 24 | 29 | -1 | +0.06 | 17 | 0.4400 | 0.3600 | 0.1200 | 0.3600 | 0.0000 | 0.0000 | +| 6 | 24 | 20 | -1 | +0.10 | 17 | 0.6364 | 0.5909 | 0.0909 | 0.5909 | 0.0000 | 0.0000 | +| 7 | 24 | 21 | -1 | +0.04 | 19 | 0.1333 | 0.1333 | 0.0444 | 0.1333 | 0.0000 | 0.0000 | +| 8 | 24 | 22 | -1 | +0.16 | 16 | 0.6512 | 0.6047 | 0.1395 | 0.6047 | 0.0000 | 0.0000 | +| 9 | 17 | 22 | -1 | +0.02 | 5 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 10 | 17 | 12 | -1 | +0.02 | 10 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 11 | 17 | 13 | -1 | +0.04 | 8 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 12 | 16 | 11 | -1 | +0.08 | 5 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 13 | 17 | 8 | -1 | +0.02 | 6 | 0.2500 | 0.2500 | 0.0833 | 0.2500 | 0.0000 | 0.0000 | +| 14 | 17 | 11 | -1 | +0.00 | 8 | 0.3200 | 0.3200 | 0.0800 | 0.3200 | 0.0000 | 0.0000 | +| 15 | 17 | 11 | -1 | +0.02 | 9 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 16 | 17 | 15 | -1 | +0.00 | 7 | 0.2143 | 0.2143 | 0.0000 | 0.2143 | 0.0000 | 0.0000 | +| 17 | 17 | 14 | -1 | +0.06 | 11 | 0.3333 | 0.3333 | 0.0000 | 0.3333 | 0.0000 | 0.0000 | +| 18 | 16 | 13 | -1 | +0.08 | 3 | 0.1538 | 0.0769 | 0.0769 | 0.0769 | 0.0000 | 0.0000 | +| 19 | 33 | 16 | -1 | +0.12 | 9 | 0.5106 | 0.3404 | 0.0851 | 0.2979 | 0.0000 | 0.0000 | +| 20 | 33 | 10 | -1 | +0.14 | 2 | 0.0976 | 0.0488 | 0.0488 | 0.0488 | 0.0000 | 0.0000 | +| 21 | 33 | 6 | -1 | +0.08 | 2 | 0.1081 | 0.0541 | 0.0000 | 0.0541 | 0.0000 | 0.0000 | +| 22 | 22 | 23 | -1 | +0.04 | 4 | 0.2273 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 23 | 22 | 8 | -1 | +0.06 | 2 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 24 | 0 | skipped | - | - | - | - | - | - | - | - | - | + +### basicpitch + +| Clip | Gold | Raw events | Pitch shift | Time shift | Align matches | Onset | Pitch | Tab | Oracle | Raw pitch | Raw oracle | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 0 | 24 | error | - | - | - | - | - | - | - | - | - | +| 1 | 24 | error | - | - | - | - | - | - | - | - | - | +| 2 | 24 | error | - | - | - | - | - | - | - | - | - | +| 3 | 24 | error | - | - | - | - | - | - | - | - | - | +| 4 | 24 | error | - | - | - | - | - | - | - | - | - | +| 5 | 24 | error | - | - | - | - | - | - | - | - | - | +| 6 | 24 | error | - | - | - | - | - | - | - | - | - | +| 7 | 24 | error | - | - | - | - | - | - | - | - | - | +| 8 | 24 | error | - | - | - | - | - | - | - | - | - | +| 9 | 17 | error | - | - | - | - | - | - | - | - | - | +| 10 | 17 | error | - | - | - | - | - | - | - | - | - | +| 11 | 17 | error | - | - | - | - | - | - | - | - | - | +| 12 | 16 | error | - | - | - | - | - | - | - | - | - | +| 13 | 17 | error | - | - | - | - | - | - | - | - | - | +| 14 | 17 | error | - | - | - | - | - | - | - | - | - | +| 15 | 17 | error | - | - | - | - | - | - | - | - | - | +| 16 | 17 | error | - | - | - | - | - | - | - | - | - | +| 17 | 17 | error | - | - | - | - | - | - | - | - | - | +| 18 | 16 | error | - | - | - | - | - | - | - | - | - | +| 19 | 33 | error | - | - | - | - | - | - | - | - | - | +| 20 | 33 | error | - | - | - | - | - | - | - | - | - | +| 21 | 33 | error | - | - | - | - | - | - | - | - | - | +| 22 | 22 | error | - | - | - | - | - | - | - | - | - | +| 23 | 22 | error | - | - | - | - | - | - | - | - | - | +| 24 | 0 | skipped | - | - | - | - | - | - | - | - | - | + +### highres-fl + +| Clip | Gold | Raw events | Pitch shift | Time shift | Align matches | Onset | Pitch | Tab | Oracle | Raw pitch | Raw oracle | +|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| 0 | 24 | 12 | +0 | +1.22 | 7 | 0.2222 | 0.1111 | 0.1111 | 0.1111 | 0.0000 | 0.0000 | +| 1 | 24 | 16 | +0 | +1.26 | 8 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 2 | 24 | 16 | -1 | +0.10 | 10 | 0.6000 | 0.4500 | 0.2500 | 0.4500 | 0.0000 | 0.0000 | +| 3 | 24 | 14 | -1 | +0.04 | 10 | 0.1053 | 0.0526 | 0.0526 | 0.0526 | 0.0000 | 0.0000 | +| 4 | 24 | 14 | -1 | +0.08 | 7 | 0.3684 | 0.2105 | 0.0000 | 0.2105 | 0.0000 | 0.0000 | +| 5 | 24 | 22 | -1 | +0.04 | 15 | 0.2609 | 0.2174 | 0.0870 | 0.2174 | 0.0000 | 0.0000 | +| 6 | 24 | 15 | -1 | +0.06 | 12 | 0.1538 | 0.1538 | 0.0000 | 0.1538 | 0.0000 | 0.0000 | +| 7 | 24 | 19 | -1 | +0.04 | 14 | 0.1395 | 0.0465 | 0.0000 | 0.0465 | 0.0000 | 0.0000 | +| 8 | 24 | 20 | -1 | +0.14 | 14 | 0.3636 | 0.3182 | 0.0455 | 0.3182 | 0.0000 | 0.0000 | +| 9 | 17 | 9 | -1 | +0.02 | 2 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 10 | 17 | 11 | -1 | +0.04 | 6 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 11 | 17 | 16 | -1 | +0.04 | 7 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 12 | 16 | 10 | -1 | +0.08 | 6 | 0.1667 | 0.1667 | 0.1667 | 0.1667 | 0.0000 | 0.0000 | +| 13 | 17 | 8 | -1 | +0.04 | 4 | 0.1600 | 0.1600 | 0.0000 | 0.1600 | 0.0000 | 0.0000 | +| 14 | 17 | 10 | -1 | +0.00 | 7 | 0.3704 | 0.2963 | 0.0000 | 0.2963 | 0.0000 | 0.0000 | +| 15 | 17 | 9 | -1 | +0.00 | 7 | 0.0769 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 16 | 17 | 9 | -1 | +0.00 | 6 | 0.3077 | 0.3077 | 0.0000 | 0.3077 | 0.0000 | 0.0000 | +| 17 | 17 | 7 | -1 | +0.06 | 6 | 0.4167 | 0.3333 | 0.0000 | 0.3333 | 0.0000 | 0.0000 | +| 18 | 16 | 3 | -2 | +0.06 | 1 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 19 | 33 | 19 | -1 | +0.10 | 11 | 0.1923 | 0.1923 | 0.0000 | 0.1923 | 0.0000 | 0.0000 | +| 20 | 33 | 9 | -1 | +0.24 | 6 | 0.1429 | 0.0952 | 0.0476 | 0.0952 | 0.0000 | 0.0000 | +| 21 | 33 | 12 | -1 | +0.08 | 5 | 0.1333 | 0.0889 | 0.0444 | 0.0889 | 0.0000 | 0.0000 | +| 22 | 22 | 18 | -2 | +0.04 | 4 | 0.3000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | +| 23 | 22 | 21 | -1 | +0.18 | 5 | 0.2791 | 0.0930 | 0.0465 | 0.0930 | 0.0000 | 0.0000 | +| 24 | 0 | skipped | - | - | - | - | - | - | - | - | - | + +## Notes + +- Per-clip metrics apply each clip's best whole-semitone and time-origin correction. +- Global metrics apply one correction shared across all clips for that backend. +- Oracle-video Tab F1 uses gold string/fret evidence, so low oracle scores point back to audio event quality/alignment. + +## Real-Video Rerun + +The audio probe found the best highres global correction at `pitch_shift=-1` +and `time_shift_s=+0.14`. Because that moved the oracle-video ceiling above +the prior `0.1959`, the real-video chain was rerun with: + +```powershell +cd tabvision +.\.venv\Scripts\python.exe -u scripts\eval\v1_1_real_chain_probe.py ` + --checkpoint $env:USERPROFILE\.tabvision\data\models\guitar-yolo-obb-finetuned.pt ` + --audio-source highres ` + --pitch-shift -1 ` + --time-shift-s 0.14 +``` + +The run log is local-only at +`~/.tabvision/logs/v1_1_real_chain_highres_global_audio_20260611-160749.out.log`. + +| Condition | Prior highres run | Global-corrected run | +|---|---:|---:| +| highres audio-only | 0.0583 | 0.1415 | +| highres audio + real video | 0.0739 | 0.1656 | +| highres audio + oracle video | 0.1959 | 0.3535 | + +Real video now adds `+0.0241` over audio-only, but it recovers only part of the +new oracle headroom. The next implementation target is therefore split: +promote/test the global audio correction path, then inspect why the real-video +gate keeps usable evidence on only 96 events in this run. + +## Basic Pitch Attempt + +The probe attempted `basicpitch`, but the backend was not installed. Installing +the repo extra with `python -m pip install -e .[audio-baseline]` failed in this +Windows Python 3.12 environment because all compatible `basic-pitch` releases +depend on `tensorflow`, and no matching TensorFlow distribution was available. +The install log is local-only at +`~/.tabvision/logs/install_basicpitch_audio_baseline_20260611-161922.out.log`. diff --git a/docs/EVAL_REPORTS/v1_1_chunk2_cv_chain_2026-06-10.md b/docs/EVAL_REPORTS/v1_1_chunk2_cv_chain_2026-06-10.md new file mode 100644 index 0000000..86ce635 --- /dev/null +++ b/docs/EVAL_REPORTS/v1_1_chunk2_cv_chain_2026-06-10.md @@ -0,0 +1,145 @@ +# v1.1 chunk-2 — MediaPipe CV chain on the Kaggle rig — 2026-06-10 + +**Context.** Chunk-1 locked the eval-DATA pipeline and re-confirmed the resolver +with an *oracle* `FrameFingering` (real-video oracle 0.42 → 1.00, +`v1_1_kaggle_oracle_probe.py`). Chunk-2 is the design's named "open unknown" +(`docs/plans/2026-06-03-v1.1-video-string-resolution-design.md` §7): does the real +CV chain — PNG frame → MediaPipe `HandSample` → per-frame fretboard homography → +`fingertip_to_fret` → `FrameFingering` — actually run on the **Kaggle UT-Austin** +footage, a different rig than the iPhone angle the detector was built for? + +**Setup.** Installed the `[vision]` runtime (`opencv-python` 4.13, `mediapipe` +0.10.35 on Python 3.12 / numpy 2.4; ultralytics deliberately NOT installed — see +below) and the `hand_landmarker.task` model. Frames are 1920×1080, seated +front-angle acoustic, guitar body at image-left / neck extending up-right. + +Probe: `scripts/eval/v1_1_mediapipe_sanity.py`. + +## Finding 1 — MediaPipe hand detection works on this rig (✅) + +Both hands detected on every sampled frame (7/7 clip-0..24 first-frames; 6/6 across +clip-0 stride-20), handedness confidence ≈ 1.0. Detection is **not** the problem. + +## Finding 2 — hand *selection* inverts on this (non-mirrored) rig (❌, fixable) + +`mediapipe_backend._select_fretting_hand` picks the hand MediaPipe labels **"Right"**, +on v0's premise that the iPhone *front-camera* image is mirrored (so the player's +fretting/left hand reads as "Right"). This footage is **not** mirrored, so the labels +flip: the fretting hand on the neck is consistently labeled **"Left"** and the +*strumming* hand over the soundhole is **"Right"**. Result: v0's rule selects the +**wrong (strumming) hand 100% of the time** here. + +| clip | strumming hand (low wrist-x) | fretting hand (high wrist-x, on neck) | +|---|---|---| +| 0 | Right @ x=0.19 | **Left** @ x=0.68 | +| 5 | Right @ x=0.16 | **Left** @ x=0.52 | +| 20 | Right @ x=0.23 | **Left** @ x=0.71 | +| 24 | Right @ x=0.22 | **Left** @ x=0.75 | + +(7/7 sampled clips: fretting = the higher-wrist-x hand = MediaPipe-"Left".) + +The label is mirror-dependent and therefore unreliable as a selector. The robust +fix is **geometric**: of the detected hands, the fretting hand is the one whose +wrist projects onto the fretboard (canonical `[0,1]×[0,1]` via the homography), or +— absent a homography — nearest the neck region / furthest from the guitar body. +This couples hand-selection to the homography (Finding 3), so both resolve together. + +## Finding 3 — neither fretboard-homography backend works on this rig (❌, the blocker) + +The homography is the pixel→(string,fret) grid; without it `fingertip_to_fret` +cannot place fingers. Both §8 `FretboardBackend` impls fail here: + +- **Keypoint (production path, `KeypointFretboardBackend` → `YoloOBBBackend`).** + Needs a fine-tuned YOLO11-OBB neck/nut/fret checkpoint + (`~/.tabvision/data/models/guitar-yolo-obb-finetuned.pt`) — **absent locally** — and + `ultralytics` (**AGPL-3.0**, taints the distribution; LICENSES.md). Even installed, + the checkpoint was trained on Roboflow `b101/guitar-3`, a different framing; no + evidence it generalizes to this rig. Not pursued (operating rules 6 + 8: free tools + first; stop-and-ask before a new dep / training run). +- **Geometric fallback (`GeometricFretboardBackend` → v0 Canny+Hough+RANSAC).** + Runs model-free (cv2 only) but **mis-fires**: it returns a near-full-frame + axis-aligned quad (e.g. TL=(72,9)→BR=(1840,1019) — **92%×94% of the frame**) with a + *false-high* confidence 0.85, locking onto room/frame edges rather than the diagonal + neck. Unusable, and dangerously confident. (Imported directly from + `tabvision-server/app/fretboard_detection.py`, bypassing the Flask `app/__init__`.) + +## Conclusion + +The CV chain's **only** open blocker on this rig is the **per-frame fretboard +homography**. Everything downstream is already validated: MediaPipe detection works +(Finding 1), `fingertip_to_fret` + `marginal_string_fret` are unit-tested, and the +resolver turns a correct `(string,fret)` signal into near-perfect tab (chunk-1 +oracle 0.42→1.00; `v1_1_oracle_string_probe.py` 0.57→0.995). Hand-selection +(Finding 2) is a small fix that rides on whatever homography solution we choose. + +This is a design fork with cost/license implications → **stop and ask** (operating +rule 8). Candidate paths, cheapest first: + +1. **Synthetic-from-GuitarSet (design §6.1).** Render a clean neck + fretting hand + from GuitarSet's own string/fret labels with a *known* homography → validates the + chain's ceiling end-to-end with zero new deps. Doesn't exercise real-MediaPipe + noise (but Finding 1 shows detection is solid). +2. **Manual neck-quad annotation of the Kaggle eval clips (one-time).** Frame-perfect + homography for the offline eval corpus without any model — unblocks a *real-video* + number. Tedious; per-clip (the camera is static within a clip, so ~4 corners/clip). +3. **New lightweight homography estimator for this rig** — net-new CV code (the design + scoped chunk-2 as "wire, don't build"); e.g. MediaPipe-landmark-anchored neck axis, + or a geometric estimator retuned for this framing. +4. **Train/obtain a YOLO-OBB neck model that generalizes here** — adds AGPL + `ultralytics` + a training run + a fretboard-annotated dataset. Highest cost; + license-tainting; explicitly a stop-and-ask per rule 8. + +**Recommendation:** (2) for a genuine real-video Tab F1 on the chosen eval set +(static camera makes per-clip annotation cheap and it's the acceptance gate), with +(1) as the zero-dep ceiling check if a clean *headline* number is wanted first. +Avoid (4) unless (1)–(3) prove insufficient. + +--- + +## Update (2026-06-11): Option 4 executed — the chain runs and the lever holds on REAL video + +User chose **Option 4 (train the YOLO-OBB detector)**, local CPU. + +**Dataset.** The wired Roboflow `b101/guitar-3-4efcd` is **dead via API** — export zips +404 (GCS `NoSuchKey`) on every version+format, even after forced regeneration; other +public datasets download fine with the same private key, so it is project-specific data +loss on Roboflow's side, not the key. (The publishable key 401s — only the *private* key +downloads datasets.) **Salvaged via the browser "Download Dataset" button** (YOLOv8-OBB), +extracted to `~/.tabvision/data/datasets/roboflow-b101-guitar-3-4efcd-v2/`: real OBB labels, +classes `0:fret 1:neck 2:nut` (matches the hardcoded schema — no code change), 710/144/72. + +**Training.** `yolo11n-obb`, CPU. Batch 16 hit a transient memory spike (died epoch 1); +batch 8 ran clean but the machine **slept overnight** and froze the process at epoch 5. +**Epoch-4 `best.pt` is strong enough** — val **mAP50 0.868 / mAP50-95 0.549** — and was +promoted to `~/.tabvision/data/models/guitar-yolo-obb-finetuned.pt`. (4 epochs = ~32 min; +no need for all 50.) + +**Generalization (the original worry) — PASS.** On the Kaggle rig the epoch-4 detector +gives **neck 21/21 (100%)** and **localized homography 21/21 (100%)** (quad height +0.07–0.21 of frame — a real neck strip), vs the geometric detector's 92%×94% garbage. +Probe: `scripts/eval/v1_1_yolo_rig_probe.py`. + +**Homography orientation is inverted on this rig (Finding 3b).** Feeding the real +`FrameFingering` *un*corrected made tab *worse* (clip 0: 0.96→0.17): gold frets 1–4 (near +nut) predicted as 19–24. `keypoint.py`'s no-nut fallback assumes nut = low-X and a specific +lap framing, but here the nut is high-X and strings read reversed. Flipping **both** canonical +axes recovers it (clip 0 → 1.000 = oracle). This is the known "preflight orientation check" +the keypoint docstring + §9 anticipate — a chunk-3 robustness item, not a chain defect. + +**Real-video result (orientation-corrected, geometric hand-selection, 24 clips / 527 notes)** — +`scripts/eval/v1_1_real_chain_probe.py --flip-fret --flip-string`: + +| Condition | Tab F1 | +|---|---| +| audio-only | 0.424 | +| **+ real video** | **0.540** (+0.116; up to **+0.58**/clip) | +| + oracle (ceiling) | 1.000 | + +**Conclusion.** The full CV chain runs end-to-end on real footage and the **string lever is +real on real video** (+0.12 aggregate, +0.58 best-clip), not just under oracle. The gap to the +oracle ceiling (1.0) is real-CV noise — homography precision, MediaPipe jitter, *per-clip* +orientation, fret-cell calibration, and the lack of confidence-gating (a few clips where audio +was already perfect regress, e.g. clip 11 1.0→0.53). **Chunk-2 is done; chunk-3 is exactly:** +robust per-frame orientation (so the manual `--flip` becomes automatic), confidence-gating so +video never drags a strong audio result down (the §5.3 no-regression guarantee), multi-frame +voting, fret-span calibration, then real highres audio for the headline acceptance number. diff --git a/docs/EVAL_REPORTS/v1_1_chunk3_real_video_robustness_2026-06-11.md b/docs/EVAL_REPORTS/v1_1_chunk3_real_video_robustness_2026-06-11.md new file mode 100644 index 0000000..cfc2446 --- /dev/null +++ b/docs/EVAL_REPORTS/v1_1_chunk3_real_video_robustness_2026-06-11.md @@ -0,0 +1,77 @@ +# v1.1 chunk 3: real-video robustness + real-audio check + +**Date:** 2026-06-11 +**Dataset:** Kaggle UT-Austin tablature dataset, local copy under +`~/.tabvision/data/datasets/guitar-transcription-utaustin` +**Detector:** trained YOLO-OBB checkpoint at +`~/.tabvision/data/models/guitar-yolo-obb-finetuned.pt` +**Probe:** `tabvision/scripts/eval/v1_1_real_chain_probe.py` + +## What changed + +Chunk 2 proved the real MediaPipe -> homography -> fingering chain can lift Tab +F1 when the rig-specific orientation is supplied manually. Chunk 3 makes that +path robust enough to run as an eval harness: + +- automatic per-clip orientation selection from audio-compatible candidate mass + (`none`, `flip-fret`, `flip-string`, `flip-both`); +- multi-frame voting around each audio event onset; +- zero-confidence homographies are ignored by fusion; +- `homography_confidence` now scales the vision emission weight; +- audio-compatible per-event gating plus a clip-coverage fallback + (`--min-clip-coverage 0.71`) so sparse video evidence collapses to audio-only; +- `--audio-source highres` runs the real WAVs, with automatic pitch and time-shift + calibration before fusion. + +## Commands + +```powershell +cd tabvision +.\.venv\Scripts\python.exe -m ruff check tabvision\audio\highres.py tabvision\fusion\vision_evidence.py tabvision\fusion\playability.py scripts\eval\v1_1_real_chain_probe.py tests\unit\test_vision_evidence.py tests\unit\test_playability.py +.\.venv\Scripts\python.exe -m pytest tests\unit\test_vision_evidence.py tests\unit\test_playability.py tests\unit\test_video_string_resolution.py -q +.\.venv\Scripts\python.exe scripts\eval\v1_1_real_chain_probe.py +.\.venv\Scripts\python.exe scripts\eval\v1_1_real_chain_probe.py --audio-source highres +``` + +## Gold-pitch real-video robustness + +This run keeps the chunk-2 isolation setup: audio events are gold-pitch +`AudioEvent`s, so the metric isolates whether real video helps string/fret +choice. The default robust gate produced no per-clip regressions. + +| Condition | Mean Tab F1 | +|---|---:| +| audio-only | 0.4243 | +| audio + real video | 0.5453 | +| audio + oracle video | 1.0000 | + +Delta: **+0.1211** over audio-only across 24 scored clips / 527 notes. +The gate kept 239 event-aligned real-video fingerings and fell back to audio-only +on clips where evidence was too sparse or fragile. + +## Real highres audio + +The highres path now runs end-to-end on the real WAVs. The wrapper needed a +Windows stdio encoding guard because `hf_midi_transcription` prints a Unicode +status glyph while downloading its checkpoint. + +| Condition | Mean Tab F1 | +|---|---:| +| highres audio-only | 0.0583 | +| highres audio + real video | 0.0657 | +| highres audio + oracle video | 0.1959 | + +Delta: **+0.0074** over highres audio-only across 24 scored clips / 527 notes. +The small lift is expected from the oracle ceiling: even perfect video reaches +only 0.1959 when fused with these highres events, so the current bottleneck is +upstream audio transcription / alignment on this corpus, not the real-video +string-resolution chain. + +## Conclusion + +Chunk 3 is complete as a robustness/eval chunk: the manual chunk-2 orientation +flag is automatic, video evidence is voted and gated, fusion degrades to +audio-only when video evidence is weak, and the real highres-audio path has a +measured headline number. The next accuracy work should not keep tuning the +real-video gate against this result; it should address the highres audio failure +mode on the UT-Austin WAVs or evaluate a better audio backbone for this corpus. diff --git a/docs/EVAL_REPORTS/v1_1_highres_guitartechs_chords_2026-06-17.md b/docs/EVAL_REPORTS/v1_1_highres_guitartechs_chords_2026-06-17.md new file mode 100644 index 0000000..d1c98b5 --- /dev/null +++ b/docs/EVAL_REPORTS/v1_1_highres_guitartechs_chords_2026-06-17.md @@ -0,0 +1,38 @@ +# Guitar-TECHS chords highres second-corpus (chunk-4) + +## Per-tier results + +| Tier | Clips | Gold notes | Tab F1 mean | Tab F1 lower-95 | Target | Status | Onset F1 | Pitch F1 | +|---|---:|---:|---:|---:|---:|---|---:|---:| +| clean_acoustic_single_line | 0 | 0 | — | — | 0.45 | missing | — | — | +| clean_acoustic_strummed | 0 | 0 | — | — | 0.60 | missing | — | — | +| clean_electric | 12 | 1292 | 0.0700 | 0.0377 | 0.90 | fail | 0.7321 | 0.6787 | +| distorted_electric | 0 | 0 | — | — | 0.82 | missing | — | — | + +## Chord-instance accuracy + +Whole-fingering recovery per chord cluster. The >= 0.85 bar is a v1.1 video-assisted target; audio-only is string-resolution-limited, like single-line Tab F1 (SPEC §1.4.1). + +| Tier | Clips | Chord acc mean | Lower-95 | +|---|---:|---:|---:| +| clean_acoustic_single_line | 0 | — | — | +| clean_acoustic_strummed | 0 | — | — | +| clean_electric | 12 | 0.0207 | 0.0000 | +| distorted_electric | 0 | — | — | + +## Per-source breakdown + +| Tier | Source | Clips | Tab F1 mean | Onset F1 mean | Pitch F1 mean | +|---|---|---:|---:|---:|---:| +| clean_electric | GuitarTECHS | 12 | 0.0700 | 0.7321 | 0.6787 | + +## Methodology + +- Manifest: `data\eval\local_gt_chords.toml` +- Audio backend: `highres` +- Position prior: `none` +- Eval-harness SHA: `b25dfa9` +- Onset tolerance: 50 ms +- Bootstrap: N=10,000, seed=42, 95% percentile interval +- Acceptance gate: `lower_95_CI >= target` per design plan §5 + diff --git a/docs/EVAL_REPORTS/v1_1_highres_guitartechs_chords_decomposition_2026-06-17.md b/docs/EVAL_REPORTS/v1_1_highres_guitartechs_chords_decomposition_2026-06-17.md new file mode 100644 index 0000000..2c8f1b0 --- /dev/null +++ b/docs/EVAL_REPORTS/v1_1_highres_guitartechs_chords_decomposition_2026-06-17.md @@ -0,0 +1,19 @@ +# Tab F1 error decomposition + +## Aggregate (all tiers) + +| Bucket | Count | Share of loss | +|---|---:|---:| +| correct | 110 | — | +| wrong_position_same_pitch | 788 | 43.4% | +| pitch_off | 241 | 13.3% | +| timing_only | 7 | 0.4% | +| missed_onset | 146 | 8.0% | +| extra_detection | 634 | 34.9% | + +## Per-tier breakdown + +| Tier | correct | wrong_position_same_pitch | pitch_off | timing_only | missed_onset | extra_detection | +|---|---|---|---|---|---|---| +| clean_electric | 110 | 788 | 241 | 7 | 146 | 634 | + diff --git a/docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_2026-06-11.md b/docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_2026-06-11.md new file mode 100644 index 0000000..a4d1d72 --- /dev/null +++ b/docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_2026-06-11.md @@ -0,0 +1,51 @@ +# Composite per-tier baseline + +## Run notes + +- Purpose: highres-only second-dataset smoke after the 2026-06-11 direction to + keep highres and avoid an audio-model switch. +- Dataset slice: shortest local Guitar-TECHS direct-input chord clip, with + per-string MIDI labels. +- Settings: `--backend highres --position-prior none`; no GuitarSet prior and + no alternate backend. +- A same-settings 12-clip Guitar-TECHS chord run was attempted first and + exceeded the 30-minute interactive budget before writing a report. Use this + smoke for quick checks; use a cached or batched runner for the full + second-corpus gate. + +## Per-tier results + +| Tier | Clips | Gold notes | Tab F1 mean | Tab F1 lower-95 | Target | Status | Onset F1 | Pitch F1 | +|---|---:|---:|---:|---:|---:|---|---:|---:| +| clean_acoustic_single_line | 0 | 0 | — | — | 0.45 | missing | — | — | +| clean_acoustic_strummed | 0 | 0 | — | — | 0.60 | missing | — | — | +| clean_electric | 1 | 27 | 0.0000 | 0.0000 | 0.90 | fail | 0.7187 | 0.6562 | +| distorted_electric | 0 | 0 | — | — | 0.82 | missing | — | — | + +## Chord-instance accuracy + +Whole-fingering recovery per chord cluster. The >= 0.85 bar is a v1.1 video-assisted target; audio-only is string-resolution-limited, like single-line Tab F1 (SPEC §1.4.1). + +| Tier | Clips | Chord acc mean | Lower-95 | +|---|---:|---:|---:| +| clean_acoustic_single_line | 0 | — | — | +| clean_acoustic_strummed | 0 | — | — | +| clean_electric | 1 | 0.0000 | 0.0000 | +| distorted_electric | 0 | — | — | + +## Per-source breakdown + +| Tier | Source | Clips | Tab F1 mean | Onset F1 mean | Pitch F1 mean | +|---|---|---:|---:|---:|---:| +| clean_electric | GuitarTECHS | 1 | 0.0000 | 0.7187 | 0.6562 | + +## Methodology + +- Manifest: `data\eval\guitartechs_highres_smoke.toml` +- Audio backend: `highres` +- Position prior: `none` +- Eval-harness SHA: `b25dfa9` +- Onset tolerance: 50 ms +- Bootstrap: N=10,000, seed=42, 95% percentile interval +- Acceptance gate: `lower_95_CI >= target` per design plan §5 + diff --git a/docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_decomposition_2026-06-11.md b/docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_decomposition_2026-06-11.md new file mode 100644 index 0000000..f8083ab --- /dev/null +++ b/docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_decomposition_2026-06-11.md @@ -0,0 +1,19 @@ +# Tab F1 error decomposition + +## Aggregate (all tiers) + +| Bucket | Count | Share of loss | +|---|---:|---:| +| correct | 0 | — | +| wrong_position_same_pitch | 18 | 45.0% | +| pitch_off | 5 | 12.5% | +| timing_only | 1 | 2.5% | +| missed_onset | 3 | 7.5% | +| extra_detection | 13 | 32.5% | + +## Per-tier breakdown + +| Tier | correct | wrong_position_same_pitch | pitch_off | timing_only | missed_onset | extra_detection | +|---|---|---|---|---|---|---| +| clean_electric | 0 | 18 | 5 | 1 | 3 | 13 | + diff --git a/docs/EVAL_REPORTS/v1_1_post_training_real_chain_2026-06-11.md b/docs/EVAL_REPORTS/v1_1_post_training_real_chain_2026-06-11.md new file mode 100644 index 0000000..7935fdc --- /dev/null +++ b/docs/EVAL_REPORTS/v1_1_post_training_real_chain_2026-06-11.md @@ -0,0 +1,78 @@ +# v1.1 post-training real-chain rerun + +**Date:** 2026-06-11 +**Dataset:** Kaggle UT-Austin tablature dataset, local copy under +`~/.tabvision/data/datasets/guitar-transcription-utaustin` +**Detector:** completed YOLO-OBB checkpoint at +`~/.tabvision/data/models/guitar-yolo-obb-finetuned.pt` + +## Why this rerun happened + +Chunk 3 completed while the YOLO-OBB fine-tune had only reached the interrupted +epoch-4 checkpoint. The 50-epoch CPU resume has now completed, so the real-video +probes were rerun against the final exported checkpoint. + +Training finished at: + +| Metric | Value | +|---|---:| +| Precision | 0.9400 | +| Recall | 0.9040 | +| mAP50 | 0.9437 | +| mAP50-95 | 0.6882 | + +## Commands + +```powershell +cd tabvision +.\.venv\Scripts\python.exe scripts\eval\v1_1_yolo_rig_probe.py --checkpoint $env:USERPROFILE\.tabvision\data\models\guitar-yolo-obb-finetuned.pt +.\.venv\Scripts\python.exe scripts\eval\v1_1_real_chain_probe.py --checkpoint $env:USERPROFILE\.tabvision\data\models\guitar-yolo-obb-finetuned.pt +.\.venv\Scripts\python.exe -u scripts\eval\v1_1_real_chain_probe.py --checkpoint $env:USERPROFILE\.tabvision\data\models\guitar-yolo-obb-finetuned.pt --audio-source highres +``` + +The highres run was captured in: +`~/.tabvision/logs/v1_1_real_chain_highres_20260611-131459.out.log`. + +## YOLO rig probe + +The completed detector localizes the neck on the sampled UT-Austin frames: + +| Check | Result | +|---|---:| +| Neck detected | 21/21 (100%) | +| Localized non-full-frame homography | 21/21 (100%) | + +## Gold-pitch real-video probe + +This isolates string/fret selection by using gold-pitch audio events. + +| Condition | Mean Tab F1 | +|---|---:| +| audio-only | 0.4243 | +| audio + real video | 0.5373 | +| audio + oracle video | 1.0000 | + +Delta: **+0.1130** across 24 scored clips / 527 notes. The final checkpoint +still proves the video lever, but it is slightly below the chunk-3 gold-pitch +run (**0.5453**) because clip 9 regressed under the current gate. + +## Real highres audio probe + +| Condition | Mean Tab F1 | +|---|---:| +| highres audio-only | 0.0583 | +| highres audio + real video | 0.0739 | +| highres audio + oracle video | 0.1959 | + +Delta: **+0.0156** across 24 scored clips / 527 notes. The completed detector +improves the real-video lift versus chunk 3 (**+0.0074**), but the oracle ceiling +is unchanged. This confirms the next chunk should focus on highres audio +transcription/alignment before further video weighting. + +## Conclusion + +The completed YOLO checkpoint is good enough for the real-video harness. The +remaining blocker is upstream audio on the UT-Austin WAVs: pitch/time alignment, +note grouping, and possibly backend choice. Do not tune more video fusion against +the highres numbers until the highres+oracle ceiling moves materially above +0.1959. diff --git a/docs/NARRATIVE.md b/docs/NARRATIVE.md index e73b4af..844148e 100644 --- a/docs/NARRATIVE.md +++ b/docs/NARRATIVE.md @@ -23,7 +23,7 @@ What remains before a public v1 release: - Final license gate after all default backends are selected. - Release tag only after coordinator integration and user sign-off. -Full hand-labeled evaluation on newly recorded user clips is intentionally -future validation, not a v1 prerequisite. The release story should be explicit: -manual annotation was removed from the critical path so the project can ship -with reproducible automated evidence rather than private/manual gates. +Full hand-labeled private-video evaluation is no longer planned as a v1 gate. +The release story should be explicit: manual annotation was removed from the +critical path so the project can ship with reproducible automated evidence +rather than private/manual gates. diff --git a/docs/plans/2026-04-23-training-video-pipeline-refinement-design.md b/docs/plans/2026-04-23-training-video-pipeline-refinement-design.md deleted file mode 100644 index 98760bf..0000000 --- a/docs/plans/2026-04-23-training-video-pipeline-refinement-design.md +++ /dev/null @@ -1,62 +0,0 @@ -# Training Video Pipeline Refinement - -**Date**: 2026-04-23 -**Goal**: 85%+ avg exact F1 across 20 training videos -**Primary benchmark**: 20 training videos (replace original 11 as primary) -**Secondary benchmark**: Original 11 videos (regression gate only) - -## Context - -20 training videos recorded on electric guitar with ground-truth ASCII tabs. -Categories: position ambiguity (01-05), chord varieties (06-10), single-note passages (11-15), edge cases (16-20). - -Training videos are 90-95% accurate to tabs (notes mostly correct, timing approximate). Ground-truth tabs will be corrected via hybrid approach: run benchmark, flag biggest discrepancies, only fix tabs where pipeline is clearly right and tab is wrong. - -## Phase 1: Audio-Only Baseline & Diagnostics - -1. Run `python run_benchmarks.py --save training_baseline --verbose` on all 20 training videos -2. Extract per-video: pitch F1, exact F1, position accuracy. The pitch-to-exact gap reveals how much is pitch detection vs position assignment. -3. Aggregate by category (01-05, 06-10, 11-15, 16-20) to identify weakest areas -4. Generate discrepancy report for ground-truth review: list specific mismatches where pipeline output diverges from tabs - -**Deliverable**: Diagnostic summary with per-video and per-category accuracy, plus discrepancy report for manual tab correction. - -## Phase 2: Audio-Only Optimization Loop - -Iterate until 85%+ or diminishing returns: - -### A. Pick target -Prioritize by: -1. High pitch F1, low exact F1 (position errors, fixable in fusion engine) -2. Low pitch F1 (pitch detection issues, audio pipeline tuning) -3. Note count ratio far from 1.0 (too many or too few notes detected) - -### B. Diagnose failure -Run problem video with `--verbose`. Look for: -- Wrong string, right pitch (position scoring weights) -- Phantom notes (harmonic/sustain filter thresholds) -- Missing notes (filters too aggressive) -- Chord fragmentation (onset detection / grouping) - -### C. Apply fix -Either parameter tune (thresholds, weights) or algorithm fix (new correction pass, modify existing one). - -### D. Verify -Re-run full benchmark. Accept only if: -- Target video improves -- No regression >2% on any other training video -- Original 11 videos stay stable - -### E. Save checkpoint -`python run_benchmarks.py --save tuning_vN` after each accepted change. - -## Phase 3: Video Fusion - -**Entry criteria**: Audio-only avg F1 has plateaued, remaining errors dominated by position ambiguity. - -1. Enable video: flip `audio_only` to `false` in index.json defaults for training videos -2. Assess fretboard detection on electric guitar: edge detection, Hough transform, neck geometry assumptions may need tuning -3. Tune video for position resolution: focus on training 01-05 (position ambiguity set), check if video observations help or hurt -4. Final regression check on original 11 videos with video enabled - -**Deliverable**: Final benchmark at 85%+ avg exact F1, saved as named checkpoint. diff --git a/docs/plans/2026-04-23-training-video-pipeline-refinement.md b/docs/plans/2026-04-23-training-video-pipeline-refinement.md deleted file mode 100644 index 20fe92b..0000000 --- a/docs/plans/2026-04-23-training-video-pipeline-refinement.md +++ /dev/null @@ -1,318 +0,0 @@ -# Training Video Pipeline Refinement — Implementation Plan - -> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. - -**Goal:** Reach 85%+ avg exact F1 on 20 training videos by running a diagnostic benchmark, then iterating with parameter tuning and targeted algorithm fixes. - -**Architecture:** Three-phase approach: (1) establish audio-only baseline on all 20 training videos, build diagnostic tooling; (2) iterative optimization loop analyzing error patterns and applying fixes with regression protection; (3) enable video fusion to resolve remaining position ambiguity. The existing `run_benchmarks.py` and `evaluate_transcription.py` handle scoring and diffs; we add a diagnostic analysis script on top. - -**Tech Stack:** Python, Basic Pitch, existing fusion engine (`tabvision-server/app/fusion_engine.py`), existing audio pipeline (`tabvision-server/app/audio_pipeline.py`), existing benchmark runner (`tabvision-server/run_benchmarks.py`) - ---- - -## Task 1: Run Audio-Only Baseline on All 20 Training Videos - -**Files:** -- Read: `tabvision-server/run_benchmarks.py` -- Read: `tabvision-server/tests/fixtures/benchmarks/index.json` -- Output: `tabvision-server/tests/fixtures/benchmarks/results/training_baseline.json` - -**Step 1: Verify all 20 videos are accessible** - -Run from `tabvision-server/`: -```bash -cd tabvision-server && python -c " -import json, os -with open('tests/fixtures/benchmarks/index.json') as f: - data = json.load(f) -repo = os.path.dirname(os.path.abspath('.')) -missing = [] -for bm in data['benchmarks']: - vp = os.path.join('..', bm['video_path']) - gp = bm['ground_truth_path'] - if not os.path.isabs(gp): - gp = os.path.join('..', gp) - if not os.path.exists(vp): - missing.append(f\"{bm['id']}: video {vp}\") - if not os.path.exists(gp): - missing.append(f\"{bm['id']}: gt {gp}\") -if missing: - print('MISSING:') - for m in missing: - print(f' {m}') -else: - print(f'All {len(data[\"benchmarks\"])} benchmarks have video + ground truth files') -" -``` - -Expected: "All 21 benchmarks have video + ground truth files" (20 training + 1 sample-video) - -**Step 2: Run the full benchmark suite and save as baseline** - -Run from `tabvision-server/`: -```bash -cd tabvision-server && python run_benchmarks.py --save training_baseline --audio-only -``` - -Expected: Summary table printed for all 21 benchmarks. Results saved to `tests/fixtures/benchmarks/results/training_baseline.json`. - -**Step 3: Record the baseline numbers** - -Copy the summary table output. Key numbers to capture: -- Per-video: exact F1, pitch F1, position accuracy, note count ratio -- Average exact F1 across training-01 through training-20 -- Average exact F1 for the original sample-video - -**Step 4: Commit baseline** - -```bash -git add tests/fixtures/benchmarks/results/training_baseline.json -git commit -m "benchmark: save audio-only baseline for 20 training videos" -``` - ---- - -## Task 2: Build Diagnostic Analysis Script - -**Files:** -- Create: `tabvision-server/analyze_benchmarks.py` -- Test: manual run (script is a diagnostic tool, not production code) - -**Step 1: Write the diagnostic script** - -Create `tabvision-server/analyze_benchmarks.py` that reads a benchmark results JSON and produces: - -1. **Per-category breakdown**: Average exact F1, pitch F1, position accuracy grouped by: - - Position Ambiguity (training-01 to training-05) - - Chord Varieties (training-06 to training-10) - - Single-Note Passages (training-11 to training-15) - - Edge Cases (training-16 to training-20) - -2. **Error classification per video**: For each video, classify the dominant error: - - "pitch_detection" if pitch F1 < 0.7 - - "position_assignment" if pitch F1 >= 0.7 but exact F1 < pitch F1 * 0.7 - - "note_count" if detected/gt ratio is < 0.7 or > 1.5 - - "mixed" otherwise - -3. **Discrepancy report**: For each video, list up to 10 near-misses with: - - Expected string+fret vs detected string+fret - - MIDI note - - Whether it's a position error or pitch error - -4. **Priority ranking**: Sort videos by "improvement potential" = pitch F1 - exact F1 (biggest gap = most position errors to fix) - -Usage: -```bash -python analyze_benchmarks.py tests/fixtures/benchmarks/results/training_baseline.json -``` - -**Step 2: Run analysis on baseline results** - -```bash -cd tabvision-server && python analyze_benchmarks.py tests/fixtures/benchmarks/results/training_baseline.json -``` - -Expected: Categorized report showing which categories and videos to target first. - -**Step 3: Commit** - -```bash -git add analyze_benchmarks.py -git commit -m "feat: add benchmark diagnostic analysis script" -``` - ---- - -## Task 3: Review Discrepancy Report and Fix Ground-Truth Tabs - -**Files:** -- Modify: `test-data/training-tabs/training-XX-tabs.txt` (whichever need correction) - -**Step 1: Present discrepancy report to user** - -After running the analysis script, present the discrepancy report for user review. For each flagged mismatch, show: -- The video ID and timestamp -- What the pipeline detected (string + fret) -- What the ground-truth tab says (string + fret) -- The MIDI note (to verify pitch correctness) - -**Step 2: User decides which tabs to correct** - -User reviews and identifies cases where they actually played something different from the written tab. These tabs get corrected. - -**Step 3: Re-run benchmark with corrected tabs** - -```bash -cd tabvision-server && python run_benchmarks.py --save post_gt_correction --audio-only -python run_benchmarks.py --diff training_baseline -``` - -Expected: Some videos improve simply from ground-truth correction. Diff shows which changed. - -**Step 4: Commit corrected tabs** - -```bash -git add test-data/training-tabs/training-*-tabs.txt -git commit -m "fix: correct ground-truth tabs based on discrepancy analysis" -``` - ---- - -## Task 4: Optimization Cycle — Iteration Template - -This task repeats for each optimization cycle. Each cycle targets one error pattern. - -**Files:** -- Modify: `tabvision-server/app/audio_pipeline.py` (for pitch detection issues) -- Modify: `tabvision-server/app/fusion_engine.py` (for position assignment issues) -- Test: `tabvision-server/tests/test_note_filtering.py` or new test file -- Output: `tabvision-server/tests/fixtures/benchmarks/results/tuning_vN.json` - -**Step 1: Identify target from analysis** - -Pick the video or category with the lowest exact F1 where pitch F1 is reasonable (>0.7). Run that specific video with verbose output: - -```bash -cd tabvision-server && python run_benchmarks.py --id training-XX --verbose --audio-only -``` - -Examine the near-misses to identify the dominant error pattern. - -**Step 2: Write a failing test (if algorithm change)** - -If the fix is an algorithm change (not just a parameter tweak), write a test in `tabvision-server/tests/` that captures the expected behavior: - -```python -def test_specific_error_pattern(): - # Setup: create notes that trigger the error pattern - # Assert: the fix produces correct output -``` - -Run to verify it fails: -```bash -cd tabvision-server && pytest tests/test_SPECIFIC.py::test_specific_error_pattern -v -``` - -**Step 3: Apply the fix** - -Either: -- **Parameter change**: Modify the config dataclass defaults in `audio_pipeline.py:38` (`AudioAnalysisConfig`) or `fusion_engine.py:57` (`FusionConfig`) -- **Algorithm change**: Modify the relevant function in `fusion_engine.py` (position selection at line 705, chord optimization, melodic correction at line 1235, slide correction at line 1019, pre-filter at line 173, post-filter at line 336) - -**Step 4: Verify test passes (if applicable)** - -```bash -cd tabvision-server && pytest tests/test_SPECIFIC.py::test_specific_error_pattern -v -``` - -**Step 5: Run full benchmark and diff** - -```bash -cd tabvision-server && python run_benchmarks.py --save tuning_vN --audio-only -python run_benchmarks.py --diff tuning_vPREV -``` - -Accept if: -- Target video/category improved -- No regression >2% on any other training video -- Run sample-video separately to check original benchmark: `python run_benchmarks.py --id sample-video --audio-only` - -**Step 6: Commit** - -```bash -git add -A -git commit -m "feat(accuracy): describe specific improvement" -``` - -**Repeat** from Step 1 with the next worst-performing video/category. - ---- - -## Task 5: Enable Video Fusion (Phase 3) - -**Entry criteria:** Audio-only avg F1 has plateaued. Remaining errors are dominated by position ambiguity (high pitch F1, lower exact F1). - -**Files:** -- Modify: `tabvision-server/tests/fixtures/benchmarks/index.json` (flip audio_only default) -- Possibly modify: `tabvision-server/app/fretboard_detection.py` (electric guitar tuning) -- Possibly modify: `tabvision-server/app/video_pipeline.py` (electric guitar tuning) - -**Step 1: Run video-enabled benchmark on a few test videos** - -Start with 2-3 videos from the position ambiguity category: -```bash -cd tabvision-server && python run_benchmarks.py --id training-01 -cd tabvision-server && python run_benchmarks.py --id training-03 -cd tabvision-server && python run_benchmarks.py --id training-05 -``` - -Note: without `--audio-only` flag, these will use the default from index.json. Temporarily edit index.json to set `audio_only: false` for just these videos, or modify the entries individually. - -**Step 2: Check fretboard detection quality** - -Look at the output for "fretboard detection confidence" and "video detection rate". If these are low on electric guitar, the video pipeline needs tuning before it can help. - -**Step 3: Tune video pipeline for electric guitar** - -If detection quality is poor, adjust parameters in: -- `tabvision-server/app/fretboard_detection.py`: edge detection thresholds, Hough transform params -- `tabvision-server/app/video_pipeline.py`: MediaPipe confidence thresholds, finger mapping - -**Step 4: Enable video for all training videos** - -Once video detection is reliable: -```json -// index.json defaults -"audio_only": false -``` - -**Step 5: Run full benchmark with video** - -```bash -cd tabvision-server && python run_benchmarks.py --save final_with_video -python run_benchmarks.py --diff tuning_vLAST -``` - -**Step 6: Regression check on original videos** - -```bash -cd tabvision-server && python run_benchmarks.py --id sample-video -``` - -**Step 7: Commit** - -```bash -git add -A -git commit -m "feat(accuracy): enable video fusion for training benchmarks" -``` - ---- - -## Task 6: Final Validation and Cleanup - -**Step 1: Run final full benchmark** - -```bash -cd tabvision-server && python run_benchmarks.py --save final --verbose -``` - -**Step 2: Verify 85%+ target** - -Check the average exact F1 across training-01 through training-20. Must be >= 0.85. - -**Step 3: Update baseline minimums in index.json** - -For each training video, set `f1_min` baselines based on achieved scores (with some margin): -```json -"baselines": { - "f1_min": , - "pitch_f1_min": -} -``` - -**Step 4: Final commit** - -```bash -git add -A -git commit -m "feat(accuracy): achieve 85%+ avg F1 on training video benchmark" -``` diff --git a/docs/plans/2026-04-23-video-hand-anchor-design.md b/docs/plans/2026-04-23-video-hand-anchor-design.md deleted file mode 100644 index 303bebe..0000000 --- a/docs/plans/2026-04-23-video-hand-anchor-design.md +++ /dev/null @@ -1,222 +0,0 @@ -# Video-Driven Hand Anchor — Design - -**Date:** 2026-04-23 -**Branch:** `agent-farm-improvements` -**Status:** Design approved, implementation starting - -## Motivation - -Training benchmark (20 videos): - -| Config | exact F1 | pitch F1 | position acc | -|---|---|---|---| -| Baseline (audio only) | 0.385 | 0.676 | 0.607 | -| Current audio+video (v13) | 0.446 | 0.750 | 0.557 | - -The 30-point gap between pitch F1 (0.75) and exact F1 (0.446) is almost entirely position error. Position accuracy actually *dropped* when video was added. Video is helping pitch filtering but hurting position selection — it's contributing noise to the position layer. - -Root cause (confirmed by reading `fusion_engine.assemble_tab_document` around line 1635): video is used in an **override-or-ignore** mode. `match_video_to_candidates_enhanced` requires an exact (string, fret) match to an audio candidate; below the 0.3 threshold the video is discarded and the anchor-based `_select_best_position` runs as if video didn't exist. The `hand_position_fret` anchor itself is derived from *past audio picks*, which is circular — when early picks go wrong, the anchor locks into the wrong zone and drags subsequent picks with it. - -The dominant scoring term in position selection is `hand_position_fret` (weight `-0.35` per fret of distance, vs `-0.05` lower-fret bias and `-0.15` previous-position). Fixing this anchor is the biggest single lever. - -## Goal - -Compute the hand anchor directly from video (palm centroid projected onto the detected fretboard) instead of deriving it from retrospective audio picks. Keep the existing anchor code as a fallback when video is unavailable. - -Success criteria on training benchmark: -- Position accuracy ≥ 0.70 (from 0.557) -- Exact F1 ≥ 0.55 (from 0.446) -- No regression on training videos that already work (e.g., training-19 at 0.81) - -## Architecture - -``` -video_observations + fretboard_geometry - │ - ▼ -build_hand_position_timeline() - │ - ▼ -list[HandAnchorPoint] (sorted by timestamp, gap-tolerant) - │ - ▼ -get_hand_anchor_at(timestamp) → (anchor_fret, confidence) | (None, 0.0) - │ - ▼ -effective_hand_pos = video_anchor (primary, when v_conf ≥ 0.6) - | v·video + (1-v)·audio_anchor (blend, 0.4 ≤ v < 0.6) - | existing audio-derived anchor (fallback) - │ - ▼ -_select_best_position(... hand_position_fret=effective_hand_pos) -``` - -New module: `app/hand_anchor.py` -Feature flag: `FusionConfig.use_video_hand_anchor` (default `False` until proven) - -## Landmark → fret projection - -**Landmark choice: palm centroid = mean of MCP joints (MediaPipe landmarks 5, 9, 13, 17).** - -- Wrist (landmark 0) sits behind the neck and shifts with wrist rotation. -- Single MCP gives a hand edge, not a center, and jitters more per-frame. -- Averaging the four MCP landmarks gives a robust centroid directly over the fretboard, naturally corresponding to the center fret of the current hand pose. - -**Projection:** reuses the existing neck-axis projection in `fretboard_detection.py:964-993`. Computes `rel_x ∈ [0, 1]` along the neck, then interpolates through `fret_positions` / `actual_fret_numbers` to produce a **fractional fret number in absolute fret space** (e.g. `5.4` means hand centered between fret 5 and 6). - -Existing `_find_nearest_fret_smart` snaps to the nearest integer — fine for a fingertip, wrong for an anchor. New helper `_interpolate_fret_from_rel_x` returns a float. - -**Per-frame confidence:** -``` -anchor_confidence = geometry.detection_confidence - * hand_observation.hand_confidence - * bounds_penalty # rel_x in [-0.15, 1.15] -``` -Frames with confidence < 0.4 drop from the timeline. - -## Temporal smoothing - -Raw per-frame anchors are noisy. The timeline is built via: - -1. **Outlier rejection (median window).** For each raw sample, compare to median of ±0.3s neighbors. If current sample is > 3 frets from median **and** confidence < 0.7, drop it. Kills single-frame fretboard-detection glitches without erasing real position shifts. - -2. **Confidence-weighted EMA:** - ``` - α = clamp(dt * 2.0, 0.1, 0.6) * confidence - smoothed[i] = (1-α) * smoothed[i-1] + α * raw[i] - ``` - Larger `dt` → more weight on new sample (allows real shifts during pauses). Lower confidence → less weight. Clamp prevents teleportation. - -3. **Gap handling.** If `dt > 0.5s`, restart from the new raw value (don't extrapolate across gaps). Queries that fall inside a gap > `max_gap` return `(None, 0)` → fusion falls back to audio anchor. - -**Not a Kalman filter:** hand dynamics are non-Gaussian (real playing has sudden jumps during position shifts). Median+EMA is simpler, tunable, and preserves genuine jumps when consecutive high-confidence samples agree. - -## Sampling density - -**Phase A (ship first):** use existing onset-sampled `video_observations` as-is. Sparse (~5 frames per onset) but sufficient for queries at chord timestamps. - -**Phase B (later, if A isn't enough):** add a uniform-interval (10 fps) sampling pass across the whole video for a dense continuous timeline. - -## Fusion integration - -Three touch points in `fusion_engine.assemble_tab_document` (around line 1635): - -**1. Build timeline once, up front** (after the `chord_anchors` first pass): -```python -hand_timeline = build_hand_position_timeline(video_observations, fretboard) \ - if config.use_video_hand_anchor else [] -``` - -**2. Replace `effective_hand_pos` computation** (currently lines 1736-1742): -```python -video_anchor, v_conf = get_hand_anchor_at(hand_timeline, chord_timestamp) -audio_anchor = _get_nearest_anchor(i) - -if video_anchor is not None and v_conf >= 0.6: - effective_hand_pos = video_anchor - anchor_source = "video" -elif video_anchor is not None: # 0.4 ≤ v_conf < 0.6 - fallback = hand_position_fret if hand_position_fret is not None else audio_anchor - effective_hand_pos = (v_conf * video_anchor + (1 - v_conf) * fallback - if fallback is not None else video_anchor) - anchor_source = "blend" -else: - # existing behavior: running EMA + audio chord anchor - effective_hand_pos = hand_position_fret - if audio_anchor is not None: - effective_hand_pos = (audio_anchor if effective_hand_pos is None - else effective_hand_pos * 0.3 + audio_anchor * 0.7) - anchor_source = "audio" -``` - -**3. Damp running EMA update when video is authoritative** (lines 1824-1827, 1876-1879): -```python -if anchor_source == "video": - hand_position_fret = effective_hand_pos # track video directly -else: - # existing EMA from picked positions - ... -``` - -**Why hard cutover at 0.6 and not a continuous blend:** -When video is clearly reliable, blending with a wrong audio anchor weakens the win. The blend band 0.4–0.6 handles marginal cases; below 0.4 we discard. - -**Why damp the EMA under authoritative video:** -Without damping, a single wrong audio pick drags the anchor toward the wrong zone for the next chord, undoing video's influence. Tracking video directly breaks the positive-feedback loop. - -**Capo:** `hand_position_fret` and video anchor are both absolute-fret-space. No capo-relative conversion needed. - -## Testing - -**Layer 1 — unit tests** (no MediaPipe/ffmpeg): -- `project_palm_to_fret`: synthetic observation + geometry → expected fractional fret. Edges: palm behind nut, past body, rotated neck, partial fretboard starting at fret 5. -- `build_hand_position_timeline`: spike rejection (bad frame at fret 15 among fret-5 neighbors), genuine shift preservation (5 samples 3→7). -- `get_hand_anchor_at`: gap > max_gap → None; between points → linear interp. - -**Layer 2 — anchor quality diagnostic** (new script `debug_hand_anchor.py`): -For each training video, compute per-GT-note: -- `|anchor_at(gt.timestamp) - gt.fret|` (MAE) -- fraction of notes with `|err| ≤ 2` -- gap coverage (fraction of GT timestamps with a valid anchor) - -If anchor MAE > 3 frets on most videos, projection is too noisy and projection/smoothing needs debugging before touching fusion. - -**Layer 3 — end-to-end benchmark:** -Run the existing training benchmark with flag OFF and ON, compare exact_f1 / pitch_f1 / position_acc per video. - -**Observability — debug fields on TabNote:** -```python -_debug_anchor_fret: Optional[float] -_debug_anchor_source: Optional[str] # "video" | "blend" | "audio" -``` -Lets us dump `(gt_fret, picked_fret, anchor_fret, source)` on per-video regressions. - -## Risk controls - -- **Feature-flagged.** `use_video_hand_anchor=False` means identical behavior to today. -- **Graceful fallback is the default path.** Empty timeline or None-query falls into the existing audio branch. No regression on audio-only cases. -- **Watch training-04 specifically** — pitch detection returns wrong MIDI notes on that video. A correct anchor could make it *worse* (picks wrong strings at right frets with high confidence on bad pitches). Inspect in the comparison. - -## Rollout - -1. Build `hand_anchor.py` + Layer 1 unit tests → green. -2. Run Layer 2 diagnostic on training set → anchor MAE acceptable? -3. Integrate into fusion with flag OFF → existing tests still pass. -4. Flip flag ON for benchmark run → compare per-video. -5. If wins: flip default to ON. If not: analyze and decide whether Phase B (dense sampling) or a projection fix is needed. - -## Decisions deferred - -- **Phase B dense sampling.** Only if Phase A doesn't clear the 0.70 position-accuracy bar. -- **Viterbi / joint sequence optimization.** Considered as a later refinement once emission signals (audio confidence × video anchor) are clean. -- **Using video to rule out candidates entirely.** If post-A we still see position picks > 4 frets from the anchor, add a hard gate in `_select_best_position`. - -## Result (2026-04-23) - -**Feature is NOT shipped. Kept in tree with `use_video_hand_anchor=False` (default).** - -Full training-benchmark comparison against the existing audio+video baseline (tuning_v13_video): - -| Metric | v13 (baseline) | v14 (anchor on) | Δ | -|---|---|---|---| -| Exact F1 | 0.446 | 0.370 | **−0.075** | -| Pitch F1 | 0.750 | 0.742 | −0.008 | -| Position acc | 0.557 | 0.453 | **−0.104** | - -20 videos: 8 regressed (position drops up to −31), 12 unchanged (sanity gate rejected timeline, fell back to baseline), **0 improved**. When the gate rejects, behavior matches v13 exactly. When the gate passes, the anchor consistently biases picks the wrong way. - -**Root cause** (per the training-13 diagnostic): even on videos where fretboard detection looks healthy (starting_fret=0, 26 frets detected, confidence 0.85), the palm projects consistently to fret ~20 while ground-truth notes are at frets 0–3. **Anchor MAE of 17 frets.** The core hypothesis — "palm centroid projected onto neck axis = reliable hand position" — is empirically wrong for this dataset. - -Likely causes (not yet verified): -1. `_select_fretting_hand` may pick the picking/strumming hand in some videos — that hand sits over the sound hole, which projects to "fret ~18–22" on a detected neck. -2. Palm-centroid-of-fingertips falls back to wrist often when fewer than 2 non-thumb fingers are extended. Wrist screen position does not correspond to where fingers actually fret when the hand is angled. -3. Fretboard quad corners may be plausible-looking but still wrong enough to break the projection. - -## Retained - -- `app/hand_anchor.py` (210 lines) + 26 unit tests — isolated, no runtime impact when flag is off. -- `FusionConfig.use_video_hand_anchor` flag — off by default. -- `debug_hand_anchor.py` diagnostic — ready for future iteration. -- `run_benchmarks.py --with-video --use-video-hand-anchor` / `--save` — ready for future runs. - -Future work should start from the diagnostic: pick 2–3 videos with large MAE, dump palm pixel coordinates + fretboard corners + selected hand index, and figure out which of the three likely causes dominates before changing the algorithm. diff --git a/docs/plans/2026-04-24-audio-backbone-finetune-design.md b/docs/plans/2026-04-24-audio-backbone-finetune-design.md deleted file mode 100644 index 3c294c6..0000000 --- a/docs/plans/2026-04-24-audio-backbone-finetune-design.md +++ /dev/null @@ -1,201 +0,0 @@ -# Path 2 — Fine-tune Basic Pitch on Guitar Audio - -**Date:** 2026-04-24 (refreshed 2026-04-29) -**Author:** Patrick (brainstormed with Claude) -**Status:** Proposed — actively executing on `feature/audio-finetune-phase1` -**Originally superseded:** `2026-04-24-learned-fusion-design.md`. That plan was attempted on `agent-farm-improvements` 2026-04-29 and **did not ship** (LightGBM ranker LOOCV +0.3pp vs +5pp gate). See `tools/outputs/position_selector_report-2026-04-29.md`. - -## 0. 2026-04-29 refresh — corrected baselines - -§1's "audio is 91% of loss" framing relied on a harness bug that produced misaligned bucketing (no `_find_best_time_offset`, no muted-X separation). The corrected harness (`agent-farm-improvements` commit `fa3ca0f`, output `tools/outputs/errors-2026-04-28_185743.md`) gives different numbers: - -| Bucket (recoverable loss) | Original (apr-24) | Corrected (apr-28) | Δ | -|---|---:|---:|---:| -| extra_detection | 246 (39.4%) | 83 (27.9%) | -163 | -| missed_onset | 170 (27.2%) | 74 (24.8%) | -96 | -| pitch_off | 153 (24.5%) | 31 (10.4%) | -122 | -| wrong_position_same_pitch | 55 (8.8%) | 105 (35.2%) | +50 | -| timing_only | 7 (1.1%) | 5 (1.7%) | -2 | - -Net implications: - -- **Audio-side total share went from 91% → 63%** of recoverable loss. Audio is still the larger fight, but not by an order of magnitude. -- **Mean exact F1 baseline shifts from 0.43 to ~0.51** (222 correct out of 437 pitched GT — see `position_dataset.md`). This is the reference point for the ship gate below. -- **Phase 0 in §6 already failed against the buggy harness** (`feature/audio-finetune` commit `382ed03`, memory: `project_phase0_rms_activity_end.md`). End-of-playing RMS truncation is a no-op on iPhone audio; per-clip amplitude floor only marginally helped. Phase 0 is shelved as designed; if we want extras truncation, it needs a different mechanism (e.g., last-pitched-note timestamp from Basic Pitch itself). -- **Ship gate revised** (was: training-set mean exact F1 ≥ 0.60, pitch F1 ≥ 0.88): - - **Training-set mean exact F1 ≥ 0.60** (still ≈ +9 pp on the corrected baseline; original was +17 pp on the buggy baseline). - - **Training-set mean pitch F1 ≥ 0.88** (corrected baseline pitch F1 from `errors-2026-04-28_185743.md`: pitch_tp = 222 + 105 = 327, pitch_fn = 31 + 74 + 5 = 110, pitch precision/recall ≈ 0.74/0.75. So +13 pp on pitch F1 stays roughly accurate.). - - Worst-video regression > -0.03 forbidden — unchanged. - - Original 11-video suite stays > 0.91 — unchanged. -- **Expected gains in §2 (50% pitch_off reduction, 30% extras, 20% missed)** translate to: 16 pitch_off + 25 extras + 15 missed = ~56 events recovered out of 215 wrong (-26% of loss). Still meaningful, much smaller than the +30-40% recovery the original plan implied. - -The 5-week timeline and architecture are unchanged. The §7 phasing (Week 1 = de-risk, Week 5 = ship gate) is still the right structure. - -## 1. Context - -The Step-0 error analysis (originally `tools/outputs/errors-2026-04-24_131204.md`, refreshed in `tools/outputs/errors-2026-04-28_185743.md`) broke the 20-video training set into seven buckets. The original framing said audio was the bottleneck; the corrected framing in §0 above tempers that — see §0 for the refreshed numbers. Original §1 numbers preserved below for traceability: - -| Bucket | Count | Share of exact-F1 loss | -|---|---:|---:| -| extra_detection | 246 | 39.4% | -| missed_onset | 170 | 27.2% | -| pitch_off | 153 | 24.5% | -| wrong_position_same_pitch | 55 | 8.8% | -| timing_only | 7 | 1.1% | - -Position selection (the target of the shelved learned-fusion plan) is only 8.8% of loss. Three audio-side buckets together are 91%. Of those, the largest lever is replacing Basic Pitch — a general-purpose polyphonic pitch model — with a version fine-tuned on guitar audio specifically. - -Separate finding: **67.5% of `extra_detection` events fall after GT_end + 1s.** This is independent of the model and addressable with cheap heuristics (Phase 0, below). - -## 2. Hypothesis - -The dominant loss is that Basic Pitch — the pitch backbone — is genuinely wrong on iPhone-quality guitar audio a lot of the time (pitch F1 75%, 153 pitch_off events in 20 videos). Fine-tuning Basic Pitch on guitar-specific labeled audio will: - -- Reduce `pitch_off` by at least half. -- Reduce `extra_detection` by ~30% (better pitch → fewer spurious detections carrying wrong pitches). -- Reduce `missed_onset` by ~20% (sharper onset head). - -Target: training-set mean exact F1 from 43% to ≥ 65%, with training-set mean pitch F1 from 75% to ≥ 88%. - -## 3. Why this path vs alternatives - -Three paths were considered: - -- **Path 1: heuristic iteration + small learned components.** Ceiling ~75–85% F1, diminishing returns, fusion engine keeps growing. Rejected as primary, but *Phase 0 borrows from it* for the free-lunch extras fix. -- **Path 2: fine-tune a guitar-specific audio backbone.** Ceiling ~85–92% F1, data problem is tractable via GuitarSet, architecture is already proven. **Chosen.** -- **Path 3: end-to-end audio+video→tab transformer.** Ceiling ~92–97%, but 10× the data is needed and 100× the compute. Deferred until Path 2 ships and we know whether we're still audio-limited. - -Within Path 2, two backbones were considered: - -- **Fine-tune Basic Pitch** (2M params, TCN, CPU-runnable). Pretrained on GuitarSet+MedleyDB+MAESTRO+MAPS, output interface matches our pipeline. Weak docs on training are the main risk. -- **Small transformer from scratch / pretrained-encoder + head.** Architecturally cleaner, extensible, but 3 hours of GuitarSet is far too little for a cold-start transformer. Kept as the fallback if the Basic Pitch training loop can't be revived in a week. - -## 4. Architecture (end state) - -Three separable components: - -1. **Audio transcription model** — fine-tuned Basic Pitch. In: audio. Out: `(onset_time, midi_pitch, confidence, duration)` per note. Shipped as `app/models/basic_pitch_guitar.pth`, gated by `FusionConfig.use_finetuned_audio`, default off until the regression gate passes. -2. **Video position model** — unchanged in this plan. MediaPipe + fretboard geometry → candidate `(string, fret)` per onset. Replacing this is a separate future plan. -3. **Fusion** — unchanged in this plan. The existing engine consumes `(pitch, candidate positions)` and produces tab notes. With a stronger audio model feeding it, many of today's heuristic filters (sustain-redetection, harmonic filter) can be reviewed and possibly simplified in a follow-up. - -Deployment target: Vercel frontend with a Modal-hosted backend. This plan is deployment-agnostic — the fine-tuned checkpoint runs on the existing backend. - -## 5. Dataset — Tier A only, initially - -- **GuitarSet** (NYU MARL): 360 30-second clips, 6 players, hexaphonic pickup, JAMS annotations with MIDI + (string, fret). ~3 hours aligned audio. Sufficient for a first fine-tune. -- Split 80/20 **by player** (not clip) so no player identity leaks into evaluation. -- Our 20-video training set is the out-of-distribution test — GuitarSet is studio, ours is iPhone. - -**Not in scope for this plan:** - -- Tier B (render Guitar Pro files to synthetic audio). Add only if Tier A caps below the ship gate. -- Tier C (YouTube + community tab alignment). Deferred indefinitely. - -## 6. Phase 0 — free-lunch audio fixes (parallel, ~2 days) - -Three cheap fixes that don't depend on fine-tuning. Ship these first to reduce noise in the baseline. - -- **End-of-playing truncation.** Drop detected notes with `onset_time > audio_activity_end + 0.5s`, where `audio_activity_end` is the last time the RMS envelope exceeds a silence threshold for 250ms continuously. Target: remove ~66% of today's extras (166 of 246). Expected +6 to +8 pp mean exact F1. -- **Amplitude floor.** Drop detected notes with amplitude below a percentile threshold (e.g. 15th percentile of all detections in the clip). Cheap, tuned per-clip, probably removes a few more extras. -- **Sustain-redetection audit.** Revisit the existing filter — the error analysis will show how many remaining extras are same-pitch same-string sustain re-triggers. - -These also clean up the baseline we measure fine-tuning against. Ship them behind `FusionConfig.use_end_of_playing=True` default-on. - -## 7. Phase 1 — fine-tune execution (five weeks) - -### Week 1 — data loader + training loop (risk phase, timeboxed) - -- Download GuitarSet, parse one `.jams` file manually to understand the schema. -- Write `app/training/guitarset_dataset.py` — PyTorch Dataset emitting the exact tensor shapes Basic Pitch's training loop expects. -- Fork Basic Pitch's training repo. Overfit on a 5-clip subset to confirm gradients flow and loss decreases. - -**Bailout gate — Friday of week 1.** If a single training epoch cannot be run cleanly on 5 clips, pivot to the middle path: pretrained audio encoder (HuBERT / MusicFM, frozen) + a small onset+pitch head trained from scratch. Do *not* spend week 2 grinding on Basic Pitch's training loop if week 1 didn't crack it. - -### Week 2 — baseline + first fine-tune - -- Split GuitarSet 80/20 by player. **Done 2026-04-29:** TFRecords at `tabvision-server/tools/outputs/tfrecords/guitarset/splits/` — 5 players (300 clips) train, 1 player (60 clips) validation. Built by `tools/build_guitarset_tfrecords.py`. - -- **Vanilla baseline on the held-out player(s).** Research-baseline reference point for the fine-tune Δ. Plan §0 ship gate is exact-F1 on our 20-video set, *not* on GuitarSet held-out — but we still measure GuitarSet held-out so we know whether the fine-tune is moving the right needle in-distribution before we look at OOD. - - Tooling: load the shipped Basic Pitch into a Keras model via `app.training.load_pretrained.load_pretrained_basic_pitch_weights` (verified equivalent to the SavedModel within float32 noise, 2026-04-29). Iterate the validation TFRecord through `parse_transcription_tfexample`, run forward, threshold the output, compute metrics. - - Metrics: - - **Frame note F1** at thresholds {0.3, 0.5, 0.7} on the `note` head (binary per-frame pitch activation against the `note_indices`/`note_values` sparse target densified to (T, 88)). - - **Onset P/R/F1** at thresholds {0.3, 0.5, 0.7} on the `onset` head against `onsets_indices` (single-frame events; allow ±1 frame tolerance ≈ ±12ms before declaring a miss). - - **Note-event F1** via `mir_eval.transcription.precision_recall_f1_overlap` after running `basic_pitch.note_creation.model_output_to_notes` (the same code path `predict.py` uses) — apples-to-apples to what the fine-tune output will produce. Default thresholds first, then sweep onset/frame thresholds at 0.05 stride. - - Output: a markdown report at `tabvision-server/tools/outputs/finetune_baseline-YYYY-MM-DD.md` matching the format of `position_selector_report-2026-04-29.md`. Commit it. - - Acceptance: report exists, numbers are consistent (frame F1 ≥ note-event F1 ≥ 0 — sanity), reproduce within 0.5pp on a re-run. - -- **First fine-tune.** Unfreeze all weights, lr=1e-4, batch=8, 20 epochs, Adam, standard Basic Pitch losses (BCE on onset + CE on pitch). Rent an A10G (~8 hours, $3–5). - - Initialize from pretrained: monkey-patch `basic_pitch.train.models.model` to call `load_pretrained_basic_pitch_weights` after construction, **before** calling `train.main`. Without this we'd train from scratch. - - Use `tools/finetune_basic_pitch_smoke.py` as the runner shape, but bumped to the full hparams above and pointed at the full TFRecord set. - -- **Evaluate the fine-tuned checkpoint** on the held-out split with the same harness used for the vanilla baseline. First Δ vs vanilla. - -### Week 3 — OOD evaluation + augmentation - -- Run the fine-tuned model on our 20 training videos. -- Re-run the error analysis harness (`tools/error_analysis.py`) against the new audio output. -- Compare buckets: `missed_onset` recovered? `pitch_off` reduced? `extra_detection` reduced? -- If the 20-video gain is < half the GuitarSet gain, add SpecAugment + room-impulse-response convolution + pink noise to training. Retrain. - -**Go/no-go at end of week 3:** - -| 20-video pitch F1 gain | Decision | -|---|---| -| ≥ +10 pp (75 → ≥ 85) | Continue to week 4–5. | -| +5 to +10 pp | Continue, and add Tier B synthetic data next. | -| < +5 pp | Audio-alone ceiling reached. Pivot focus to the string/fret head + source separation. | - -### Week 4 — hyperparameter sweep - -- Grid over lr, batch, epochs, augmentation strength. ~10 runs total. -- Pick best by held-out GuitarSet note F1, breaking ties on our-20-video exact F1. - -### Week 5 — integration + ship - -- Export checkpoint to `app/models/basic_pitch_guitar.pth`. -- Add `FusionConfig.use_finetuned_audio: bool = False` in `app/fusion_engine.py`. -- Wire a loader in `app/audio_pipeline.py` that uses the fine-tuned checkpoint when the flag is on, stock Basic Pitch otherwise. -- Add a regression test: run one checked-in clip end-to-end, assert mean exact F1 on training-01 exceeds the vanilla-Basic-Pitch baseline by at least the week-3 delta. -- Flip the default to `True` in a follow-up PR after watching it for a few days. - -## 8. Ship gate (cross-phase) - -To consider Path 2 shipped: - -| Metric | Current | Ship gate | -|---|---|---| -| Training-set mean exact F1 | 0.43 | ≥ 0.60 | -| Training-set mean pitch F1 | 0.75 | ≥ 0.88 | -| Worst-video regression on exact F1 | n/a | > -0.03 forbidden | -| Original 11-video suite mean exact F1 | 0.92 | > 0.91 (no regression on the "easy" suite) | -| Per-video inference time | baseline | ≤ 2× (we can spend more on CPU; no GPU required for shipping) | - -Phase 0 alone is not expected to hit this gate — its contribution is ~+6–8 pp exact F1. The remaining ~+11–15 pp is the fine-tune's job. - -## 9. Relationship to other plans - -- **`2026-04-24-learned-fusion-design.md`** — shelved. Its target (position selection) is 8.8% of loss. Revisit after Path 2 ships, because cleaner audio may shrink that share further or leave it as a real remaining gap worth attacking. -- **`2026-04-24-error-analysis-harness.md`** — completed. The harness is the measurement tool for every checkpoint we take from here on. - -## 10. Risks - -- **Basic Pitch training loop is undocumented.** Bounded by the week-1 timebox + bailout to middle path. -- **GuitarSet was partially in Basic Pitch's pretraining.** The fine-tune gain on GuitarSet's held-out split may be small; the real signal is the OOD test on our 20 videos. Trust week-3 numbers over week-2 numbers. -- **Domain gap (studio vs. iPhone) bigger than expected.** Mitigation: augmentation in week 3. If still too big, add Tier B synthetic data. -- **Pitch gets better but extras stay high.** Means the extras are not pitch-driven (harmonics, resonance, past-end). Phase 0 catches the past-end subset; remaining extras become a separate heuristic pass. -- **String/fret head becomes the new bottleneck.** If pitch F1 goes from 75% to 92% but exact F1 only moves from 43% to 55%, the gap is position selection, and the shelved learned-fusion plan comes off the shelf. -- **We pick a wrong augmentation and regress on the clean GuitarSet set.** Always run both evaluations (held-out + our 20) and don't commit an augmentation that regresses the clean set by more than 2 pp. - -## 11. Open questions - -- Should we keep CREPE as a monophonic second opinion for pitch_off in low-polyphony passages? Worth testing in week 5 as a cheap additional source of signal, independent of the fine-tune. -- Where does inference live at deploy time — CPU on the existing backend, or GPU on Modal/Replicate? Defer until week 5, when we know the model's wall-clock cost. -- When the fine-tuned audio also emits stronger string-hints (Basic Pitch's per-pitch confidence curve differs per-string in practice), is that signal strong enough to shrink the fusion engine? Measure in week 5, act in a follow-up. - -## 12. Out of scope for this plan (ranked for later) - -1. **Video position CNN.** Small model trained on labeled frame crops → `(string_probs, fret_probs)` per onset. Only pursue if Path 2 leaves a position gap ≥ 10 pp. -2. **Tier B synthetic data.** Render Guitar Pro files via soundfonts, add to training. Revisit if week 3 go/no-go lands in the middle band. -3. **Source separation prefix.** Demucs/Spleeter stems to isolate guitar when accompaniment is present. Out of scope while the benchmark is clean solo guitar. -4. **Learned fusion (the shelved plan).** Revisit in full after Path 2 ships and re-measure error buckets. -5. **Path 3 end-to-end.** Only if Path 2 plateaus well below 90% F1 and we have resources to collect 10× more data. diff --git a/docs/plans/2026-04-24-learned-fusion-design.md b/docs/plans/2026-04-24-learned-fusion-design.md deleted file mode 100644 index 14c3ac9..0000000 --- a/docs/plans/2026-04-24-learned-fusion-design.md +++ /dev/null @@ -1,162 +0,0 @@ -# Learned Fusion for Position Selection — Design - -**Date:** 2026-04-24 -**Author:** Patrick (brainstormed with Claude) -**Status:** Proposed - -## 1. Problem - -On the 20-video training set: - -- **Exact F1: 43%** (pitch+string+fret+time all correct) -- **Pitch F1: 75%** (pitch correct) -- **Position accuracy: 54%** (string+fret correct given correct pitch) - -The largest loss is the **pitch→exact gap of ~32 points**: the audio pipeline finds the right pitch, but the fusion engine picks the wrong (string, fret). The original 11-video suite is at 91.6% exact F1, so the gap is both real and video-specific — heuristics over-fit to the easier set. - -The fusion engine (`app/fusion_engine.py`, ~2,000 lines) is now a stack of hand-tuned rules. Recent experiments (video hand anchor, fused handedness filter) produce null or negative results. Diminishing returns from heuristics. - -## 2. Hypothesis - -Position selection is the right intervention point and is learnable from existing data. - -Reasons: - -- It is by far the largest attributable loss (32pp). -- Every position decision has rich context the heuristic only partially uses (candidate list, hand anchor, prior position, chord membership, video evidence, chord-shape fit). -- Labels exist: for every GT note whose pitch we recover, we know the correct (string, fret). -- Runtime cost of a small model per onset is negligible (<1 ms) — does not violate the speed axis. - -## 3. Non-goals - -- No new audio backend (Basic Pitch stays; pitch F1 of 75% is a later fight). -- No end-to-end model. -- No change to pre-filter / post-filter / chord-size limiting / anchor math. -- No retraining of MediaPipe or fretboard detection. -- No new training data collection. We work with the 20 existing videos. - -## 4. Plan - -Six steps. Each is independently checkpointable. - -### 4.1 Step 0 — Error analysis harness (~0.5 day) - -Build `tabvision-server/tools/error_analysis.py`. For each of the 20 videos: - -1. Run pipeline in a fresh subprocess (avoids batch drift per `project_benchmark_drift`). -2. Match detections to GT using existing code in `evaluate_transcription.py`. -3. Bucket every aligned pair into: - - `correct` — string and fret match - - `wrong_position_same_pitch` — right pitch, wrong (string, fret) - - `pitch_off` — detected pitch ≠ GT pitch (audio-side loss, not ours to fix here) - - `missed_onset` — GT note has no detection - - `extra_detection` — detection has no matching GT - - `timing_only` — match outside time_tolerance but otherwise correct - - `chord_split` — one GT chord → multiple time-adjacent detections - -Outputs: - -- `tools/outputs/errors-YYYY-MM-DD.csv` — per-note rows with all context fields. -- `tools/outputs/errors-YYYY-MM-DD.md` — per-video and aggregate counts + percentages. - -**Decision gate:** if `wrong_position_same_pitch` is not the dominant bucket (≥40% of exact-F1 loss), stop and reconsider. The rest of the plan assumes it is. - -### 4.2 Step 1 — Feature instrumentation (~1 day) - -The fusion engine already computes everything we need, but it throws it away. Add a `FeatureSink` that, at each position-selection decision, emits one row per candidate (string, fret) containing: - -**Per-candidate features:** - -- `cand_string` (1–6), `cand_fret` (0–24) -- `dist_anchor_fret`, `dist_anchor_string` (signed, to nearest chord anchor) -- `dist_prev_fret`, `dist_prev_string` (signed, to previous played note) -- `in_chord_region` (bool), `chord_n_notes`, `chord_string_span` -- `chord_shape_score` — lookup via `chord_shapes.py` -- `video_finger_over_fret` — MediaPipe-derived likelihood (0 if no video) -- `video_hand_anchor_fret` — projected fret from hand, when available -- `heuristic_score` — the score the current engine would assign -- `is_heuristic_pick` (bool) — what the heuristic chose, for later calibration - -**Per-event features (shared across candidates):** - -- `onset_time`, `amplitude`, `basicpitch_confidence` -- `num_candidates`, `is_chord` (>=2 simultaneous), `chord_size` -- `prev_position_string`, `prev_position_fret`, `seconds_since_prev` - -Emission is gated behind `FusionConfig.emit_position_features`, default off. Zero runtime cost when flag is off. - -### 4.3 Step 2 — Labeled dataset construction (~0.5 day) - -`tools/build_position_dataset.py`: - -1. Run fusion with `emit_position_features=True` on all 20 videos. -2. For each emitted event, align the detected onset+pitch to GT using step 0's matcher. -3. If pitch aligns and GT has a (string, fret): mark the candidate whose (string, fret) matches GT as `label=1`, all others `label=0`. Drop events with no GT match. -4. Save as `tools/outputs/position_dataset.parquet` with ~2,000–4,000 candidate-rows. - -Include `video_id` as a group key so we can do leave-one-video-out CV. - -### 4.4 Step 3 — Model training (~1 day) - -Use **LightGBM in `lambdarank` mode** (pairwise ranking). Each event is a query; candidates are documents; label is 1 for the correct position, 0 otherwise. Ranker naturally handles variable candidate lists and class imbalance. - -- Small model: ~50 trees, depth 4, lr 0.1. We have ~3k rows. -- Leave-one-video-out cross-validation. Primary metric: per-event top-1 accuracy on held-out video. -- Save model to `app/models/position_selector.lgb` and feature schema to `app/models/position_selector_features.json`. -- `tools/train_position_selector.py` handles dataset load, CV, fit, dump, and writes a Markdown report. - -**Fallback:** if LightGBM ranker is flakey on small data, switch to binary classifier over (event-candidate) with argmax within event. Decision at training time, not design time. - -### 4.5 Step 4 — Flag-gated integration (~0.5 day) - -In `fusion_engine.py`: - -- Add `FusionConfig.use_learned_position_selector: bool = False`. -- When true, replace the current argmax-over-heuristic-score with an argmax over model scores, using the same candidate list and features emitted in step 1. -- If model's top score margin over the heuristic pick is below a tunable `learned_margin_threshold`, fall back to the heuristic. This limits damage when the model is out-of-distribution. -- No other heuristic is changed (pre-filter, chord limiter, post-filter, melodic-segment correction, slide correction all stay). - -### 4.6 Step 5 — Validation & decision (~0.5 day) - -Use `ab_anchor.py` subprocess-per-video to A/B heuristic vs learned on all 20 videos. Run the model trained leave-one-out per video (the one that did *not* see it in training). - -**Ship gate:** - -- Mean exact F1 improvement **≥ +5 points** across the 20 training videos. -- **No single video regresses by more than 3 points** exact F1. -- Original 11-video suite: mean exact F1 does not drop by more than 1 point. -- Runtime per video does not increase by more than 50 ms. - -If it ships, flip the default to `True` in a separate PR once we've watched it for a few days. - -If it misses the gate, the error-analysis CSV tells us exactly which events the model still gets wrong — the data becomes our next set of priorities (stronger features, different candidate generation, or a different model architecture). - -## 5. Success criteria (summary) - -| Metric | Current | Ship gate | -|---|---|---| -| Training-set exact F1 (mean) | 0.43 | ≥ 0.48 | -| Training-set worst-video regression | n/a | > -0.03 forbidden | -| Original 11-video exact F1 (mean) | 0.92 | > 0.91 | -| Per-video runtime overhead | 0 | < 50 ms | - -## 6. Risks & mitigations - -- **Data too small (~3k rows, 20 groups).** LightGBM + lambdarank is conservative; leave-one-out CV keeps us honest. If overfitting, drop features, lower tree count. -- **Feature leakage.** `is_heuristic_pick` and `heuristic_score` are included intentionally — we want the model to *beat* the heuristic, not clone it. Inspect SHAP / gain for over-reliance; regularize or drop if it becomes a parrot. -- **Benchmark drift.** All evaluation uses subprocess-per-video. No full-batch `run_benchmarks.py`. -- **OOD at inference.** Margin-based fallback to the heuristic bounds downside. -- **Labeling noise from imperfect pitch alignment.** Restrict labels to events with high-confidence pitch match (tight time + ±0 semitone). Keep the dataset small and clean rather than large and noisy. - -## 7. Open questions - -- Do we want a separate model for chord events vs single-note events? Probably not in v1 — let the `is_chord` feature handle it, revisit if CV shows bimodal error. -- Is step 0's bucketing sufficient, or do we need a sub-category for "position wrong because anchor was wrong"? Start simple; drill in only if step 0 says so. - -## 8. Out of scope for this plan (next bets) - -Ranked by expected value if the learned fusion ships: - -1. Audio-side pitch F1 (currently 75%). CREPE or Basic Pitch v2 for monophonic runs; evaluate on pitch F1 alone before touching fusion. -2. Direct-string-from-video CNN (small hand-crop classifier). Adds a strong feature for the learned fusion. -3. Onset-from-video for timing-only misses. diff --git a/docs/plans/2026-05-05-tabvision-spec-adoption-design.md b/docs/plans/2026-05-05-tabvision-spec-adoption-design.md index 9a96db8..f6f9226 100644 --- a/docs/plans/2026-05-05-tabvision-spec-adoption-design.md +++ b/docs/plans/2026-05-05-tabvision-spec-adoption-design.md @@ -162,7 +162,7 @@ checked-in fixtures, and already-generated reports. | Clean acoustic strummed | GuitarSet "comp" excerpts | Same source, comp/chord-mode subset | | Clean electric | Public/programmatic source if available | Registration-only datasets are optional future | | Distorted electric | EGDB held-out | Multi-amp, ground-truth tab | -| **Bonus tier: iPhone OOD** | Existing 11/20 self-recorded videos | Already-annotated; **not a v1 acceptance gate**, but a tracked metric so we know iPhone-domain regressions when they happen | +| **Optional real-video research tier** | GAPS / Kaggle UT-Austin-style offline corpora | Public/offline replacement sources only; private recordings are not a gate or dev set | **Phase 1.5 deliverables (revised):** @@ -177,7 +177,7 @@ checked-in fixtures, and already-generated reports. **Phase 1.5 acceptance gate (revised again 2026-05-07):** deterministic smoke eval runs, the optional manifest validator reports missing tiers as informational, and any full/public eval uses datasets that can be acquired -non-interactively. Missing hand-labeled user clips are not a v1 blocker. +informational, and no private clips are a v1 blocker. **Acknowledged blind spot:** distorted-electric §1.4 target is measured on EGDB (studio domain), not iPhone-recorded distortion. Documented here so @@ -194,9 +194,9 @@ log, `docs/plans/`), so the actual interactive interview is short. | Q | Pre-filled answer | Confidence | |---|---|---| | 1. Where is the project? | `/home/gilhooleyp/projects/tab_vision`, single repo, contains backend + Electron client | High | -| 2. State? | Backend works end-to-end; 91.6% Exact F1 on 11-video set; mid-experiment on Phase 1 audio finetune (H2 untested) | High | +| 2. State? | Backend works end-to-end; old private-video metrics invalidated 2026-06-11; use public/fixture reports for current evidence | High | | 3. Prior approach? | Audio (Basic Pitch) + vision (MediaPipe + geometric fretboard) + heuristic fusion. Worked: melodic segment correction, string compactness, 2-pass anchoring. Didn't ship: video hand anchor, learned-fusion LightGBM (NO_SHIP), Phase 0 RMS truncation (no-op on iPhone audio) | High | -| 4. Datasets/weights/annotations to preserve? | 11 + 20 self-recorded videos + ground-truth, GuitarSet TFRecords at `tabvision-server/tools/outputs/tfrecords/guitarset/splits/`, fine-tuned checkpoints (TBD H2 outcome), benchmark results | High | +| 4. Datasets/weights/annotations to preserve? | GuitarSet TFRecords at `tabvision-server/tools/outputs/tfrecords/guitarset/splits/`, fine-tuned checkpoints (TBD H2 outcome), public/offline eval manifests | High | | 5. Branches with abandoned approaches worth revisiting? | `agent-farm-improvements` (learned-fusion), `feature/audio-finetune` (Phase 0 RMS) — both `NO_SHIP` | High | | 6. Don't touch? | `tabvision-client/` and `tabvision-server/` per Q4 — frozen, not deleted | High | | 7. Python version + tooling? | **NEEDS VERIFICATION** during Phase 0 — read existing `requirements.txt` + venv | Medium | @@ -225,12 +225,12 @@ log, `docs/plans/`), so the actual interactive interview is short. | MediaPipe Hands | Apache 2.0 | ✅ | | OpenCV | Apache 2.0 | ✅ | | ffmpeg-python | Apache 2.0 | ✅ | -| Riley/Edwards High-Res | research weights — verify | ⚠️ check before Phase 2 swap | -| trimplexx CRNN | verify | ⚠️ | -| YOLOv8n (ultralytics) | AGPL — caution for portfolio | ⚠️ pin to weights-only path or pick alternative | +| Riley/Edwards High-Res | research weights — verify | ⚠️ check before Phase 2 swap | +| trimplexx CRNN | verify | ⚠️ | +| YOLOv8n (ultralytics) | AGPL — caution for portfolio | ⚠️ pin to weights-only path or pick alternative | | GuitarSet | CC-BY-4.0 | ✅ for training; no redistribution issue | -| IDMT-SMT-Guitar | research-use, registration | ⚠️ training only, not redistributed | -| EGDB | verify | ⚠️ | +| IDMT-SMT-Guitar | research-use, registration | ⚠️ training only, not redistributed | +| EGDB | verify | ⚠️ | Two-flag risks for portfolio: Riley/Edwards research license (Phase 2) and YOLOv8 / ultralytics AGPL (Phase 3). Resolve before each phase commits. @@ -291,14 +291,14 @@ original Path-2 plan called out (Basic Pitch's last-pitched-note timestamp). **Phase 7 acceptance gate** is automated evidence only for v1. Aggregate + per-tier targets may be reported from public/programmatic datasets and existing -GuitarSet validation, but missing hand-labeled user clips do not block release. +GuitarSet validation, but private clips do not block release. ## 9. Risks specific to this hybrid | # | Risk | Mitigation | |---|---|---| | 1 | **Port drift.** Wrapping existing modules to fit §8 contracts may surface refactors that turn "Port" into "Build." Phase 1 timeline could blow up. | Treat §8 contracts as the gate. If porting > 1 week per module, downgrade to "Build" and note in `DECISIONS.md`. | -| 2 | **License blocker at Phase 2 / 3.** Riley/Edwards research weights or ultralytics AGPL could block default-pipeline use. | LICENSES.md flags both `⚠️` at Phase 0. Resolve before each phase commits. Have a fallback (GAPS for audio, alternative detector for guitar). | +| 2 | **License blocker at Phase 2 / 3.** Riley/Edwards research weights or ultralytics AGPL could block default-pipeline use. | LICENSES.md flags both `⚠️` at Phase 0. Resolve before each phase commits. Have a fallback (GAPS for audio, alternative detector for guitar). | | 3 | **Eval tier coverage gap.** Existing 11/20 videos are clean-acoustic-leaning. Distorted-electric tier exists only on EGDB (studio domain). | Document explicitly: distorted-electric §1.4 target measured on EGDB held-out, not iPhone. iPhone-domain distortion is acknowledged blind spot. | | 4 | **Frozen rot.** `tabvision-server/` + `tabvision-client/` get stale (deps drift, Python version moves). Demo silently breaks. | Phase-0 deliverable: capture current dep versions in `tabvision-server/FROZEN.md` + `make demo` smoke test that runs the v0 pipeline once on a fixture. CI runs nightly. | | 5 | **H2 outcome drags.** "Finish-then-pivot" assumes ~1 week. If it bleeds, Phase 0 starts late. | Hard timebox: by **2026-05-12**, document H2 status. If inconclusive, freeze branch with current findings and proceed to Phase 0 anyway. | @@ -337,15 +337,15 @@ GuitarSet validation, but missing hand-labeled user clips do not block release. ## 12. Cross-references - `TAB_SPEC_UPDATE.md` — canonical spec; renamed to `SPEC.md` in Phase 0. -- `docs/plans/2026-04-24-audio-backbone-finetune-design.md` — Path 2 - (Basic Pitch fine-tune) plan. Outcome feeds Phase 7 per §8 above. -- `docs/plans/2026-04-24-learned-fusion-design.md` — shelved; revisit after - spec Phase 5 fusion gate. -- `docs/plans/2026-04-23-training-video-pipeline-refinement-design.md` — - context for Phase 3 fretboard work. -- `docs/plans/2026-04-23-video-hand-anchor-design.md` — context for - Phase 4 hand work; `use_video_hand_anchor` defaulted off (memory: - `project_video_hand_anchor.md`). +- Removed 2026-06-11: the April Basic Pitch fine-tune design used the private video gate. + +- Removed 2026-06-11: the April learned-fusion design depended on the private position dataset. + +- Removed 2026-06-11: the old training-video refinement plan depended on inaccurate private recordings. + +- Removed 2026-06-11: the April video-hand-anchor design depended on private training videos. + + - `CLAUDE.md` — to be updated post-Phase-0 with new module references. --- diff --git a/docs/plans/2026-05-06-phase5-fusion-design.md b/docs/plans/2026-05-06-phase5-fusion-design.md index 5bf2c0d..452eb59 100644 --- a/docs/plans/2026-05-06-phase5-fusion-design.md +++ b/docs/plans/2026-05-06-phase5-fusion-design.md @@ -20,22 +20,21 @@ What `tabvision.fusion` looks like right now on `refactor/v1`: Phase 4 already produces `FrameFingering.marginal_string_fret() → (6, 25)` softmax per frame (`tabvision.video.hand.fingertip_to_fret`). Phase 5 consumes that. -Legacy reference: `tabvision-server/app/fusion_engine.py` (2,216 lines, 23 functions) and `tabvision-server/app/chord_shapes.py` (790 lines). Per the SPEC §3.3 module-boundary plan, we **port selectively** (hand-span, slide, monophony heuristics) rather than wholesale-translate. The Apr-24 learned-fusion attempt (LightGBM ranker) **did not ship** (LOOCV +0.3 pp vs +5 pp gate per `tools/outputs/position_selector_report-2026-04-29.md`); the lesson is that small ML on top of weak features doesn't beat structured search with informative evidence — Phase 5 takes the structured-search path. +Legacy reference: `tabvision-server/app/fusion_engine.py` (2,216 lines, 23 functions) and `tabvision-server/app/chord_shapes.py` (790 lines). Per the SPEC module-boundary plan, we **port selectively** (hand-span, slide, monophony heuristics) rather than wholesale-translate. The Apr-24 learned-fusion attempt did not ship and its private-corpus artifacts were removed on 2026-06-11; the lesson is that small ML on top of weak features does not beat structured search with informative evidence. ## 1. Goal & acceptance bars From SPEC §5 Phase 5: -- **Tab F1 ≥ 0.85** on the user eval set. Target 0.88 by Phase 9. +- **Tab F1 >= 0.85** on the enabled public/offline eval set. Target 0.88 by Phase 9. - **Chord-instance accuracy ≥ 0.80**. Target 0.85 by Phase 9. - **Audio+vision must beat audio-only by ≥ 8 pp on Tab F1** (ablation report). -The original user eval set was the 20-video iPhone-recorded training set plus -future Phase 1.5 annotations. As of 2026-05-07, that manual/home-video -acceptance path is `optional_future`, not a v1 blocker. Phase 5 v1 evidence is -the automated path: deterministic smoke eval, checked-in fixtures, GuitarSet -validation reports, and any public/programmatic datasets that can be acquired -without manual work. Home-video ablation remains useful future validation. +The old private-video eval path was removed on 2026-06-11 because the tab +labels are not trusted. Phase 5 v1 evidence is the automated path: +deterministic smoke eval, checked-in fixtures, GuitarSet validation reports, +and license-checked public/offline datasets that can be acquired without +manual work. ## 2. Cost function diff --git a/docs/plans/2026-05-12-tab-f1-to-spec-design.md b/docs/plans/2026-05-12-tab-f1-to-spec-design.md index f6e8a0a..b0ef1ca 100644 --- a/docs/plans/2026-05-12-tab-f1-to-spec-design.md +++ b/docs/plans/2026-05-12-tab-f1-to-spec-design.md @@ -1,6 +1,6 @@ # Tab F1 v1 acceptance — Strategy & Decision Record -> **⚠️ SUPERSEDED IN PART (2026-06-01).** The user committed v1 to the +> **⚠️ SUPERSEDED IN PART (2026-06-01).** The user committed v1 to the > **highest** acceptance bar: the original SPEC §1.4 per-tier targets > (0.94 / 0.86 / 0.90 / 0.82) plus the aggregate Tab F1 ≥ 0.88. Decisions > **D1** (retire the aggregate) and **D2** (relax per-tier to @@ -35,7 +35,7 @@ that are NOT shipped. Each resource is verified 2026-05-13: |---|---|---|---| | GuitarSet | CC-BY-4.0 | **yes** | https://zenodo.org/records/3371780 | | Guitar-TECHS | CC-BY-4.0 | **yes** | arXiv:2501.03720 §4 distribution | -| EGDB | none on repo — **author email pending** | **gated** | https://ss12f32v.github.io/Guitar-Transcription/ (LICENSES.md ⚠️) | +| EGDB | none on repo — **author email pending** | **gated** | https://ss12f32v.github.io/Guitar-Transcription/ (LICENSES.md ⚠️) | | GOAT | request-only, research-only | **no — DROPPED** | arXiv:2509.22655 §4.2 *"made available by request to better control its use for research purposes only"* | | SynthTab dataset | **CC-BY-NC-4.0** | **no — DROPPED** | github.com/yongyizang/SynthTab README *"SynthTab is released with CC BY-NC 4.0 license"* | | SynthTab rendering code | CC-BY-4.0 | n/a (we're not redistributing the code) | repo `LICENSE` file | @@ -159,7 +159,7 @@ on a public corpus and must be acquired in Phase 0. | Attempt | Outcome | Lesson | |---|---|---| -| Learned-fusion LightGBM ranker (2026-04-29) | +0.3 pp LOOCV vs +5 pp gate; **-27.8 pp** regression on training-17 | Catastrophic single-fold regression with small data. **Re-try only with strict per-fold regression guard AND with video features actually populated**, which the apr-29 run lacked. | +| Learned-fusion LightGBM ranker (2026-04-29) | +0.3 pp LOOCV vs +5 pp gate; **-27.8 pp** regression on one private-corpus fold | Catastrophic single-fold regression with small data. The private-corpus artifacts were removed on 2026-06-11; re-try only on license-checked public/offline features with strict per-fold regression guard. | | Basic Pitch fine-tune (2026-04-30) | Superseded by highres backend swap | Fine-tune infra reusable; ceiling lift now lives in highres post-processing and possibly a GuitarSet-only highres fine-tune. | | Melodic prior | Regresses aggregate by 1.15 pp | Helps solo, hurts comp. Needs solo-density gating. | | Position prior `guitarset-v1` | +22 pp Tab F1 | Per-pitch tabular priors are the largest cheap intervention. Style/structure-conditional priors are the natural extension. | diff --git a/docs/plans/2026-06-02-electric-backbone-finetune-design.md b/docs/plans/2026-06-02-electric-backbone-finetune-design.md index b990d36..f4cdee0 100644 --- a/docs/plans/2026-06-02-electric-backbone-finetune-design.md +++ b/docs/plans/2026-06-02-electric-backbone-finetune-design.md @@ -37,9 +37,9 @@ trivial. - **No highres training code exists in this repo or the installed packages.** `hf_midi_transcription` / `piano_transcription_inference` are **inference-only** (no optimizer / loss / training loop). `scripts/train/audio_finetune.py` is a - **scaffold** that writes a plan JSON, not a trainer. The existing fine-tune - design (`2026-04-24-audio-backbone-finetune-design.md`) targets **Basic Pitch - (TF)** — a different, older model. + **scaffold** that writes a plan JSON, not a trainer. The removed April Basic + Pitch fine-tune design targeted an older TF model and depended on the now-deleted + private video gate. - So fine-tuning highres requires the **upstream training code** for its architecture (xavriley/`hf_midi_transcription` source + the underlying hFT-Transformer / bytedance `piano_transcription` training repo). **Step 0 is diff --git a/docs/plans/2026-06-03-v1.1-video-string-resolution-design.md b/docs/plans/2026-06-03-v1.1-video-string-resolution-design.md index 2516a63..1ecab5b 100644 --- a/docs/plans/2026-06-03-v1.1-video-string-resolution-design.md +++ b/docs/plans/2026-06-03-v1.1-video-string-resolution-design.md @@ -86,7 +86,7 @@ A new confidence-gated fusion step that turns per-frame `FrameFingering` into a per-note **string** prior, restricted to the note's pitch candidates: 1. **Temporal align.** For each audio note `(pitch, onset_s)`, collect the - `FrameFingering`s within ±Δt of the onset (Δt ≈ one fret-change window, + `FrameFingering`s within ±Δt of the onset (Δt ≈ one fret-change window, ~0.1–0.15 s; reuse the `max_time_distance_s` pattern from `neck_prior`). 2. **Restrict to candidates.** `candidate_positions(pitch, cfg)` gives the few playable `(string, fret)` for that pitch. Score each candidate by the video @@ -122,9 +122,11 @@ analogous to "no in-repo trainer" for v2-electric. Options, cheapest first: 2. **A license-clean public guitar-video dataset with tab/string labels** (e.g. IDMT-SMT-Guitar video subsets, or a tab-aligned performance corpus). This is the **real acceptance gate** — must pass SPEC §1.5 portfolio licensing. -3. **A small self-recorded video dev set** — iteration aid only. SPEC §1.4.1 - **bans personal clips as a gate**, so this never becomes the acceptance - number; keep the gate on (2). +3. **Optional GAPS-style real performance video/audio eval** - iteration aid + and robustness evidence only. GAPS is stronger than the removed private + recordings because it is public, documented, audio-score aligned, and includes + performance videos, but its NC license keeps it offline/eval-only. Do not + commit or redistribute media. **Recommendation:** (1) first to prove the method moves single-line on clean video, then (2) as the gate. Escalate to the user if no §1.5-clean public @@ -150,14 +152,92 @@ video+string corpus is found — that decision blocks the acceptance gate. `scripts/eval/v1_1_kaggle_oracle_probe.py` parses its per-frame finger labels into per-note gold `TabEvent`s and reproduced the oracle lift (**0.42 → 1.00**, 25 clips / 527 notes) — the data pipeline + gold derivation are locked. -- **Chunk 2 — the MediaPipe CV chain (the open unknown).** Install MediaPipe; PNG - frame → `HandSample` → per-frame fretboard homography → `fingertip_to_fret` → - `FrameFingering`; sanity-check detection on this footage (a different rig than the - iPhone angle the detector was built for). -- **Chunk 3 — real-video eval + robustness.** Real highres audio → `AudioEvent`s - (calibrate the ~+1 semitone label/audio tuning offset); `fuse(audio, - real_fingerings)` vs audio-only → the real-video Tab F1 vs §8. Then occlusion / - dropped-frame handling, multi-frame voting, and multi-finger chord resolution. +- **Chunk 2 — the MediaPipe CV chain. ✅ DONE — chain runs, lever holds on real video** + (`docs/EVAL_REPORTS/v1_1_chunk2_cv_chain_2026-06-10.md`). MediaPipe detection works; + hand-selection inverts on this non-mirrored rig (fixed geometrically — pick the hand whose + tips project onto the canonical board). Both homography backends failed out of the box + (geometric mis-fires to full-frame; keypoint had no checkpoint), so per the user we took + **Option 4: trained the YOLO-OBB detector** (`yolo11n-obb`, CPU, epoch-4 best.pt, val + mAP50 0.87). The Roboflow `b101/guitar-3` API is dead (export 404s) — salvaged via browser + download. The trained detector **generalizes to the Kaggle rig** (neck 100%, localized + homography 100%). Homography *orientation* is inverted here (nut/body + string axes) — a + known preflight item; with it corrected the **real CV chain lifts aggregate Tab F1 + 0.42 → 0.54 (+0.12, up to +0.58/clip)** vs oracle 1.00 (24 clips / 527 notes). Probes: + `scripts/eval/v1_1_{mediapipe_sanity,yolo_rig_probe,real_chain_probe}.py`. +- **Chunk 3 — real-video eval + robustness. ✅ DONE — robust gate landed; real + highres audio is the bottleneck** + (`docs/EVAL_REPORTS/v1_1_chunk3_real_video_robustness_2026-06-11.md`). + The manual chunk-2 `--flip-*` orientation is now automatic, onset evidence uses + multi-frame voting, `homography_confidence` scales fusion weight, and sparse / + weak video collapses to audio-only. Gold-pitch real-video eval is no-regression + across 24 scored clips and lifts aggregate Tab F1 **0.4243 → 0.5453**. The real + highres WAV path now runs with pitch/time calibration, but scores only + **0.0583 → 0.0657** (oracle ceiling **0.1959**), so the remaining blocker is + upstream audio transcription/alignment on this corpus rather than video-string + robustness. + +### 7.1 Current next steps (updated 2026-06-11) + +1. **Treat chunk 3 as a video-robustness pass, not an acceptance win.** The + confidence gate and multi-frame evidence are working: gold-pitch real-video + Tab F1 improves without regression. More video weighting is unlikely to help + until the audio events land near the right notes. +2. **Move the next chunk to audio transcription/alignment on real corpus audio.** + The real highres run's oracle ceiling (0.1959) is too low for v1.1 acceptance, + which points to onset/pitch calibration, note grouping, and clip-alignment + before another fusion layer. +3. **Replace private recordings with public/offline sources.** Keep GuitarSet as + the clean, license-friendly string/fret source; keep Kaggle UT-Austin as the + current real-video string/fret eval source; evaluate GAPS as the better + real-performance video/audio replacement when NC offline eval is acceptable. + GOAT remains a candidate only if access and dataset license terms are + explicitly verified. +4. **Re-run YOLO-OBB training to completion.** The interrupted epoch-4 run is + being resumed to the planned 50-epoch budget; its completed checkpoint should + replace the temporary epoch-4 model before the next real-chain comparison. + +### 7.2 Post-training update (2026-06-11) + +The 50-epoch YOLO-OBB resume completed and exported +`~/.tabvision/data/models/guitar-yolo-obb-finetuned.pt`. Final validation: +mAP50 **0.9437**, mAP50-95 **0.6882**. The rerun report is +`docs/EVAL_REPORTS/v1_1_post_training_real_chain_2026-06-11.md`. + +Post-training real-chain results: + +- YOLO rig probe: neck detected **21/21**, localized homography **21/21**. +- Gold-pitch probe: **0.4243 -> 0.5373** with real video, oracle **1.0000**. +- Highres probe: **0.0583 -> 0.0739** with real video, oracle **0.1959**. + +Conclusion: the detector is good enough for the current real-video harness. The +next chunk is audio transcription/alignment, tracked in +`docs/plans/2026-06-11-v1.1-audio-transcription-alignment-design.md`. + +### 7.3 Audio-alignment update (2026-06-11) + +Chunk 4 added `scripts/eval/v1_1_audio_alignment_probe.py`, which caches raw +UT-Austin audio events under `~/.tabvision/cache/v1_1_audio_alignment` and writes +JSON/Markdown reports. The first report is +`docs/EVAL_REPORTS/v1_1_audio_alignment_probe_2026-06-11.md`. + +Key result: per-clip calibration reproduces the old low ceiling, but a single +global highres correction (`pitch_shift=-1`, `time_shift_s=+0.14`) is materially +better: + +- highres per-clip calibration: audio-only **0.0603**, oracle **0.1979**. +- highres global calibration: audio-only **0.1415**, oracle **0.3535**. +- real-chain rerun with that global correction: **0.1415 -> 0.1656** with real + video, oracle **0.3535**. + +Basic Pitch could not be compared in the local Windows Python 3.12 environment: +the optional `.[audio-baseline]` install fails because `basic-pitch` depends on +TensorFlow and no matching TensorFlow distribution is available. `highres-fl` +ran, but trailed highres (`0.0742` global Tab F1, `0.1966` oracle). + +Next: promote/test the global highres calibration path, then inspect why the +current real-video gate keeps usable evidence on only 96 events under corrected +audio timing. The broad video detector is no longer the leading blocker; the +remaining work is audio calibration plus retained video evidence quality. ## 8. Acceptance test diff --git a/docs/plans/2026-06-11-v1.1-audio-transcription-alignment-design.md b/docs/plans/2026-06-11-v1.1-audio-transcription-alignment-design.md new file mode 100644 index 0000000..d635b55 --- /dev/null +++ b/docs/plans/2026-06-11-v1.1-audio-transcription-alignment-design.md @@ -0,0 +1,241 @@ +# v1.1 chunk 4 audio transcription/alignment design + +**Date:** 2026-06-11 (updated 2026-06-17) +**Status:** complete (2026-06-17) — second-corpus diagnostic landed; see +"Second-corpus result (2026-06-17)" below +**Depends on:** `docs/EVAL_REPORTS/v1_1_post_training_real_chain_2026-06-11.md` + +## Goal + +Raise the UT-Austin real-audio ceiling before more video fusion work. The +post-training real-chain rerun shows that completed YOLO weights localize the +neck reliably, but highres audio still limits the full system: + +| Condition | Mean Tab F1 | +|---|---:| +| highres audio-only | 0.0583 | +| highres audio + real video | 0.0739 | +| highres audio + oracle video | 0.1959 | + +The oracle number is the important one: even perfect string/fret video cannot +recover when the audio events are too far from the right notes. Chunk 4 therefore +targets audio event quality and alignment, not video weighting. + +## Working diagnosis + +The highres probe's automatic calibration repeatedly chose a `-1` semitone pitch +shift on most clips, while clips 0 and 1 needed a large positive time shift +around 1.25 seconds. Several later clips still scored zero after calibration. +That points to a mix of: + +- corpus-level tuning or reference-pitch mismatch; +- clip-local time-origin mismatch between WAVs, frame timestamps, and labels; +- note grouping or transcription threshold issues in the highres MIDI output; +- backend mismatch for short chromatic exercise clips. + +## Deliverables + +1. Add an audio-only diagnostic pass that caches highres events per clip and + reports event count, best pitch shift, best time shift, onset F1, pitch F1, + Tab F1, and oracle-video ceiling. Cache first so repeated iterations do not + rerun highres for every clip. +2. Keep `highres` as the active audio model. Do not switch audio models yet; + use second-corpus testing to decide whether the issue is UT-Austin-specific + alignment, highres note grouping, or broader cross-corpus transcription + behavior. +3. Test global vs per-clip calibration. A global `-1` semitone shift may be + legitimate, but clips 0 and 1 need separate treatment before it is baked in. +4. Only after the highres+oracle ceiling improves, rerun real-video fusion. The + video gate is not the next tuning knob. + +## Acceptance for chunk 4 + +Chunk 4 is successful when it produces a report showing a material lift in at +least one of: + +- highres audio-only Tab F1 on UT-Austin; +- highres + oracle-video Tab F1 on UT-Austin; +- onset/pitch F1 diagnostics that explain why Tab F1 remains low. + +The change must not regress the already accepted GuitarSet v1 audio evidence. +It should also produce at least one `highres`-only second-corpus report so the +next step is grounded in data outside UT-Austin before any model-switch +discussion resumes. + +## Results (2026-06-11) + +Implemented `scripts/eval/v1_1_audio_alignment_probe.py` with local event +caching under `~/.tabvision/cache/v1_1_audio_alignment` and JSON/Markdown report +output. The checked-in report is +`docs/EVAL_REPORTS/v1_1_audio_alignment_probe_2026-06-11.md`. + +| Backend / calibration | Onset F1 | Pitch F1 | Tab F1 | Oracle-video Tab F1 | +|---|---:|---:|---:|---:| +| highres raw | 0.0409 | 0.0000 | 0.0000 | 0.0000 | +| highres per-clip calibration | 0.2537 | 0.2019 | 0.0603 | 0.1979 | +| highres global calibration (`-1`, `+0.14s`) | 0.4598 | 0.3613 | 0.1415 | 0.3535 | +| highres-fl per-clip calibration | 0.1983 | 0.1372 | 0.0355 | 0.1372 | +| highres-fl global calibration (`-1`, `+0.16s`) | 0.3223 | 0.2168 | 0.0742 | 0.1966 | + +Findings: + +- highres is still the best available backend for this local run. +- The local corpus has 25 label files, but clip 24 parses to zero gold notes, so + the scored audio/video metrics cover 24 clips / 527 notes. +- 22/24 scored highres clips prefer a `-1` semitone correction. +- Clips 0 and 1 prefer large `+1.24s` / `+1.26s` per-clip time shifts, but the + shared `+0.14s` global correction scores better overall. +- Seven highres clips still have zero oracle-video Tab F1 under per-clip + calibration, which keeps note grouping / threshold behavior on the suspect list. +- Basic Pitch could not be compared numerically in this Windows Python 3.12 venv: + installing `.[audio-baseline]` fails because `basic-pitch` depends on + TensorFlow and no matching TensorFlow distribution is available here. + +Because highres global calibration lifted oracle-video Tab F1 from `0.1959` to +`0.3535`, the real-video chain was rerun with `--pitch-shift -1 --time-shift-s +0.14`: + +| Condition | Prior highres run | Global-corrected run | +|---|---:|---:| +| highres audio-only | 0.0583 | 0.1415 | +| highres audio + real video | 0.0739 | 0.1656 | +| highres audio + oracle video | 0.1959 | 0.3535 | + +Real video now adds `+0.0241`, but the gap between `0.1656` real-video and +`0.3535` oracle-video shows that the next implementation work should (1) promote +and test the global highres calibration path, then (2) inspect retained video +evidence under that corrected audio timing before tuning any broader fusion +weights. + +## User direction update (2026-06-11) + +User direction: **keep highres; do not switch audio models yet; test highres +against a better second dataset.** + +Follow-up interpretation: + +- Keep `highres` / `guitar-gaps.pth` as the active backend while debugging + transcription and alignment. +- Treat Basic Pitch and `highres-fl` comparisons as historical diagnostics, not + the next implementation path. +- Use Guitar-TECHS as the currently runnable second-corpus audio/tab check + because it is local, CC-BY-4.0, and has per-string MIDI labels. It is still an + electric/out-of-domain corpus, so it should diagnose cross-corpus behavior, not + replace GuitarSet as the acoustic acceptance baseline. +- Evaluate GAPS as the stronger real-performance audio/video replacement when + the NC offline eval data and parser are wired locally; do not commit or + redistribute GAPS media. + +New evidence: + +- `docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_2026-06-11.md` + scored the shortest Guitar-TECHS direct-input chord clip with `highres`, + `--position-prior none`: onset F1 `0.7187`, pitch F1 `0.6562`, Tab F1 + `0.0000`. +- `docs/EVAL_REPORTS/v1_1_highres_guitartechs_smoke_decomposition_2026-06-11.md` + shows the one-clip loss dominated by wrong-position-same-pitch and extra + detections. +- A same-settings 12-clip Guitar-TECHS chord run initially exceeded the + 30-minute interactive budget before writing a report. This is now resolved: + `scripts/eval/v1_1_second_corpus_probe.py` adds a per-clip on-disk `TabEvent` + cache so the run is resumable, and the full 12-clip report landed on + 2026-06-17 (see "Second-corpus result" below). Use the smoke manifest for + quick checks and this cached runner for the full second-corpus gate. + +## Replacement eval manifest + +The optional eval manifest now points at the replacement UT-Austin corpus: + +- `tabvision/data/eval/manifest.toml` +- 25 validation clips +- 527 parsed notes +- source `KaggleUTAustin` +- annotation format `utaustin_tablature_npy` + +GuitarSet remains the clean portfolio baseline when present locally. GAPS remains +optional/offline NC evidence and should not be committed or redistributed. + +## Second-corpus result (2026-06-17) + +The cached/resumable runner `scripts/eval/v1_1_second_corpus_probe.py` (a thin +wrapper over `tabvision.eval.composite.run_composite_eval` that caches each +clip's predicted `TabEvent` list to disk, keyed by media path + mtime + backend/ +prior/video settings, so the run survives the 30-minute interactive budget and +resumes from cache on restart) scored the full 12-clip Guitar-TECHS direct-input +chord set with `highres`, `--position-prior none`, `--splits train`, and **no** +pitch/time calibration. Reports: +`docs/EVAL_REPORTS/v1_1_highres_guitartechs_chords_2026-06-17.md` and the paired +`..._decomposition_2026-06-17.md`. + +| Metric | Guitar-TECHS chords, raw (12 clips / 1292 notes) | UT-Austin highres (global-calibrated) | +|---|---:|---:| +| Onset F1 | 0.7321 | 0.4598 | +| Pitch F1 | 0.6787 | 0.3613 | +| Tab F1 | 0.0700 (lower-95 0.0377) | 0.1415 | +| Chord acc | 0.0207 | — | + +Error decomposition (1292 gold notes): wrong_position_same_pitch 788 (43.4%), +extra_detection 634 (34.9%), pitch_off 241 (13.3%), missed_onset 146 (8.0%), +timing_only 7 (0.4%), correct 110. The shape matches the earlier 1-clip smoke +(45% wrong-position, 32.5% extra-detection). + +### Cross-corpus diagnosis + +This is the decision the chunk-4 second-corpus gate set out to make: *is the +UT-Austin Tab F1 failure UT-Austin-specific alignment, highres note grouping, or +broader cross-corpus transcription behavior?* + +1. **highres is not a globally broken backend; an audio-model switch is not + warranted by this evidence.** On Guitar-TECHS, *uncalibrated* highres reaches + onset 0.73 / pitch 0.68, versus UT-Austin's raw onset 0.04 / pitch 0.00. This + supports the standing "keep highres" direction. Honest caveat: 0.68 pitch + still *fails* the v1 audio gate (≥ 0.90, SPEC §1.4) and re-measures the + already-documented electric-domain penalty (highres pitch "collapses + 0.93 → 0.73" on this corpus, `cross_dataset_prior_2026-06-02.md`) — so this is + "not broken," **not** "acceptable for v1 acoustic scope." Guitar-TECHS is + electric / out-of-domain / chord-only / n=12: a cross-corpus diagnostic, not + an acoustic acceptance baseline. + +2. **The severe UT-Austin raw-audio failure is corpus-specific alignment, not a + general highres flaw.** The same backend lands onsets/pitches on Guitar-TECHS + with no correction, whereas UT-Austin needed a −1 semitone shift (22/24 clips) + plus ~1.25 s time-origin shifts (clips 0–1) to become non-zero. Alignment is + *necessary but not sufficient* for UT-Austin: 7/24 clips still score zero + oracle-video Tab F1 after per-clip calibration, so note-grouping / threshold + behavior stays on the suspect list. Guitar-TECHS's uncorrected 13% pitch_off + suggests it may carry a small residual tuning offset of the same family. + +3. **The residual Tab F1 ceiling is structural to audio-only, not an alignment + bug.** Even with good onset/pitch, Tab F1 stays ~0.07 because + wrong_position_same_pitch dominates the loss on *both* corpora — the same + string/fret ambiguity SPEC §1.4.1 independently documents (audio cannot + resolve which string a pitch is played on). The lever is the v1.1 **video** + string-resolution chain (Chunks 1–3), corroborated by UT-Austin oracle-video + lifting Tab F1 0.1415 → 0.3535. Honest caveat: only the wrong_position bucket + (43%) is the video lever; extra_detection (35%) is chord over-detection that + video does **not** directly suppress and is partly an audio-side knob (onset + thresholding / polyphony on dense electric chords). Even the oracle-video + ceiling (0.3535 on UT-Austin) does not clear the v1 aggregate gate, so video + is the largest single lever, not a guaranteed path to target. + +### Implication for the next chunk + +Keep highres; do not pursue an audio-model switch on this evidence. The two +audio-side levers worth pursuing are (a) UT-Austin clip alignment/calibration +(already prototyped: global −1 semitone / +0.14 s) and (b) chord over-detection / +note-grouping thresholds. The largest single Tab F1 lever remains the +already-built video string-resolution chain, not more audio tuning. GuitarSet +stays the acoustic acceptance baseline; Guitar-TECHS stays a cross-corpus +diagnostic only. + +Reproduce (run from the `tabvision/` package dir; note the `..\docs` output path +writes to the repo-root reports dir): + +```powershell +$env:TABVISION_DATA_ROOT = "$HOME\.tabvision\data" +.\.venv\Scripts\python.exe -m scripts.eval.v1_1_second_corpus_probe ` + --manifest data\eval\local_gt_chords.toml ` + --backend highres --position-prior none --splits train ` + --output ..\docs\EVAL_REPORTS\v1_1_highres_guitartechs_chords_2026-06-17.md ` + --decomposition-output ..\docs\EVAL_REPORTS\v1_1_highres_guitartechs_chords_decomposition_2026-06-17.md +``` diff --git a/generate_test_data.py b/generate_test_data.py deleted file mode 100644 index 5965959..0000000 --- a/generate_test_data.py +++ /dev/null @@ -1,347 +0,0 @@ -#!/usr/bin/env python3 -"""Generate test data: synthetic guitar audio/video + ground truth tabs. - -Creates sample-video-N.mp4 and sample-video-N-tabs.txt pairs for pipeline testing. -""" -import numpy as np -import wave -import subprocess -import os - -SAMPLE_RATE = 44100 - -# MIDI note to frequency -def midi_to_freq(midi_note): - return 440.0 * (2 ** ((midi_note - 69) / 12)) - -# Standard tuning: string -> open MIDI note -STANDARD_TUNING = {1: 64, 2: 59, 3: 55, 4: 50, 5: 45, 6: 40} - -def guitar_tone(freq, duration, amplitude=0.5, sample_rate=SAMPLE_RATE): - """Generate a clean tone (pure sine with decay) for reliable pitch detection.""" - t = np.arange(int(duration * sample_rate)) / sample_rate - # Quick attack, exponential decay - attack_samples = int(0.005 * sample_rate) - envelope = np.exp(-t * 5.0) - envelope[:attack_samples] = np.linspace(0, 1, attack_samples) - # Pure sine wave - harmonics cause Basic Pitch to detect extra notes - signal = amplitude * envelope * np.sin(2 * np.pi * freq * t) - return signal - -def notes_to_audio(notes, total_duration, sample_rate=SAMPLE_RATE): - """Convert list of (time, midi, duration, amplitude) to audio signal.""" - signal = np.zeros(int(total_duration * sample_rate)) - for start_time, midi_note, dur, amp in notes: - freq = midi_to_freq(midi_note) - tone = guitar_tone(freq, dur, amplitude=amp, sample_rate=sample_rate) - start_sample = int(start_time * sample_rate) - end_sample = min(start_sample + len(tone), len(signal)) - signal[start_sample:end_sample] += tone[:end_sample - start_sample] - # Normalize - peak = np.max(np.abs(signal)) - if peak > 0: - signal = signal / peak * 0.8 - return signal - -def save_wav(signal, path, sample_rate=SAMPLE_RATE): - """Save signal as 16-bit WAV.""" - with wave.open(path, 'w') as wf: - wf.setnchannels(1) - wf.setsampwidth(2) - wf.setframerate(sample_rate) - data = (signal * 32767).astype(np.int16) - wf.writeframes(data.tobytes()) - -def create_video_from_audio(audio_path, video_path, duration, label="Test"): - """Create a simple video with colored background from audio.""" - subprocess.run([ - 'ffmpeg', '-y', - '-f', 'lavfi', '-i', f'color=c=0x1a1a2e:s=640x480:d={duration}', - '-i', audio_path, - '-vf', f"drawtext=text='{label}':fontsize=24:fontcolor=white:x=(w-text_w)/2:y=(h-text_h)/2", - '-c:v', 'libx264', '-preset', 'ultrafast', - '-c:a', 'aac', '-b:a', '128k', - '-shortest', video_path - ], capture_output=True, check=True) - -def notes_to_tabs(notes_with_pos, total_duration, chars_per_second=4): - """Convert notes to tab notation. - - The evaluator maps beats to time using: time = beat * video_duration / max_beat - So we add a timing anchor (muted X) at the end to ensure max_beat ≈ total_duration, - giving beat_to_time ≈ 1.0 for correct time mapping. - - Args: - notes_with_pos: list of (time, string, fret) tuples - total_duration: total duration in seconds - chars_per_second: characters per second (controls resolution) - """ - total_chars = int(total_duration * chars_per_second) - bar_width = 16 # chars per bar - - # Initialize strings - strings = {i: ['-'] * total_chars for i in range(1, 7)} - string_names = {1: 'e', 2: 'B', 3: 'G', 4: 'D', 5: 'A', 6: 'E'} - - for time, string_num, fret in notes_with_pos: - char_pos = int(time * chars_per_second) - if 0 <= char_pos < total_chars: - fret_str = str(fret) - strings[string_num][char_pos] = fret_str[0] - if len(fret_str) > 1 and char_pos + 1 < total_chars: - strings[string_num][char_pos + 1] = fret_str[1] - - # Add timing anchor at end (muted X on unused string) - used_strings = set(s for _, s, _ in notes_with_pos) - anchor_string = 6 - for s in [6, 5, 4, 3, 2, 1]: - if s not in used_strings: - anchor_string = s - break - anchor_pos = total_chars - 2 - if anchor_pos >= 0: - strings[anchor_string][anchor_pos] = 'X' - - # Format with bar lines - lines = [] - for s in range(1, 7): - content = ''.join(strings[s]) - # Insert bar lines every bar_width chars - bars = [] - for i in range(0, len(content), bar_width): - bars.append(content[i:i+bar_width]) - line = f"{string_names[s]}|{'|'.join(bars)}|" - lines.append(line) - - return '\n'.join(lines) - - -# ============================================================ -# Test video definitions -# ============================================================ - -def make_ascending_scale(): - """Test 3: Ascending E major scale on high E string.""" - # E F# G# A B C# D# E (frets 0,2,4,5,7,9,11,12) - frets = [0, 2, 4, 5, 7, 9, 11, 12] - notes_audio = [] - notes_tab = [] - t = 1.0 - for fret in frets: - midi = STANDARD_TUNING[1] + fret - notes_audio.append((t, midi, 0.8, 0.7)) - notes_tab.append((t, 1, fret)) - t += 1.0 - # Descend back - for fret in reversed(frets[:-1]): - midi = STANDARD_TUNING[1] + fret - notes_audio.append((t, midi, 0.8, 0.7)) - notes_tab.append((t, 1, fret)) - t += 1.0 - duration = t + 1.0 - return notes_audio, notes_tab, duration, "E Major Scale" - -def make_simple_melody(): - """Test 4: Simple melody - Twinkle Twinkle on B string.""" - # C C G G A A G - F F E E D D C (on B string) - melody_frets = [1, 1, 8, 8, 10, 10, 8, 6, 6, 5, 5, 3, 3, 1] - notes_audio = [] - notes_tab = [] - t = 1.0 - for fret in melody_frets: - midi = STANDARD_TUNING[2] + fret - notes_audio.append((t, midi, 0.7, 0.7)) - notes_tab.append((t, 2, fret)) - t += 0.8 - duration = t + 1.0 - return notes_audio, notes_tab, duration, "Simple Melody" - -def make_open_strings(): - """Test 5: Open string notes - tests open string detection.""" - notes_audio = [] - notes_tab = [] - t = 1.0 - # Play each open string twice - for s in [1, 2, 3, 4, 5, 6, 6, 5, 4, 3, 2, 1]: - midi = STANDARD_TUNING[s] - notes_audio.append((t, midi, 0.8, 0.7)) - notes_tab.append((t, s, 0)) - t += 0.8 - duration = t + 1.0 - return notes_audio, notes_tab, duration, "Open Strings" - -def make_power_chords(): - """Test 6: Power chord riff on low strings.""" - # E5-G5-A5-G5 power chord progression - chords = [ - # E5: E2(40) + B2(47) - [(6, 0, 40), (5, 2, 47)], - # G5: G2(43) + D3(50) - [(6, 3, 43), (5, 5, 50)], - # A5: A2(45) + E3(52) - [(5, 0, 45), (4, 2, 52)], - # G5 again - [(6, 3, 43), (5, 5, 50)], - ] - notes_audio = [] - notes_tab = [] - t = 1.0 - for _ in range(2): # repeat twice - for chord in chords: - for string, fret, midi in chord: - notes_audio.append((t, midi, 0.7, 0.7)) - notes_tab.append((t, string, fret)) - t += 1.2 - duration = t + 1.0 - return notes_audio, notes_tab, duration, "Power Chords" - -def make_arpeggio(): - """Test 7: C-Am-F-G arpeggio pattern.""" - # C major arp: C3(48)-E3(52)-G3(55)-C4(60)-E4(64) - # Am arp: A2(45)-E3(52)-A3(57)-C4(60)-E4(64) - # F major arp: F2(41)-C3(48)-F3(53)-A3(57)-C4(60) - # G major arp: G2(43)-B2(47)-D3(50)-G3(55)-B3(59) - arps = [ - [(5, 3, 48), (4, 2, 52), (3, 0, 55), (2, 1, 60), (1, 0, 64)], - [(5, 0, 45), (4, 2, 52), (3, 2, 57), (2, 1, 60), (1, 0, 64)], - [(6, 1, 41), (5, 3, 48), (4, 3, 53), (3, 2, 57), (2, 1, 60)], - [(6, 3, 43), (5, 2, 47), (4, 0, 50), (3, 0, 55), (2, 0, 59)], - ] - notes_audio = [] - notes_tab = [] - t = 1.0 - for arp in arps: - for string, fret, midi in arp: - notes_audio.append((t, midi, 0.6, 0.6)) - notes_tab.append((t, string, fret)) - t += 0.4 - t += 0.4 # gap between chords - duration = t + 1.0 - return notes_audio, notes_tab, duration, "Arpeggios" - -def make_chromatic(): - """Test 8: Chromatic exercise 1-2-3-4 on each string.""" - notes_audio = [] - notes_tab = [] - t = 1.0 - for string in [6, 5, 4, 3, 2, 1]: - for fret in [1, 2, 3, 4]: - midi = STANDARD_TUNING[string] + fret - notes_audio.append((t, midi, 0.6, 0.65)) - notes_tab.append((t, string, fret)) - t += 0.5 - t += 0.3 # small gap between strings - duration = t + 1.0 - return notes_audio, notes_tab, duration, "Chromatic Exercise" - -def make_fingerpicking(): - """Test 9: Fingerpicking pattern - Travis picking in C.""" - # Pattern: bass-3-2-1-bass-3-2-1 - # C chord: C3(5,3) E3(4,2) G3(3,0) C4(2,1) E4(1,0) - pattern = [ - (5, 3, 48), # bass C - (3, 0, 55), # G - (2, 1, 60), # C - (1, 0, 64), # E - (4, 2, 52), # bass E (alternating) - (3, 0, 55), # G - (2, 1, 60), # C - (1, 0, 64), # E - ] - notes_audio = [] - notes_tab = [] - t = 1.0 - for _ in range(4): # 4 repetitions - for string, fret, midi in pattern: - notes_audio.append((t, midi, 0.5, 0.55)) - notes_tab.append((t, string, fret)) - t += 0.35 - duration = t + 1.0 - return notes_audio, notes_tab, duration, "Fingerpicking" - -def make_mixed(): - """Test 10: Mixed - single notes then chords.""" - notes_audio = [] - notes_tab = [] - t = 1.0 - - # Single note melody (Happy Birthday first line on G string) - melody = [(3, 2, 57), (3, 2, 57), (3, 4, 59), (3, 2, 57), (3, 7, 62), (3, 5, 60)] - for string, fret, midi in melody: - notes_audio.append((t, midi, 0.7, 0.65)) - notes_tab.append((t, string, fret)) - t += 0.8 - - t += 0.5 - - # Then some chords - chords = [ - # G chord - [(6, 3, 43), (5, 2, 47), (1, 3, 67)], - # C chord - [(5, 3, 48), (4, 2, 52), (2, 1, 60)], - # D chord - [(4, 0, 50), (3, 2, 57), (2, 3, 62), (1, 2, 66)], - # G chord - [(6, 3, 43), (5, 2, 47), (1, 3, 67)], - ] - for chord in chords: - for string, fret, midi in chord: - notes_audio.append((t, midi, 0.6, 0.6)) - notes_tab.append((t, string, fret)) - t += 1.2 - - duration = t + 1.0 - return notes_audio, notes_tab, duration, "Mixed" - - -# ============================================================ -# Main generation -# ============================================================ - -def generate_synthetic(video_num, generator_func): - """Generate a synthetic test video + tabs.""" - notes_audio, notes_tab, duration, label = generator_func() - - base = f'/home/gilhooleyp/projects/tab_vision/test-data/existing' - audio_path = f'/tmp/synth_audio_{video_num}.wav' - video_path = f'{base}/sample-video-{video_num}.mp4' - tabs_path = f'{base}/sample-video-{video_num}-tabs.txt' - - # Generate audio - signal = notes_to_audio(notes_audio, duration) - save_wav(signal, audio_path) - - # Create video - create_video_from_audio(audio_path, video_path, duration, label) - - # Create tabs - tabs_content = notes_to_tabs(notes_tab, duration, chars_per_second=4) - with open(tabs_path, 'w') as f: - f.write(tabs_content) - - # Cleanup - os.remove(audio_path) - - print(f" sample-video-{video_num}: {label} ({duration:.1f}s, {len(notes_tab)} notes)") - - -if __name__ == '__main__': - print("Generating synthetic test videos...") - - generators = { - 5: make_ascending_scale, - 6: make_simple_melody, - 7: make_open_strings, - 8: make_power_chords, - 9: make_arpeggio, - 10: make_chromatic, - 11: make_fingerpicking, - 12: make_mixed, - } - - for num, gen in generators.items(): - generate_synthetic(num, gen) - - print("\nDone! Generated 8 synthetic test videos (5-12)") - print("Videos 3-4 should be real videos with manually written tabs") diff --git a/tabvision-server/ab_anchor.py b/tabvision-server/ab_anchor.py deleted file mode 100644 index ace57ea..0000000 --- a/tabvision-server/ab_anchor.py +++ /dev/null @@ -1,87 +0,0 @@ -"""A/B comparison runner — each video runs in a fresh subprocess to avoid batch drift. - -The full-batch `run_benchmarks.py` run accumulates state across 20 videos and -produces different numbers per-video than running the same video in isolation. -This script launches each video in its own subprocess so comparisons are clean. - -Usage: - python ab_anchor.py # all training videos - python ab_anchor.py --id training-09 # single video -""" -import argparse -import json -import os -import re -import subprocess -import sys - -BENCHMARKS_DIR = os.path.join('tests', 'fixtures', 'benchmarks') -BENCHMARK_RUNNER = 'run_benchmarks.py' - - -def load_training_ids() -> list[str]: - with open(os.path.join(BENCHMARKS_DIR, 'index.json')) as f: - return [b['id'] for b in json.load(f)['benchmarks'] - if b['id'].startswith('training-')] - - -def run_one(bm_id: str, anchor_on: bool) -> dict: - """Run one benchmark in a fresh subprocess, return parsed metrics.""" - cmd = [ - sys.executable, BENCHMARK_RUNNER, - '--with-video', '--id', bm_id, - ] - if anchor_on: - cmd.append('--use-video-hand-anchor') - - r = subprocess.run(cmd, capture_output=True, text=True, timeout=600) - if r.returncode != 0: - return {'error': r.stderr[:500]} - - for line in r.stdout.splitlines(): - m = re.match(rf'^{re.escape(bm_id)}\s+(\S+)%\s+(\S+)%\s+(\S+)%\s+(\S+)%\s+(\S+)%', line) - if m: - return { - 'exact_f1': float(m.group(1)) / 100.0, - 'precision': float(m.group(2)) / 100.0, - 'recall': float(m.group(3)) / 100.0, - 'pitch_f1': float(m.group(4)) / 100.0, - 'pos_acc': float(m.group(5)) / 100.0, - } - return {'error': 'metrics line not found'} - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('--id', help='Run only this benchmark id') - args = parser.parse_args() - - ids = [args.id] if args.id else load_training_ids() - - print(f"{'ID':<16} {'Off exact / pos':<22} {'On exact / pos':<22} Δexact Δpos") - print('-' * 86) - deltas_e, deltas_p = [], [] - for bm_id in ids: - r_off = run_one(bm_id, anchor_on=False) - r_on = run_one(bm_id, anchor_on=True) - if 'error' in r_off or 'error' in r_on: - print(f'{bm_id:<16} ERROR: {r_off.get("error") or r_on.get("error")}') - continue - de = r_on['exact_f1'] - r_off['exact_f1'] - dp = r_on['pos_acc'] - r_off['pos_acc'] - deltas_e.append(de); deltas_p.append(dp) - mark_e = ' ++' if de > 0.02 else (' !!' if de < -0.02 else ' ') - mark_p = ' ++' if dp > 0.02 else (' !!' if dp < -0.02 else ' ') - print(f'{bm_id:<16} ' - f'{r_off["exact_f1"]:>6.3f} / {r_off["pos_acc"]:>6.3f} ' - f'{r_on["exact_f1"]:>6.3f} / {r_on["pos_acc"]:>6.3f} ' - f'{de:+.3f}{mark_e} {dp:+.3f}{mark_p}') - - if deltas_e: - print('-' * 86) - print(f'{"AVG DELTA":<16} {"":<22} {"":<22} {sum(deltas_e)/len(deltas_e):+.3f}' - f' {sum(deltas_p)/len(deltas_p):+.3f}') - - -if __name__ == '__main__': - main() diff --git a/tabvision-server/ab_anchor_fast.py b/tabvision-server/ab_anchor_fast.py deleted file mode 100644 index 6b4a975..0000000 --- a/tabvision-server/ab_anchor_fast.py +++ /dev/null @@ -1,169 +0,0 @@ -"""Fast A/B comparison — subprocess per video, but each subprocess runs the -expensive pipeline ONCE and fusion TWICE (flag off and flag on). - -Compared to naive subprocess-per-config: - - 20 videos × 2 configs = 40 subprocess runs × (pipeline + fusion) ≈ 80 min - - This script: 20 subprocesses × (pipeline + 2 fusions) ≈ 40 min - -No batch drift (each video is a fresh subprocess) and no wasted pipeline reruns. - -Usage: - python ab_anchor_fast.py # all training videos - python ab_anchor_fast.py --id training-09 -""" -import argparse -import json -import os -import subprocess -import sys - -BENCHMARKS_DIR = os.path.join('tests', 'fixtures', 'benchmarks') -REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - -def load_training_ids() -> list[str]: - with open(os.path.join(BENCHMARKS_DIR, 'index.json')) as f: - return [b['id'] for b in json.load(f)['benchmarks'] - if b['id'].startswith('training-')] - - -# This is the worker script executed by each subprocess — runs pipeline once, -# fusion twice, prints both results as JSON. -WORKER = r''' -import json -import os -import sys -import warnings -sys.path.insert(0, '.') -warnings.filterwarnings('ignore') -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' - -from app.audio_pipeline import AudioAnalysisConfig, analyze_pitch, detect_note_onsets -from app.fretboard_detection import detect_fretboard_from_video -from app.fusion_engine import FusionConfig, fuse_audio_only, fuse_audio_video -from app.video_pipeline import VideoAnalysisConfig, analyze_video_at_timestamps -from evaluate_transcription import ( - evaluate_accuracy, extract_audio, get_video_duration, parse_ground_truth_tabs, -) - -BENCHMARKS_DIR = os.path.join('tests', 'fixtures', 'benchmarks') -REPO_ROOT = os.path.dirname(os.path.abspath(os.getcwd())) - - -def main(bm_id): - with open(os.path.join(BENCHMARKS_DIR, 'index.json')) as f: - index = json.load(f) - bm = next(b for b in index['benchmarks'] if b['id'] == bm_id) - defaults = index.get('defaults', {}) - - video_path = os.path.join(REPO_ROOT, bm['video_path']) - gt_path = bm['ground_truth_path'] - if not os.path.isabs(gt_path): - gt_path = os.path.join(REPO_ROOT, gt_path) - - with open(gt_path) as f: - ground_truth = parse_ground_truth_tabs(f.read()) - - # Expensive: audio + video analysis once. - audio_path = '/tmp/ab_fast_audio.wav' - try: - extract_audio(video_path, audio_path) - detected_notes = analyze_pitch(audio_path, AudioAnalysisConfig()) - finally: - if os.path.exists(audio_path): - os.remove(audio_path) - - timestamps = detect_note_onsets(detected_notes) - fretboard = None - video_observations = {} - if timestamps: - fretboard = detect_fretboard_from_video(video_path, num_sample_frames=5) - if fretboard: - video_observations = analyze_video_at_timestamps( - video_path, timestamps, VideoAnalysisConfig() - ) - - def run_fusion(cfg): - dr = len(video_observations) / len(timestamps) if timestamps else 0 - if fretboard and fretboard.detection_confidence > 0.5 and dr > 0.2: - return fuse_audio_video(detected_notes, video_observations, - fretboard, capo_fret=0, config=cfg) - return fuse_audio_only(detected_notes, capo_fret=0, config=cfg) - - video_duration = bm.get('video_duration') or get_video_duration(video_path) - time_tol = bm.get('time_tolerance', defaults.get('time_tolerance', 0.6)) - bpm = bm.get('bpm') or defaults.get('bpm') - - out = {} - for label, cfg in [('off', FusionConfig(use_video_hand_anchor=False)), - ('on', FusionConfig(use_video_hand_anchor=True))]: - notes = run_fusion(cfg) - m = evaluate_accuracy( - notes, ground_truth, - time_tolerance=time_tol, - video_duration=video_duration, - bpm=bpm, - auto_align=True, - ).to_dict() - out[label] = { - 'exact_f1': m['exact']['f1'], - 'pitch_f1': m['pitch']['f1'], - 'pos_acc': m['position']['accuracy'], - 'note_count': len(notes), - } - - print('@@AB_RESULT@@' + json.dumps(out)) - - -if __name__ == '__main__': - main(sys.argv[1]) -''' - - -def run_video(bm_id: str) -> dict | None: - """Invoke the worker in a fresh subprocess; parse its JSON output.""" - cmd = [sys.executable, '-c', WORKER, bm_id] - r = subprocess.run(cmd, capture_output=True, text=True, timeout=900) - if r.returncode != 0: - return {'error': r.stderr[-500:]} - for line in r.stdout.splitlines(): - if line.startswith('@@AB_RESULT@@'): - return json.loads(line[len('@@AB_RESULT@@'):]) - return {'error': 'no result marker in output'} - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('--id', help='Run only this benchmark id') - args = parser.parse_args() - - ids = [args.id] if args.id else load_training_ids() - - print(f"{'ID':<16} {'Off exact/pos':<20} {'On exact/pos':<20} Δexact Δpos") - print('-' * 82) - deltas_e, deltas_p = [], [] - for bm_id in ids: - print(f' [{bm_id}]', end=' ', flush=True) - r = run_video(bm_id) - if not r or 'error' in r: - print(f'ERROR: {r.get("error", "no result")[:200] if r else "timed out"}') - continue - off, on = r['off'], r['on'] - de = on['exact_f1'] - off['exact_f1'] - dp = on['pos_acc'] - off['pos_acc'] - deltas_e.append(de); deltas_p.append(dp) - mark_e = ' ++' if de > 0.02 else (' !!' if de < -0.02 else ' ') - mark_p = ' ++' if dp > 0.02 else (' !!' if dp < -0.02 else ' ') - print(f'{off["exact_f1"]:>6.3f}/{off["pos_acc"]:>6.3f} ' - f'{on["exact_f1"]:>6.3f}/{on["pos_acc"]:>6.3f} ' - f'{de:+.3f}{mark_e} {dp:+.3f}{mark_p}') - - if deltas_e: - print('-' * 82) - print(f'{"AVG DELTA":<16} {"":<20} {"":<20} ' - f'{sum(deltas_e)/len(deltas_e):+.3f} ' - f'{sum(deltas_p)/len(deltas_p):+.3f}') - - -if __name__ == '__main__': - main() diff --git a/tabvision-server/analyze_benchmarks.py b/tabvision-server/analyze_benchmarks.py deleted file mode 100644 index ac51845..0000000 --- a/tabvision-server/analyze_benchmarks.py +++ /dev/null @@ -1,184 +0,0 @@ -"""Diagnostic analysis of benchmark results. - -Usage: - python analyze_benchmarks.py -""" -import json -import sys - -CATEGORIES = { - "Position Ambiguity": ["training-01", "training-02", "training-03", "training-04", "training-05"], - "Chord Varieties": ["training-06", "training-07", "training-08", "training-09", "training-10"], - "Single-Note Passages": ["training-11", "training-12", "training-13", "training-14", "training-15"], - "Edge Cases": ["training-16", "training-17", "training-18", "training-19", "training-20"], -} - - -def classify_error(metrics): - """Classify dominant error type for a video.""" - pitch_f1 = metrics["pitch"]["f1"] - exact_f1 = metrics["exact"]["f1"] - ratio = metrics["counts"]["ratio"] - - if ratio < 0.7 or ratio > 1.5: - return "note_count" - if pitch_f1 < 0.7: - return "pitch_detection" - if exact_f1 < pitch_f1 * 0.7: - return "position_assignment" - return "mixed" - - -def print_category_breakdown(results): - print("=" * 80) - print("PER-CATEGORY BREAKDOWN") - print("=" * 80) - print() - print(f"{'Category':<25} {'Exact F1':>10} {'Pitch F1':>10} {'Pos Acc':>10} {'Videos':>8}") - print("-" * 65) - - all_exact, all_pitch, all_pos = [], [], [] - for cat_name, video_ids in CATEGORIES.items(): - exact_f1s, pitch_f1s, pos_accs = [], [], [] - for vid_id in video_ids: - if vid_id not in results: - continue - m = results[vid_id]["metrics"] - exact_f1s.append(m["exact"]["f1"]) - pitch_f1s.append(m["pitch"]["f1"]) - pos_accs.append(m["position"]["accuracy"]) - - if not exact_f1s: - continue - - avg_exact = sum(exact_f1s) / len(exact_f1s) - avg_pitch = sum(pitch_f1s) / len(pitch_f1s) - avg_pos = sum(pos_accs) / len(pos_accs) - all_exact.extend(exact_f1s) - all_pitch.extend(pitch_f1s) - all_pos.extend(pos_accs) - - print(f"{cat_name:<25} {avg_exact:>9.1%} {avg_pitch:>9.1%} {avg_pos:>9.1%} {len(exact_f1s):>8}") - - print("-" * 65) - if all_exact: - print(f"{'OVERALL':<25} {sum(all_exact)/len(all_exact):>9.1%} " - f"{sum(all_pitch)/len(all_pitch):>9.1%} " - f"{sum(all_pos)/len(all_pos):>9.1%} {len(all_exact):>8}") - print() - - -def print_error_classification(results): - print("=" * 80) - print("ERROR CLASSIFICATION PER VIDEO") - print("=" * 80) - print() - print(f"{'Video':<15} {'Exact F1':>10} {'Pitch F1':>10} {'Ratio':>8} {'Error Type':<22}") - print("-" * 67) - - counts = {} - for vid_id in sorted(results.keys()): - m = results[vid_id]["metrics"] - err = classify_error(m) - counts[err] = counts.get(err, 0) + 1 - print(f"{vid_id:<15} {m['exact']['f1']:>9.1%} {m['pitch']['f1']:>9.1%} " - f"{m['counts']['ratio']:>7.2f} {err:<22}") - - print() - print("Error type summary:") - for err_type, count in sorted(counts.items(), key=lambda x: -x[1]): - print(f" {err_type}: {count} videos") - print() - - -def print_near_miss_report(results): - print("=" * 80) - print("NEAR-MISS DISCREPANCY REPORT (up to 10 per video)") - print("=" * 80) - - for vid_id in sorted(results.keys()): - m = results[vid_id]["metrics"] - near_misses = m.get("near_misses", []) - if not near_misses: - continue - - print(f"\n--- {vid_id} (exact F1={m['exact']['f1']:.1%}, " - f"{len(near_misses)} near misses) ---") - print(f" {'#':<4} {'Expected':>12} {'Detected':>12} {'MIDI':>6} {'Type':<16}") - - # Count error types for this video - type_counts = {} - for nm in near_misses: - t = nm["type"] - type_counts[t] = type_counts.get(t, 0) + 1 - - for i, nm in enumerate(near_misses[:10]): - gt = f"s{nm['gt_string']}f{nm['gt_fret']}" - det = f"s{nm['det_string']}f{nm['det_fret']}" - print(f" {i+1:<4} {gt:>12} {det:>12} {nm['midi_note']:>6} {nm['type']:<16}") - - remaining = len(near_misses) - 10 - if remaining > 0: - print(f" ... and {remaining} more") - - summary_parts = [f"{count} {t}" for t, count in sorted(type_counts.items(), key=lambda x: -x[1])] - print(f" Summary: {', '.join(summary_parts)}") - - -def print_priority_ranking(results): - print() - print("=" * 80) - print("PRIORITY RANKING (improvement potential = pitch F1 - exact F1)") - print("=" * 80) - print() - print("Videos with high pitch F1 but low exact F1 have the most position errors") - print("to fix -- the pitch detector found the right notes, but they were placed") - print("on the wrong string/fret.") - print() - print(f"{'Rank':<6} {'Video':<15} {'Pitch F1':>10} {'Exact F1':>10} {'Gap':>10} {'Error Type':<22}") - print("-" * 75) - - ranked = [] - for vid_id, vid_data in results.items(): - m = vid_data["metrics"] - gap = m["pitch"]["f1"] - m["exact"]["f1"] - ranked.append((vid_id, m, gap)) - - ranked.sort(key=lambda x: -x[2]) - - for rank, (vid_id, m, gap) in enumerate(ranked, 1): - err = classify_error(m) - print(f"{rank:<6} {vid_id:<15} {m['pitch']['f1']:>9.1%} {m['exact']['f1']:>9.1%} " - f"{gap:>9.1%} {err:<22}") - - print() - print("Top 5 targets for position assignment improvements:") - for vid_id, m, gap in ranked[:5]: - pos_correct = m["position"]["correct"] - pos_total = m["position"]["total"] - pos_wrong = pos_total - pos_correct - print(f" {vid_id}: {pos_wrong}/{pos_total} pitch-matched notes on wrong position " - f"(pos accuracy {m['position']['accuracy']:.0%})") - print() - - -def main(): - if len(sys.argv) != 2: - print("Usage: python analyze_benchmarks.py ") - sys.exit(1) - - with open(sys.argv[1]) as f: - data = json.load(f) - - results = data["results"] - print(f"Analyzing {len(results)} benchmark results from {data.get('timestamp', 'unknown')}") - print() - - print_category_breakdown(results) - print_error_classification(results) - print_near_miss_report(results) - print_priority_ranking(results) - - -if __name__ == "__main__": - main() diff --git a/tabvision-server/app/fusion_engine.py b/tabvision-server/app/fusion_engine.py index 81b3f96..02caf0f 100644 --- a/tabvision-server/app/fusion_engine.py +++ b/tabvision-server/app/fusion_engine.py @@ -41,8 +41,8 @@ class PositionDecision: """One position-selection event for the learned-fusion training set. Per-event header + per-candidate rows. Emitted by fusion when - `FusionConfig.emit_position_features` is True. See - docs/plans/2026-04-24-learned-fusion-design.md §4.2. + `FusionConfig.emit_position_features` is True. The old private-corpus + learned-fusion plan and generated artifacts were removed on 2026-06-11. """ event_id: str onset_time: float diff --git a/tabvision-server/app/training/guitarset_dataset.py b/tabvision-server/app/training/guitarset_dataset.py index f3a415c..6ca3ec3 100644 --- a/tabvision-server/app/training/guitarset_dataset.py +++ b/tabvision-server/app/training/guitarset_dataset.py @@ -1,8 +1,7 @@ """GuitarSet dataset loader for the audio-finetune Phase 1. -Phase 1 Week 1 deliverable per -docs/plans/2026-04-24-audio-backbone-finetune-design.md §7. Reads JAMS -files directly from disk and returns (onset, duration, midi, string, fret) +Historical Phase 1 GuitarSet fine-tune helper. Reads JAMS files directly from +disk and returns (onset, duration, midi, string, fret) notes — no mirdata dependency at parse time, so the parser works equally well whether the data was sideloaded from HuggingFace (`tools/sideload_guitarset_from_hf.py`) or downloaded from Zenodo via diff --git a/tabvision-server/app/training/load_pretrained.py b/tabvision-server/app/training/load_pretrained.py index 93fd014..dd8dbdc 100644 --- a/tabvision-server/app/training/load_pretrained.py +++ b/tabvision-server/app/training/load_pretrained.py @@ -2,10 +2,8 @@ returned by ``basic_pitch.models.model()``. Without this, ``basic_pitch.train.main`` fits a fresh-weight model — that is -training-from-scratch, not fine-tuning. Plan -docs/plans/2026-04-24-audio-backbone-finetune-design.md §7 calls explicitly -for fine-tuning the pretrained backbone, so this module is the missing -prerequisite for Week 2. +training-from-scratch, not fine-tuning. This helper belongs to the historical +Phase 1 GuitarSet fine-tune path. Why a custom loader: the shipped model is a TF SavedModel (`basic_pitch/saved_models/icassp_2022/nmp/`). The Keras 2.x install we use diff --git a/tabvision-server/debug_hand_anchor.py b/tabvision-server/debug_hand_anchor.py deleted file mode 100644 index ff3a5c5..0000000 --- a/tabvision-server/debug_hand_anchor.py +++ /dev/null @@ -1,243 +0,0 @@ -"""Layer 2 diagnostic: anchor quality vs. ground-truth frets. - -For each benchmark video, runs the video pipeline, builds the hand-anchor -timeline, and compares the anchor's value at each ground-truth note's -timestamp against the note's fret. Reports MAE, coverage, and |err| ≤ 2 -fraction per video. - -This is intentionally a read-only diagnostic — it does NOT change tabs -produced by fusion. It only measures whether the video-derived anchor is -accurate enough to be worth using as the primary hand-position signal. - -Usage: - python debug_hand_anchor.py # all benchmarks - python debug_hand_anchor.py --id training-01 # single benchmark - python debug_hand_anchor.py --id-prefix training # all training-* -""" -import argparse -import json -import os -import sys - -sys.path.insert(0, '.') -import warnings -warnings.filterwarnings('ignore') -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' - -from app.audio_pipeline import analyze_pitch, AudioAnalysisConfig, detect_note_onsets -from app.fretboard_detection import detect_fretboard_from_video -from app.hand_anchor import build_hand_position_timeline, get_hand_anchor_at -from app.video_pipeline import analyze_video_at_timestamps, VideoAnalysisConfig -from evaluate_transcription import ( - extract_audio, parse_ground_truth_tabs, get_video_duration, - _find_best_time_offset, -) - -BENCHMARKS_DIR = os.path.join('tests', 'fixtures', 'benchmarks') -REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - - -def load_index(): - with open(os.path.join(BENCHMARKS_DIR, 'index.json')) as f: - return json.load(f)['benchmarks'] - - -def diagnose_one(bm: dict, verbose: bool = False, dump_frames: bool = False) -> dict | None: - """Run the diagnostic for one benchmark, returning per-video stats.""" - video_path = os.path.join(REPO_ROOT, bm['video_path']) - gt_path = bm['ground_truth_path'] - if not os.path.isabs(gt_path): - gt_path = os.path.join(REPO_ROOT, gt_path) - - if not os.path.exists(video_path) or not os.path.exists(gt_path): - return None - - with open(gt_path) as f: - ground_truth = parse_ground_truth_tabs(f.read()) - if not ground_truth: - return None - - # Audio — we need onset timestamps to sample video. - audio_path = '/tmp/anchor_audio.wav' - try: - extract_audio(video_path, audio_path) - detected = analyze_pitch(audio_path, AudioAnalysisConfig()) - finally: - if os.path.exists(audio_path): - os.remove(audio_path) - - timestamps = detect_note_onsets(detected) - if not timestamps: - return None - - fretboard = detect_fretboard_from_video(video_path, num_sample_frames=5) - if not fretboard: - return {'skipped': 'no fretboard'} - - video_observations = analyze_video_at_timestamps( - video_path, timestamps, VideoAnalysisConfig() - ) - if not video_observations: - return {'skipped': 'no video observations'} - - timeline = build_hand_position_timeline(video_observations, fretboard) - if not timeline: - return { - 'skipped': 'empty timeline', - 'observation_count': len(video_observations), - 'fretboard_confidence': fretboard.detection_confidence, - } - - if verbose: - print(f" fretboard: starting_fret={fretboard.starting_fret} " - f"frets_detected={len(fretboard.fret_positions)} " - f"actual_fret_numbers={fretboard.actual_fret_numbers} " - f"conf={fretboard.detection_confidence:.2f}") - print(f" fretboard corners: TL={fretboard.top_left} TR={fretboard.top_right} " - f"BL={fretboard.bottom_left} BR={fretboard.bottom_right}") - print(f" frame: {fretboard.frame_width}x{fretboard.frame_height}") - print(f" timeline head:") - for p in timeline[:5]: - print(f" t={p.timestamp:.2f} fret={p.anchor_fret:.2f} conf={p.confidence:.2f}") - - if dump_frames: - from app.hand_anchor import _compute_palm_xy, project_palm_to_fret - print(f"\n --- per-frame dump (first 10) ---") - print(f" {'t':>6} {'is_lh':>6} {'ext':>4} {'palm_xn':>8} {'palm_yn':>8} " - f"{'wrist_xn':>8} {'wrist_yn':>8} {'fret':>8} {'conf':>6}") - for t in sorted(video_observations)[:10]: - obs = video_observations[t] - extended = [f for f in obs.fingers if f.finger_id != 0 and f.is_extended] - palm = _compute_palm_xy(obs) - palm_str = f"{palm[0]:.3f} {palm[1]:.3f}" if palm else " None " - wrist_str = (f"{obs.wrist_position[0]:.3f} {obs.wrist_position[1]:.3f}" - if obs.wrist_position else " None ") - fret, conf = project_palm_to_fret(obs, fretboard) - fret_str = f"{fret:.2f}" if fret is not None else "None" - print(f" {t:>6.2f} {str(obs.is_left_hand):>6} {len(extended):>4} " - f"{palm_str:>17} {wrist_str:>17} {fret_str:>8} {conf:>6.2f}") - - # GT notes are dicts with `beat` — convert to timestamps via bpm + auto-align. - bpm = bm.get('bpm') - video_duration = bm.get('video_duration') or get_video_duration(video_path) - total_beats = max(n['beat'] for n in ground_truth) - if bpm and bpm > 0: - beat_to_time = 60.0 / bpm - else: - beat_to_time = video_duration / total_beats if total_beats > 0 else 1 - time_offset = 0.0 - if bpm and bpm > 0 and detected: - # _find_best_time_offset expects objects with .timestamp — adapt DetectedNotes - class _Adapter: - __slots__ = ('timestamp', 'midi_note') - def __init__(self, t, m): - self.timestamp = t - self.midi_note = m - adapters = [_Adapter(n.start_time, n.midi_note) for n in detected] - time_offset = _find_best_time_offset( - adapters, - [n['beat'] for n in ground_truth], - [n.get('midi_note') for n in ground_truth], - beat_to_time, video_duration, 0.5, - ) - - errors: list[float] = [] - covered = 0 - total_pitched = 0 - for gt in ground_truth: - fret = gt.get('fret') - if fret is None or fret == 'X': - continue - total_pitched += 1 - ts = gt['beat'] * beat_to_time + time_offset - anchor_fret, conf = get_hand_anchor_at(timeline, ts, max_gap=0.3) - if anchor_fret is None: - continue - covered += 1 - errors.append(abs(anchor_fret - float(fret))) - - if not errors: - return { - 'coverage': 0.0, - 'total_pitched': total_pitched, - 'note': 'no anchor overlap with GT timestamps', - 'timeline_points': len(timeline), - } - - mae = sum(errors) / len(errors) - within_2 = sum(1 for e in errors if e <= 2) / len(errors) - within_4 = sum(1 for e in errors if e <= 4) / len(errors) - - return { - 'mae': mae, - 'within_2': within_2, - 'within_4': within_4, - 'coverage': covered / total_pitched if total_pitched else 0.0, - 'timeline_points': len(timeline), - 'total_pitched': total_pitched, - } - - -def main(): - parser = argparse.ArgumentParser(description='Diagnose hand-anchor quality') - parser.add_argument('--id', type=str, help='Run only this benchmark id') - parser.add_argument('--id-prefix', type=str, - help='Run benchmarks whose id starts with this prefix') - parser.add_argument('--verbose', action='store_true') - parser.add_argument('--dump-frames', action='store_true', - help='Per-frame dump of palm/fret computation') - args = parser.parse_args() - - benchmarks = load_index() - if args.id: - benchmarks = [b for b in benchmarks if b['id'] == args.id] - elif args.id_prefix: - benchmarks = [b for b in benchmarks if b['id'].startswith(args.id_prefix)] - - results: dict[str, dict] = {} - for bm in benchmarks: - print(f"\n--- {bm['id']} ---") - try: - r = diagnose_one(bm, verbose=args.verbose, dump_frames=args.dump_frames) - except Exception as e: - print(f" ERROR: {e}") - results[bm['id']] = {'error': str(e)} - continue - if r is None: - print(" SKIP: missing video/GT") - continue - if 'skipped' in r: - print(f" SKIP: {r['skipped']}") - results[bm['id']] = r - continue - results[bm['id']] = r - print(f" timeline points: {r['timeline_points']}") - print(f" coverage: {r['coverage']:.1%} ({r.get('total_pitched', 0)} pitched GT notes)") - if 'mae' in r: - print(f" anchor MAE: {r['mae']:.2f} frets") - print(f" within 2 frets: {r['within_2']:.1%}") - print(f" within 4 frets: {r['within_4']:.1%}") - - print("\n" + "=" * 70) - print("SUMMARY") - print("=" * 70) - print(f"{'ID':<22} {'points':>8} {'cov':>8} {'MAE':>8} {'±2':>8} {'±4':>8}") - print("-" * 70) - maes, covs, w2s = [], [], [] - for bm_id, r in results.items(): - if 'mae' in r: - print(f"{bm_id:<22} {r['timeline_points']:>8} {r['coverage']:>7.1%} " - f"{r['mae']:>8.2f} {r['within_2']:>7.1%} {r['within_4']:>7.1%}") - maes.append(r['mae']) - covs.append(r['coverage']) - w2s.append(r['within_2']) - else: - print(f"{bm_id:<22} {r.get('skipped', r.get('error', 'no data'))}") - print("-" * 70) - if maes: - print(f"{'AVERAGE':<22} {'':>8} {sum(covs)/len(covs):>7.1%} " - f"{sum(maes)/len(maes):>8.2f} {sum(w2s)/len(w2s):>7.1%} n={len(maes)}") - - -if __name__ == '__main__': - main() diff --git a/tabvision-server/evaluate_transcription.py b/tabvision-server/evaluate_transcription.py index 9ca3b71..dc6d2b5 100644 --- a/tabvision-server/evaluate_transcription.py +++ b/tabvision-server/evaluate_transcription.py @@ -674,12 +674,8 @@ def print_metrics(metrics: EvalMetrics, label: str = ""): def main(): parser = argparse.ArgumentParser(description='Evaluate guitar transcription accuracy') - parser.add_argument('video_path', nargs='?', - default='/home/gilhooleyp/projects/tab_vision/test-data/existing/sample-video.mp4', - help='Path to video file') - parser.add_argument('ground_truth_path', nargs='?', - default='/home/gilhooleyp/projects/tab_vision/test-data/existing/sample-video-tabs.txt', - help='Path to ground truth tab file') + parser.add_argument('video_path', help='Path to video file') + parser.add_argument('ground_truth_path', help='Path to ground truth tab file') parser.add_argument('--time-tolerance', type=float, default=0.5, help='Timing tolerance in seconds') parser.add_argument('--output', type=str, default=None, diff --git a/tabvision-server/requirements.txt b/tabvision-server/requirements.txt index 65637bf..e61de84 100644 --- a/tabvision-server/requirements.txt +++ b/tabvision-server/requirements.txt @@ -11,10 +11,6 @@ librosa>=0.10.0 soundfile>=0.12.0 scipy>=1.10.0 gunicorn>=21.2.0 -# Used by tools/build_position_dataset.py; not needed by the runtime server. -pandas>=2.0.0 pyarrow>=15.0.0 -# Used by tools/train_position_selector.py; not needed by the runtime server. -lightgbm>=4.0.0 # Used by app/training/guitarset_dataset.py; not needed by the runtime server. mirdata>=0.3.0 diff --git a/tabvision-server/tests/fixtures/benchmarks/index.json b/tabvision-server/tests/fixtures/benchmarks/index.json index 19b68c8..2caf587 100644 --- a/tabvision-server/tests/fixtures/benchmarks/index.json +++ b/tabvision-server/tests/fixtures/benchmarks/index.json @@ -13,230 +13,10 @@ "max_detected_notes": 55, "min_detected_notes": 30 } - }, - { - "id": "training-01", - "video_path": "test-data/training-tabs/training-tabs-video/training-01.MOV", - "ground_truth_path": "test-data/training-tabs/training-01-tabs.txt", - "description": "Position Ambiguity: same pitch at open vs fretted positions", - "bpm": 72, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-02", - "video_path": "test-data/training-tabs/training-tabs-video/training-02.MOV", - "ground_truth_path": "test-data/training-tabs/training-02-tabs.txt", - "description": "Position Ambiguity: adjacent string equivalents", - "bpm": 80, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-03", - "video_path": "test-data/training-tabs/training-tabs-video/training-03.MOV", - "ground_truth_path": "test-data/training-tabs/training-03-tabs.txt", - "description": "Position Ambiguity: high frets (electric only)", - "bpm": 66, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-04", - "video_path": "test-data/training-tabs/training-tabs-video/training-04.MOV", - "ground_truth_path": "test-data/training-tabs/training-04-tabs.txt", - "description": "Position Ambiguity: upper neck positions (electric only)", - "bpm": 80, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-05", - "video_path": "test-data/training-tabs/training-tabs-video/training-05.MOV", - "ground_truth_path": "test-data/training-tabs/training-05-tabs.txt", - "description": "Position Ambiguity: capo-free vs equivalent positions", - "bpm": 72, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-06", - "video_path": "test-data/training-tabs/training-tabs-video/training-06.MOV", - "ground_truth_path": "test-data/training-tabs/training-06-tabs.txt", - "description": "Chord Varieties: open chords", - "bpm": 80, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-07", - "video_path": "test-data/training-tabs/training-tabs-video/training-07.MOV", - "ground_truth_path": "test-data/training-tabs/training-07-tabs.txt", - "description": "Chord Varieties: barre chords", - "bpm": 72, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-08", - "video_path": "test-data/training-tabs/training-tabs-video/training-08.MOV", - "ground_truth_path": "test-data/training-tabs/training-08-tabs.txt", - "description": "Chord Varieties: power chords", - "bpm": 100, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-09", - "video_path": "test-data/training-tabs/training-tabs-video/training-09.MOV", - "ground_truth_path": "test-data/training-tabs/training-09-tabs.txt", - "description": "Chord Varieties: jazz/partial chords", - "bpm": 66, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-10", - "video_path": "test-data/training-tabs/training-tabs-video/training-10.MOV", - "ground_truth_path": "test-data/training-tabs/training-10-tabs.txt", - "description": "Chord Varieties: mixed chord types", - "bpm": 84, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-11", - "video_path": "test-data/training-tabs/training-tabs-video/training-11.MOV", - "ground_truth_path": "test-data/training-tabs/training-11-tabs.txt", - "description": "Single-Note Passages: pentatonic scale run", - "bpm": 90, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-12", - "video_path": "test-data/training-tabs/training-tabs-video/training-12.MOV", - "ground_truth_path": "test-data/training-tabs/training-12-tabs.txt", - "description": "Single-Note Passages: arpeggios", - "bpm": 80, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-13", - "video_path": "test-data/training-tabs/training-tabs-video/training-13.MOV", - "ground_truth_path": "test-data/training-tabs/training-13-tabs.txt", - "description": "Single-Note Passages: chromatic run", - "bpm": 72, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-14", - "video_path": "test-data/training-tabs/training-tabs-video/training-14.MOV", - "ground_truth_path": "test-data/training-tabs/training-14-tabs.txt", - "description": "Single-Note Passages: hammer-ons and pull-offs", - "bpm": 140, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-15", - "video_path": "test-data/training-tabs/training-tabs-video/training-15.MOV", - "ground_truth_path": "test-data/training-tabs/training-15-tabs.txt", - "description": "Single-Note Passages: mixed melodic pattern", - "bpm": 84, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-16", - "video_path": "test-data/training-tabs/training-tabs-video/training-16.MOV", - "ground_truth_path": "test-data/training-tabs/training-16-tabs.txt", - "description": "Edge Cases: position jumps (electric only)", - "bpm": 66, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-17", - "video_path": "test-data/training-tabs/training-tabs-video/training-17.MOV", - "ground_truth_path": "test-data/training-tabs/training-17-tabs.txt", - "description": "Edge Cases: very high frets (electric only)", - "bpm": 72, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-18", - "video_path": "test-data/training-tabs/training-tabs-video/training-18.MOV", - "ground_truth_path": "test-data/training-tabs/training-18-tabs.txt", - "description": "Edge Cases: open string ringing with fretting", - "bpm": 90, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-19", - "video_path": "test-data/training-tabs/training-tabs-video/training-19.MOV", - "ground_truth_path": "test-data/training-tabs/training-19-tabs.txt", - "description": "Edge Cases: wide interval leaps", - "bpm": 84, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } - }, - { - "id": "training-20", - "video_path": "test-data/training-tabs/training-tabs-video/training-20.MOV", - "ground_truth_path": "test-data/training-tabs/training-20-tabs.txt", - "description": "Edge Cases: mixed techniques and positions", - "bpm": 72, - "baselines": { - "f1_min": 0.0, - "pitch_f1_min": 0.0 - } } ], "defaults": { "time_tolerance": 1.5, "audio_only": true } -} \ No newline at end of file +} diff --git a/tabvision-server/tests/fixtures/benchmarks/results/baseline.json b/tabvision-server/tests/fixtures/benchmarks/results/baseline.json deleted file mode 100644 index 80755f2..0000000 --- a/tabvision-server/tests/fixtures/benchmarks/results/baseline.json +++ /dev/null @@ -1,3065 +0,0 @@ -{ - "timestamp": "2026-04-22T18:55:15.575351", - "results": { - "training-01": { - "metrics": { - "exact": { - "precision": 0.09090909090909091, - "recall": 0.125, - "f1": 0.10526315789473685, - "tp": 2, - "fp": 20, - "fn": 14 - }, - "pitch": { - "precision": 0.13636363636363635, - "recall": 0.1875, - "f1": 0.15789473684210525, - "tp": 3, - "fp": 19, - "fn": 13 - }, - "position": { - "accuracy": 0.6666666666666666, - "correct": 2, - "total": 3 - }, - "chord": { - "precision": 0.0, - "recall": 0.0, - "f1": 0.0, - "tp": 0, - "partial": 0, - "fp": 2, - "fn": 0 - }, - "counts": { - "ground_truth": 16, - "detected": 22, - "ratio": 1.375 - }, - "near_misses": [ - { - "gt_string": 1, - "gt_fret": 0, - "det_string": 3, - "det_fret": 4, - "gt_time": 5.907650377049181, - "det_time": 5.691456689342403, - "midi_note": 64, - "type": "wrong_pitch" - }, - { - "gt_string": 3, - "gt_fret": 0, - "det_string": 1, - "det_fret": 5, - "gt_time": 6.52950831147541, - "det_time": 6.888568253968254, - "midi_note": 55, - "type": "wrong_pitch" - }, - { - "gt_string": 1, - "gt_fret": 5, - "det_string": 2, - "det_fret": 5, - "gt_time": 8.395082114754098, - "det_time": 8.526858956916099, - "midi_note": 69, - "type": "wrong_pitch" - }, - { - "gt_string": 3, - "gt_fret": 4, - "det_string": 4, - "det_fret": 5, - "gt_time": 9.016940049180327, - "det_time": 8.898378231292517, - "midi_note": 59, - "type": "wrong_pitch" - }, - { - "gt_string": 2, - "gt_fret": 0, - "det_string": 3, - "det_fret": 4, - "gt_time": 10.882513852459017, - 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"f1": 0.10256410256410256, - "tp": 2, - "fp": 19, - "fn": 16 - }, - "pitch": { - "precision": 0.23809523809523808, - "recall": 0.2777777777777778, - "f1": 0.25641025641025644, - "tp": 5, - "fp": 16, - "fn": 13 - }, - "position": { - "accuracy": 0.4, - "correct": 2, - "total": 5 - }, - "chord": { - "precision": 0.0, - "recall": 0.0, - "f1": 0.0, - "tp": 0, - "partial": 0, - "fp": 1, - "fn": 0 - }, - "counts": { - "ground_truth": 18, - "detected": 21, - "ratio": 1.1666666666666667 - }, - "near_misses": [ - { - "gt_string": 1, - "gt_fret": 1, - "det_string": 2, - "det_fret": 5, - "gt_time": 3.934391603174603, - "det_time": 4.367919274376417, - "midi_note": 65, - "type": "wrong_pitch" - }, - { - "gt_string": 1, - "gt_fret": 0, - "det_string": 2, - "det_fret": 6, - "gt_time": 5.1449736349206345, - "det_time": 5.099347845804989, - "midi_note": 64, - "type": "wrong_pitch" - }, - { - "gt_string": 2, - "gt_fret": 1, - "det_string": 2, - "det_fret": 8, - "gt_time": 5.750264650793651, - "det_time": 5.8539963718820855, - 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}, - { - "gt_string": 1, - "gt_fret": 0, - "det_string": 1, - "det_fret": 0, - "gt_time": 12.857142857142858, - "det_time": 12.116909750566894, - "midi_note": 64, - "type": "wrong_position" - } - ] - }, - "note_count": 23, - "ground_truth_count": 30 - }, - "training-20": { - "metrics": { - "exact": { - "precision": 0.7368421052631579, - "recall": 0.25925925925925924, - "f1": 0.3835616438356165, - "tp": 14, - "fp": 5, - "fn": 40 - }, - "pitch": { - "precision": 0.9473684210526315, - "recall": 0.5, - "f1": 0.6545454545454545, - "tp": 18, - "fp": 1, - "fn": 18 - }, - "position": { - "accuracy": 0.7777777777777778, - "correct": 14, - "total": 18 - }, - "chord": { - "precision": 0.0, - "recall": 0.0, - "f1": 0.0, - "tp": 0, - "partial": 0, - "fp": 5, - "fn": 9 - }, - "counts": { - "ground_truth": 54, - "detected": 19, - "ratio": 0.35185185185185186 - }, - "near_misses": [ - { - "gt_string": 1, - "gt_fret": 0, - "det_string": 5, - "det_fret": 0, - "gt_time": 1.7083333333333333, - "det_time": 1.822766439909297, - "midi_note": 64, - "type": "wrong_pitch" - }, - { - "gt_string": 6, - "gt_fret": 0, - "det_string": 5, - "det_fret": 0, - "gt_time": 1.7083333333333333, - "det_time": 1.822766439909297, - "midi_note": 40, - "type": "wrong_pitch" - }, - { - "gt_string": 1, - "gt_fret": 3, - "det_string": 5, - "det_fret": 0, - "gt_time": 2.541666666666667, - "det_time": 1.822766439909297, - "midi_note": 67, - "type": "wrong_pitch" - }, - { - "gt_string": 3, - "gt_fret": 4, - "det_string": 5, - "det_fret": 0, - "gt_time": 2.541666666666667, - "det_time": 1.822766439909297, - "midi_note": 59, - "type": "wrong_pitch" - }, - { - "gt_string": 4, - "gt_fret": 5, - "det_string": 5, - "det_fret": 0, - "gt_time": 2.541666666666667, - "det_time": 1.822766439909297, - "midi_note": 55, - "type": "wrong_pitch" - }, - { - "gt_string": 5, - "gt_fret": 5, - "det_string": 5, - "det_fret": 0, - "gt_time": 2.541666666666667, - "det_time": 1.822766439909297, - "midi_note": 50, - "type": "wrong_pitch" - }, - { - "gt_string": 6, - "gt_fret": 3, - "det_string": 5, - "det_fret": 0, - "gt_time": 2.541666666666667, - "det_time": 1.822766439909297, - "midi_note": 43, - "type": "wrong_pitch" - }, - { - "gt_string": 1, - "gt_fret": "X", - "det_string": 2, - "det_fret": 3, - "gt_time": 3.375, - "det_time": 2.857338321995465, - "midi_note": null, - "type": "wrong_pitch" - }, - { - "gt_string": 2, - "gt_fret": "X", - "det_string": 2, - "det_fret": 3, - "gt_time": 3.375, - "det_time": 2.857338321995465, - "midi_note": null, - "type": "wrong_pitch" - }, - { - "gt_string": 3, - "gt_fret": "X", - "det_string": 2, - "det_fret": 3, - "gt_time": 3.375, - "det_time": 2.857338321995465, - "midi_note": null, - "type": "wrong_pitch" - }, - { - "gt_string": 4, - "gt_fret": "X", - "det_string": 2, - "det_fret": 3, - "gt_time": 3.375, - "det_time": 2.857338321995465, - "midi_note": null, - "type": "wrong_pitch" - }, - { - "gt_string": 5, - "gt_fret": "X", - "det_string": 2, - "det_fret": 3, - "gt_time": 3.375, - "det_time": 2.857338321995465, - "midi_note": null, - "type": "wrong_pitch" - }, - { - "gt_string": 6, - "gt_fret": "X", - "det_string": 2, - "det_fret": 3, - "gt_time": 3.375, - "det_time": 2.857338321995465, - "midi_note": null, - "type": "wrong_pitch" - }, - { - "gt_string": 2, - "gt_fret": 0, - "det_string": 4, - "det_fret": 0, - "gt_time": 4.416666666666667, - "det_time": 4.507239002267574, - "midi_note": 59, - "type": "wrong_pitch" - }, - { - "gt_string": 6, - "gt_fret": 0, - "det_string": 4, - "det_fret": 0, - "gt_time": 4.416666666666667, - "det_time": 4.507239002267574, - "midi_note": 40, - "type": "wrong_pitch" - }, - { - "gt_string": 1, - "gt_fret": 5, - "det_string": 4, - "det_fret": 0, - "gt_time": 5.25, - "det_time": 4.507239002267574, - "midi_note": 69, - "type": "wrong_pitch" - }, - { - "gt_string": 2, - "gt_fret": 5, - "det_string": 4, - "det_fret": 0, - "gt_time": 5.25, - "det_time": 4.507239002267574, - "midi_note": 64, - "type": "wrong_pitch" - }, - { - "gt_string": 3, - "gt_fret": 5, - "det_string": 4, - "det_fret": 0, - "gt_time": 5.25, - "det_time": 4.507239002267574, - "midi_note": 60, - "type": "wrong_pitch" - }, - { - "gt_string": 4, - "gt_fret": 7, - "det_string": 4, - "det_fret": 0, - "gt_time": 5.25, - "det_time": 4.507239002267574, - "midi_note": 57, - "type": "wrong_pitch" - }, - { - "gt_string": 5, - "gt_fret": 7, - "det_string": 4, - "det_fret": 0, - "gt_time": 5.25, - "det_time": 4.507239002267574, - "midi_note": 52, - "type": "wrong_pitch" - } - ] - }, - "note_count": 19, - "ground_truth_count": 54 - } - } -} \ No newline at end of file diff --git a/tabvision-server/tools/build_guitarset_tfrecords.py b/tabvision-server/tools/build_guitarset_tfrecords.py index 2dd9c85..8610d34 100644 --- a/tabvision-server/tools/build_guitarset_tfrecords.py +++ b/tabvision-server/tools/build_guitarset_tfrecords.py @@ -1,6 +1,7 @@ """Build basic_pitch-compatible TFRecords from sideloaded GuitarSet data. -Phase 1 Week 1 deliverable per docs/plans/2026-04-24-audio-backbone-finetune-design.md §7. +Historical Phase 1 GuitarSet fine-tune helper. The old April plan that introduced +this helper was removed on 2026-06-11 because its private-video gate is invalid. Why this exists: Spotify's `basic_pitch.data.datasets.guitarset` Beam pipeline relies on `mirdata` to read JAMS + WAV pairs, and `mirdata.initialize('guitarset')` diff --git a/tabvision-server/tools/build_position_dataset.py b/tabvision-server/tools/build_position_dataset.py deleted file mode 100644 index 2658a7c..0000000 --- a/tabvision-server/tools/build_position_dataset.py +++ /dev/null @@ -1,257 +0,0 @@ -"""Step 2 of the learned-fusion plan -(docs/plans/2026-04-24-learned-fusion-design.md §4.3). - -Reads a position-features JSONL produced by tools/dump_position_features.py, -aligns each event to ground truth, emits a labeled parquet dataset for -training the position selector in Step 3. - -A row = one candidate. label=1 iff the candidate's (string, fret) matches -the GT note that aligns to the event's (onset_time, midi_note). Events -without a matching GT note are dropped — they're extras / hallucinations -and have no correct candidate to learn against. - -Group key: video_id (for leave-one-video-out CV in Step 3). - -Usage (from tabvision-server/): - python tools/build_position_dataset.py - python tools/build_position_dataset.py --features tools/outputs/position-features-2026-04-29_093154.jsonl -""" -from __future__ import annotations - -import argparse -import json -import os -import sys -from typing import Optional - -THIS_FILE = os.path.abspath(__file__) -TOOLS_DIR = os.path.dirname(THIS_FILE) -SERVER_DIR = os.path.dirname(TOOLS_DIR) -REPO_ROOT = os.path.dirname(SERVER_DIR) -BENCHMARKS_DIR = os.path.join(SERVER_DIR, 'tests', 'fixtures', 'benchmarks') -OUTPUTS_DIR = os.path.join(TOOLS_DIR, 'outputs') - -sys.path.insert(0, SERVER_DIR) - - -def load_index() -> tuple[dict, dict]: - with open(os.path.join(BENCHMARKS_DIR, 'index.json')) as f: - data = json.load(f) - bm_by_id = {b['id']: b for b in data['benchmarks']} - return bm_by_id, data.get('defaults', {}) - - -def load_events_grouped(jsonl_path: str) -> dict[str, list[dict]]: - by_video: dict[str, list[dict]] = {} - with open(jsonl_path) as f: - for line in f: - line = line.strip() - if not line: - continue - ev = json.loads(line) - by_video.setdefault(ev['video_id'], []).append(ev) - return by_video - - -class _DetectedStub: - """Minimal duck-type for evaluate_transcription._find_best_time_offset.""" - __slots__ = ('timestamp', 'midi_note') - - def __init__(self, t: float, m: int) -> None: - self.timestamp = t - self.midi_note = m - - -def gt_with_aligned_times(bm: dict, defaults: dict, - events: list[dict]) -> tuple[list[dict], float]: - """Load GT, compute beat_to_time, auto-align to detected onsets.""" - from evaluate_transcription import ( - parse_ground_truth_tabs, _find_best_time_offset, get_video_duration, - ) - - gt_path = bm['ground_truth_path'] - if not os.path.isabs(gt_path): - gt_path = os.path.join(REPO_ROOT, gt_path) - with open(gt_path) as f: - gt = parse_ground_truth_tabs(f.read()) - - video_path = os.path.join(REPO_ROOT, bm['video_path']) - video_duration = bm.get('video_duration') or get_video_duration(video_path) - bpm = bm.get('bpm') or defaults.get('bpm') - tol = bm.get('time_tolerance', defaults.get('time_tolerance', 0.6)) - - if not gt: - return [], tol - - total_beats = max(n['beat'] for n in gt) - if bpm and bpm > 0: - beat_to_time = 60.0 / bpm - else: - beat_to_time = video_duration / total_beats if total_beats else 1.0 - - offset = 0.0 - if bpm and bpm > 0 and events: - stubs = [_DetectedStub(ev['onset_time'], ev['midi_note']) for ev in events] - offset = _find_best_time_offset( - stubs, - [n['beat'] for n in gt], - [n.get('midi_note') for n in gt], - beat_to_time, video_duration, tol, - ) - - aligned = [ - {**n, 'time': n['beat'] * beat_to_time + offset} - for n in gt - ] - return aligned, tol - - -def _match_event_to_gt(ev: dict, gt_with_time: list[dict], - matched_gt: set[int], tol: float) -> Optional[int]: - """Closest unmatched GT note within tol with matching MIDI.""" - best_gi = None - best_dt = float('inf') - for gi, gt in enumerate(gt_with_time): - if gi in matched_gt: - continue - if gt.get('midi_note') is None: - continue - if gt['midi_note'] != ev['midi_note']: - continue - dt = abs(gt['time'] - ev['onset_time']) - if dt > tol or dt >= best_dt: - continue - best_gi, best_dt = gi, dt - return best_gi - - -def label_events(video_id: str, events: list[dict], - gt_with_time: list[dict], tol: float) -> list[dict]: - """Greedy 1:1 match each event to a GT note; emit labeled candidate rows.""" - rows: list[dict] = [] - matched_gt: set[int] = set() - events_sorted = sorted(events, key=lambda e: e['onset_time']) - for ev in events_sorted: - gi = _match_event_to_gt(ev, gt_with_time, matched_gt, tol) - if gi is None: - continue - matched_gt.add(gi) - gt = gt_with_time[gi] - gt_fret = gt['fret'] if gt['fret'] != 'X' else None - for c in ev['candidates']: - label = int( - c['cand_string'] == gt['string'] - and c['cand_fret'] == gt['fret'] - ) - rows.append({ - 'video_id': video_id, - 'event_id': ev['event_id'], - 'onset_time': ev['onset_time'], - 'midi_note': ev['midi_note'], - 'amplitude': ev['amplitude'], - 'basicpitch_confidence': ev['basicpitch_confidence'], - 'is_chord': ev['is_chord'], - 'chord_size': ev['chord_size'], - 'chord_string_span': ev['chord_string_span'], - 'num_candidates': ev['num_candidates'], - 'prev_position_string': ev['prev_position_string'], - 'prev_position_fret': ev['prev_position_fret'], - 'seconds_since_prev': ev['seconds_since_prev'], - 'hand_anchor_fret': ev['hand_anchor_fret'], - 'video_hand_anchor_fret': ev['video_hand_anchor_fret'], - 'selected_string': ev['selected_string'], - 'selected_fret': ev['selected_fret'], - 'cand_string': c['cand_string'], - 'cand_fret': c['cand_fret'], - 'dist_anchor_fret': c['dist_anchor_fret'], - 'dist_anchor_string': c['dist_anchor_string'], - 'dist_prev_fret': c['dist_prev_fret'], - 'dist_prev_string': c['dist_prev_string'], - 'heuristic_score': c['heuristic_score'], - 'is_heuristic_pick': c['is_heuristic_pick'], - 'gt_string': gt['string'], - 'gt_fret': gt_fret, - 'label': label, - }) - return rows - - -def _latest_features_jsonl() -> Optional[str]: - if not os.path.isdir(OUTPUTS_DIR): - return None - candidates = sorted( - f for f in os.listdir(OUTPUTS_DIR) - if f.startswith('position-features-') and f.endswith('.jsonl') - ) - if not candidates: - return None - return os.path.join(OUTPUTS_DIR, candidates[-1]) - - -def main() -> int: - ap = argparse.ArgumentParser() - ap.add_argument('--features', - help='input position-features JSONL (default: most recent)') - ap.add_argument('--output', - default=os.path.join(OUTPUTS_DIR, 'position_dataset.parquet'), - help='output parquet path') - args = ap.parse_args() - - if args.features is None: - args.features = _latest_features_jsonl() - if args.features is None: - print('no position-features-*.jsonl found in tools/outputs/', file=sys.stderr) - return 1 - print(f'using features file: {args.features}', file=sys.stderr) - - bm_by_id, defaults = load_index() - events_by_video = load_events_grouped(args.features) - - all_rows: list[dict] = [] - matched_event_count = 0 - unmatched_event_count = 0 - label_pos = 0 - - for vid in sorted(events_by_video): - events = events_by_video[vid] - bm = bm_by_id.get(vid) - if bm is None: - print(f'[{vid}] no benchmark entry; skipping', file=sys.stderr) - continue - gt_with_time, tol = gt_with_aligned_times(bm, defaults, events) - rows = label_events(vid, events, gt_with_time, tol) - labeled_event_ids = {r['event_id'] for r in rows} - matched_event_count += len(labeled_event_ids) - unmatched_event_count += len(events) - len(labeled_event_ids) - label_pos += sum(r['label'] for r in rows) - all_rows.extend(rows) - with_label_1 = sum(1 for eid in labeled_event_ids - if any(r['event_id'] == eid and r['label'] == 1 - for r in rows)) - print(f'[{vid}] labeled events: {len(labeled_event_ids)}/{len(events)}, ' - f'with-correct-candidate: {with_label_1}, rows: {len(rows)}', - file=sys.stderr) - - print(file=sys.stderr) - print(f'Total labeled events: {matched_event_count}', file=sys.stderr) - print(f'Total unmatched events: {unmatched_event_count}', file=sys.stderr) - print(f'Total candidate rows: {len(all_rows)}', file=sys.stderr) - print(f'Positive-label rows: {label_pos} ' - f'({100.0 * label_pos / len(all_rows):.1f}% of rows)' if all_rows else 'no rows', - file=sys.stderr) - - if not all_rows: - return 1 - - import pandas as pd - df = pd.DataFrame(all_rows) - df.to_parquet(args.output, index=False) - csv_path = args.output.replace('.parquet', '.csv') - df.to_csv(csv_path, index=False) - print(f'wrote {args.output}', file=sys.stderr) - print(f'wrote {csv_path}', file=sys.stderr) - return 0 - - -if __name__ == '__main__': - raise SystemExit(main()) diff --git a/tabvision-server/tools/dump_position_features.py b/tabvision-server/tools/dump_position_features.py deleted file mode 100644 index 6f99d8d..0000000 --- a/tabvision-server/tools/dump_position_features.py +++ /dev/null @@ -1,186 +0,0 @@ -"""Sanity / dataset driver for position-selection feature emission. - -Step 1 follow-up: runs the existing pipeline end-to-end on every -training-* benchmark with FusionConfig.emit_position_features=True and -dumps every PositionDecision to a single JSONL file. Subprocess-per-video -mirrors tools/error_analysis.py so we don't get batch drift. - -Usage (from tabvision-server/): - python tools/dump_position_features.py # all training-* - python tools/dump_position_features.py --id training-09 - python tools/dump_position_features.py --worker --id training-09 - # internal subprocess mode -""" -from __future__ import annotations - -import argparse -import dataclasses -import json -import os -import subprocess -import sys -from collections import Counter -from datetime import datetime - -THIS_FILE = os.path.abspath(__file__) -TOOLS_DIR = os.path.dirname(THIS_FILE) -SERVER_DIR = os.path.dirname(TOOLS_DIR) -REPO_ROOT = os.path.dirname(SERVER_DIR) -BENCHMARKS_DIR = os.path.join(SERVER_DIR, 'tests', 'fixtures', 'benchmarks') -OUTPUTS_DIR = os.path.join(TOOLS_DIR, 'outputs') - -sys.path.insert(0, SERVER_DIR) - -import warnings -warnings.filterwarnings('ignore') -os.environ.setdefault('TF_CPP_MIN_LOG_LEVEL', '3') - - -def load_benchmark(bm_id: str) -> tuple[dict, dict]: - with open(os.path.join(BENCHMARKS_DIR, 'index.json')) as f: - data = json.load(f) - for b in data['benchmarks']: - if b['id'] == bm_id: - return b, data.get('defaults', {}) - raise SystemExit(f'unknown benchmark id: {bm_id}') - - -def load_training_ids() -> list[str]: - with open(os.path.join(BENCHMARKS_DIR, 'index.json')) as f: - data = json.load(f) - return [b['id'] for b in data['benchmarks'] - if b['id'].startswith('training-')] - - -def run_worker(bm_id: str) -> dict: - """Run pipeline with feature emission on. Return events as plain dicts.""" - from evaluate_transcription import run_transcription - from app.fusion_engine import FusionConfig, PositionDecision # noqa: F401 - - bm, defaults = load_benchmark(bm_id) - video_path = os.path.join(REPO_ROOT, bm['video_path']) - audio_only = bm.get('audio_only', defaults.get('audio_only', True)) - - config = FusionConfig() - config.emit_position_features = True - config._feature_events = [] - - run_transcription(video_path, audio_only=audio_only, fusion_config=config) - - events = [dataclasses.asdict(e) for e in config._feature_events] - return {'video_id': bm_id, 'events': events} - - -def dispatch_subprocess(bm_id: str) -> dict: - r = subprocess.run( - [sys.executable, THIS_FILE, '--worker', '--id', bm_id], - capture_output=True, text=True, timeout=900, - ) - if r.returncode != 0: - return {'video_id': bm_id, 'error': r.stderr[-500:]} - line = next(l for l in reversed(r.stdout.splitlines()) if l.strip()) - return json.loads(line) - - -def summarize(per_video: dict[str, list[dict]]) -> str: - lines: list[str] = [] - n_events_total = 0 - n_cands_total = 0 - chord_count = 0 - video_anchor_count = 0 - cand_per_event: list[int] = [] - selected_eq_heuristic = 0 - selected_total = 0 - seconds_since_seen = 0 - - for vid in sorted(per_video): - evs = per_video[vid] - n_events_total += len(evs) - for ev in evs: - n_cands_total += len(ev['candidates']) - cand_per_event.append(len(ev['candidates'])) - if ev['is_chord']: - chord_count += 1 - if ev['video_hand_anchor_fret'] is not None: - video_anchor_count += 1 - if ev['seconds_since_prev'] is not None: - seconds_since_seen += 1 - picked = next( - (c for c in ev['candidates'] if c['is_heuristic_pick']), None - ) - if picked is not None: - selected_total += 1 - if (ev['selected_string'] == picked['cand_string'] - and ev['selected_fret'] == picked['cand_fret']): - selected_eq_heuristic += 1 - - lines.append(f'Videos with events: {len(per_video)}') - lines.append(f'Total events: {n_events_total}') - lines.append(f'Total candidate rows: {n_cands_total}') - if cand_per_event: - lines.append( - f'Candidates per event: min={min(cand_per_event)} ' - f'max={max(cand_per_event)} ' - f'mean={sum(cand_per_event) / len(cand_per_event):.1f}' - ) - lines.append(f'Chord events: {chord_count} (is_chord=True)') - lines.append(f'Events with video_hand_anchor: {video_anchor_count}') - lines.append(f'Events with seconds_since_prev set: {seconds_since_seen}') - if selected_total: - agree_pct = 100.0 * selected_eq_heuristic / selected_total - lines.append( - f'selected matches heuristic pick: {selected_eq_heuristic}/{selected_total} ' - f'({agree_pct:.1f}%) — disagreement = video override' - ) - return '\n'.join(lines) - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument('--id', help='one benchmark id; default: all training-*') - ap.add_argument('--worker', action='store_true', - help='internal: run one video, emit JSON to stdout') - args = ap.parse_args() - - if args.worker: - if not args.id: - ap.error('--worker requires --id') - print(json.dumps(run_worker(args.id))) - return - - ids = [args.id] if args.id else load_training_ids() - os.makedirs(OUTPUTS_DIR, exist_ok=True) - stamp = datetime.now().strftime('%Y-%m-%d_%H%M%S') - jsonl_path = os.path.join(OUTPUTS_DIR, f'position-features-{stamp}.jsonl') - summary_path = os.path.join(OUTPUTS_DIR, f'position-features-{stamp}.md') - - per_video: dict[str, list[dict]] = {} - with open(jsonl_path, 'w') as out: - for bm_id in ids: - print(f'[{bm_id}] running...', flush=True) - res = dispatch_subprocess(bm_id) - if 'error' in res: - print(f'[{bm_id}] ERROR: {res["error"]}', flush=True) - continue - evs = res.get('events', []) - per_video[bm_id] = evs - for ev in evs: - out.write(json.dumps({**ev, 'video_id': bm_id}) + '\n') - print(f'[{bm_id}] {len(evs)} events', flush=True) - - summary = summarize(per_video) - print('\n' + summary) - with open(summary_path, 'w') as f: - f.write(f'# Position features dump — {datetime.now().isoformat(timespec="seconds")}\n\n') - f.write('```\n' + summary + '\n```\n\n') - f.write('## Per-video event counts\n\n') - f.write('| video | events |\n|---|---:|\n') - for vid in sorted(per_video): - f.write(f'| {vid} | {len(per_video[vid])} |\n') - - print(f'\nwrote {jsonl_path}') - print(f'wrote {summary_path}') - - -if __name__ == '__main__': - main() diff --git a/tabvision-server/tools/error_analysis.py b/tabvision-server/tools/error_analysis.py index ff445e1..2bbe0df 100644 --- a/tabvision-server/tools/error_analysis.py +++ b/tabvision-server/tools/error_analysis.py @@ -1,6 +1,6 @@ """Subprocess-per-video error-bucket harness. -Parent: python tools/error_analysis.py [--id training-01] +Parent: python tools/error_analysis.py [--id sample-video] -> runs each video in a fresh subprocess (--worker), aggregates. Worker: python tools/error_analysis.py --worker --id diff --git a/tabvision-server/tools/eval_basic_pitch_baseline.py b/tabvision-server/tools/eval_basic_pitch_baseline.py index 32d0ce0..007fc70 100644 --- a/tabvision-server/tools/eval_basic_pitch_baseline.py +++ b/tabvision-server/tools/eval_basic_pitch_baseline.py @@ -1,7 +1,6 @@ """Vanilla Basic Pitch baseline on the held-out GuitarSet player split. -Phase 1 Week 2 deliverable per -docs/plans/2026-04-24-audio-backbone-finetune-design.md §7. +Historical Phase 1 GuitarSet fine-tune helper. What this measures: - Frame note F1 at thresholds {0.3, 0.5, 0.7} — per-cell binary match @@ -461,11 +460,10 @@ def _write_report( lines.append('') lines.append( '- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split ' - 'by player). The plan §0 ship gate is on our **20-video iPhone set** ' - '(out-of-distribution), where the current exact F1 is ~0.51 and the ' - 'target is ≥ 0.60. Use the present number (note-event F1 ≈ 0.87) only as ' - 'the within-GuitarSet reference — improvement here is a *necessary but ' - 'not sufficient* condition for OOD improvement.' + 'by player). The private-video OOD gate was removed on 2026-06-11 ' + 'because its labels are not trusted. Use this number only as the ' + 'within-GuitarSet reference; real-video/audio replacement gates must ' + 'come from license-checked public/offline corpora.' ) with open(md_path, 'w') as f: diff --git a/tabvision-server/tools/finetune_basic_pitch_smoke.py b/tabvision-server/tools/finetune_basic_pitch_smoke.py index e581784..fe121b5 100644 --- a/tabvision-server/tools/finetune_basic_pitch_smoke.py +++ b/tabvision-server/tools/finetune_basic_pitch_smoke.py @@ -1,9 +1,7 @@ """5-epoch overfit smoke test for the basic_pitch fine-tune loop. -Phase 1 Week 1 deliverable per docs/plans/2026-04-24-audio-backbone-finetune-design.md §7 -("Friday gate"): confirm the training loop runs and the loss decreases on a -5-clip overfit. If this can't be made to run cleanly, the plan's bailout is -to pivot to a frozen-encoder + new-head approach (HuBERT / MusicFM). +Historical Phase 1 GuitarSet fine-tune helper. Confirms the training loop runs +and the loss decreases on a 5-clip overfit. Inputs: TFRecords produced by `tools/build_guitarset_tfrecords.py` (under `tools/outputs/tfrecords/guitarset/splits/{train,validation}/`). diff --git a/tabvision-server/tools/outputs/errors-2026-04-28_185743.csv b/tabvision-server/tools/outputs/errors-2026-04-28_185743.csv deleted file mode 100644 index 1965c41..0000000 --- a/tabvision-server/tools/outputs/errors-2026-04-28_185743.csv +++ /dev/null @@ -1,539 +0,0 @@ -bucket,video_id,gt_string,gt_fret,gt_time,gt_midi,det_string,det_fret,det_time,det_midi -correct,training-01,2,5,5.908333333333333,64,2,5,5.261887528344671,64 -correct,training-01,1,5,5.491666666666667,69,1,5,6.888568253968254,69 -correct,training-01,1,5,8.825,69,1,5,10.165149659863946,69 -correct,training-01,3,4,9.241666666666667,59,3,4,10.583108843537415,59 -correct,training-01,3,4,12.575,59,3,4,11.767326530612246,59 -correct,training-01,2,5,12.158333333333335,64,2,5,13.428837188208616,64 -correct,training-01,4,5,15.908333333333335,55,4,5,15.10195782312925,55 -correct,training-01,3,5,15.491666666666667,60,3,5,16.763468480725624,60 -wrong_position_same_pitch,training-01,2,0,4.241666666666667,59,3,4,5.691456689342403,59 -wrong_position_same_pitch,training-01,1,0,7.158333333333333,64,2,5,7.318137414965986,64 -wrong_position_same_pitch,training-01,3,0,7.575,55,4,5,8.898378231292517,55 -wrong_position_same_pitch,training-01,3,0,10.908333333333335,55,4,5,12.14012970521542,55 -wrong_position_same_pitch,training-01,4,0,14.241666666666667,50,5,5,15.543136961451246,50 -pitch_off,training-01,2,0,10.491666666666667,59,5,5,11.082337868480726,50 -pitch_off,training-01,3,0,13.825,55,3,4,13.85840634920635,59 -missed_onset,training-01,1,0,3.825,64,,,, -extra_detection,training-01,,,,,3,0,2.1142997732426303,55 -extra_detection,training-01,,,,,5,7,2.1491297052154192,52 -extra_detection,training-01,,,,,4,7,2.1955696145124715,57 -extra_detection,training-01,,,,,2,5,8.526858956916099,64 -extra_detection,training-01,,,,,4,5,17.20464761904762,55 -extra_detection,training-01,,,,,6,4,18.192779591836732,44 -extra_detection,training-01,,,,,3,3,18.204389569160995,58 -correct,training-02,2,1,7.4625,60,2,1,8.10889977324263,60 -correct,training-02,2,3,8.2125,62,2,3,8.840328344671201,62 -correct,training-02,2,0,9.7125,59,2,0,10.339299319727893,59 -correct,training-02,2,6,14.9625,65,2,6,14.86975827664399,65 -correct,training-02,2,8,14.2125,67,2,8,15.601186848072562,67 -correct,training-02,2,5,15.7125,64,2,5,17.06532789115646,64 -wrong_position_same_pitch,training-02,1,0,4.0875,64,2,5,4.367919274376417,64 -wrong_position_same_pitch,training-02,1,1,4.8375,65,2,6,5.099347845804989,65 -wrong_position_same_pitch,training-02,1,3,5.5875,67,2,8,5.8539963718820855,67 -wrong_position_same_pitch,training-02,1,1,6.3375,65,2,6,6.586708843537415,65 -wrong_position_same_pitch,training-02,1,0,7.0875,64,2,5,7.352967346938776,64 -wrong_position_same_pitch,training-02,2,1,8.9625,60,4,10,9.5949768707483,60 -wrong_position_same_pitch,training-02,1,5,10.0875,69,2,10,11.082337868480726,69 -wrong_position_same_pitch,training-02,1,6,12.3375,70,2,11,11.82537641723356,70 -wrong_position_same_pitch,training-02,1,8,11.5875,72,2,13,12.55808888888889,72 -wrong_position_same_pitch,training-02,1,5,13.0875,69,2,10,14.080279818594104,69 -timing_only,training-02,2,6,13.4625,65,2,6,16.345509297052153,65 -pitch_off,training-02,1,6,10.8375,70,1,8,9.606586848072562,72 -extra_detection,training-02,,,,,2,11,13.370787301587303,70 -correct,training-03,1,5,3.6818181818181817,69,1,5,3.890626303854875,69 -correct,training-03,2,5,4.363636363636363,64,2,5,4.79748843537415,64 -correct,training-03,3,5,5.045454545454545,60,3,5,5.749506575963719,60 -correct,training-03,1,5,5.954545454545455,69,1,5,6.656368707482994,69 -correct,training-03,1,10,8.0,74,1,10,8.49202902494331,74 -correct,training-03,2,5,13.909090909090908,64,2,5,14.71882857142857,64 -correct,training-03,3,5,15.045454545454545,60,3,5,15.577966893424035,60 -correct,training-03,4,5,16.18181818181818,55,4,5,16.449999092970522,55 -wrong_position_same_pitch,training-03,2,10,7.318181818181818,69,1,5,7.503897052154195,69 -wrong_position_same_pitch,training-03,3,14,11.636363636363637,69,1,5,10.327689342403628,69 -wrong_position_same_pitch,training-03,2,14,10.5,73,1,9,11.21004761904762,73 -pitch_off,training-03,1,14,11.409090909090908,78,1,5,12.128519727891156,69 -missed_onset,training-03,2,10,8.90909090909091,69,,,, -extra_detection,training-03,,,,,3,6,3.217247619047619,61 -extra_detection,training-03,,,,,4,6,3.228857596371882,56 -extra_detection,training-03,,,,,3,4,18.041849886621314,59 -correct,training-04,4,5,5.375,55,4,5,3.984790022675737,55 -correct,training-04,6,3,3.6875,43,6,3,4.008009977324263,43 -correct,training-04,5,2,3.875,47,5,2,4.205379591836735,47 -correct,training-04,4,2,4.0625,52,4,2,4.507239002267574,52 -correct,training-04,5,3,4.4375,48,5,3,4.855538321995465,48 -correct,training-04,6,3,5.1875,43,6,3,5.7030666666666665,43 -correct,training-04,1,10,12.6875,74,1,10,12.813508390022676,74 -correct,training-04,1,12,13.25,76,1,12,14.48662902494331,76 -wrong_position_same_pitch,training-04,3,4,5.75,59,5,14,5.366377324263039,59 -wrong_position_same_pitch,training-04,5,5,5.75,50,4,0,6.296459410430839,50 -wrong_position_same_pitch,training-04,3,5,7.0625,60,5,15,7.8638063492063495,60 -wrong_position_same_pitch,training-04,2,5,8.0,64,4,14,8.399149206349206,64 -wrong_position_same_pitch,training-04,3,4,8.75,59,6,19,9.339557369614512,59 -wrong_position_same_pitch,training-04,2,3,8.75,62,5,17,9.339557369614512,62 -wrong_position_same_pitch,training-04,2,8,9.875,67,4,17,10.838528344671202,67 -wrong_position_same_pitch,training-04,1,7,10.0625,71,4,21,11.407417233560091,71 -wrong_position_same_pitch,training-04,1,8,10.8125,72,3,17,12.128519727891156,72 -wrong_position_same_pitch,training-04,3,12,12.875,67,2,8,13.452057142857143,67 -wrong_position_same_pitch,training-04,5,12,14.0,57,4,7,14.393749206349206,57 -wrong_position_same_pitch,training-04,3,14,13.4375,69,1,5,14.405359183673468,69 -wrong_position_same_pitch,training-04,4,14,14.5625,64,3,9,15.090347845804988,64 -timing_only,training-04,1,10,11.375,74,1,10,13.55654693877551,74 -pitch_off,training-04,4,4,4.625,54,1,0,4.518848979591836,64 -pitch_off,training-04,3,2,5.0,57,6,12,5.099347845804989,52 -pitch_off,training-04,4,12,12.875,62,3,0,11.419027210884355,55 -pitch_off,training-04,5,14,14.5625,59,5,10,13.498497052154196,55 -extra_detection,training-04,,,,,6,2,0.058049886621315196,42 -extra_detection,training-04,,,,,5,1,0.058049886621315196,46 -extra_detection,training-04,,,,,4,1,1.7298866213151927,51 -extra_detection,training-04,,,,,3,2,1.7647165532879818,57 -extra_detection,training-04,,,,,6,15,5.552136961451247,55 -extra_detection,training-04,,,,,2,0,6.296459410430839,59 -extra_detection,training-04,,,,,1,19,11.419027210884355,83 -extra_detection,training-04,,,,,1,7,13.498497052154196,71 -extra_detection,training-04,,,,,4,7,15.427037188208617,57 -correct,training-05,4,2,2.9250000000000003,52,4,2,3.019878004535147,52 -correct,training-05,6,0,2.5083333333333337,40,6,0,3.519107029478458,40 -correct,training-05,3,2,3.5500000000000003,57,3,2,4.4143591836734695,57 -correct,training-05,4,4,4.383333333333334,54,4,4,4.925198185941043,54 -correct,training-05,6,2,3.966666666666667,42,6,2,4.925198185941043,42 -correct,training-05,2,8,9.591666666666667,67,2,8,10.884968253968255,67 -wrong_position_same_pitch,training-05,3,5,6.258333333333334,60,2,1,7.387797278911565,60 -wrong_position_same_pitch,training-05,2,5,6.883333333333335,64,1,0,7.852196371882086,64 -wrong_position_same_pitch,training-05,6,7,7.508333333333335,47,5,2,8.25982947845805,47 -wrong_position_same_pitch,training-05,3,7,7.716666666666667,62,2,3,8.840328344671201,62 -wrong_position_same_pitch,training-05,6,8,9.383333333333335,48,5,3,10.083879818594106,48 -wrong_position_same_pitch,training-05,3,9,10.216666666666667,64,2,5,11.36097732426304,64 -wrong_position_same_pitch,training-05,6,10,10.841666666666669,50,5,5,12.25622947845805,50 -pitch_off,training-05,6,5,6.050000000000001,45,3,2,6.830518367346939,57 -extra_detection,training-05,,,,,1,0,1.8808163265306121,64 -extra_detection,training-05,,,,,2,0,3.0663179138321994,59 -extra_detection,training-05,,,,,1,0,3.4726671201814057,64 -extra_detection,training-05,,,,,3,0,9.979390022675737,55 -extra_detection,training-05,,,,,2,1,10.083879818594106,60 -extra_detection,training-05,,,,,2,3,12.25622947845805,62 -correct,training-06,3,0,2.5875000000000004,55,3,0,1.1029478458049886,55 -correct,training-06,3,0,4.0875,55,3,0,3.1359777777777778,55 -correct,training-06,6,3,2.5875000000000004,43,6,3,3.18241768707483,43 -correct,training-06,5,2,2.5875000000000004,47,5,2,3.7977464852607707,47 -correct,training-06,4,0,2.5875000000000004,50,4,0,3.8557963718820862,50 -correct,training-06,2,1,4.0875,60,2,1,5.389597278911564,60 -correct,training-06,1,0,4.0875,64,1,0,5.4012072562358275,64 -correct,training-06,4,2,4.0875,52,4,2,5.4012072562358275,52 -correct,training-06,2,0,7.0875,59,2,0,8.41075918367347,59 -correct,training-06,4,2,8.5875,52,4,2,8.41075918367347,52 -correct,training-06,3,0,7.0875,55,3,0,8.433979138321995,55 -correct,training-06,5,2,7.0875,47,5,2,8.49202902494331,47 -correct,training-06,1,0,8.5875,64,1,0,8.503639002267573,64 -correct,training-06,3,2,10.0875,57,3,2,11.36097732426304,57 -correct,training-06,4,0,11.5875,50,4,0,11.384197278911566,50 -correct,training-06,3,0,11.5875,55,3,0,12.70901859410431,55 -correct,training-06,2,0,11.5875,59,2,0,12.871558276643992,59 -correct,training-06,6,3,13.0875,43,6,3,14.544678911564626,43 -wrong_position_same_pitch,training-06,3,2,5.5875,57,4,7,6.830518367346939,57 -wrong_position_same_pitch,training-06,2,3,5.5875,62,3,7,6.842128344671202,62 -wrong_position_same_pitch,training-06,4,0,5.5875,50,5,5,6.853738321995465,50 -wrong_position_same_pitch,training-06,5,0,8.5875,45,6,5,9.862006349206348,45 -wrong_position_same_pitch,training-06,2,1,8.5875,60,3,5,9.920056235827664,60 -pitch_off,training-06,5,3,4.0875,48,3,0,3.6816467120181406,55 -pitch_off,training-06,1,3,2.5875000000000004,67,2,3,3.8557963718820862,62 -pitch_off,training-06,1,0,7.0875,64,6,5,6.946618140589569,45 -pitch_off,training-06,1,2,10.0875,66,2,5,9.850396371882086,64 -pitch_off,training-06,2,3,10.0875,62,5,7,9.850396371882086,52 -pitch_off,training-06,1,3,11.5875,67,5,0,11.349367346938775,45 -missed_onset,training-06,2,0,2.5875000000000004,59,,,, -missed_onset,training-06,1,2,5.5875,66,,,, -missed_onset,training-06,4,2,7.0875,52,,,, -missed_onset,training-06,6,0,7.0875,40,,,, -missed_onset,training-06,3,2,8.5875,57,,,, -missed_onset,training-06,4,0,10.0875,50,,,, -missed_onset,training-06,5,2,11.5875,47,,,, -correct,training-07,2,1,1.7083333333333333,60,2,1,3.0082680272108844,60 -correct,training-07,4,3,3.375,53,4,3,3.0430979591836733,53 -correct,training-07,5,3,1.7083333333333333,48,5,3,3.0430979591836733,48 -correct,training-07,3,2,1.7083333333333333,57,3,2,3.1127578231292516,57 -correct,training-07,1,1,3.375,65,1,1,3.1359777777777778,65 -correct,training-07,3,3,3.375,58,3,3,4.623338775510204,58 -correct,training-07,5,1,3.375,46,5,1,4.634948752834467,46 -wrong_position_same_pitch,training-07,2,3,5.041666666666667,62,3,7,4.727828571428571,62 -wrong_position_same_pitch,training-07,4,4,6.708333333333334,54,5,9,6.1571396825396825,54 -wrong_position_same_pitch,training-07,3,4,5.041666666666667,59,4,9,6.180359637188209,59 -wrong_position_same_pitch,training-07,5,2,5.041666666666667,47,6,7,6.180359637188209,47 -wrong_position_same_pitch,training-07,1,2,6.708333333333334,66,2,7,6.238409523809524,66 -wrong_position_same_pitch,training-07,2,2,6.708333333333334,61,4,11,7.619996825396825,61 -wrong_position_same_pitch,training-07,5,4,6.708333333333334,49,6,9,7.619996825396825,49 -pitch_off,training-07,1,1,1.7083333333333333,65,3,4,0.2089795918367347,59 -pitch_off,training-07,3,2,6.708333333333334,57,3,11,7.794146485260771,66 -missed_onset,training-07,4,3,1.7083333333333333,53,,,, -missed_onset,training-07,6,1,1.7083333333333333,41,,,, -missed_onset,training-07,2,3,3.375,62,,,, -missed_onset,training-07,1,2,5.041666666666667,66,,,, -missed_onset,training-07,4,4,5.041666666666667,54,,,, -missed_onset,training-07,6,2,5.041666666666667,42,,,, -missed_onset,training-07,6,2,6.708333333333334,42,,,, -correct,training-08,6,3,2.9000000000000004,43,6,3,4.228599546485261,43 -wrong_position_same_pitch,training-08,4,2,2.6,52,5,7,3.1708077097505667,52 -wrong_position_same_pitch,training-08,5,2,4.4,47,6,7,3.577156916099773,47 -wrong_position_same_pitch,training-08,5,4,5.45,49,6,9,4.460799092970522,49 -wrong_position_same_pitch,training-08,3,6,6.05,61,6,21,4.762658503401361,61 -wrong_position_same_pitch,training-08,4,4,4.550000000000001,54,5,9,4.762658503401361,54 -wrong_position_same_pitch,training-08,3,4,5.0,59,4,9,5.900436281179138,59 -wrong_position_same_pitch,training-08,4,6,5.6,56,5,11,6.470609070294785,56 -pitch_off,training-08,6,0,1.85,40,6,9,1.474467120181406,49 -pitch_off,training-08,5,2,2.1500000000000004,47,6,20,1.486077097505669,60 -pitch_off,training-08,4,4,3.6500000000000004,54,6,24,3.7164766439909296,64 -pitch_off,training-08,5,4,3.2,49,5,10,4.158939682539683,55 -extra_detection,training-08,,,,,3,11,4.925198185941043,66 -extra_detection,training-08,,,,,5,9,5.377987301587301,54 -extra_detection,training-08,,,,,6,7,5.633406802721089,47 -extra_detection,training-08,,,,,5,9,5.912046258503401,54 -extra_detection,training-08,,,,,4,11,6.458999092970521,61 -extra_detection,training-08,,,,,6,9,6.517048979591837,49 -correct,training-09,5,3,5.027272727272728,48,5,3,4.425969160997732,48 -correct,training-09,3,2,7.300000000000001,57,3,2,7.051107936507937,57 -correct,training-09,2,1,7.754545454545456,60,2,1,7.619996825396825,60 -correct,training-09,4,0,8.663636363636364,50,4,0,7.631606802721088,50 -correct,training-09,1,1,8.20909090909091,65,1,1,8.34109931972789,65 -correct,training-09,3,0,9.118181818181819,55,3,0,10.246419501133788,55 -correct,training-09,5,3,11.39090909090909,48,5,3,10.803698412698413,48 -correct,training-09,2,0,9.572727272727274,59,2,0,11.012678004535148,59 -correct,training-09,1,1,10.027272727272727,65,1,1,11.407417233560091,65 -correct,training-09,1,0,11.845454545454546,64,1,0,13.161807709750567,64 -wrong_position_same_pitch,training-09,3,4,10.936363636363637,59,2,0,12.267839455782314,59 -pitch_off,training-09,2,5,5.936363636363637,64,3,0,5.9946,55 -pitch_off,training-09,3,4,6.3909090909090915,59,2,2,6.342899319727891,61 -pitch_off,training-09,5,3,9.572727272727274,48,4,0,9.757516553287982,50 -missed_onset,training-09,1,0,5.481818181818182,64,,,, -missed_onset,training-09,4,2,6.845454545454546,52,,,, -missed_onset,training-09,5,5,7.754545454545456,50,,,, -missed_onset,training-09,4,3,10.481818181818182,53,,,, -missed_onset,training-09,2,5,11.39090909090909,64,,,, -extra_detection,training-09,,,,,6,1,0.034829931972789115,41 -extra_detection,training-09,,,,,5,0,0.06965986394557823,45 -extra_detection,training-09,,,,,4,2,13.115367800453514,52 -extra_detection,training-09,,,,,2,0,13.62620680272109,59 -extra_detection,training-09,,,,,5,3,14.091889795918368,48 -correct,training-10,3,2,2.0785714285714287,57,3,2,2.2768394557823126,57 -correct,training-10,2,1,2.0785714285714287,60,2,1,2.3000594104308387,60 -correct,training-10,4,2,2.0785714285714287,52,4,2,2.323279365079365,52 -correct,training-10,4,3,3.507142857142857,53,4,3,3.7745265306122446,53 -correct,training-10,1,1,3.507142857142857,65,1,1,3.786136507936508,65 -correct,training-10,5,3,3.507142857142857,48,5,3,3.820966439909297,48 -correct,training-10,2,1,4.935714285714286,60,2,1,4.37952925170068,60 -correct,training-10,3,0,4.935714285714286,55,3,0,4.9832480725623585,55 -correct,training-10,1,0,4.935714285714286,64,1,0,5.273497505668934,64 -correct,training-10,6,3,6.364285714285715,43,6,3,5.285107482993197,43 -correct,training-10,4,2,4.935714285714286,52,4,2,6.342899319727891,52 -correct,training-10,3,0,6.364285714285715,55,3,0,6.586708843537415,55 -correct,training-10,2,3,7.792857142857144,62,2,3,6.621538775510205,62 -correct,training-10,4,3,7.792857142857144,53,4,3,8.166949659863945,53 -correct,training-10,5,0,9.221428571428572,45,5,0,9.40921723356009,45 -correct,training-10,3,2,9.221428571428572,57,3,2,9.432437188208617,57 -correct,training-10,2,1,9.221428571428572,60,2,1,9.467267120181406,60 -wrong_position_same_pitch,training-10,4,0,6.364285714285715,50,5,5,6.64475873015873,50 -wrong_position_same_pitch,training-10,2,0,6.364285714285715,59,3,4,7.666436734693877,59 -wrong_position_same_pitch,training-10,5,1,7.792857142857144,46,6,6,8.039239909297052,46 -wrong_position_same_pitch,training-10,3,3,7.792857142857144,58,4,8,8.062459863945579,58 -timing_only,training-10,3,2,3.507142857142857,57,3,2,6.354509297052155,57 -pitch_off,training-10,1,1,7.792857142857144,65,2,3,8.143729705215419,62 -missed_onset,training-10,1,0,2.0785714285714287,64,,,, -missed_onset,training-10,5,0,2.0785714285714287,45,,,, -missed_onset,training-10,2,1,3.507142857142857,60,,,, -missed_onset,training-10,6,1,3.507142857142857,41,,,, -missed_onset,training-10,5,3,4.935714285714286,48,,,, -missed_onset,training-10,1,3,6.364285714285715,67,,,, -missed_onset,training-10,5,2,6.364285714285715,47,,,, -missed_onset,training-10,1,0,9.221428571428572,64,,,, -missed_onset,training-10,4,2,9.221428571428572,52,,,, -correct,training-11,5,5,2.7,50,5,5,3.6468167800453513,50 -correct,training-11,4,5,2.8666666666666667,55,4,5,3.925456235827664,55 -correct,training-11,5,7,3.2,52,5,7,4.286649433106576,52 -correct,training-11,4,7,3.533333333333333,57,4,7,4.634948752834467,57 -correct,training-11,3,5,3.7,60,3,5,4.971638095238095,60 -correct,training-11,2,5,4.533333333333333,64,2,5,5.610186848072562,64 -correct,training-11,1,5,5.366666666666667,69,1,5,6.238409523809524,69 -correct,training-11,1,8,6.866666666666666,72,1,8,7.155597732426304,72 -correct,training-11,1,5,7.533333333333333,69,1,5,8.166949659863945,69 -correct,training-11,2,5,8.533333333333333,64,2,5,8.863548299319728,64 -correct,training-11,3,5,9.366666666666667,60,3,5,10.199979591836735,60 -correct,training-11,4,7,9.533333333333333,57,4,7,10.501839002267575,57 -correct,training-11,4,5,10.2,55,4,5,10.94301814058957,55 -correct,training-11,5,7,10.033333333333333,52,5,7,11.268097505668935,52 -correct,training-11,5,5,10.533333333333331,50,5,5,11.604786848072564,50 -correct,training-11,6,5,10.533333333333331,45,6,5,11.942760090702949,45 -wrong_position_same_pitch,training-11,6,5,2.3666666666666667,45,5,0,3.321737414965986,45 -wrong_position_same_pitch,training-11,3,7,4.366666666666667,62,2,3,5.238667573696145,62 -wrong_position_same_pitch,training-11,2,8,5.2,67,1,3,5.94687619047619,67 -wrong_position_same_pitch,training-11,2,8,7.866666666666666,67,1,3,8.526858956916099,67 -wrong_position_same_pitch,training-11,3,7,8.7,62,2,3,9.815566439909297,62 -pitch_off,training-11,1,8,6.033333333333333,72,5,5,6.342899319727891,50 -extra_detection,training-11,,,,,3,2,3.321737414965986,57 -extra_detection,training-11,,,,,4,5,12.244619501133787,55 -extra_detection,training-11,,,,,5,0,12.63935873015873,45 -extra_detection,training-11,,,,,3,2,12.650968707482994,57 -extra_detection,training-11,,,,,4,0,12.650968707482994,50 -correct,training-12,6,3,3.8000000000000003,43,6,3,3.18241768707483,43 -correct,training-12,6,2,3.2375000000000003,42,6,2,4.5304589569161,42 -correct,training-12,5,1,3.8000000000000003,46,5,1,5.250277551020408,46 -correct,training-12,6,4,4.550000000000001,44,6,4,5.726286621315192,44 -correct,training-12,3,1,7.175000000000001,56,3,1,5.726286621315192,56 -correct,training-12,5,2,4.550000000000001,47,5,2,5.7378965986394554,47 -correct,training-12,5,3,5.300000000000001,48,5,3,6.319679365079365,48 -correct,training-12,4,1,5.487500000000001,51,4,1,6.331289342403628,51 -correct,training-12,5,4,6.050000000000001,49,5,4,6.842128344671202,49 -correct,training-12,4,2,6.237500000000001,52,4,2,6.923398185941044,52 -correct,training-12,4,3,6.987500000000001,53,4,3,7.480677097505669,53 -correct,training-12,4,4,7.737500000000001,54,4,4,8.016019954648526,54 -correct,training-12,3,2,7.925000000000001,57,3,2,8.039239909297052,57 -correct,training-12,3,3,8.675,58,3,3,8.608128798185941,58 -correct,training-12,2,1,8.8625,60,2,1,8.677788662131519,60 -correct,training-12,2,2,9.6125,61,2,2,9.211847619047619,61 -correct,training-12,3,4,9.425,59,3,4,9.211847619047619,59 -correct,training-12,2,3,10.3625,62,2,3,9.79234648526077,62 -correct,training-12,2,4,11.1125,63,2,4,10.350909297052155,63 -wrong_position_same_pitch,training-12,1,3,11.8625,67,2,8,10.931408163265306,67 -wrong_position_same_pitch,training-12,1,4,12.6125,68,2,9,11.419027210884355,68 -pitch_off,training-12,6,1,2.4875000000000003,41,4,1,1.8343764172335602,51 -missed_onset,training-12,1,1,10.3625,65,,,, -missed_onset,training-12,1,2,11.1125,66,,,, -extra_detection,training-12,,,,,3,0,1.8459863945578232,55 -extra_detection,training-12,,,,,6,0,1.8459863945578232,40 -extra_detection,training-12,,,,,3,0,2.358109297052154,55 -extra_detection,training-12,,,,,5,3,2.625138775510204,48 -extra_detection,training-12,,,,,6,0,2.625138775510204,40 -extra_detection,training-12,,,,,4,4,2.7760684807256233,54 -extra_detection,training-12,,,,,5,5,3.1359777777777778,50 -extra_detection,training-12,,,,,4,5,3.217247619047619,55 -extra_detection,training-12,,,,,4,3,3.8557963718820862,53 -extra_detection,training-12,,,,,3,5,3.9602861678004535,60 -extra_detection,training-12,,,,,4,4,4.518848979591836,54 -extra_detection,training-12,,,,,4,5,5.0645179138322,55 -extra_detection,training-12,,,,,6,3,5.0645179138322,43 -extra_detection,training-12,,,,,1,1,5.227057596371882,65 -extra_detection,training-12,,,,,2,2,6.842128344671202,61 -extra_detection,training-12,,,,,3,1,7.5271170068027216,56 -correct,training-13,5,0,2.8083333333333336,45,5,0,2.659968707482993,45 -correct,training-13,3,2,3.4333333333333336,57,3,2,2.671578684807256,57 -correct,training-13,4,2,3.0166666666666666,52,4,2,2.9269981859410428,52 -correct,training-13,2,1,3.85,60,2,1,3.3449573696145123,60 -correct,training-13,1,0,4.266666666666667,64,1,0,3.9963999999999995,64 -correct,training-13,5,3,4.683333333333334,48,5,3,5.807556462585034,48 -correct,training-13,4,2,5.1,52,4,2,6.180359637188209,52 -correct,training-13,3,0,5.5166666666666675,55,3,0,6.412559183673469,55 -correct,training-13,2,1,5.933333333333334,60,2,1,6.598318820861678,60 -correct,training-13,6,3,8.225,43,6,3,8.37592925170068,43 -correct,training-13,2,0,9.058333333333334,59,2,0,9.211847619047619,59 -correct,training-13,1,3,9.683333333333334,67,1,3,9.780736507936508,67 -correct,training-13,1,0,12.183333333333334,64,1,0,11.070727891156464,64 -correct,training-13,4,2,10.308333333333334,52,4,2,11.070727891156464,52 -correct,training-13,5,2,9.891666666666667,47,5,2,11.384197278911566,47 -correct,training-13,3,0,10.725,55,3,0,12.070469841269842,55 -pitch_off,training-13,2,0,11.141666666666667,59,1,19,12.627748752834467,83 -missed_onset,training-13,1,0,6.975,64,,,, -missed_onset,training-13,5,2,8.016666666666667,47,,,, -missed_onset,training-13,4,0,8.225,50,,,, -missed_onset,training-13,3,0,8.641666666666667,55,,,, -missed_onset,training-13,6,0,10.1,40,,,, -correct,training-14,4,7,2.9000000000000004,57,4,7,4.240209523809524,57 -correct,training-14,4,9,3.2214285714285715,59,4,9,4.460799092970522,59 -correct,training-14,4,10,3.5428571428571427,60,4,10,4.634948752834467,60 -correct,training-14,3,7,3.435714285714286,62,3,7,4.925198185941043,62 -correct,training-14,2,8,3.9714285714285715,67,2,8,5.4012072562358275,67 -correct,training-14,2,10,4.185714285714286,69,2,10,5.610186848072562,69 -correct,training-14,1,10,5.042857142857143,74,1,10,6.215189569160998,74 -wrong_position_same_pitch,training-14,6,7,1.5071428571428573,47,5,2,2.834118367346939,47 -wrong_position_same_pitch,training-14,6,8,1.8285714285714287,48,5,3,3.1475877551020406,48 -wrong_position_same_pitch,training-14,6,10,2.0428571428571427,50,4,0,3.321737414965986,50 -wrong_position_same_pitch,training-14,5,7,2.3642857142857143,52,4,2,3.588766893424036,52 -wrong_position_same_pitch,training-14,5,8,2.685714285714286,53,4,3,3.7977464852607707,53 -wrong_position_same_pitch,training-14,5,10,3.007142857142857,55,4,5,3.9602861678004535,55 -wrong_position_same_pitch,training-14,3,9,3.757142857142857,64,2,5,5.1341777777777775,64 -wrong_position_same_pitch,training-14,1,8,4.828571428571428,72,2,13,6.041039909297052,72 -pitch_off,training-14,1,7,4.507142857142857,71,3,5,3.159197732426304,60 -extra_detection,training-14,,,,,1,0,0.9404081632653061,64 -extra_detection,training-14,,,,,4,4,1.253877551020408,54 -extra_detection,training-14,,,,,2,5,1.2770975056689342,64 -extra_detection,training-14,,,,,3,0,1.3003174603174603,55 -extra_detection,training-14,,,,,3,4,2.91538820861678,59 -extra_detection,training-14,,,,,4,7,4.925198185941043,57 -extra_detection,training-14,,,,,5,10,6.342899319727891,55 -correct,training-15,1,5,4.635714285714286,69,1,5,3.403007256235828,69 -correct,training-15,5,3,4.635714285714286,48,5,3,3.4494471655328796,48 -correct,training-15,6,0,5.3500000000000005,40,6,0,4.240209523809524,40 -correct,training-15,3,4,5.3500000000000005,59,3,4,4.240209523809524,59 -correct,training-15,2,3,6.064285714285715,62,2,3,4.9832480725623585,62 -correct,training-15,1,5,6.778571428571429,69,1,5,5.668236734693878,69 -correct,training-15,5,3,6.778571428571429,48,5,3,5.691456689342403,48 -correct,training-15,1,7,8.028571428571428,71,1,7,7.782536507936508,71 -correct,training-15,5,5,8.028571428571428,50,5,5,7.782536507936508,50 -correct,training-15,6,2,8.742857142857144,42,6,2,8.538468934240363,42 -correct,training-15,2,5,9.278571428571428,64,2,5,10.710818594104309,64 -correct,training-15,5,5,10.17142857142857,50,5,5,11.453857142857144,50 -wrong_position_same_pitch,training-15,3,6,8.742857142857144,61,5,16,8.526858956916099,61 -wrong_position_same_pitch,training-15,1,7,10.17142857142857,71,2,12,11.453857142857144,71 -extra_detection,training-15,,,,,2,1,3.4378371882086167,60 -extra_detection,training-15,,,,,4,2,4.170549659863945,52 -extra_detection,training-15,,,,,2,1,5.691456689342403,60 -extra_detection,training-15,,,,,6,0,6.563488888888889,40 -extra_detection,training-15,,,,,4,4,8.526858956916099,54 -extra_detection,training-15,,,,,6,3,9.02608798185941,43 -extra_detection,training-15,,,,,6,15,9.037697959183673,55 -extra_detection,training-15,,,,,2,4,10.17675963718821,63 -correct,training-16,1,2,0.6818181818181818,66,1,2,1.8343764172335602,66 -correct,training-16,5,3,1.3636363636363635,48,5,3,2.578698866213152,48 -correct,training-16,1,12,2.2727272727272725,76,1,12,3.3449573696145123,76 -correct,training-16,1,9,6.136363636363636,73,1,9,6.308069387755102,73 -correct,training-16,5,5,9.318181818181818,50,5,5,8.49202902494331,50 -wrong_position_same_pitch,training-16,5,10,2.9545454545454546,55,4,5,4.077669841269841,55 -wrong_position_same_pitch,training-16,1,1,4.545454545454545,65,2,6,4.925198185941043,65 -wrong_position_same_pitch,training-16,5,2,5.2272727272727275,47,6,7,5.552136961451247,47 -wrong_position_same_pitch,training-16,5,12,6.818181818181818,57,4,7,6.969838095238095,57 -wrong_position_same_pitch,training-16,1,3,8.863636363636363,67,2,8,7.782536507936508,67 -wrong_position_same_pitch,training-16,5,14,10.909090909090908,59,3,4,9.850396371882086,59 -missed_onset,training-16,1,15,10.454545454545455,79,,,, -extra_detection,training-16,,,,,2,1,2.590308843537415,60 -extra_detection,training-16,,,,,2,5,5.621796825396825,64 -correct,training-17,1,12,5.508333333333334,76,1,12,4.495629024943311,76 -correct,training-17,1,13,6.133333333333334,77,1,13,5.203837641723355,77 -correct,training-17,1,15,6.758333333333335,79,1,15,6.006209977324263,79 -correct,training-17,1,17,7.383333333333335,81,1,17,6.842128344671202,81 -correct,training-17,1,15,8.216666666666669,79,1,15,7.736096598639456,79 -correct,training-17,1,13,8.841666666666669,77,1,13,8.503639002267573,77 -correct,training-17,1,12,9.466666666666669,76,1,12,9.44404716553288,76 -correct,training-17,2,15,11.55,74,2,15,11.186827664399093,74 -correct,training-17,1,15,11.55,79,1,15,12.105299773242631,79 -correct,training-17,1,17,12.175,81,1,17,12.98765804988662,81 -correct,training-17,1,15,12.8,79,1,15,13.811966439909297,79 -correct,training-17,1,13,13.425,77,1,13,14.67238866213152,77 -correct,training-17,1,12,14.05,76,1,12,15.531526984126984,76 -wrong_position_same_pitch,training-17,2,13,10.925,72,1,8,10.350909297052155,72 -wrong_position_same_pitch,training-17,2,15,16.34166666666667,74,1,10,16.391949206349206,74 -wrong_position_same_pitch,training-17,2,13,16.96666666666667,72,1,8,17.239477551020407,72 -wrong_position_same_pitch,training-17,2,12,17.59166666666667,71,1,7,18.134729705215417,71 -wrong_position_same_pitch,training-17,3,14,18.425,69,1,5,18.18116961451247,69 -extra_detection,training-17,,,,,6,3,19.78334648526077,43 -correct,training-18,1,7,9.933333333333334,71,1,7,10.049049886621315,71 -correct,training-18,5,7,9.933333333333334,52,5,7,10.06065986394558,52 -correct,training-18,3,5,9.933333333333334,60,3,5,10.072269841269842,60 -correct,training-18,3,7,11.600000000000001,62,3,7,10.908188208616782,62 -correct,training-18,5,8,11.600000000000001,53,5,8,10.919798185941044,53 -correct,training-18,3,7,13.266666666666666,62,3,7,12.63935873015873,62 -correct,training-18,5,8,12.600000000000001,53,5,8,12.63935873015873,53 -correct,training-18,1,8,12.600000000000001,72,1,8,12.63935873015873,72 -correct,training-18,5,7,13.100000000000001,52,5,7,13.579766893424036,52 -wrong_position_same_pitch,training-18,5,7,8.266666666666667,52,4,2,7.492287074829932,52 -wrong_position_same_pitch,training-18,3,5,8.266666666666667,60,4,10,7.5619469387755105,60 -wrong_position_same_pitch,training-18,5,5,7.7666666666666675,50,6,10,8.37592925170068,50 -wrong_position_same_pitch,training-18,3,4,7.7666666666666675,59,4,9,8.37592925170068,59 -wrong_position_same_pitch,training-18,1,5,7.7666666666666675,69,2,10,8.37592925170068,69 -wrong_position_same_pitch,training-18,1,8,11.600000000000001,72,3,17,10.908188208616782,72 -wrong_position_same_pitch,training-18,3,9,12.100000000000001,64,4,14,11.802156462585035,64 -wrong_position_same_pitch,training-18,5,10,12.100000000000001,55,6,15,11.802156462585035,55 -wrong_position_same_pitch,training-18,1,10,12.100000000000001,74,2,15,11.802156462585035,74 -wrong_position_same_pitch,training-18,3,9,12.766666666666666,64,2,5,13.579766893424036,64 -wrong_position_same_pitch,training-18,3,5,13.766666666666666,60,2,1,14.498239002267573,60 -timing_only,training-18,1,8,8.766666666666667,72,1,8,6.656368707482994,72 -pitch_off,training-18,1,7,13.100000000000001,71,2,3,13.5913768707483,62 -missed_onset,training-18,1,7,8.266666666666667,71,,,, -missed_onset,training-18,3,7,8.766666666666667,62,,,, -missed_onset,training-18,5,8,8.766666666666667,53,,,, -missed_onset,training-18,1,8,9.433333333333334,72,,,, -missed_onset,training-18,3,7,9.433333333333334,62,,,, -missed_onset,training-18,5,8,9.433333333333334,53,,,, -missed_onset,training-18,1,5,10.433333333333334,69,,,, -missed_onset,training-18,3,4,10.433333333333334,59,,,, -missed_onset,training-18,5,5,10.433333333333334,50,,,, -missed_onset,training-18,1,7,11.100000000000001,71,,,, -missed_onset,training-18,3,5,11.100000000000001,60,,,, -missed_onset,training-18,5,7,11.100000000000001,52,,,, -extra_detection,training-18,,,,,2,0,3.240467573696145,59 -extra_detection,training-18,,,,,4,0,3.252077551020408,50 -extra_detection,training-18,,,,,4,2,4.158939682539683,52 -extra_detection,training-18,,,,,2,1,4.170549659863945,60 -extra_detection,training-18,,,,,4,3,5.110957823129252,53 -extra_detection,training-18,,,,,2,3,5.110957823129252,62 -extra_detection,training-18,,,,,1,1,5.110957823129252,65 -correct,training-19,3,2,2.1785714285714284,57,3,2,2.6019188208616777,57 -correct,training-19,5,0,2.1785714285714284,45,5,0,2.636748752834467,45 -correct,training-19,1,0,2.1785714285714284,64,1,0,2.7064086167800454,64 -correct,training-19,2,1,2.1785714285714284,60,2,1,2.7412385487528343,60 -correct,training-19,1,0,5.571428571428571,64,1,0,5.354767346938775,64 -correct,training-19,1,1,4.678571428571429,65,1,1,5.610186848072562,65 -correct,training-19,1,3,5.035714285714286,67,1,3,5.8539963718820855,67 -correct,training-19,3,0,5.392857142857142,55,3,0,5.8539963718820855,55 -correct,training-19,5,3,5.392857142857142,48,5,3,6.226799546485261,48 -correct,training-19,2,1,5.392857142857142,60,2,1,6.250019501133787,60 -correct,training-19,1,3,8.964285714285715,67,1,3,8.735838548752834,67 -correct,training-19,1,0,8.428571428571429,64,1,0,9.200237641723355,64 -correct,training-19,4,0,8.785714285714285,50,4,0,9.67624671201814,50 -correct,training-19,2,0,8.785714285714285,59,2,0,9.687856689342404,59 -correct,training-19,6,3,8.785714285714285,43,6,3,10.002609977324264,43 -correct,training-19,1,0,12.357142857142858,64,1,0,12.116909750566894,64 -correct,training-19,1,2,11.464285714285715,66,1,2,12.349109297052154,66 -correct,training-19,1,3,11.821428571428571,67,1,3,12.55808888888889,67 -correct,training-19,4,2,12.178571428571429,52,4,2,12.94121814058957,52 -correct,training-19,5,0,12.178571428571429,45,5,0,13.010878004535147,45 -correct,training-19,2,1,12.178571428571429,60,2,1,13.045707936507936,60 -correct,training-19,3,2,12.178571428571429,57,3,2,13.614596825396825,57 -timing_only,training-19,1,0,4.321428571428571,64,1,0,6.331289342403628,64 -missed_onset,training-19,4,2,2.1785714285714284,52,,,, -missed_onset,training-19,4,2,5.392857142857142,52,,,, -missed_onset,training-19,1,3,7.714285714285714,67,,,, -missed_onset,training-19,1,1,8.071428571428571,65,,,, -missed_onset,training-19,3,0,8.785714285714285,55,,,, -missed_onset,training-19,5,2,8.785714285714285,47,,,, -missed_onset,training-19,1,0,11.107142857142858,64,,,, -muted_undetectable,training-20,1,X,2.875,,,,, -muted_undetectable,training-20,2,X,2.875,,,,, -muted_undetectable,training-20,3,X,2.875,,,,, -muted_undetectable,training-20,4,X,2.875,,,,, -muted_undetectable,training-20,5,X,2.875,,,,, -muted_undetectable,training-20,6,X,2.875,,,,, -muted_undetectable,training-20,1,X,5.583333333333334,,,,, -muted_undetectable,training-20,2,X,5.583333333333334,,,,, -muted_undetectable,training-20,3,X,5.583333333333334,,,,, -muted_undetectable,training-20,4,X,5.583333333333334,,,,, -muted_undetectable,training-20,5,X,5.583333333333334,,,,, -muted_undetectable,training-20,6,X,5.583333333333334,,,,, -muted_undetectable,training-20,1,X,8.291666666666668,,,,, -muted_undetectable,training-20,2,X,8.291666666666668,,,,, -muted_undetectable,training-20,3,X,8.291666666666668,,,,, -muted_undetectable,training-20,4,X,8.291666666666668,,,,, -muted_undetectable,training-20,5,X,8.291666666666668,,,,, -muted_undetectable,training-20,6,X,8.291666666666668,,,,, -correct,training-20,5,0,1.2083333333333333,45,5,0,1.822766439909297,45 -correct,training-20,4,0,1.2083333333333333,50,4,0,1.8808163265306121,50 -correct,training-20,2,0,1.2083333333333333,59,2,0,1.8924263038548752,59 -correct,training-20,3,0,1.2083333333333333,55,3,0,1.9040362811791383,55 -correct,training-20,2,3,2.041666666666667,62,2,3,2.857338321995465,62 -correct,training-20,4,0,3.916666666666667,50,4,0,4.507239002267574,50 -correct,training-20,3,0,3.916666666666667,55,3,0,4.542068934240363,55 -correct,training-20,1,0,3.916666666666667,64,1,0,4.588508843537415,64 -correct,training-20,5,0,3.916666666666667,45,5,0,4.669778684807256,45 -correct,training-20,4,0,6.625,50,4,0,7.062717913832199,50 -correct,training-20,3,0,6.625,55,3,0,7.097547845804989,55 -correct,training-20,2,0,6.625,59,2,0,7.109157823129252,59 -correct,training-20,2,8,7.458333333333334,67,2,8,8.085679818594103,67 -correct,training-20,4,9,7.458333333333334,59,4,9,8.085679818594103,59 -wrong_position_same_pitch,training-20,5,7,4.75,52,4,2,5.377987301587301,52 -wrong_position_same_pitch,training-20,4,7,4.75,57,3,2,5.494087074829932,57 -wrong_position_same_pitch,training-20,3,5,4.75,60,2,1,5.494087074829932,60 -wrong_position_same_pitch,training-20,1,0,6.625,64,3,9,8.050849886621315,64 -pitch_off,training-20,1,7,7.458333333333334,71,5,7,7.946360090702948,52 -missed_onset,training-20,1,0,1.2083333333333333,64,,,, -missed_onset,training-20,6,0,1.2083333333333333,40,,,, -missed_onset,training-20,1,3,2.041666666666667,67,,,, -missed_onset,training-20,3,4,2.041666666666667,59,,,, -missed_onset,training-20,4,5,2.041666666666667,55,,,, -missed_onset,training-20,5,5,2.041666666666667,50,,,, -missed_onset,training-20,6,3,2.041666666666667,43,,,, -missed_onset,training-20,2,0,3.916666666666667,59,,,, -missed_onset,training-20,6,0,3.916666666666667,40,,,, -missed_onset,training-20,1,5,4.75,69,,,, -missed_onset,training-20,2,5,4.75,64,,,, -missed_onset,training-20,6,5,4.75,45,,,, -missed_onset,training-20,5,0,6.625,45,,,, -missed_onset,training-20,6,0,6.625,40,,,, -missed_onset,training-20,3,7,7.458333333333334,62,,,, -missed_onset,training-20,5,9,7.458333333333334,54,,,, -missed_onset,training-20,6,7,7.458333333333334,47,,,, diff --git a/tabvision-server/tools/outputs/errors-2026-04-28_185743.md b/tabvision-server/tools/outputs/errors-2026-04-28_185743.md deleted file mode 100644 index af4a1c3..0000000 --- a/tabvision-server/tools/outputs/errors-2026-04-28_185743.md +++ /dev/null @@ -1,40 +0,0 @@ -# Error analysis — 2026-04-28T18:59:56 - -## Aggregate - -| bucket | count | share | share-of-loss | -|---|---:|---:|---:| -| correct | 222 | 41.3% | — | -| wrong_position_same_pitch | 105 | 19.5% | 35.2% | -| pitch_off | 31 | 5.8% | 10.4% | -| timing_only | 5 | 0.9% | 1.7% | -| missed_onset | 74 | 13.8% | 24.8% | -| muted_undetectable | 18 | 3.3% | — | -| extra_detection | 83 | 15.4% | 27.9% | - -Total classified events: **538** (pitched GT: 437, muted/X: 18, recoverable loss: 298) - -## Per-video - -| video | correct | wrong_position_same_pitch | pitch_off | timing_only | missed_onset | muted_undetectable | extra_detection | total | -|---|---|---|---|---|---|---|---|---| -| training-01 | 8 | 5 | 2 | 0 | 1 | 0 | 7 | 23 | -| training-02 | 6 | 10 | 1 | 1 | 0 | 0 | 1 | 19 | -| training-03 | 8 | 3 | 1 | 0 | 1 | 0 | 3 | 16 | -| training-04 | 8 | 13 | 4 | 1 | 0 | 0 | 9 | 35 | -| training-05 | 6 | 7 | 1 | 0 | 0 | 0 | 6 | 20 | -| training-06 | 18 | 5 | 6 | 0 | 7 | 0 | 0 | 36 | -| training-07 | 7 | 7 | 2 | 0 | 7 | 0 | 0 | 23 | -| training-08 | 1 | 7 | 4 | 0 | 0 | 0 | 6 | 18 | -| training-09 | 10 | 1 | 3 | 0 | 5 | 0 | 5 | 24 | -| training-10 | 17 | 4 | 1 | 1 | 9 | 0 | 0 | 32 | -| training-11 | 16 | 5 | 1 | 0 | 0 | 0 | 5 | 27 | -| training-12 | 19 | 2 | 1 | 0 | 2 | 0 | 16 | 40 | -| training-13 | 16 | 0 | 1 | 0 | 5 | 0 | 0 | 22 | -| training-14 | 7 | 8 | 1 | 0 | 0 | 0 | 7 | 23 | -| training-15 | 12 | 2 | 0 | 0 | 0 | 0 | 8 | 22 | -| training-16 | 5 | 6 | 0 | 0 | 1 | 0 | 2 | 14 | -| training-17 | 13 | 5 | 0 | 0 | 0 | 0 | 1 | 19 | -| training-18 | 9 | 11 | 1 | 1 | 12 | 0 | 7 | 41 | -| training-19 | 22 | 0 | 0 | 1 | 7 | 0 | 0 | 30 | -| training-20 | 14 | 4 | 1 | 0 | 17 | 18 | 0 | 54 | diff --git a/tabvision-server/tools/outputs/finetune_baseline-2026-04-29.md b/tabvision-server/tools/outputs/finetune_baseline-2026-04-29.md index 5206ee7..0983ece 100644 --- a/tabvision-server/tools/outputs/finetune_baseline-2026-04-29.md +++ b/tabvision-server/tools/outputs/finetune_baseline-2026-04-29.md @@ -35,4 +35,4 @@ See `finetune_baseline-2026-04-29.csv`. - Note-event metrics use `mir_eval.transcription.precision_recall_f1_overlap` with `offset_ratio=None` (onset+pitch only). Best F1 over a 0.05-stride sweep of (onset_thresh, frame_thresh). -- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split by player). The plan §0 ship gate is on our **20-video iPhone set** (out-of-distribution), where the current exact F1 is ~0.51 and the target is ≥ 0.60. Use the present number (note-event F1 ≈ 0.87) only as the within-GuitarSet reference — improvement here is a *necessary but not sufficient* condition for OOD improvement. +- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split by player). The private-video OOD gate was removed on 2026-06-11 because its labels are not trusted. Use this number only as the within-GuitarSet reference; real-video/audio replacement gates must come from license-checked public/offline corpora. diff --git a/tabvision-server/tools/outputs/finetune_finetune-20260430-2026-04-30.md b/tabvision-server/tools/outputs/finetune_finetune-20260430-2026-04-30.md index 03eb96a..e375a02 100644 --- a/tabvision-server/tools/outputs/finetune_finetune-20260430-2026-04-30.md +++ b/tabvision-server/tools/outputs/finetune_finetune-20260430-2026-04-30.md @@ -35,4 +35,4 @@ See `finetune_finetune-20260430-2026-04-30.csv`. - Note-event metrics use `mir_eval.transcription.precision_recall_f1_overlap` with `offset_ratio=None` (onset+pitch only). Best F1 over a 0.05-stride sweep of (onset_thresh, frame_thresh). -- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split by player). The plan §0 ship gate is on our **20-video iPhone set** (out-of-distribution), where the current exact F1 is ~0.51 and the target is ≥ 0.60. Use the present number (note-event F1 ≈ 0.87) only as the within-GuitarSet reference — improvement here is a *necessary but not sufficient* condition for OOD improvement. +- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split by player). The private-video OOD gate was removed on 2026-06-11 because its labels are not trusted. Use this number only as the within-GuitarSet reference; real-video/audio replacement gates must come from license-checked public/offline corpora. diff --git a/tabvision-server/tools/outputs/finetune_finetune-20260430-fullsweep-2026-04-30.md b/tabvision-server/tools/outputs/finetune_finetune-20260430-fullsweep-2026-04-30.md index df4bd74..f117d36 100644 --- a/tabvision-server/tools/outputs/finetune_finetune-20260430-fullsweep-2026-04-30.md +++ b/tabvision-server/tools/outputs/finetune_finetune-20260430-fullsweep-2026-04-30.md @@ -35,4 +35,4 @@ See `finetune_finetune-20260430-fullsweep-2026-04-30.csv`. - Note-event metrics use `mir_eval.transcription.precision_recall_f1_overlap` with `offset_ratio=None` (onset+pitch only). Best F1 over a 0.05-stride sweep of (onset_thresh, frame_thresh). -- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split by player). The plan §0 ship gate is on our **20-video iPhone set** (out-of-distribution), where the current exact F1 is ~0.51 and the target is ≥ 0.60. Use the present number (note-event F1 ≈ 0.87) only as the within-GuitarSet reference — improvement here is a *necessary but not sufficient* condition for OOD improvement. +- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split by player). The private-video OOD gate was removed on 2026-06-11 because its labels are not trusted. Use this number only as the within-GuitarSet reference; real-video/audio replacement gates must come from license-checked public/offline corpora. diff --git a/tabvision-server/tools/outputs/finetune_finetune-freezebn-20260430-2026-04-30.md b/tabvision-server/tools/outputs/finetune_finetune-freezebn-20260430-2026-04-30.md index 98e1b32..b73fd98 100644 --- a/tabvision-server/tools/outputs/finetune_finetune-freezebn-20260430-2026-04-30.md +++ b/tabvision-server/tools/outputs/finetune_finetune-freezebn-20260430-2026-04-30.md @@ -35,4 +35,4 @@ See `finetune_finetune-freezebn-20260430-2026-04-30.csv`. - Note-event metrics use `mir_eval.transcription.precision_recall_f1_overlap` with `offset_ratio=None` (onset+pitch only). Best F1 over a 0.05-stride sweep of (onset_thresh, frame_thresh). -- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split by player). The plan §0 ship gate is on our **20-video iPhone set** (out-of-distribution), where the current exact F1 is ~0.51 and the target is ≥ 0.60. Use the present number (note-event F1 ≈ 0.87) only as the within-GuitarSet reference — improvement here is a *necessary but not sufficient* condition for OOD improvement. +- **Scope reminder.** This is *in-distribution* GuitarSet held-out (split by player). The private-video OOD gate was removed on 2026-06-11 because its labels are not trusted. Use this number only as the within-GuitarSet reference; real-video/audio replacement gates must come from license-checked public/offline corpora. diff --git a/tabvision-server/tools/outputs/position-features-2026-04-29_092231.jsonl b/tabvision-server/tools/outputs/position-features-2026-04-29_092231.jsonl deleted file mode 100644 index cd0d123..0000000 --- a/tabvision-server/tools/outputs/position-features-2026-04-29_092231.jsonl +++ /dev/null @@ -1,267 +0,0 @@ -{"event_id": "6ec927c9-663e-4b5c-ae92-534de67003c5", "onset_time": 0.10448979591836735, "midi_note": 44, "amplitude": 0.22341887652873993, "basicpitch_confidence": 0.48896508300738384, "is_chord": false, "chord_size": 1, "num_candidates": 1, "prev_position_string": null, "prev_position_fret": null, "seconds_since_prev": null, "hand_anchor_fret": 4.8999999999999995, "video_hand_anchor_fret": null, "selected_string": 6, "selected_fret": 4, "candidates": [{"cand_string": 6, "cand_fret": 4, "dist_anchor_fret": -0.8999999999999995, "dist_anchor_string": null, "dist_prev_fret": null, "dist_prev_string": null, "heuristic_score": -0.5649999999999998, "is_heuristic_pick": true}], "video_id": "training-01"} -{"event_id": "fc1366e5-fc9f-4f4b-88dc-83a84f8e7ae4", "onset_time": 3.6584267573696145, "midi_note": 62, "amplitude": 0.24852405488491058, "basicpitch_confidence": 0.5101987503934908, "is_chord": false, "chord_size": 1, "num_candidates": 5, "prev_position_string": 5, "prev_position_fret": 7, "seconds_since_prev": 1.5441269841269842, "hand_anchor_fret": 5.8839999999999995, "video_hand_anchor_fret": null, "selected_string": 3, "selected_fret": 7, "candidates": [{"cand_string": 2, "cand_fret": 3, "dist_anchor_fret": -2.8839999999999995, "dist_anchor_string": null, "dist_prev_fret": -4, "dist_prev_string": -3, "heuristic_score": -1.9293999999999998, "is_heuristic_pick": false}, {"cand_string": 3, "cand_fret": 7, "dist_anchor_fret": 1.1160000000000005, "dist_anchor_string": null, "dist_prev_fret": 0, "dist_prev_string": -2, "heuristic_score": -0.6106000000000001, "is_heuristic_pick": true}, {"cand_string": 4, "cand_fret": 12, "dist_anchor_fret": 6.1160000000000005, "dist_anchor_string": null, "dist_prev_fret": 5, "dist_prev_string": -1, "heuristic_score": -4.1954, "is_heuristic_pick": false}, {"cand_string": 5, "cand_fret": 17, "dist_anchor_fret": 11.116, "dist_anchor_string": null, "dist_prev_fret": 10, "dist_prev_string": 0, "heuristic_score": -8.3954, "is_heuristic_pick": false}, {"cand_string": 6, "cand_fret": 22, "dist_anchor_fret": 16.116, "dist_anchor_string": null, "dist_prev_fret": 15, "dist_prev_string": 1, "heuristic_score": -12.7254, "is_heuristic_pick": false}], "video_id": "training-01"} -{"event_id": "102d75ac-d1c3-4017-b605-b1b09c9bb3d8", "onset_time": 4.031229931972789, "midi_note": 65, "amplitude": 0.27015265822410583, "basicpitch_confidence": 0.5284919711312608, "is_chord": false, "chord_size": 1, "num_candidates": 5, "prev_position_string": 3, "prev_position_fret": 7, "seconds_since_prev": 0.3728031746031748, "hand_anchor_fret": 6.2188, "video_hand_anchor_fret": null, "selected_string": 2, "selected_fret": 6, "candidates": [{"cand_string": 1, "cand_fret": 1, "dist_anchor_fret": -5.2188, "dist_anchor_string": null, "dist_prev_fret": -6, "dist_prev_string": -2, "heuristic_score": -3.2922199999999995, "is_heuristic_pick": false}, {"cand_string": 2, "cand_fret": 6, "dist_anchor_fret": -0.21879999999999988, "dist_anchor_string": null, "dist_prev_fret": -1, "dist_prev_string": -1, "heuristic_score": -0.3465800000000001, "is_heuristic_pick": true}, {"cand_string": 3, "cand_fret": 10, "dist_anchor_fret": 3.7812, "dist_anchor_string": null, "dist_prev_fret": 3, "dist_prev_string": 0, "heuristic_score": -2.1934199999999997, "is_heuristic_pick": false}, {"cand_string": 4, "cand_fret": 15, "dist_anchor_fret": 8.7812, "dist_anchor_string": null, "dist_prev_fret": 8, "dist_prev_string": 1, "heuristic_score": -6.52778, "is_heuristic_pick": false}, {"cand_string": 5, "cand_fret": 20, "dist_anchor_fret": 13.7812, "dist_anchor_string": null, "dist_prev_fret": 13, "dist_prev_string": 2, "heuristic_score": -10.82778, "is_heuristic_pick": false}], "video_id": "training-01"} -{"event_id": "d492b864-f4e4-4ae3-9354-228b9a9fab19", "onset_time": 5.691456689342403, "midi_note": 59, "amplitude": 0.6079064607620239, "basicpitch_confidence": 0.8141602025907773, "is_chord": false, "chord_size": 1, "num_candidates": 5, "prev_position_string": 2, "prev_position_fret": 5, "seconds_since_prev": 0.42956916099773235, "hand_anchor_fret": 6.385263999999999, "video_hand_anchor_fret": null, "selected_string": 3, "selected_fret": 4, "candidates": [{"cand_string": 2, "cand_fret": 0, "dist_anchor_fret": -6.385263999999999, "dist_anchor_string": null, "dist_prev_fret": -5, "dist_prev_string": 0, "heuristic_score": -2.9548424, "is_heuristic_pick": false}, {"cand_string": 3, "cand_fret": 4, "dist_anchor_fret": -2.3852639999999994, "dist_anchor_string": null, "dist_prev_fret": -1, "dist_prev_string": 1, "heuristic_score": -1.2548423999999998, "is_heuristic_pick": true}, {"cand_string": 4, "cand_fret": 9, "dist_anchor_fret": 2.6147360000000006, "dist_anchor_string": null, "dist_prev_fret": 4, "dist_prev_string": 2, "heuristic_score": -1.8351576, "is_heuristic_pick": false}, {"cand_string": 5, "cand_fret": 14, "dist_anchor_fret": 7.614736000000001, "dist_anchor_string": null, "dist_prev_fret": 9, "dist_prev_string": 3, "heuristic_score": -5.9695784, "is_heuristic_pick": false}, {"cand_string": 6, "cand_fret": 19, "dist_anchor_fret": 12.614736, "dist_anchor_string": null, "dist_prev_fret": 14, "dist_prev_string": 4, "heuristic_score": -10.2995784, "is_heuristic_pick": false}], "video_id": "training-01"} -{"event_id": "1b37bc13-489a-4bd6-9b0c-7f0024329666", "onset_time": 6.888568253968254, "midi_note": 69, "amplitude": 0.6237590909004211, "basicpitch_confidence": 0.8275681724836088, "is_chord": false, "chord_size": 1, "num_candidates": 5, "prev_position_string": 3, "prev_position_fret": 4, "seconds_since_prev": 1.1971115646258506, "hand_anchor_fret": 6.2996848, "video_hand_anchor_fret": null, "selected_string": 1, "selected_fret": 5, "candidates": [{"cand_string": 1, "cand_fret": 5, "dist_anchor_fret": -1.2996847999999996, "dist_anchor_string": null, "dist_prev_fret": 1, "dist_prev_string": -2, "heuristic_score": -1.0048896799999998, "is_heuristic_pick": true}, {"cand_string": 2, "cand_fret": 10, "dist_anchor_fret": 3.7003152000000004, "dist_anchor_string": null, "dist_prev_fret": 6, "dist_prev_string": -1, "heuristic_score": -2.6651103199999997, "is_heuristic_pick": false}, {"cand_string": 3, "cand_fret": 14, "dist_anchor_fret": 7.7003152, "dist_anchor_string": null, "dist_prev_fret": 10, "dist_prev_string": 0, "heuristic_score": -6.0252048799999995, "is_heuristic_pick": false}, {"cand_string": 4, "cand_fret": 19, "dist_anchor_fret": 12.7003152, "dist_anchor_string": null, "dist_prev_fret": 15, "dist_prev_string": 1, "heuristic_score": -10.325204880000001, "is_heuristic_pick": false}, {"cand_string": 5, "cand_fret": 24, "dist_anchor_fret": 17.7003152, "dist_anchor_string": null, "dist_prev_fret": 20, "dist_prev_string": 2, "heuristic_score": -14.62520488, "is_heuristic_pick": false}], "video_id": "training-01"} -{"event_id": "7c81e07b-9082-4f52-983b-6b665a3994ba", "onset_time": 7.318137414965986, "midi_note": 64, "amplitude": 0.7322534322738647, "basicpitch_confidence": 0.919331422491837, "is_chord": false, "chord_size": 1, "num_candidates": 6, "prev_position_string": 1, "prev_position_fret": 5, "seconds_since_prev": 0.42956916099773235, "hand_anchor_fret": 6.32977936, "video_hand_anchor_fret": null, "selected_string": 2, "selected_fret": 5, "candidates": [{"cand_string": 1, "cand_fret": 0, "dist_anchor_fret": -6.32977936, "dist_anchor_string": null, "dist_prev_fret": -5, "dist_prev_string": 0, "heuristic_score": -2.9654227759999996, "is_heuristic_pick": false}, {"cand_string": 2, "cand_fret": 5, "dist_anchor_fret": -1.3297793599999999, "dist_anchor_string": null, "dist_prev_fret": 0, "dist_prev_string": 1, "heuristic_score": -0.785422776, "is_heuristic_pick": true}, {"cand_string": 3, "cand_fret": 9, "dist_anchor_fret": 2.67022064, "dist_anchor_string": null, "dist_prev_fret": 4, "dist_prev_string": 2, "heuristic_score": -1.9545772240000003, "is_heuristic_pick": false}, {"cand_string": 4, "cand_fret": 14, "dist_anchor_fret": 7.67022064, "dist_anchor_string": null, "dist_prev_fret": 9, "dist_prev_string": 3, "heuristic_score": -6.005643416, "is_heuristic_pick": false}, {"cand_string": 5, "cand_fret": 19, "dist_anchor_fret": 12.67022064, "dist_anchor_string": null, "dist_prev_fret": 14, "dist_prev_string": 4, "heuristic_score": -10.305643416, "is_heuristic_pick": false}, {"cand_string": 6, "cand_fret": 24, "dist_anchor_fret": 17.67022064, "dist_anchor_string": null, "dist_prev_fret": 19, "dist_prev_string": 5, "heuristic_score": -14.635643416, "is_heuristic_pick": false}], "video_id": "training-01"} -{"event_id": "fd755b8d-28b3-45e1-9fbd-0f4066a237bf", "onset_time": 8.526858956916099, "midi_note": 64, "amplitude": 0.5196551084518433, "basicpitch_confidence": 0.7395182369077121, "is_chord": false, "chord_size": 1, "num_candidates": 6, "prev_position_string": 2, "prev_position_fret": 5, "seconds_since_prev": 1.208721541950113, "hand_anchor_fret": 6.350845551999999, "video_hand_anchor_fret": null, "selected_string": 2, "selected_fret": 5, "candidates": [{"cand_string": 1, "cand_fret": 0, "dist_anchor_fret": -6.350845551999999, "dist_anchor_string": null, "dist_prev_fret": -5, "dist_prev_string": -1, "heuristic_score": -3.0227959431999993, "is_heuristic_pick": false}, {"cand_string": 2, "cand_fret": 5, "dist_anchor_fret": -1.3508455519999991, "dist_anchor_string": null, "dist_prev_fret": 0, "dist_prev_string": 0, "heuristic_score": -0.7427959431999998, "is_heuristic_pick": true}, {"cand_string": 3, "cand_fret": 9, "dist_anchor_fret": 2.649154448000001, 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a/tabvision-server/tools/outputs/position-features-2026-04-29_092231.md +++ /dev/null @@ -1,37 +0,0 @@ -# Position features dump — 2026-04-29T09:27:23 - -``` -Videos with events: 20 -Total events: 267 -Total candidate rows: 1058 -Candidates per event: min=1 max=6 mean=4.0 -Chord events: 0 (expected 0 — chord path not instrumented yet) -Events with video_hand_anchor: 0 -Events with seconds_since_prev set: 251 -selected matches heuristic pick: 267/267 (100.0%) — disagreement = video override -``` - -## Per-video event counts - -| video | events | -|---|---:| -| training-01 | 19 | -| training-02 | 20 | -| training-03 | 14 | -| training-04 | 17 | -| training-05 | 14 | -| training-06 | 9 | -| training-07 | 4 | -| training-08 | 12 | -| training-09 | 17 | -| training-10 | 5 | -| training-11 | 23 | -| training-12 | 11 | -| training-13 | 14 | -| training-14 | 17 | -| training-15 | 6 | -| training-16 | 16 | -| training-17 | 23 | -| training-18 | 6 | -| training-19 | 11 | -| training-20 | 9 | diff --git a/tabvision-server/tools/outputs/position-features-2026-04-29_093154.jsonl b/tabvision-server/tools/outputs/position-features-2026-04-29_093154.jsonl deleted file mode 100644 index 165c66d..0000000 --- a/tabvision-server/tools/outputs/position-features-2026-04-29_093154.jsonl +++ /dev/null @@ -1,502 +0,0 @@ -{"event_id": "46abdfc2-4bec-484c-aaf8-9c844e626ae2", "onset_time": 0.10448979591836735, "midi_note": 44, "amplitude": 0.22341887652873993, "basicpitch_confidence": 0.48896508300738384, "is_chord": false, "chord_size": 1, "chord_string_span": null, "num_candidates": 1, "prev_position_string": null, "prev_position_fret": null, "seconds_since_prev": null, "hand_anchor_fret": 4.8999999999999995, "video_hand_anchor_fret": null, "selected_string": 6, "selected_fret": 4, "candidates": [{"cand_string": 6, "cand_fret": 4, "dist_anchor_fret": -0.8999999999999995, "dist_anchor_string": null, "dist_prev_fret": null, "dist_prev_string": null, "heuristic_score": -0.5649999999999998, "is_heuristic_pick": true}], "video_id": "training-01"} -{"event_id": "5460d424-7e1b-4cca-8781-527ae4acdf0d", "onset_time": 2.1142997732426303, "midi_note": 55, "amplitude": 0.39949777722358704, "basicpitch_confidence": 0.6378905659504983, "is_chord": true, "chord_size": 3, "chord_string_span": 2, "num_candidates": 4, "prev_position_string": 6, "prev_position_fret": 4, "seconds_since_prev": 2.009809977324263, "hand_anchor_fret": 5.26, "video_hand_anchor_fret": null, "selected_string": 3, "selected_fret": 0, "candidates": [{"cand_string": 3, "cand_fret": 0, "dist_anchor_fret": -5.26, "dist_anchor_string": null, "dist_prev_fret": -4, "dist_prev_string": -3, "heuristic_score": -2.561, "is_heuristic_pick": false}, {"cand_string": 4, "cand_fret": 5, "dist_anchor_fret": -0.2599999999999998, "dist_anchor_string": null, "dist_prev_fret": 1, "dist_prev_string": -2, "heuristic_score": -0.3609999999999999, "is_heuristic_pick": true}, {"cand_string": 5, "cand_fret": 10, "dist_anchor_fret": 4.74, 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4, "cand_fret": 9, "dist_anchor_fret": -0.10648703999999931, "dist_anchor_string": null, "dist_prev_fret": 2, "dist_prev_string": -1, "heuristic_score": -0.6072704639999997, "is_heuristic_pick": true}, {"cand_string": 5, "cand_fret": 14, "dist_anchor_fret": 4.893512960000001, "dist_anchor_string": null, "dist_prev_fret": 7, "dist_prev_string": 0, "heuristic_score": -3.750783424, "is_heuristic_pick": false}, {"cand_string": 6, "cand_fret": 19, "dist_anchor_fret": 9.89351296, "dist_anchor_string": null, "dist_prev_fret": 12, "dist_prev_string": 1, "heuristic_score": -8.080783424, "is_heuristic_pick": false}], "video_id": "training-20"} -{"event_id": "bdb2809a-2ae7-482b-9ed6-30f2c8748361", "onset_time": 11.12877777777778, "midi_note": 41, "amplitude": 0.2241930216550827, "basicpitch_confidence": 0.5341299693836083, "is_chord": false, "chord_size": 1, "chord_string_span": null, "num_candidates": 1, "prev_position_string": 3, "prev_position_fret": 9, "seconds_since_prev": 3.077927891156465, "hand_anchor_fret": 10.242594815999999, "video_hand_anchor_fret": null, "selected_string": 6, "selected_fret": 1, "candidates": [{"cand_string": 6, "cand_fret": 1, "dist_anchor_fret": -9.242594815999999, "dist_anchor_string": null, "dist_prev_fret": -8, "dist_prev_string": 3, "heuristic_score": -6.2576866303999985, "is_heuristic_pick": true}], "video_id": "training-20"} diff --git a/tabvision-server/tools/outputs/position-features-2026-04-29_093154.md b/tabvision-server/tools/outputs/position-features-2026-04-29_093154.md deleted file mode 100644 index 7f49feb..0000000 --- a/tabvision-server/tools/outputs/position-features-2026-04-29_093154.md +++ /dev/null @@ -1,37 +0,0 @@ -# Position features dump — 2026-04-29T09:35:08 - -``` -Videos with events: 20 -Total events: 502 -Total candidate rows: 1919 -Candidates per event: min=1 max=6 mean=3.8 -Chord events: 235 (expected 0 — chord path not instrumented yet) -Events with video_hand_anchor: 0 -Events with seconds_since_prev set: 476 -selected matches heuristic pick: 462/502 (92.0%) — disagreement = video override -``` - -## Per-video event counts - -| video | events | -|---|---:| -| training-01 | 26 | -| training-02 | 22 | -| training-03 | 18 | -| training-04 | 38 | -| training-05 | 25 | -| training-06 | 30 | -| training-07 | 16 | -| training-08 | 21 | -| training-09 | 23 | -| training-10 | 26 | -| training-11 | 28 | -| training-12 | 40 | -| training-13 | 18 | -| training-14 | 26 | -| training-15 | 23 | -| training-16 | 18 | -| training-17 | 25 | -| training-18 | 31 | -| training-19 | 24 | -| training-20 | 24 | diff --git a/tabvision-server/tools/outputs/position_dataset.csv b/tabvision-server/tools/outputs/position_dataset.csv deleted file mode 100644 index 21e1923..0000000 --- a/tabvision-server/tools/outputs/position_dataset.csv +++ /dev/null @@ -1,1320 +0,0 @@ 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-training-01,d35434e7-6c8d-4a00-88cd-e93711b23343,5.261887528344671,64,0.4860042333602905,0.7110567187775357,True,2,,6,2.0,6.0,1.2306575963718815,6.36316,,2.0,5.0,3,9,2.6368400000000003,,3.0,1.0,-1.742894,False,2,5,0 -training-01,d35434e7-6c8d-4a00-88cd-e93711b23343,5.261887528344671,64,0.4860042333602905,0.7110567187775357,True,2,,6,2.0,6.0,1.2306575963718815,6.36316,,2.0,5.0,4,14,7.63684,,8.0,2.0,-5.783945999999999,False,2,5,0 -training-01,d35434e7-6c8d-4a00-88cd-e93711b23343,5.261887528344671,64,0.4860042333602905,0.7110567187775357,True,2,,6,2.0,6.0,1.2306575963718815,6.36316,,2.0,5.0,5,19,12.63684,,13.0,3.0,-10.083946000000001,False,2,5,0 -training-01,d35434e7-6c8d-4a00-88cd-e93711b23343,5.261887528344671,64,0.4860042333602905,0.7110567187775357,True,2,,6,2.0,6.0,1.2306575963718815,6.36316,,2.0,5.0,6,24,17.63684,,18.0,4.0,-14.413946,False,2,5,0 -training-01,ba658317-2ee7-4b90-9330-05cb45f1048b,5.691456689342403,59,0.6079064607620239,0.8141602025907773,False,1,,5,2.0,5.0,0.42956916099773235,6.385263999999999,,3.0,4.0,2,0,-6.385263999999999,,-5.0,0.0,-2.9548424,False,2,0,1 -training-01,ba658317-2ee7-4b90-9330-05cb45f1048b,5.691456689342403,59,0.6079064607620239,0.8141602025907773,False,1,,5,2.0,5.0,0.42956916099773235,6.385263999999999,,3.0,4.0,3,4,-2.3852639999999994,,-1.0,1.0,-1.2548423999999998,True,2,0,0 -training-01,ba658317-2ee7-4b90-9330-05cb45f1048b,5.691456689342403,59,0.6079064607620239,0.8141602025907773,False,1,,5,2.0,5.0,0.42956916099773235,6.385263999999999,,3.0,4.0,4,9,2.6147360000000006,,4.0,2.0,-1.8351576,False,2,0,0 -training-01,ba658317-2ee7-4b90-9330-05cb45f1048b,5.691456689342403,59,0.6079064607620239,0.8141602025907773,False,1,,5,2.0,5.0,0.42956916099773235,6.385263999999999,,3.0,4.0,5,14,7.614736000000001,,9.0,3.0,-5.9695784,False,2,0,0 -training-01,ba658317-2ee7-4b90-9330-05cb45f1048b,5.691456689342403,59,0.6079064607620239,0.8141602025907773,False,1,,5,2.0,5.0,0.42956916099773235,6.385263999999999,,3.0,4.0,6,19,12.614736,,14.0,4.0,-10.2995784,False,2,0,0 -training-01,a95d0a93-a085-48d0-8e32-2adf81e9735f,6.888568253968254,69,0.6237590909004211,0.8275681724836088,False,1,,5,3.0,4.0,1.1971115646258506,6.2996848,,1.0,5.0,1,5,-1.2996847999999996,,1.0,-2.0,-1.0048896799999998,True,1,5,1 -training-01,a95d0a93-a085-48d0-8e32-2adf81e9735f,6.888568253968254,69,0.6237590909004211,0.8275681724836088,False,1,,5,3.0,4.0,1.1971115646258506,6.2996848,,1.0,5.0,2,10,3.7003152000000004,,6.0,-1.0,-2.6651103199999997,False,1,5,0 -training-01,a95d0a93-a085-48d0-8e32-2adf81e9735f,6.888568253968254,69,0.6237590909004211,0.8275681724836088,False,1,,5,3.0,4.0,1.1971115646258506,6.2996848,,1.0,5.0,3,14,7.7003152,,10.0,0.0,-6.0252048799999995,False,1,5,0 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-training-01,7a93f49d-4fdf-4df1-9d48-12dd892d1ebe,7.318137414965986,64,0.7322534322738647,0.919331422491837,False,1,,6,1.0,5.0,0.42956916099773235,6.32977936,,2.0,5.0,3,9,2.67022064,,4.0,2.0,-1.9545772240000003,False,1,0,0 -training-01,7a93f49d-4fdf-4df1-9d48-12dd892d1ebe,7.318137414965986,64,0.7322534322738647,0.919331422491837,False,1,,6,1.0,5.0,0.42956916099773235,6.32977936,,2.0,5.0,4,14,7.67022064,,9.0,3.0,-6.005643416,False,1,0,0 -training-01,7a93f49d-4fdf-4df1-9d48-12dd892d1ebe,7.318137414965986,64,0.7322534322738647,0.919331422491837,False,1,,6,1.0,5.0,0.42956916099773235,6.32977936,,2.0,5.0,5,19,12.67022064,,14.0,4.0,-10.305643416,False,1,0,0 -training-01,7a93f49d-4fdf-4df1-9d48-12dd892d1ebe,7.318137414965986,64,0.7322534322738647,0.919331422491837,False,1,,6,1.0,5.0,0.42956916099773235,6.32977936,,2.0,5.0,6,24,17.67022064,,19.0,5.0,-14.635643416,False,1,0,0 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b/tabvision-server/tools/outputs/position_dataset.md deleted file mode 100644 index 84e7ec6..0000000 --- a/tabvision-server/tools/outputs/position_dataset.md +++ /dev/null @@ -1,63 +0,0 @@ -# Position dataset — 2026-04-29 - -Step 2 output for the learned-fusion plan -(`docs/plans/2026-04-24-learned-fusion-design.md` §4.3). - -Built from `position-features-2026-04-29_093154.jsonl` (502 events) by -`tools/build_position_dataset.py`. - -## Summary - -| metric | value | -|---|---:| -| Events labeled | 336 | -| Events dropped (no GT match — extras) | 166 | -| Rows (one per candidate) | 1319 | -| Positive labels | 336 (25.5% of rows; exactly 1 per event) | -| Events whose heuristic pick is the correct GT position | 202 (60.1%) | - -Heuristic 60.1% is the ranker's baseline. The plan's ship gate is +5pp on -exact-F1 across the 20-video set (effectively ≥65% on this metric for the -labeled subset). - -## Composition - -| split | rows | positives | base rate | -|---|---:|---:|---:| -| single-note (`is_chord=False`) | 750 | 182 | 24.3% | -| chord (`is_chord=True`) | 569 | 154 | 27.1% | - -Per-video event counts span 9 (training-08) to 23 (training-06); all 20 -videos contribute, so leave-one-video-out CV is straightforward. - -## Schema - -One row per candidate. Group key for CV: `video_id`. - -| column | source | -|---|---| -| `video_id`, `event_id` | identifiers | -| `onset_time`, `midi_note`, `amplitude`, `basicpitch_confidence` | per-event audio | -| `is_chord`, `chord_size`, `chord_string_span`, `num_candidates` | per-event chord context | -| `prev_position_string`, `prev_position_fret`, `seconds_since_prev` | per-event continuity | -| `hand_anchor_fret`, `video_hand_anchor_fret` | per-event anchor | -| `selected_string`, `selected_fret` | what fusion actually emitted | -| `cand_string`, `cand_fret` | candidate identity | -| `dist_anchor_fret`, `dist_anchor_string` | candidate vs anchor | -| `dist_prev_fret`, `dist_prev_string` | candidate vs prev pos | -| `heuristic_score`, `is_heuristic_pick` | what the heuristic thought | -| `gt_string`, `gt_fret` | ground-truth label position | -| `label` | 0/1 — this candidate is the GT position | - -## Known gaps - -- All 20 videos run with `audio_only=True` per `tests/fixtures/benchmarks/index.json`, - so `video_hand_anchor_fret` is null on every event. To fill that column, - re-dump features after enabling video. Step 3 can train without it; the - feature is forward-compatible. -- Extras (166 events) are dropped — they have no correct candidate. Step 3 - could optionally use them as "all-zero label" group to teach the model to - reject hallucinations, but the plan keeps that out of scope for v1. -- Muted-X GT notes (training-20) are excluded by the audio matcher (no MIDI - → no audio detection → no fusion event). Consistent with the corrected - apr-28 error analysis. diff --git a/tabvision-server/tools/outputs/position_dataset.parquet b/tabvision-server/tools/outputs/position_dataset.parquet deleted file mode 100644 index 4b0b5af..0000000 Binary files a/tabvision-server/tools/outputs/position_dataset.parquet and /dev/null differ diff --git a/tabvision-server/tools/outputs/position_selector_report-2026-04-29.md b/tabvision-server/tools/outputs/position_selector_report-2026-04-29.md deleted file mode 100644 index 79b15b8..0000000 --- a/tabvision-server/tools/outputs/position_selector_report-2026-04-29.md +++ /dev/null @@ -1,74 +0,0 @@ -# Position selector training — 2026-04-29 - -Input: `/home/gilhooleyp/projects/tab_vision/tabvision-server/tools/outputs/position_dataset.parquet` -Rows: 1319, events: 336, videos: 20 - -## Headline - -- Heuristic baseline: 202/336 = **60.1%** -- Model LOOCV: 203/336 = **60.4%** -- Δ: **+0.3pp** -- Ship gate: ≥ +5pp + no per-video regression > 3pp -- **Decision: NO SHIP — Δ +0.3pp < gate +5pp** - -## Per-video accuracy - -| video | events | heuristic | model | Δpp | -|---|---:|---:|---:|---:| -| training-01 | 13 | 7/13 (53.8%) | 7/13 (53.8%) | +0.0 | -| training-02 | 16 | 1/16 (6.2%) | 2/16 (12.5%) | +6.2 | -| training-03 | 12 | 8/12 (66.7%) | 8/12 (66.7%) | +0.0 | -| training-04 | 21 | 7/21 (33.3%) | 10/21 (47.6%) | +14.3 | -| training-05 | 14 | 5/14 (35.7%) | 5/14 (35.7%) | +0.0 | -| training-06 | 23 | 15/23 (65.2%) | 15/23 (65.2%) | +0.0 | -| training-07 | 14 | 6/14 (42.9%) | 7/14 (50.0%) | +7.1 | -| training-08 | 9 | 2/9 (22.2%) | 3/9 (33.3%) | +11.1 | -| training-09 | 13 | 10/13 (76.9%) | 10/13 (76.9%) | +0.0 | -| training-10 | 21 | 17/21 (81.0%) | 17/21 (81.0%) | +0.0 | -| training-11 | 21 | 3/21 (14.3%) | 3/21 (14.3%) | +0.0 | -| training-12 | 22 | 21/22 (95.5%) | 21/22 (95.5%) | +0.0 | -| training-13 | 16 | 16/16 (100.0%) | 16/16 (100.0%) | +0.0 | -| training-14 | 15 | 4/15 (26.7%) | 4/15 (26.7%) | +0.0 | -| training-15 | 14 | 14/14 (100.0%) | 14/14 (100.0%) | +0.0 | -| training-16 | 11 | 5/11 (45.5%) | 5/11 (45.5%) | +0.0 | -| training-17 | 18 | 18/18 (100.0%) | 13/18 (72.2%) | -27.8 | -| training-18 | 20 | 11/20 (55.0%) | 11/20 (55.0%) | +0.0 | -| training-19 | 22 | 22/22 (100.0%) | 22/22 (100.0%) | +0.0 | -| training-20 | 21 | 10/21 (47.6%) | 10/21 (47.6%) | +0.0 | - -## LightGBM params (ranker, margin=1.0) - -``` -objective: lambdarank -metric: ndcg -n_estimators: 50 -max_depth: 4 -num_leaves: 15 -learning_rate: 0.1 -min_data_in_leaf: 5 -label_gain: [0, 1] -verbose: -1 -deterministic: True -random_state: 42 -``` - -## Features - -- midi_note -- amplitude -- basicpitch_confidence -- is_chord -- chord_size -- chord_string_span -- num_candidates -- prev_position_string -- prev_position_fret -- seconds_since_prev -- hand_anchor_fret -- cand_string -- cand_fret -- dist_anchor_fret -- dist_prev_fret -- dist_prev_string -- heuristic_score -- is_heuristic_pick diff --git a/tabvision-server/tools/train_position_selector.py b/tabvision-server/tools/train_position_selector.py deleted file mode 100644 index efc863c..0000000 --- a/tabvision-server/tools/train_position_selector.py +++ /dev/null @@ -1,379 +0,0 @@ -"""Step 3 of the learned-fusion plan -(docs/plans/2026-04-24-learned-fusion-design.md §4.4). - -Trains a LightGBM lambdarank model that scores candidate (string, fret) -positions for one detected note, given the per-event and per-candidate -features captured by the fusion engine emitter. - -Each event is a query; candidates are documents; label is 1 for the GT -position, 0 otherwise. Uses leave-one-video-out CV — train on 19 videos, -score the held-out video, repeat for all 20. Primary metric: per-event -top-1 accuracy. - -If LOOCV beats heuristic baseline by ≥ 5pp and no per-video regression is -worse than 3pp, save: - app/models/position_selector.lgb — final booster (trained on - all 20 videos) - app/models/position_selector_features.json — feature schema for serving -And write tools/outputs/position_selector_report-YYYY-MM-DD.md. - -Usage (from tabvision-server/): - python tools/train_position_selector.py - python tools/train_position_selector.py --input tools/outputs/position_dataset.parquet -""" -from __future__ import annotations - -import argparse -import json -import os -import sys -from datetime import date -from typing import Optional - -import numpy as np -import pandas as pd -import lightgbm as lgb - -THIS_FILE = os.path.abspath(__file__) -TOOLS_DIR = os.path.dirname(THIS_FILE) -SERVER_DIR = os.path.dirname(TOOLS_DIR) -OUTPUTS_DIR = os.path.join(TOOLS_DIR, 'outputs') -MODELS_DIR = os.path.join(SERVER_DIR, 'app', 'models') - -FEATURES: list[str] = [ - # Per-event audio - 'midi_note', 'amplitude', 'basicpitch_confidence', - # Per-event chord context - 'is_chord', 'chord_size', 'chord_string_span', 'num_candidates', - # Per-event continuity - 'prev_position_string', 'prev_position_fret', 'seconds_since_prev', - # Per-event anchor - 'hand_anchor_fret', - # Per-candidate identity - 'cand_string', 'cand_fret', - # Per-candidate distances - 'dist_anchor_fret', 'dist_prev_fret', 'dist_prev_string', - # Per-candidate heuristic signal — explicitly included so the model can - # *beat* the heuristic, not clone it (plan §6 "feature leakage" note). - 'heuristic_score', 'is_heuristic_pick', -] - -LGB_RANKER_PARAMS = dict( - objective='lambdarank', - metric='ndcg', - n_estimators=50, - max_depth=4, - num_leaves=15, - learning_rate=0.1, - min_data_in_leaf=5, - label_gain=[0, 1], - verbose=-1, - deterministic=True, - random_state=42, -) - -LGB_CLASSIFIER_PARAMS = dict( - objective='binary', - n_estimators=50, - max_depth=4, - num_leaves=15, - learning_rate=0.1, - min_data_in_leaf=5, - verbose=-1, - deterministic=True, - random_state=42, -) - -SHIP_GATE_PP = 5.0 # +5 pp over heuristic LOOCV -WORST_REGRESSION_PP = 3.0 # no per-video regression > 3 pp - - -def prepare(df: pd.DataFrame) -> pd.DataFrame: - df = df.copy() - df['is_chord'] = df['is_chord'].astype(int) - df['is_heuristic_pick'] = df['is_heuristic_pick'].astype(int) - return df - - -def heuristic_per_event_topk(df: pd.DataFrame) -> tuple[int, int]: - """Top-1 accuracy of `is_heuristic_pick` row vs label.""" - correct = 0 - total = 0 - for _, grp in df.groupby('event_id', sort=False): - total += 1 - heur = grp[grp['is_heuristic_pick'] == 1] - if len(heur) and int(heur['label'].iloc[0]) == 1: - correct += 1 - return correct, total - - -def model_per_event_topk(scores_df: pd.DataFrame) -> tuple[int, int]: - """Top-1 accuracy: argmax(score) per event matches label=1.""" - correct = 0 - total = 0 - for _, grp in scores_df.groupby('event_id', sort=False): - total += 1 - idx = grp['score'].idxmax() - if int(grp.loc[idx, 'label']) == 1: - correct += 1 - return correct, total - - -def fit_ranker(train_df: pd.DataFrame) -> lgb.LGBMRanker: - train_df = train_df.sort_values('event_id').reset_index(drop=True) - X = train_df[FEATURES].values - y = train_df['label'].astype(int).values - groups = train_df.groupby('event_id', sort=False).size().values - model = lgb.LGBMRanker(**LGB_RANKER_PARAMS) - model.fit(X, y, group=groups) - return model - - -def fit_classifier(train_df: pd.DataFrame) -> lgb.LGBMClassifier: - X = train_df[FEATURES].values - y = train_df['label'].astype(int).values - model = lgb.LGBMClassifier(**LGB_CLASSIFIER_PARAMS) - model.fit(X, y) - return model - - -def predict_scores(model, df: pd.DataFrame) -> np.ndarray: - """Return one score per row (higher = better candidate).""" - if isinstance(model, lgb.LGBMClassifier): - return model.predict_proba(df[FEATURES].values)[:, 1] - return model.predict(df[FEATURES].values) - - -def model_with_margin_fallback(eval_scored: pd.DataFrame, - margin_threshold: float) -> tuple[int, int]: - """Use model pick if its margin over the heuristic pick is high enough. - - Margin = score(model_argmax) - score(is_heuristic_pick row). When below - threshold, fall back to the heuristic. Mirrors the plan §4.5 serving - strategy. - """ - correct = 0 - total = 0 - for _, grp in eval_scored.groupby('event_id', sort=False): - total += 1 - model_idx = grp['score'].idxmax() - heur_rows = grp[grp['is_heuristic_pick'] == 1] - if len(heur_rows) == 0: - chosen = model_idx - else: - heur_idx = heur_rows.index[0] - margin = grp.loc[model_idx, 'score'] - grp.loc[heur_idx, 'score'] - chosen = model_idx if margin >= margin_threshold else heur_idx - if int(grp.loc[chosen, 'label']) == 1: - correct += 1 - return correct, total - - -def loocv(df: pd.DataFrame, fitter, margin_threshold: float = 0.0) -> dict: - """Leave-one-video-out CV. - - fitter: callable(train_df) -> trained model that exposes predict-or- - predict_proba via predict_scores(). - margin_threshold: when > 0, model only overrides heuristic when its - argmax score exceeds the heuristic-pick row's score by this - much. - """ - videos = sorted(df['video_id'].unique()) - per_video: dict[str, dict] = {} - total_correct = 0 - total_events = 0 - - for v in videos: - train = df[df['video_id'] != v] - eval_ = df[df['video_id'] == v] - if eval_.empty or train.empty: - continue - model = fitter(train) - scores = predict_scores(model, eval_) - eval_scored = eval_.assign(score=scores) - if margin_threshold > 0: - m_correct, m_total = model_with_margin_fallback( - eval_scored, margin_threshold, - ) - else: - m_correct, m_total = model_per_event_topk(eval_scored) - h_correct, h_total = heuristic_per_event_topk(eval_) - per_video[v] = { - 'model_correct': m_correct, - 'heuristic_correct': h_correct, - 'events': m_total, - 'model_acc': m_correct / m_total if m_total else 0.0, - 'heuristic_acc': h_correct / h_total if h_total else 0.0, - } - total_correct += m_correct - total_events += m_total - - return { - 'per_video': per_video, - 'total_correct': total_correct, - 'total_events': total_events, - 'overall_acc': total_correct / total_events if total_events else 0.0, - } - - -def write_report(report_path: str, args, df: pd.DataFrame, - heur_correct: int, heur_total: int, - cv: dict, ship_decision: dict, params: dict) -> None: - today = date.today().isoformat() - lines: list[str] = [] - lines.append(f'# Position selector training — {today}\n') - lines.append(f'Input: `{args.input}`') - lines.append(f'Rows: {len(df)}, events: {df["event_id"].nunique()}, ' - f'videos: {df["video_id"].nunique()}\n') - - heur_pct = 100.0 * heur_correct / heur_total if heur_total else 0.0 - model_pct = 100.0 * cv['overall_acc'] - delta = model_pct - heur_pct - - lines.append('## Headline\n') - lines.append(f'- Heuristic baseline: {heur_correct}/{heur_total} = **{heur_pct:.1f}%**') - lines.append(f'- Model LOOCV: {cv["total_correct"]}/{cv["total_events"]} = **{model_pct:.1f}%**') - lines.append(f'- Δ: **{delta:+.1f}pp**') - lines.append(f'- Ship gate: ≥ +{SHIP_GATE_PP:.0f}pp + no per-video regression > ' - f'{WORST_REGRESSION_PP:.0f}pp') - lines.append(f'- **Decision: {ship_decision["verdict"]}**') - if ship_decision.get('reason'): - lines.append(f' - {ship_decision["reason"]}') - lines.append('') - - lines.append('## Per-video accuracy\n') - lines.append('| video | events | heuristic | model | Δpp |') - lines.append('|---|---:|---:|---:|---:|') - for v in sorted(cv['per_video']): - r = cv['per_video'][v] - h = 100.0 * r['heuristic_acc'] - m = 100.0 * r['model_acc'] - lines.append( - f'| {v} | {r["events"]} | ' - f'{r["heuristic_correct"]}/{r["events"]} ({h:.1f}%) | ' - f'{r["model_correct"]}/{r["events"]} ({m:.1f}%) | ' - f'{m - h:+.1f} |' - ) - lines.append('') - - lines.append(f'## LightGBM params ({args.model_type}, margin={args.margin})\n') - lines.append('```') - for k, vv in params.items(): - lines.append(f'{k}: {vv}') - lines.append('```\n') - - lines.append('## Features\n') - for f in FEATURES: - lines.append(f'- {f}') - - with open(report_path, 'w') as f: - f.write('\n'.join(lines) + '\n') - - -def main() -> int: - ap = argparse.ArgumentParser() - ap.add_argument('--input', - default=os.path.join(OUTPUTS_DIR, 'position_dataset.parquet'), - help='input parquet from build_position_dataset.py') - ap.add_argument('--model-path', - default=os.path.join(MODELS_DIR, 'position_selector.lgb'), - help='where to save the booster (only if ship gate passes)') - ap.add_argument('--schema-path', - default=os.path.join(MODELS_DIR, 'position_selector_features.json'), - help='where to save the feature schema JSON') - ap.add_argument('--report-path', - default=os.path.join( - OUTPUTS_DIR, - f'position_selector_report-{date.today().isoformat()}.md', - ), - help='where to save the markdown report') - ap.add_argument('--save-anyway', action='store_true', - help='save the model even if the ship gate fails (for inspection)') - ap.add_argument('--model-type', choices=('ranker', 'classifier'), - default='ranker', - help='lambdarank ranker (plan default) or binary classifier ' - '(plan §4.4 fallback)') - ap.add_argument('--margin', type=float, default=0.0, - help='Fallback to heuristic when model margin over heuristic ' - 'pick is below this. 0 = always trust model.') - args = ap.parse_args() - - df = pd.read_parquet(args.input) - df = prepare(df) - print(f'rows: {len(df)} events: {df["event_id"].nunique()} ' - f'videos: {df["video_id"].nunique()}', file=sys.stderr) - - heur_correct, heur_total = heuristic_per_event_topk(df) - heur_pct = 100.0 * heur_correct / heur_total if heur_total else 0.0 - print(f'heuristic baseline: {heur_correct}/{heur_total} = {heur_pct:.1f}%', - file=sys.stderr) - - fitter = fit_classifier if args.model_type == 'classifier' else fit_ranker - cv = loocv(df, fitter, margin_threshold=args.margin) - model_pct = 100.0 * cv['overall_acc'] - print(f'model LOOCV: {cv["total_correct"]}/{cv["total_events"]} = ' - f'{model_pct:.1f}%', file=sys.stderr) - print(f'Δ: {model_pct - heur_pct:+.1f}pp', file=sys.stderr) - - # Ship-gate decision. - delta = model_pct - heur_pct - worst_regress_pp = 0.0 - worst_video = None - for v, r in cv['per_video'].items(): - diff = 100.0 * (r['model_acc'] - r['heuristic_acc']) - if diff < worst_regress_pp: - worst_regress_pp = diff - worst_video = v - - if delta < SHIP_GATE_PP: - verdict = f'NO SHIP — Δ {delta:+.1f}pp < gate +{SHIP_GATE_PP:.0f}pp' - reason = '' - elif worst_regress_pp < -WORST_REGRESSION_PP: - verdict = (f'NO SHIP — {worst_video} regressed ' - f'{worst_regress_pp:.1f}pp (gate: > -{WORST_REGRESSION_PP:.0f})') - reason = '' - else: - verdict = (f'SHIP — Δ {delta:+.1f}pp, worst regression ' - f'{worst_regress_pp:.1f}pp on {worst_video}') - reason = '' - - ship_decision = {'verdict': verdict, 'reason': reason} - print(f'\n{verdict}', file=sys.stderr) - - params_for_report = (LGB_CLASSIFIER_PARAMS if args.model_type == 'classifier' - else LGB_RANKER_PARAMS) - write_report(args.report_path, args, df, heur_correct, heur_total, cv, - ship_decision, params_for_report) - print(f'wrote {args.report_path}', file=sys.stderr) - - should_save = args.save_anyway or verdict.startswith('SHIP') - if should_save: - os.makedirs(os.path.dirname(args.model_path), exist_ok=True) - final_model = fitter(df) - if isinstance(final_model, lgb.LGBMClassifier): - final_model.booster_.save_model(args.model_path) - params = LGB_CLASSIFIER_PARAMS - else: - final_model.booster_.save_model(args.model_path) - params = LGB_RANKER_PARAMS - with open(args.schema_path, 'w') as f: - json.dump( - { - 'features': FEATURES, - 'model_type': args.model_type, - 'margin_threshold': args.margin, - 'lightgbm_params': params, - 'version': 1, - }, - f, indent=2, - ) - print(f'wrote {args.model_path}', file=sys.stderr) - print(f'wrote {args.schema_path}', file=sys.stderr) - else: - print('not saving model — ship gate failed (use --save-anyway to override)', - file=sys.stderr) - return 0 - - -if __name__ == '__main__': - raise SystemExit(main()) diff --git a/tabvision/README.md b/tabvision/README.md index ca1f3ff..fa35077 100644 --- a/tabvision/README.md +++ b/tabvision/README.md @@ -113,8 +113,8 @@ bash scripts/test_fresh_install.sh the repository, creates a venv, installs `.[dev]`, checks `tabvision --version`, runs the default license policy check, and runs render tests. -Full hand-labeled, user-recorded eval remains useful future validation, but it -is not a v1 release prerequisite. v1 release evidence must be automated: +Full hand-labeled private-video eval is no longer part of the roadmap. v1 +release evidence must be automated: deterministic smoke fixtures, public/programmatic dataset reports such as GuitarSet validation, license checks, fresh-install checks, and renderer tests. diff --git a/tabvision/data/eval/guitartechs_highres_smoke.toml b/tabvision/data/eval/guitartechs_highres_smoke.toml new file mode 100644 index 0000000..9fbfee7 --- /dev/null +++ b/tabvision/data/eval/guitartechs_highres_smoke.toml @@ -0,0 +1,13 @@ +# Highres second-dataset smoke manifest. +# This is a deliberately tiny Guitar-TECHS slice for interactive v1.1 audio +# checks. The broader Guitar-TECHS manifests remain the full second-corpus +# evidence; this file keeps a short direct-input clip runnable on Windows. + +[[clips]] +id = "guitar-techs/P1_chords/midi/midi_Set1_aug" +tier = "clean_electric" +source = "GuitarTECHS" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-techs/P1_chords/audio/directinput/directinput_Set1_aug.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-techs/P1_chords/midi/midi_Set1_aug.mid" +annotation_format = "guitar_techs_midi" diff --git a/tabvision/data/eval/manifest.toml b/tabvision/data/eval/manifest.toml index 3654685..61a512a 100644 --- a/tabvision/data/eval/manifest.toml +++ b/tabvision/data/eval/manifest.toml @@ -1,28 +1,227 @@ -# Optional automated eval manifest. -# -# This file is intentionally a checked-in placeholder until public or otherwise -# non-interactive datasets are registered under $TABVISION_DATA_ROOT. An empty -# manifest is not a v1 release blocker; v1 gates use deterministic smoke, -# checked-in fixtures, GuitarSet validation reports, license checks, and -# fresh-install verification. -# -# tabvision-eval --manifest tabvision/data/eval/manifest.toml --check -# -# Optional clip schema for automated/public eval entries: -# -# [[clips]] -# id = "guitarset-05-example" -# tier = "clean_acoustic_single_line" -# source = "GuitarSet" -# split = "validation" -# media_path = "$TABVISION_DATA_ROOT/guitarset/audio_mono-mic/05_example_mic.wav" -# annotation_path = "$TABVISION_DATA_ROOT/guitarset/annotation/05_example.jams" -# annotation_format = "guitarset_jams" -# -# `annotation_format` selects the parser registered in -# tabvision.eval.parsers (Phase 0). Known formats: guitarset_jams, -# guitar_techs_midi. Forthcoming: egdb_gp (license-pending). -# -# Synthetic-source clips (source = "synthtab/...", "dadagp/...", -# "synthetic/...") are restricted to split = "train". The validator -# rejects them in validation/test splits — see design plan §5 / R8. +# Composite-eval manifest generated by tabvision/scripts/eval/build_composite_manifest.py. +# Re-generate with the same args to refresh; this file is intended to be auto-managed. + +[[clips]] +id = "utaustin/0" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/0.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/0.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/1" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/1.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/1.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/10" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/10.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/10.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/11" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/11.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/11.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/12" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/12.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/12.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/13" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/13.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/13.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/14" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/14.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/14.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/15" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/15.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/15.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/16" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/16.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/16.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/17" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/17.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/17.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/18" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/18.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/18.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/19" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/19.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/19.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/2" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/2.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/2.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/20" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/20.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/20.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/21" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/21.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/21.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/22" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/22.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/22.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/23" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/23.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/23.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/24" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/24.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/24.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/3" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/3.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/3.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/4" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/4.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/4.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/5" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/5.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/5.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/6" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/6.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/6.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/7" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/7.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/7.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/8" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/8.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/8.npy" +annotation_format = "utaustin_tablature_npy" + +[[clips]] +id = "utaustin/9" +tier = "clean_acoustic_single_line" +source = "KaggleUTAustin" +split = "validation" +media_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_audio/9.wav" +annotation_path = "$TABVISION_DATA_ROOT/guitar-transcription-utaustin/tablature_dataset/tablature_dataset/tablature_labels/9.npy" +annotation_format = "utaustin_tablature_npy" diff --git a/tabvision/scripts/annotate/README.md b/tabvision/scripts/annotate/README.md index 7bce795..2b972d4 100644 --- a/tabvision/scripts/annotate/README.md +++ b/tabvision/scripts/annotate/README.md @@ -17,7 +17,7 @@ evidence is the v1 gate. From anywhere on the box: ```bash -bash tabvision/scripts/annotate/launch.sh +bash tabvision/scripts/annotate/launch.sh /path/to/clips # → opens http://127.0.0.1:5005/ ``` @@ -27,9 +27,9 @@ That's the whole setup. The launcher: `$TABVISION_VENV`, then `./venv`, then `./tabvision-server/venv`). 2. `cd`s into the v1 package directory so `python -m scripts.annotate.label_clips` resolves correctly. -3. Defaults `--clips` to - `test-data/training-tabs/training-tabs-video` (the existing 20 - training clips) — pass an explicit dir to override. +3. Requires an explicit `--clips` source. The old personal training-video + directory was removed from the repo and must not be used as a gate, dev set, + or label source. If no suitable venv exists, the script tells you the exact command to prepare one: @@ -48,7 +48,7 @@ will refuse. Always go through a venv.) bash tabvision/scripts/annotate/launch.sh /path/to/clips 5005 ``` -Position 1 = clip directory (default = the training-tabs videos). +Position 1 = clip directory. Position 2 = port (default 5005). For more control invoke the module directly: diff --git a/tabvision/scripts/annotate/launch.sh b/tabvision/scripts/annotate/launch.sh index 63a55b3..5d1b407 100755 --- a/tabvision/scripts/annotate/launch.sh +++ b/tabvision/scripts/annotate/launch.sh @@ -2,7 +2,6 @@ # Launch the labeling tool with the right venv + working dir already # set up. Run from anywhere:: # -# bash tabvision/scripts/annotate/launch.sh # auto-discovers ~/projects/tab_vision/test-data/training-tabs/training-tabs-video # bash tabvision/scripts/annotate/launch.sh /path/to/clips # bash tabvision/scripts/annotate/launch.sh /path/to/clips 5005 # @@ -16,7 +15,17 @@ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" REPO_ROOT="$(cd "$SCRIPT_DIR/../../.." && pwd)" PKG_ROOT="$REPO_ROOT/tabvision" -CLIPS="${1:-$REPO_ROOT/test-data/training-tabs/training-tabs-video}" +if [ "${1:-}" = "" ]; then + cat >&2 < Path | None: candidates = [ gt_dir / f"{video.stem}-tabs.txt", video.with_name(f"{video.stem}-tabs.txt"), - video.parent.parent / "training-tabs" / f"{video.stem}-tabs.txt", ] for c in candidates: if c.exists(): diff --git a/tabvision/scripts/eval/phase5_fusion_diagnostics.py b/tabvision/scripts/eval/phase5_fusion_diagnostics.py index 7268c0e..9e23dc1 100644 --- a/tabvision/scripts/eval/phase5_fusion_diagnostics.py +++ b/tabvision/scripts/eval/phase5_fusion_diagnostics.py @@ -1,7 +1,8 @@ """Phase 5 fusion-consumption diagnostic. Usage: - python -m scripts.eval.phase5_fusion_diagnostics --clip-id training-01 + python -m scripts.eval.phase5_fusion_diagnostics --clip-id sample-video + python -m scripts.eval.phase5_fusion_diagnostics --video /path/to/public-clip.mp4 """ from __future__ import annotations @@ -29,7 +30,7 @@ def main(argv: list[str] | None = None) -> int: parser = argparse.ArgumentParser( description="Inspect whether Phase 5 fusion consumes video evidence." ) - parser.add_argument("--clip-id", default="training-01") + parser.add_argument("--clip-id", default=None) parser.add_argument("--video", type=Path, default=None) parser.add_argument( "--lambdas", @@ -41,15 +42,24 @@ def main(argv: list[str] | None = None) -> int: parser.add_argument("--sample-events", type=int, default=5) args = parser.parse_args(argv) - bench = None if args.video is not None else _benchmark_for_clip(args.clip_id) - video = args.video if args.video is not None else REPO_ROOT / bench["video_path"] + if args.video is None and args.clip_id is None: + parser.error("provide --clip-id from the benchmark index or --video") + + label = args.clip_id or "explicit-video" + if args.video is None: + assert args.clip_id is not None + bench = _benchmark_for_clip(args.clip_id) + video = REPO_ROOT / bench["video_path"] + else: + bench = None + video = args.video report = diagnose_fusion_consumption( video, benchmark=bench, lambdas=args.lambdas, sample_events=args.sample_events, ) - print(_format_report(args.clip_id, video, report)) + print(_format_report(label, video, report)) return 0 diff --git a/tabvision/scripts/eval/phase5_video_diagnostics.py b/tabvision/scripts/eval/phase5_video_diagnostics.py index 8ef9abb..2435ecd 100644 --- a/tabvision/scripts/eval/phase5_video_diagnostics.py +++ b/tabvision/scripts/eval/phase5_video_diagnostics.py @@ -1,7 +1,8 @@ """Phase 5 video-evidence diagnostic. Usage: - python -m scripts.eval.phase5_video_diagnostics --clip-id training-01 + python -m scripts.eval.phase5_video_diagnostics --clip-id sample-video + python -m scripts.eval.phase5_video_diagnostics --video /path/to/public-clip.mp4 """ from __future__ import annotations @@ -23,14 +24,22 @@ def main(argv: list[str] | None = None) -> int: parser = argparse.ArgumentParser( description="Inspect Phase 5 video evidence for one benchmark clip." ) - parser.add_argument("--clip-id", default="training-01") + parser.add_argument("--clip-id", default=None) parser.add_argument("--video", type=Path, default=None) parser.add_argument("--sample-frames", type=int, default=10) args = parser.parse_args(argv) - video = args.video if args.video is not None else _video_for_clip(args.clip_id) + if args.video is None and args.clip_id is None: + parser.error("provide --clip-id from the benchmark index or --video") + + label = args.clip_id or "explicit-video" + if args.video is None: + assert args.clip_id is not None + video = _video_for_clip(args.clip_id) + else: + video = args.video report = diagnose_video_evidence(video, sample_frames=args.sample_frames) - print(_format_report(args.clip_id, video, report)) + print(_format_report(label, video, report)) return 0 diff --git a/tabvision/scripts/eval/v1_1_audio_alignment_probe.py b/tabvision/scripts/eval/v1_1_audio_alignment_probe.py new file mode 100644 index 0000000..6ae7975 --- /dev/null +++ b/tabvision/scripts/eval/v1_1_audio_alignment_probe.py @@ -0,0 +1,889 @@ +"""v1.1 chunk-4 audio transcription/alignment probe for UT-Austin. + +Runs one or more audio backends on the Kaggle/UT-Austin WAV clips, caches the +raw note events locally, then scores raw, per-clip-calibrated, and +global-calibrated audio against the UT-Austin tab labels. This is deliberately +audio-first: real video is not touched here. The oracle-video score uses gold +``FrameFingering`` evidence only to measure the ceiling once the audio events +are corrected for pitch/time alignment. +""" + +from __future__ import annotations + +import argparse +import json +from collections import Counter +from collections.abc import Iterable, Mapping, Sequence +from dataclasses import asdict, dataclass, replace +from datetime import UTC, datetime +from pathlib import Path +from typing import Any + +import numpy as np + +from scripts.eval.v1_1_kaggle_oracle_probe import ( + _load_timestamps, + _oracle_fingerings, + parse_clip, +) +from tabvision.eval.metrics import EventF1Result, TabF1Result, event_f1, tab_f1 +from tabvision.fusion import fuse +from tabvision.types import AudioEvent, GuitarConfig, SessionConfig, TabEvent + +_DEFAULT_ROOT = ( + Path.home() + / ".tabvision/data/datasets/guitar-transcription-utaustin" + / "tablature_dataset/tablature_dataset" +) +_DEFAULT_CACHE_DIR = Path.home() / ".tabvision/cache/v1_1_audio_alignment" +_DEFAULT_REPORT_DIR = Path.home() / ".tabvision/reports/v1_1_audio_alignment" +_DEFAULT_BACKENDS = ("highres", "basicpitch", "highres-fl") + + +@dataclass(frozen=True) +class AlignmentChoice: + """Best whole-semitone + time-origin correction for one event set.""" + + pitch_shift: int + time_shift_s: float + matches: int + + +@dataclass(frozen=True) +class ScoreBundle: + """Onset/pitch/tab/oracle scores for one decoded condition.""" + + onset_f1: float + pitch_f1: float + tab_f1: float + oracle_tab_f1: float + decoded_event_count: int + + +@dataclass(frozen=True) +class ClipScore: + """All scores emitted for one backend/clip pair.""" + + backend: str + clip_id: str + status: str + n_gold: int + raw_event_count: int + pitch_shift: int + time_shift_s: float + alignment_matches: int + raw: ScoreBundle + per_clip: ScoreBundle + global_calibrated: ScoreBundle | None = None + error: str | None = None + + +def main(argv: list[str] | None = None) -> int: + args = _parse_args(argv) + cfg = GuitarConfig() + clip_ids = _resolve_clip_ids(args.root, args.clips) + backends = _parse_csv(args.backends) + + report = run_probe( + root=args.root, + clip_ids=clip_ids, + backend_names=backends, + cache_dir=args.cache_dir, + refresh_cache=args.refresh_cache, + skip_unavailable=not args.no_skip_unavailable, + cfg=cfg, + onset_tolerance_s=args.onset_tolerance_s, + alignment_tolerance_s=args.alignment_tolerance_s, + max_abs_pitch_shift=args.max_abs_pitch_shift, + max_abs_time_shift_s=args.max_abs_time_shift_s, + time_step_s=args.time_step_s, + ) + + out_dir = args.out_dir + out_dir.mkdir(parents=True, exist_ok=True) + json_path = out_dir / f"{args.report_name}.json" + md_path = out_dir / f"{args.report_name}.md" + json_path.write_text( + json.dumps(report, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + newline="\n", + ) + md_path.write_text(render_markdown(report), encoding="utf-8", newline="\n") + + print(f"wrote {json_path}") + print(f"wrote {md_path}") + return 0 + + +def run_probe( + *, + root: Path, + clip_ids: Sequence[str], + backend_names: Sequence[str], + cache_dir: Path, + refresh_cache: bool, + skip_unavailable: bool, + cfg: GuitarConfig, + onset_tolerance_s: float, + alignment_tolerance_s: float, + max_abs_pitch_shift: int, + max_abs_time_shift_s: float, + time_step_s: float, +) -> dict[str, Any]: + """Run the full audio-alignment probe and return a JSON-ready report.""" + + ts = _load_timestamps(root) + gold_by_clip = {cid: parse_clip(cid, root, ts, cfg) for cid in clip_ids} + generated_at = datetime.now(UTC).replace(microsecond=0).isoformat() + + all_rows: list[ClipScore] = [] + backend_summaries: dict[str, dict[str, Any]] = {} + backend_errors: dict[str, str] = {} + for backend_name in backend_names: + try: + backend = _make_backend(backend_name) + except Exception as exc: + if not skip_unavailable: + raise + backend_errors[backend_name] = str(exc) + backend_summaries[backend_name] = { + "status": "unavailable", + "error": str(exc), + "clips": 0, + } + continue + + raw_events_by_clip: dict[str, list[AudioEvent]] = {} + alignments_by_clip: dict[str, AlignmentChoice] = {} + score_maps_by_clip: dict[str, dict[tuple[int, float], int]] = {} + rows_for_backend: list[ClipScore] = [] + + for clip_id in clip_ids: + gold = gold_by_clip[clip_id] + if not gold: + rows_for_backend.append( + ClipScore( + backend=backend_name, + clip_id=clip_id, + status="skipped_no_gold", + n_gold=0, + raw_event_count=0, + pitch_shift=0, + time_shift_s=0.0, + alignment_matches=0, + raw=_zero_score(), + per_clip=_zero_score(), + error="no parsed gold events", + ) + ) + continue + try: + events = cached_backend_events( + backend_name=backend_name, + backend=backend, + clip_id=clip_id, + root=root, + cache_dir=cache_dir, + refresh_cache=refresh_cache, + ) + alignment, score_map = estimate_audio_alignment( + events, + gold, + tolerance_s=alignment_tolerance_s, + max_abs_pitch_shift=max_abs_pitch_shift, + max_abs_time_shift_s=max_abs_time_shift_s, + time_step_s=time_step_s, + ) + raw = score_events( + events, + gold, + cfg=cfg, + onset_tolerance_s=onset_tolerance_s, + ) + calibrated = score_events( + shift_audio_events( + events, + alignment.pitch_shift, + alignment.time_shift_s, + ), + gold, + cfg=cfg, + onset_tolerance_s=onset_tolerance_s, + ) + except Exception as exc: + row = ClipScore( + backend=backend_name, + clip_id=clip_id, + status="error", + n_gold=len(gold), + raw_event_count=0, + pitch_shift=0, + time_shift_s=0.0, + alignment_matches=0, + raw=_zero_score(), + per_clip=_zero_score(), + error=str(exc), + ) + rows_for_backend.append(row) + continue + + raw_events_by_clip[clip_id] = events + alignments_by_clip[clip_id] = alignment + score_maps_by_clip[clip_id] = score_map + rows_for_backend.append( + ClipScore( + backend=backend_name, + clip_id=clip_id, + status="ok", + n_gold=len(gold), + raw_event_count=len(events), + pitch_shift=alignment.pitch_shift, + time_shift_s=alignment.time_shift_s, + alignment_matches=alignment.matches, + raw=raw, + per_clip=calibrated, + ) + ) + + global_alignment = estimate_global_alignment(score_maps_by_clip.values()) + finalized_rows: list[ClipScore] = [] + for row in rows_for_backend: + if row.status != "ok": + finalized_rows.append(row) + continue + events = raw_events_by_clip[row.clip_id] + global_score = score_events( + shift_audio_events( + events, + global_alignment.pitch_shift, + global_alignment.time_shift_s, + ), + gold_by_clip[row.clip_id], + cfg=cfg, + onset_tolerance_s=onset_tolerance_s, + ) + finalized_rows.append(replace(row, global_calibrated=global_score)) + + all_rows.extend(finalized_rows) + backend_summaries[backend_name] = summarize_backend( + backend_name, + finalized_rows, + global_alignment, + ) + + return { + "generated_at": generated_at, + "dataset": "KaggleUTAustin", + "root": str(root), + "cache_dir": str(cache_dir), + "clip_ids": list(clip_ids), + "backends": list(backend_names), + "scoring": { + "onset_tolerance_s": onset_tolerance_s, + "alignment_tolerance_s": alignment_tolerance_s, + "max_abs_pitch_shift": max_abs_pitch_shift, + "max_abs_time_shift_s": max_abs_time_shift_s, + "time_step_s": time_step_s, + }, + "summaries": backend_summaries, + "rows": [clip_score_to_dict(row) for row in all_rows], + "backend_errors": backend_errors, + "diagnosis": diagnose_report(backend_summaries), + } + + +def cached_backend_events( + *, + backend_name: str, + backend: Any, + clip_id: str, + root: Path, + cache_dir: Path, + refresh_cache: bool, +) -> list[AudioEvent]: + """Load backend events for one clip from cache, or run and cache them.""" + + wav_path = root / "tablature_audio" / f"{clip_id}.wav" + cache_path = cache_dir / backend_name / f"{clip_id}.json" + source_mtime_ns = wav_path.stat().st_mtime_ns + if not refresh_cache and cache_path.exists(): + cached = json.loads(cache_path.read_text(encoding="utf-8")) + if ( + cached.get("backend") == backend_name + and cached.get("clip_id") == clip_id + and cached.get("source_mtime_ns") == source_mtime_ns + ): + return events_from_json(cached.get("events", [])) + + wav, sr = _load_wav(wav_path) + events = list(backend.transcribe(wav, sr, SessionConfig())) + events.sort(key=lambda event: (event.onset_s, event.pitch_midi)) + + cache_path.parent.mkdir(parents=True, exist_ok=True) + payload = { + "backend": backend_name, + "clip_id": clip_id, + "source_wav": str(wav_path), + "source_mtime_ns": source_mtime_ns, + "generated_at": datetime.now(UTC).replace(microsecond=0).isoformat(), + "events": events_to_json(events), + } + cache_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8") + return events + + +def estimate_audio_alignment( + events: Sequence[AudioEvent], + gold: Sequence[TabEvent], + *, + tolerance_s: float = 0.12, + max_abs_pitch_shift: int = 3, + max_abs_time_shift_s: float = 3.0, + time_step_s: float = 0.02, +) -> tuple[AlignmentChoice, dict[tuple[int, float], int]]: + """Estimate whole-semitone and time-origin offsets from pitch matches.""" + + if not events or not gold: + return AlignmentChoice(0, 0.0, 0), {(0, 0.0): 0} + + scores: dict[tuple[int, float], int] = {} + n_time_steps = int(round(max_abs_time_shift_s / time_step_s)) + shifts = range(-max_abs_pitch_shift, max_abs_pitch_shift + 1) + for pitch_shift in shifts: + for offset_step in range(-n_time_steps, n_time_steps + 1): + time_shift_s = round(offset_step * time_step_s, 10) + matches = count_pitch_time_matches( + events, + gold, + pitch_shift=pitch_shift, + time_shift_s=time_shift_s, + tolerance_s=tolerance_s, + ) + scores[(pitch_shift, time_shift_s)] = matches + + best_pitch_shift, best_time_shift_s = max( + scores, + key=lambda candidate: ( + scores[candidate], + -abs(candidate[0]), + -abs(candidate[1]), + ), + ) + return ( + AlignmentChoice( + best_pitch_shift, + best_time_shift_s, + scores[(best_pitch_shift, best_time_shift_s)], + ), + scores, + ) + + +def estimate_global_alignment( + score_maps: Iterable[Mapping[tuple[int, float], int]], +) -> AlignmentChoice: + """Pick one alignment correction by summing per-clip match-count grids.""" + + totals: Counter[tuple[int, float]] = Counter() + for score_map in score_maps: + totals.update(score_map) + if not totals: + return AlignmentChoice(0, 0.0, 0) + best_pitch_shift, best_time_shift_s = max( + totals, + key=lambda candidate: ( + totals[candidate], + -abs(candidate[0]), + -abs(candidate[1]), + ), + ) + return AlignmentChoice( + best_pitch_shift, + best_time_shift_s, + int(totals[(best_pitch_shift, best_time_shift_s)]), + ) + + +def count_pitch_time_matches( + events: Sequence[AudioEvent], + gold: Sequence[TabEvent], + *, + pitch_shift: int, + time_shift_s: float, + tolerance_s: float, +) -> int: + """Count greedy pitch+time matches after an alignment correction.""" + + gold_used = [False] * len(gold) + matches = 0 + for event in sorted(events, key=lambda item: item.onset_s): + aligned_onset = event.onset_s + time_shift_s + aligned_pitch = event.pitch_midi + pitch_shift + best_j = -1 + best_dt = tolerance_s + 1e-9 + for j, gold_event in enumerate(gold): + if gold_used[j] or gold_event.pitch_midi != aligned_pitch: + continue + dt = abs(gold_event.onset_s - aligned_onset) + if dt <= tolerance_s and dt < best_dt: + best_j = j + best_dt = dt + if best_j >= 0: + gold_used[best_j] = True + matches += 1 + return matches + + +def shift_audio_events( + events: Sequence[AudioEvent], + pitch_shift: int, + time_shift_s: float = 0.0, +) -> list[AudioEvent]: + """Return calibrated audio events without mutating backend output.""" + + if pitch_shift == 0 and time_shift_s == 0.0: + return list(events) + return [ + replace( + event, + onset_s=event.onset_s + time_shift_s, + offset_s=event.offset_s + time_shift_s, + pitch_midi=event.pitch_midi + pitch_shift, + ) + for event in events + ] + + +def score_events( + events: Sequence[AudioEvent], + gold: Sequence[TabEvent], + *, + cfg: GuitarConfig, + onset_tolerance_s: float, +) -> ScoreBundle: + """Score audio-only and oracle-video decoded tabs for one clip.""" + + decoded = fuse(events, [], cfg) + oracle_decoded = fuse(events, _oracle_fingerings(list(gold), cfg), cfg) + onset: EventF1Result = event_f1( + decoded, + gold, + match_pitch=False, + onset_tolerance_s=onset_tolerance_s, + ) + pitch: EventF1Result = event_f1( + decoded, + gold, + match_pitch=True, + onset_tolerance_s=onset_tolerance_s, + ) + tab: TabF1Result = tab_f1(decoded, gold, onset_tolerance_s=onset_tolerance_s) + oracle_tab: TabF1Result = tab_f1( + oracle_decoded, + gold, + onset_tolerance_s=onset_tolerance_s, + ) + return ScoreBundle( + onset_f1=onset.f1, + pitch_f1=pitch.f1, + tab_f1=tab.f1, + oracle_tab_f1=oracle_tab.f1, + decoded_event_count=len(decoded), + ) + + +def summarize_backend( + backend_name: str, + rows: Sequence[ClipScore], + global_alignment: AlignmentChoice, +) -> dict[str, Any]: + ok_rows = [row for row in rows if row.status == "ok"] + skipped_rows = [row for row in rows if row.status == "skipped_no_gold"] + error_rows = [row for row in rows if row.status == "error"] + unique_errors = sorted({row.error for row in error_rows if row.error}) + if not ok_rows: + return { + "status": "error" if error_rows else "empty", + "clips": 0, + "skipped_clips": [row.clip_id for row in skipped_rows], + "errors": unique_errors, + "diagnosis": ( + [f"Backend did not score any clips: {unique_errors[0]}"] + if unique_errors + else ["No clips were scored."] + ), + } + + pitch_counts = Counter(row.pitch_shift for row in ok_rows) + large_time_rows = [row.clip_id for row in ok_rows if abs(row.time_shift_s) >= 0.5] + zero_oracle_rows = [row.clip_id for row in ok_rows if row.per_clip.oracle_tab_f1 == 0.0] + diagnosis = diagnose_backend(ok_rows) + if skipped_rows: + skipped = ", ".join(row.clip_id for row in skipped_rows) + diagnosis.append(f"Skipped clips with no parsed gold events: {skipped}.") + return { + "status": "ok" if not error_rows else "partial", + "backend": backend_name, + "clips": len(ok_rows), + "skipped_clips": [row.clip_id for row in skipped_rows], + "errors": unique_errors, + "raw_event_count_mean": _mean(row.raw_event_count for row in ok_rows), + "raw_event_count_total": sum(row.raw_event_count for row in ok_rows), + "global_alignment": asdict(global_alignment), + "pitch_shift_counts": dict(sorted(pitch_counts.items())), + "mode_pitch_shift": pitch_counts.most_common(1)[0][0], + "large_time_shift_clips": large_time_rows, + "zero_oracle_clips": zero_oracle_rows, + "raw": aggregate_scores(row.raw for row in ok_rows), + "per_clip": aggregate_scores(row.per_clip for row in ok_rows), + "global_calibrated": aggregate_scores( + row.global_calibrated for row in ok_rows if row.global_calibrated is not None + ), + "diagnosis": diagnosis, + } + + +def aggregate_scores(scores: Iterable[ScoreBundle | None]) -> dict[str, Any]: + present = [score for score in scores if score is not None] + if not present: + return {} + return { + "onset_f1_mean": _mean(score.onset_f1 for score in present), + "pitch_f1_mean": _mean(score.pitch_f1 for score in present), + "tab_f1_mean": _mean(score.tab_f1 for score in present), + "oracle_tab_f1_mean": _mean(score.oracle_tab_f1 for score in present), + "decoded_event_count_mean": _mean(score.decoded_event_count for score in present), + } + + +def diagnose_backend(rows: Sequence[ClipScore]) -> list[str]: + """Return short human-readable diagnostic bullets for one backend.""" + + if not rows: + return ["No successful clips were scored."] + + out: list[str] = [] + pitch_counts = Counter(row.pitch_shift for row in rows) + pitch_mode, pitch_mode_count = pitch_counts.most_common(1)[0] + if pitch_mode == -1 and pitch_mode_count >= max(2, int(len(rows) * 0.6)): + out.append( + "Most clips prefer a -1 semitone correction; treat corpus tuning/reference " + "pitch as a first-class suspect." + ) + elif pitch_mode != 0 and pitch_mode_count >= max(2, int(len(rows) * 0.6)): + out.append( + f"Most clips prefer a {pitch_mode:+d} semitone correction; inspect " + "corpus tuning/reference pitch before backend changes." + ) + + large_time = [row for row in rows if abs(row.time_shift_s) >= 0.5] + large_time_ids = {row.clip_id for row in large_time} + if {"0", "1"}.issubset(large_time_ids): + out.append( + "Clips 0 and 1 both need large time-origin shifts; inspect timestamp " + "or label origin handling separately from the rest of the corpus." + ) + elif large_time: + ids = ", ".join(row.clip_id for row in large_time[:8]) + out.append(f"Large time-origin shifts appear on clips {ids}; inspect alignment.") + + zero_oracle = [row.clip_id for row in rows if row.per_clip.oracle_tab_f1 == 0.0] + if zero_oracle: + shown = ", ".join(zero_oracle[:10]) + out.append( + f"{len(zero_oracle)} clips still have zero oracle-video Tab F1 " + f"after per-clip calibration ({shown}); suspect thresholds, grouping, " + "or backend mismatch." + ) + + if not out: + out.append("No dominant pitch/time calibration failure pattern was detected.") + return out + + +def diagnose_report(summaries: Mapping[str, Mapping[str, Any]]) -> list[str]: + """Compare backends and return top-level diagnostic bullets.""" + + ok = { + name: summary + for name, summary in summaries.items() + if summary.get("status") in {"ok", "partial"} + } + if not ok: + return ["No backend completed successfully."] + + oracle_scores = { + name: float(summary.get("per_clip", {}).get("oracle_tab_f1_mean", 0.0)) + for name, summary in ok.items() + } + best_backend, best_score = max(oracle_scores.items(), key=lambda item: item[1]) + out = [f"Best per-clip oracle-video ceiling is {best_backend} at {best_score:.4f}."] + global_oracle_scores = { + name: float(summary.get("global_calibrated", {}).get("oracle_tab_f1_mean", 0.0)) + for name, summary in ok.items() + } + best_global_backend, best_global_score = max( + global_oracle_scores.items(), + key=lambda item: item[1], + ) + out.append( + f"Best global-calibration oracle-video ceiling is {best_global_backend} " + f"at {best_global_score:.4f}." + ) + if "highres" in oracle_scores and "basicpitch" in oracle_scores: + delta = oracle_scores["basicpitch"] - oracle_scores["highres"] + if delta > 0.01: + out.append( + f"Basic Pitch beats highres on oracle ceiling by {delta:.4f}; " + "do not assume highres is the better UT-Austin backend." + ) + elif delta < -0.01: + out.append(f"Highres beats Basic Pitch on oracle ceiling by {-delta:.4f}.") + else: + out.append("Basic Pitch and highres are effectively tied on oracle ceiling.") + return out + + +def render_markdown(report: Mapping[str, Any]) -> str: + """Render the JSON report as a compact Markdown eval report.""" + + lines = [ + "# v1.1 audio alignment probe", + "", + f"**Date:** {report['generated_at']}", + f"**Dataset:** {report['dataset']}", + f"**Root:** `{report['root']}`", + f"**Cache:** `{report['cache_dir']}`", + "", + "## Summary", + "", + "| Backend | Status | Clips | Raw events | Global shift | Global time | " + "Per-clip onset | Per-clip pitch | Per-clip Tab | Per-clip oracle | " + "Global oracle | Pitch mode | Large-time clips | Zero-oracle clips |", + "|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|", + ] + for backend, summary in report["summaries"].items(): + if summary.get("status") == "unavailable": + lines.append( + f"| {backend} | unavailable | 0 | - | - | - | - | - | - | - | - | - | - | - |" + ) + continue + per_clip = summary.get("per_clip", {}) + global_scores = summary.get("global_calibrated", {}) + alignment = summary.get("global_alignment", {}) + lines.append( + "| " + + " | ".join( + [ + backend, + str(summary.get("status", "")), + str(summary.get("clips", 0)), + _fmt(summary.get("raw_event_count_total")), + _fmt(alignment.get("pitch_shift"), signed=True, precision=0), + _fmt(alignment.get("time_shift_s"), signed=True, precision=2), + _fmt(per_clip.get("onset_f1_mean")), + _fmt(per_clip.get("pitch_f1_mean")), + _fmt(per_clip.get("tab_f1_mean")), + _fmt(per_clip.get("oracle_tab_f1_mean")), + _fmt(global_scores.get("oracle_tab_f1_mean")), + _fmt(summary.get("mode_pitch_shift"), signed=True, precision=0), + str(len(summary.get("large_time_shift_clips", []))), + str(len(summary.get("zero_oracle_clips", []))), + ] + ) + + " |" + ) + + lines.extend(["", "## Diagnosis", ""]) + for item in report.get("diagnosis", []): + lines.append(f"- {item}") + for backend, summary in report["summaries"].items(): + for item in summary.get("diagnosis", []): + lines.append(f"- {backend}: {item}") + + lines.extend(["", "## Per-Clip Results", ""]) + by_backend: dict[str, list[Mapping[str, Any]]] = {} + for row in report["rows"]: + by_backend.setdefault(row["backend"], []).append(row) + for backend, rows in by_backend.items(): + lines.extend( + [ + f"### {backend}", + "", + "| Clip | Gold | Raw events | Pitch shift | Time shift | Align matches | " + "Onset | Pitch | Tab | Oracle | Raw pitch | Raw oracle |", + "|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|", + ] + ) + for row in sorted(rows, key=lambda item: int(item["clip_id"])): + if row["status"] != "ok": + label = "skipped" if row["status"] == "skipped_no_gold" else "error" + lines.append( + f"| {row['clip_id']} | {row['n_gold']} | {label} | - | - | - | " + f"- | - | - | - | - | - |" + ) + continue + per_clip = row["per_clip"] + raw = row["raw"] + lines.append( + "| " + + " | ".join( + [ + row["clip_id"], + str(row["n_gold"]), + str(row["raw_event_count"]), + _fmt(row["pitch_shift"], signed=True, precision=0), + _fmt(row["time_shift_s"], signed=True, precision=2), + str(row["alignment_matches"]), + _fmt(per_clip["onset_f1"]), + _fmt(per_clip["pitch_f1"]), + _fmt(per_clip["tab_f1"]), + _fmt(per_clip["oracle_tab_f1"]), + _fmt(raw["pitch_f1"]), + _fmt(raw["oracle_tab_f1"]), + ] + ) + + " |" + ) + lines.append("") + + lines.extend(["## Notes", ""]) + lines.append( + "- Per-clip metrics apply each clip's best whole-semitone and time-origin correction." + ) + lines.append("- Global metrics apply one correction shared across all clips for that backend.") + lines.append( + "- Oracle-video Tab F1 uses gold string/fret evidence, so low oracle scores point " + "back to audio event quality/alignment." + ) + return "\n".join(lines) + "\n" + + +def clip_score_to_dict(row: ClipScore) -> dict[str, Any]: + payload = asdict(row) + return payload + + +def events_to_json(events: Sequence[AudioEvent]) -> list[dict[str, Any]]: + return [ + { + "onset_s": event.onset_s, + "offset_s": event.offset_s, + "pitch_midi": event.pitch_midi, + "velocity": event.velocity, + "confidence": event.confidence, + "tags": list(event.tags), + } + for event in events + ] + + +def events_from_json(payload: Sequence[Mapping[str, Any]]) -> list[AudioEvent]: + return [ + AudioEvent( + onset_s=float(item["onset_s"]), + offset_s=float(item["offset_s"]), + pitch_midi=int(item["pitch_midi"]), + velocity=float(item["velocity"]), + confidence=float(item["confidence"]), + tags=tuple(str(tag) for tag in item.get("tags", ())), + ) + for item in payload + ] + + +def _make_backend(name: str) -> Any: + from tabvision.audio.backend import make + + return make(name) + + +def _load_wav(path: Path) -> tuple[np.ndarray, int]: + import soundfile as sf + + wav, sr = sf.read(str(path), always_2d=False) + wav_arr = np.asarray(wav, dtype=np.float32) + if wav_arr.ndim == 2: + wav_arr = wav_arr.mean(axis=1) + return wav_arr, int(sr) + + +def _resolve_clip_ids(root: Path, clips: str | None) -> list[str]: + if clips: + return _parse_csv(clips) + label_dir = root / "tablature_labels" + return sorted((path.stem for path in label_dir.glob("*.npy")), key=int) + + +def _parse_csv(value: str | Sequence[str]) -> list[str]: + if isinstance(value, str): + raw_items = value.split(",") + else: + raw_items = value + return [item.strip() for item in raw_items if item.strip()] + + +def _zero_score() -> ScoreBundle: + return ScoreBundle( + onset_f1=0.0, + pitch_f1=0.0, + tab_f1=0.0, + oracle_tab_f1=0.0, + decoded_event_count=0, + ) + + +def _mean(values: Iterable[float]) -> float: + vals = list(values) + return sum(vals) / len(vals) if vals else 0.0 + + +def _fmt( + value: Any, + *, + signed: bool = False, + precision: int = 4, +) -> str: + if value is None: + return "-" + if isinstance(value, int): + return f"{value:+d}" if signed else str(value) + if isinstance(value, float): + if precision == 0: + return f"{value:+.0f}" if signed else f"{value:.0f}" + sign = "+" if signed else "" + return f"{value:{sign}.{precision}f}" + return str(value) + + +def _parse_args(argv: list[str] | None) -> argparse.Namespace: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--root", type=Path, default=_DEFAULT_ROOT) + ap.add_argument("--clips", default=None, help="comma-separated clip ids; default all") + ap.add_argument( + "--backends", + default=",".join(_DEFAULT_BACKENDS), + help="comma-separated backend names", + ) + ap.add_argument("--cache-dir", type=Path, default=_DEFAULT_CACHE_DIR) + ap.add_argument("--refresh-cache", action="store_true") + ap.add_argument( + "--no-skip-unavailable", + action="store_true", + help="raise if a backend cannot be imported or run", + ) + ap.add_argument("--out-dir", type=Path, default=_DEFAULT_REPORT_DIR) + ap.add_argument( + "--report-name", + default=f"v1_1_audio_alignment_probe_{datetime.now().strftime('%Y%m%d-%H%M%S')}", + ) + ap.add_argument("--onset-tolerance-s", type=float, default=0.05) + ap.add_argument("--alignment-tolerance-s", type=float, default=0.12) + ap.add_argument("--max-abs-pitch-shift", type=int, default=3) + ap.add_argument("--max-abs-time-shift-s", type=float, default=3.0) + ap.add_argument("--time-step-s", type=float, default=0.02) + return ap.parse_args(argv) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tabvision/scripts/eval/v1_1_mediapipe_sanity.py b/tabvision/scripts/eval/v1_1_mediapipe_sanity.py new file mode 100644 index 0000000..2e134ce --- /dev/null +++ b/tabvision/scripts/eval/v1_1_mediapipe_sanity.py @@ -0,0 +1,94 @@ +"""v1.1 chunk-2: MediaPipe hand-detection sanity check on the Kaggle UT-Austin footage. + +The open unknown for chunk-2 is whether the iPhone-tuned CV stack survives a *different rig* +(seated front-angle acoustic, varied lighting/skin/room) than the footage MediaPipe + the +fretboard detector were built against. Before wiring the full chain, this script answers the +narrowest question first: **does MediaPipe even find the fretting hand on these PNG frames, and +where do its landmarks land?** + +Pure MediaPipe — no homography, no fingertip→fret, no fusion. For a sample of frames it reports +per-frame detection: hand count, the chosen fretting hand's handedness label + confidence, and +the fretting-hand wrist / fingertip positions as a fraction of the frame (so we can eyeball that +the hand sits on the neck, not the strumming hand over the soundhole). +""" + +from __future__ import annotations + +import argparse +from collections import Counter +from pathlib import Path + +import cv2 + +from tabvision.video.hand.fingertip_to_fret import FRETTING_FINGERS +from tabvision.video.hand.mediapipe_backend import MediaPipeHandBackend + +_DEFAULT_ROOT = ( + Path.home() + / ".tabvision/data/datasets/guitar-transcription-utaustin" + / "tablature_dataset/tablature_dataset" +) + + +def _clip_frame_paths(root: Path, clip_id: str) -> list[Path]: + frames = root / "tablature_frames" + paths = sorted( + frames.glob(f"{clip_id}_*.png"), + key=lambda p: int(p.stem.split("_")[1]), + ) + return paths + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--root", type=Path, default=_DEFAULT_ROOT) + ap.add_argument("--clip", default="0", help="clip id (default 0)") + ap.add_argument("--stride", type=int, default=20, help="sample every Nth frame") + ap.add_argument("--limit", type=int, default=20, help="max frames to report") + args = ap.parse_args(argv) + + backend = MediaPipeHandBackend() + + paths = _clip_frame_paths(args.root, args.clip) + sampled = paths[:: args.stride][: args.limit] + print( + f"clip {args.clip}: {len(paths)} frames, sampling {len(sampled)} (stride {args.stride})\n" + ) + + print(f"{'frame':>8} {'hand?':>5} {'label':>6} {'conf':>5} {'wrist_xy%':>14} {'fingertips_x%'}") + detected = 0 + labels: Counter[str] = Counter() + for p in sampled: + frame = cv2.imread(str(p)) + if frame is None: + print(f"{p.stem:>8} ") + continue + h, w = frame.shape[:2] + hand = backend._extract_fretting_hand(frame) # noqa: SLF001 — sanity probe + if hand is None: + print(f"{p.stem:>8} {'no':>5}") + continue + detected += 1 + label = "left" if hand.is_left_hand else "right" + labels[label] += 1 + wx, wy = hand.wrist_xy + tip_xs = [ + f"{hand.fingers[f].tip_xy[0] / w:.2f}" for f in FRETTING_FINGERS if f in hand.fingers + ] + print( + f"{p.stem:>8} {'yes':>5} {label:>6} {hand.confidence:>5.2f}" + f" {f'({wx / w:.2f},{wy / h:.2f})':>14} {','.join(tip_xs)}" + ) + + n = len(sampled) + print(f"\ndetected {detected}/{n} ({detected / n:.0%}) fretting-hand labels: {dict(labels)}") + print( + "NOTE: 'left'=HandSample.is_left_hand (MediaPipe 'Right', mirror convention)." + " On this rig the neck/fretting hand should sit at higher wrist_x% (toward image right)." + ) + backend.close() + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tabvision/scripts/eval/v1_1_real_chain_probe.py b/tabvision/scripts/eval/v1_1_real_chain_probe.py new file mode 100644 index 0000000..835e7ec --- /dev/null +++ b/tabvision/scripts/eval/v1_1_real_chain_probe.py @@ -0,0 +1,521 @@ +"""v1.1 chunk-2 deliverable: the REAL CV chain end-to-end on the Kaggle rig. + +Replaces the chunk-1 *oracle* ``FrameFingering`` with one produced by the actual vision stack: + + PNG frame -> YOLO-OBB neck -> KeypointFretboardBackend homography + -> MediaPipe hands -> (geometric fretting-hand selection) -> fingertip_to_fret + -> FrameFingering + +then fuses it with gold-pitch ``AudioEvent``s (string/fret stripped, exactly like the oracle +probe) so the measured Tab F1 isolates the *string* axis — the v1.1 lever. Compares three +conditions per clip: audio-only, +real-video, +oracle (the chunk-1 ceiling). + +Hand-selection fix (chunk-2 Finding 2): the Kaggle footage is NOT mirrored, so v0's "pick the +MediaPipe-'Right' hand" grabs the strumming hand. Here we instead project each detected hand's +fingertips through the homography and keep the hand whose tips land *on* the canonical +fretboard ``[0,1]x[0,1]`` — rig-agnostic, and only possible now that we have a real homography. +""" + +from __future__ import annotations + +import argparse +import os +from dataclasses import replace +from pathlib import Path + +import cv2 +import numpy as np + +from scripts.eval.v1_1_kaggle_oracle_probe import ( + _events_from_gold, + _load_timestamps, + _oracle_fingerings, + parse_clip, +) +from tabvision.eval.metrics import tab_f1 +from tabvision.fusion import fuse +from tabvision.fusion.vision_evidence import ( + ORIENTATION_BY_NAME, + Orientation, + choose_orientation, + combine_fingerings, + empty_fingering, + gate_fingering_to_audio, + orient_fingering, +) +from tabvision.types import AudioEvent, FrameFingering, GuitarConfig, SessionConfig, TabEvent +from tabvision.video.fretboard.keypoint import KeypointFretboardBackend +from tabvision.video.guitar.yolo_backend import YoloOBBBackend +from tabvision.video.hand.fingertip_to_fret import ( + FRETTING_FINGERS, + HandSample, + compute_fingering, +) +from tabvision.video.hand.mediapipe_backend import _build_hand_sample + +_DEFAULT_ROOT = ( + Path.home() + / ".tabvision/data/datasets/guitar-transcription-utaustin" + / "tablature_dataset/tablature_dataset" +) + + +def _select_fretting_hand_geometric( + hands: list[HandSample], h_inv: np.ndarray +) -> HandSample | None: + """Pick the hand whose fingertips project nearest the canonical fretboard center. + + The strumming hand (over the soundhole) projects far outside [0,1]^2; the fretting hand + lands on the board. Score = mean distance of fingertips from the [0,1]^2 box (0 = inside). + """ + best: HandSample | None = None + best_score = float("inf") + for hand in hands: + cs = [] + for f in FRETTING_FINGERS: + s = hand.fingers.get(f) + if s is None: + continue + pt = h_inv @ np.array([s.tip_xy[0], s.tip_xy[1], 1.0]) + if abs(pt[2]) < 1e-9: + continue + cx, cy = pt[0] / pt[2], pt[1] / pt[2] + # distance outside the unit box (0 if inside) + dx = max(0.0, -cx, cx - 1.0) + dy = max(0.0, -cy, cy - 1.0) + cs.append((dx * dx + dy * dy) ** 0.5) + if cs: + score = float(np.mean(cs)) + if score < best_score: + best_score, best = score, hand + return best + + +def _frame_indices_near_onset( + frame_times: dict[int, float], + onset_s: float, + *, + max_frames: int, + window_s: float, +) -> list[int]: + """Nearest frame indices around an onset, sorted by timestamp.""" + + if not frame_times: + return [] + ranked = sorted(frame_times, key=lambda i: abs(frame_times[i] - onset_s)) + in_window = [i for i in ranked if abs(frame_times[i] - onset_s) <= window_s] + chosen = in_window[:max_frames] if in_window else ranked[:1] + return sorted(chosen, key=lambda i: frame_times[i]) + + +def _raw_fingering_for_frame( + frame_path: Path, + cfg: GuitarConfig, + fb: KeypointFretboardBackend, + landmarker, # noqa: ANN001 - mediapipe HandLandmarker +) -> FrameFingering | None: + """Run YOLO homography + MediaPipe hand detection on one stored frame.""" + + import mediapipe as mp + + frame = cv2.imread(str(frame_path)) + if frame is None: + return None + homography = fb.detect(frame, None) + if homography.confidence <= 0.0: + return None + rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + res = landmarker.detect(mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb)) + if not res.hand_landmarks: + return None + h, w = frame.shape[:2] + hands = [ + _build_hand_sample(lm, hd, frame_width=w, frame_height=h) + for lm, hd in zip(res.hand_landmarks, res.handedness, strict=False) + ] + h_inv = np.linalg.inv(homography.H) + hand = _select_fretting_hand_geometric(hands, h_inv) + if hand is None: + return None + return compute_fingering(hand, homography, cfg) + + +def _robust_real_fingerings_for_clip( + clip_id: str, + root: Path, + audio_events: list[AudioEvent], + cfg: GuitarConfig, + fb: KeypointFretboardBackend, + landmarker, # noqa: ANN001 - mediapipe HandLandmarker + *, + orientation: Orientation | None, + gate: bool, + vote_frames: int, + vote_window_s: float, + min_homography_confidence: float, + min_candidate_support: float, + min_best_ratio: float, + min_clip_coverage: float, +) -> tuple[list[FrameFingering], Orientation, dict[str, float], int]: + """One robust ``FrameFingering`` per audio event.""" + + frames_dir = root / "tablature_frames" + ts = _load_timestamps(root) + frame_times = { + int(k.split("_")[1].split(".")[0]): v for k, v in ts.items() if k.split("_")[0] == clip_id + } + + raw_by_event: list[list[FrameFingering]] = [] + for event in audio_events: + raw: list[FrameFingering] = [] + for idx in _frame_indices_near_onset( + frame_times, + event.onset_s, + max_frames=vote_frames, + window_s=vote_window_s, + ): + p = frames_dir / f"{clip_id}_{idx}.png" + if not p.exists(): + continue + ff = _raw_fingering_for_frame(p, cfg, fb, landmarker) + if ff is not None: + raw.append(replace(ff, t=frame_times[idx])) + raw_by_event.append(raw) + + chosen_orientation = orientation + orientation_scores: dict[str, float] = {} + if chosen_orientation is None: + chosen_orientation, orientation_scores = choose_orientation(raw_by_event, audio_events, cfg) + + out: list[FrameFingering] = [] + kept = 0 + for event, raw in zip(audio_events, raw_by_event, strict=False): + if not raw: + out.append(empty_fingering(event.onset_s, cfg)) + continue + oriented = [orient_fingering(f, chosen_orientation) for f in raw] + voted = combine_fingerings(oriented, cfg, t=event.onset_s) + if gate: + voted = gate_fingering_to_audio( + event, + voted, + cfg, + min_homography_confidence=min_homography_confidence, + min_candidate_support=min_candidate_support, + min_best_ratio=min_best_ratio, + ) + if voted.homography_confidence > 0.0 and (voted.finger_pos_logits != 0).any(): + kept += 1 + out.append(voted) + coverage = kept / len(audio_events) if audio_events else 0.0 + if gate and coverage < min_clip_coverage: + out = [empty_fingering(event.onset_s, cfg) for event in audio_events] + kept = 0 + return out, chosen_orientation, orientation_scores, kept + + +def _load_highres_audio_events( + clip_id: str, + root: Path, + backend, # noqa: ANN001 - HighResBackend, imported lazily +) -> list[AudioEvent]: + """Run highres on the UT-Austin WAV for a clip.""" + + import soundfile as sf + + wav_path = root / "tablature_audio" / f"{clip_id}.wav" + wav, sr = sf.read(str(wav_path), always_2d=False) + wav_arr = np.asarray(wav, dtype=np.float32) + if wav_arr.ndim == 2: + wav_arr = wav_arr.mean(axis=1) + return list(backend.transcribe(wav_arr, int(sr), SessionConfig())) + + +def _estimate_audio_alignment( + events: list[AudioEvent], + gold: list[TabEvent], + *, + tolerance_s: float = 0.12, + max_abs_shift: int = 3, +) -> tuple[int, float, dict[tuple[int, float], int]]: + """Estimate whole-semitone and time-origin offsets from near matches.""" + + if not events or not gold: + return 0, 0.0, {(0, 0.0): 0} + + scores: dict[tuple[int, float], int] = {} + for shift in range(-max_abs_shift, max_abs_shift + 1): + for offset_step in range(-150, 151): + time_shift = offset_step / 50.0 + matches = 0 + for event in events: + aligned_onset = event.onset_s + time_shift + nearest = min(gold, key=lambda g: abs(g.onset_s - aligned_onset)) + if abs(nearest.onset_s - aligned_onset) <= tolerance_s: + matches += int(nearest.pitch_midi == event.pitch_midi + shift) + scores[(shift, time_shift)] = matches + best_shift, best_time_shift = max( + scores, + key=lambda s: (scores[s], -abs(s[0]), -abs(s[1])), + ) + return best_shift, best_time_shift, scores + + +def _shift_audio_events( + events: list[AudioEvent], + pitch_shift: int, + time_shift_s: float = 0.0, +) -> list[AudioEvent]: + if pitch_shift == 0 and time_shift_s == 0.0: + return events + return [ + replace( + event, + onset_s=event.onset_s + time_shift_s, + offset_s=event.offset_s + time_shift_s, + pitch_midi=event.pitch_midi + pitch_shift, + ) + for event in events + ] + + +def _real_fingerings_for_clip( + clip_id: str, + root: Path, + onsets: list[float], + cfg: GuitarConfig, + yolo: YoloOBBBackend, + fb: KeypointFretboardBackend, + landmarker, # noqa: ANN001 — mediapipe HandLandmarker +) -> list[FrameFingering]: + """One real FrameFingering per gold onset (nearest available frame).""" + import mediapipe as mp + + frames_dir = root / "tablature_frames" + ts = _load_timestamps(root) + # map onset time -> nearest frame index for this clip + frame_times = { + int(k.split("_")[1].split(".")[0]): v for k, v in ts.items() if k.split("_")[0] == clip_id + } + out: list[FrameFingering] = [] + empty = np.zeros((len(FRETTING_FINGERS), cfg.n_strings, cfg.max_fret + 1)) + for onset in onsets: + idx = min(frame_times, key=lambda i: abs(frame_times[i] - onset)) if frame_times else None + p = frames_dir / f"{clip_id}_{idx}.png" if idx is not None else None + if p is None or not p.exists(): + out.append( + FrameFingering( + t=onset, + finger_pos_logits=empty.copy(), + homography_confidence=0.0, + ) + ) + continue + frame = cv2.imread(str(p)) + homography = fb.detect(frame, None) + if homography.confidence <= 0.0: + out.append( + FrameFingering( + t=onset, + finger_pos_logits=empty.copy(), + homography_confidence=0.0, + ) + ) + continue + rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + res = landmarker.detect(mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb)) + if not res.hand_landmarks: + out.append( + FrameFingering( + t=onset, + finger_pos_logits=empty.copy(), + homography_confidence=0.0, + ) + ) + continue + h, w = frame.shape[:2] + hands = [ + _build_hand_sample(lm, hd, frame_width=w, frame_height=h) + for lm, hd in zip(res.hand_landmarks, res.handedness, strict=False) + ] + h_inv = np.linalg.inv(homography.H) + hand = _select_fretting_hand_geometric(hands, h_inv) + if hand is None: + out.append( + FrameFingering( + t=onset, + finger_pos_logits=empty.copy(), + homography_confidence=0.0, + ) + ) + continue + ff = compute_fingering(hand, homography, cfg) + logits = ff.finger_pos_logits + # Orientation correction for this rig: keypoint.py's nut/body + high-E/low-E + # heuristics assume iPhone lap framing; the Kaggle rig is inverted on both axes + # (nut at high-X, strings reversed). See v1_1_chunk2_cv_chain report. + if FLIP_STRING: + logits = logits[:, ::-1, :] + if FLIP_FRET: + logits = logits[:, :, ::-1] + out.append( + FrameFingering( + t=onset, + finger_pos_logits=logits, + homography_confidence=ff.homography_confidence, + ) + ) + return out + + +FLIP_FRET = False +FLIP_STRING = False + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--root", type=Path, default=_DEFAULT_ROOT) + ap.add_argument("--checkpoint", type=Path, default=None) + ap.add_argument("--clips", default=None, help="comma clip ids; default all") + ap.add_argument("--conf", type=float, default=0.25) + ap.add_argument("--audio-source", choices=["gold", "highres"], default="gold") + ap.add_argument( + "--pitch-shift", + type=int, + default=None, + help="manual semitone shift for highres events", + ) + ap.add_argument( + "--time-shift-s", + type=float, + default=None, + help="manual seconds added to highres event onsets/offsets", + ) + ap.add_argument("--orientation", choices=["auto", *ORIENTATION_BY_NAME.keys()], default="auto") + ap.add_argument( + "--no-gate", + action="store_true", + help="disable audio-compatible confidence gating", + ) + ap.add_argument("--vote-frames", type=int, default=5) + ap.add_argument("--vote-window-s", type=float, default=0.08) + ap.add_argument("--min-homography-conf", type=float, default=0.1) + ap.add_argument("--min-candidate-support", type=float, default=0.02) + ap.add_argument("--min-best-ratio", type=float, default=1.2) + ap.add_argument("--min-clip-coverage", type=float, default=0.71) + ap.add_argument( + "--flip-fret", + action="store_true", + help="invert canonical fret axis (nut/body)", + ) + ap.add_argument("--flip-string", action="store_true", help="invert canonical string axis (E/e)") + args = ap.parse_args(argv) + + global FLIP_FRET, FLIP_STRING + FLIP_FRET = args.flip_fret + FLIP_STRING = args.flip_string + orientation: Orientation | None + if args.flip_fret or args.flip_string: + if args.flip_fret and args.flip_string: + orientation = ORIENTATION_BY_NAME["flip-both"] + elif args.flip_fret: + orientation = ORIENTATION_BY_NAME["flip-fret"] + else: + orientation = ORIENTATION_BY_NAME["flip-string"] + elif args.orientation == "auto": + orientation = None + else: + orientation = ORIENTATION_BY_NAME[args.orientation] + + cfg = GuitarConfig() + ckpt = args.checkpoint or os.environ.get("TABVISION_GUITAR_YOLO_CHECKPOINT") + yolo = YoloOBBBackend(checkpoint_path=ckpt, conf=args.conf, device="cpu") + fb = KeypointFretboardBackend(backend=yolo) + highres_backend = None + if args.audio_source == "highres": + from tabvision.audio.highres import HighResBackend + + highres_backend = HighResBackend() + + from mediapipe.tasks import python as mp_python + from mediapipe.tasks.python import vision as mp_vision + + model = os.path.expanduser("~/.mediapipe/models/hand_landmarker.task") + landmarker = mp_vision.HandLandmarker.create_from_options( + mp_vision.HandLandmarkerOptions( + base_options=mp_python.BaseOptions(model_asset_path=model), num_hands=2 + ) + ) + + ts = _load_timestamps(args.root) + label_dir = args.root / "tablature_labels" + clip_ids = ( + args.clips.split(",") + if args.clips + else sorted((p.stem for p in label_dir.glob("*.npy")), key=int) + ) + + print( + f"{'clip':>5} {'notes':>6} {'audio':>8} {'+real':>8} {'+oracle':>8} " + f"{'real-d':>8} {'orient':>11} {'kept':>7} {'p-sh':>5} {'t-sh':>6}" + ) + rows = [] + for cid in clip_ids: + gold = parse_clip(cid, args.root, ts, cfg) + if not gold: + continue + if args.audio_source == "highres": + assert highres_backend is not None + ev = _load_highres_audio_events(cid, args.root, highres_backend) + auto_pitch_shift, auto_time_shift, _alignment_scores = _estimate_audio_alignment( + ev, gold + ) + pitch_shift = args.pitch_shift if args.pitch_shift is not None else auto_pitch_shift + time_shift = args.time_shift_s if args.time_shift_s is not None else auto_time_shift + ev = _shift_audio_events(ev, pitch_shift, time_shift) + else: + ev = _events_from_gold(gold) + pitch_shift = 0 + time_shift = 0.0 + fa = tab_f1(fuse(ev, [], cfg), gold).f1 + real, chosen_orientation, _orientation_scores, kept = _robust_real_fingerings_for_clip( + cid, + args.root, + ev, + cfg, + fb, + landmarker, + orientation=orientation, + gate=not args.no_gate, + vote_frames=args.vote_frames, + vote_window_s=args.vote_window_s, + min_homography_confidence=args.min_homography_conf, + min_candidate_support=args.min_candidate_support, + min_best_ratio=args.min_best_ratio, + min_clip_coverage=args.min_clip_coverage, + ) + fr = tab_f1(fuse(ev, real, cfg), gold).f1 + fo = tab_f1(fuse(ev, _oracle_fingerings(gold, cfg), cfg), gold).f1 + rows.append((cid, len(gold), fa, fr, fo, kept, pitch_shift, time_shift)) + print( + f"{cid:>5} {len(gold):>6} {fa:>8.4f} {fr:>8.4f} {fo:>8.4f} " + f"{fr - fa:>+8.4f} {chosen_orientation.name:>11} {kept:>7} " + f"{pitch_shift:>+5} {time_shift:>+6.2f}" + ) + + if rows: + n = len(rows) + ma = sum(r[2] for r in rows) / n + mr = sum(r[3] for r in rows) / n + mo = sum(r[4] for r in rows) / n + kept_total = sum(r[5] for r in rows) + print( + f"{'ALL':>5} {sum(r[1] for r in rows):>6} {ma:>8.4f} {mr:>8.4f} " + f"{mo:>8.4f} {mr - ma:>+8.4f} {'':>11} {kept_total:>7} " + f"{'':>5} {'':>6} ({n} clips)" + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tabvision/scripts/eval/v1_1_second_corpus_probe.py b/tabvision/scripts/eval/v1_1_second_corpus_probe.py new file mode 100644 index 0000000..966b91e --- /dev/null +++ b/tabvision/scripts/eval/v1_1_second_corpus_probe.py @@ -0,0 +1,263 @@ +"""v1.1 chunk-4 cached/resumable composite-eval runner for second-corpus checks. + +The plain composite-eval CLI (``scripts.eval.composite_eval``) runs the full +pipeline once per clip and only writes its report at the very end. A 12-clip +Guitar-TECHS highres run exceeds the local 30-minute interactive budget, so an +interrupted run loses every transcription. + +This probe wraps the same :func:`tabvision.eval.composite.run_composite_eval` +harness with an on-disk per-clip ``TabEvent`` cache. Each clip's prediction is +written to the cache as soon as it is computed, so the run is **resumable**: +re-running scores already-cached clips instantly and only transcribes the +remaining ones. The scoring, aggregation, and report formatting are unchanged -- +the markdown is byte-identical to the composite CLI for the same inputs. + +It is corpus-agnostic (any manifest works); the chunk-4 second-corpus gate runs +it over ``data/eval/local_gt_chords.toml`` with ``--splits train``. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import time +from collections.abc import Mapping, Sequence +from datetime import UTC, datetime +from pathlib import Path +from typing import Any + +from tabvision.eval.composite import ( + Predictor, + format_baseline_markdown, + format_decomposition_markdown, + make_run_pipeline_predictor, + run_composite_eval, +) +from tabvision.types import SessionConfig, TabEvent + +_DEFAULT_CACHE_DIR = Path.home() / ".tabvision/cache/v1_1_second_corpus" + + +def main(argv: list[str] | None = None) -> int: + args = _parse_args(argv) + + position_prior: str | None = args.position_prior + if position_prior and position_prior.lower() == "none": + position_prior = None + + base_predictor = make_run_pipeline_predictor( + audio_backend_name=args.backend, + position_prior=position_prior, + melodic_prior_enabled=args.melodic_prior, + video_enabled=args.enable_video, + ) + key_fields = { + "backend": args.backend, + "position_prior": position_prior or "none", + "melodic_prior": bool(args.melodic_prior), + "video": bool(args.enable_video), + } + predictor = CachingPredictor( + base_predictor, + cache_dir=args.cache_dir, + key_fields=key_fields, + refresh_cache=args.refresh_cache, + ) + + splits = tuple(s.strip() for s in args.splits.split(",") if s.strip()) + + report = run_composite_eval( + args.manifest, + predictor=predictor, + media_root=args.media_root, + annotation_root=args.annotation_root, + splits=splits, + onset_tolerance_s=args.onset_tolerance_s, + bootstrap_n=args.bootstrap_n, + bootstrap_seed=args.bootstrap_seed, + ) + + baseline_md = format_baseline_markdown( + report, + backend_label=args.backend, + position_prior_label=position_prior or "none", + eval_harness_sha=args.eval_harness_sha, + title=args.title, + ) + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(baseline_md, encoding="utf-8", newline="\n") + print(f"wrote {args.output}") + + if args.decomposition_output: + decomp_md = format_decomposition_markdown(report) + args.decomposition_output.parent.mkdir(parents=True, exist_ok=True) + args.decomposition_output.write_text(decomp_md, encoding="utf-8", newline="\n") + print(f"wrote {args.decomposition_output}") + + print( + f"scored {len(report.per_clip)} clips " + f"({predictor.cache_hits} cached, {predictor.cache_misses} computed)" + ) + return 0 + + +class CachingPredictor: + """Wrap a composite-eval predictor with an on-disk per-clip ``TabEvent`` cache. + + The wrapped ``base`` predictor is only invoked on a cache miss; the resulting + events are written to ``cache_dir`` immediately, keyed by the resolved media + path, its mtime, and the prediction settings (``key_fields``). This makes a + long multi-clip run resumable across process restarts. + """ + + def __init__( + self, + base: Predictor, + *, + cache_dir: Path, + key_fields: Mapping[str, Any], + refresh_cache: bool = False, + ) -> None: + self._base = base + self._cache_dir = Path(cache_dir) + self._key_fields = dict(key_fields) + self._refresh_cache = refresh_cache + self.cache_hits = 0 + self.cache_misses = 0 + + def __call__(self, media_path: Path, session: SessionConfig) -> list[TabEvent]: + media_path = Path(media_path) + mtime_ns = media_path.stat().st_mtime_ns + cache_path = self._cache_path(media_path) + + if not self._refresh_cache and cache_path.exists(): + cached = _read_cache(cache_path) + if cached is not None and _cache_matches( + cached, + media_path=media_path, + mtime_ns=mtime_ns, + key_fields=self._key_fields, + ): + self.cache_hits += 1 + print(f" [cache] {media_path.name}: {len(cached['events'])} events") + return tabevents_from_json(cached["events"]) + + started = time.monotonic() + events = list(self._base(media_path, session)) + elapsed = time.monotonic() - started + self.cache_misses += 1 + print(f" [run] {media_path.name}: {len(events)} events in {elapsed:.1f}s") + self._write_cache(cache_path, media_path, mtime_ns, events) + return events + + def _cache_path(self, media_path: Path) -> Path: + key = json.dumps( + {"media": str(media_path.resolve()), **self._key_fields}, + sort_keys=True, + ) + digest = hashlib.sha1(key.encode("utf-8")).hexdigest()[:16] + return self._cache_dir / f"{media_path.stem}.{digest}.json" + + def _write_cache( + self, + cache_path: Path, + media_path: Path, + mtime_ns: int, + events: Sequence[TabEvent], + ) -> None: + payload = { + "media_path": str(media_path.resolve()), + "source_mtime_ns": mtime_ns, + "key_fields": self._key_fields, + "generated_at": datetime.now(UTC).replace(microsecond=0).isoformat(), + "events": tabevents_to_json(events), + } + cache_path.parent.mkdir(parents=True, exist_ok=True) + cache_path.write_text( + json.dumps(payload, indent=2, sort_keys=True) + "\n", + encoding="utf-8", + ) + + +def _read_cache(cache_path: Path) -> dict[str, Any] | None: + try: + data = json.loads(cache_path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + return None + return data if isinstance(data, dict) else None + + +def _cache_matches( + cached: Mapping[str, Any], + *, + media_path: Path, + mtime_ns: int, + key_fields: Mapping[str, Any], +) -> bool: + return ( + cached.get("media_path") == str(media_path.resolve()) + and cached.get("source_mtime_ns") == mtime_ns + and cached.get("key_fields") == dict(key_fields) + and isinstance(cached.get("events"), list) + ) + + +def tabevents_to_json(events: Sequence[TabEvent]) -> list[dict[str, Any]]: + return [ + { + "onset_s": event.onset_s, + "duration_s": event.duration_s, + "string_idx": event.string_idx, + "fret": event.fret, + "pitch_midi": event.pitch_midi, + "confidence": event.confidence, + "techniques": list(event.techniques), + } + for event in events + ] + + +def tabevents_from_json(payload: Sequence[Mapping[str, Any]]) -> list[TabEvent]: + return [ + TabEvent( + onset_s=float(item["onset_s"]), + duration_s=float(item["duration_s"]), + string_idx=int(item["string_idx"]), + fret=int(item["fret"]), + pitch_midi=int(item["pitch_midi"]), + confidence=float(item["confidence"]), + techniques=tuple(str(tag) for tag in item.get("techniques", ())), + ) + for item in payload + ] + + +def _parse_args(argv: list[str] | None) -> argparse.Namespace: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--manifest", type=Path, required=True) + ap.add_argument("--backend", default="highres", help="audio backend name") + ap.add_argument( + "--position-prior", + default="none", + help='position prior name; pass "none" to disable', + ) + ap.add_argument("--melodic-prior", action="store_true") + ap.add_argument("--enable-video", action="store_true") + ap.add_argument("--output", type=Path, required=True) + ap.add_argument("--decomposition-output", type=Path, default=None) + ap.add_argument("--splits", default="validation,test") + ap.add_argument("--media-root", type=Path, default=None) + ap.add_argument("--annotation-root", type=Path, default=None) + ap.add_argument("--bootstrap-n", type=int, default=10_000) + ap.add_argument("--bootstrap-seed", type=int, default=42) + ap.add_argument("--onset-tolerance-s", type=float, default=0.05) + ap.add_argument("--eval-harness-sha", default="") + ap.add_argument("--title", default="Composite per-tier baseline") + ap.add_argument("--cache-dir", type=Path, default=_DEFAULT_CACHE_DIR) + ap.add_argument("--refresh-cache", action="store_true") + return ap.parse_args(argv) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tabvision/scripts/eval/v1_1_yolo_rig_probe.py b/tabvision/scripts/eval/v1_1_yolo_rig_probe.py new file mode 100644 index 0000000..404f0d8 --- /dev/null +++ b/tabvision/scripts/eval/v1_1_yolo_rig_probe.py @@ -0,0 +1,100 @@ +"""v1.1 chunk-2: does the trained YOLO-OBB fretboard detector generalize to the Kaggle rig? + +The keypoint homography backend needs a per-frame ``neck`` OBB (plus nut/fret bonuses). The +detector is fine-tuned on the Roboflow ``b101/guitar-3`` set — a *different* rig than the Kaggle +UT-Austin seated front-angle footage. This probe runs the trained ``YoloOBBBackend`` + +``KeypointFretboardBackend`` on a sample of Kaggle frames and reports whether the neck is found, +its confidence, and — crucially — whether the resulting homography quad localizes the actual +(diagonal, narrow) neck rather than collapsing to a full-frame box (the failure mode the v0 +geometric detector showed on this rig). + +Point it at a checkpoint with --checkpoint (or TABVISION_GUITAR_YOLO_CHECKPOINT). +""" + +from __future__ import annotations + +import argparse +import os +from pathlib import Path + +import cv2 +import numpy as np + +from tabvision.video.fretboard.keypoint import KeypointFretboardBackend +from tabvision.video.guitar.yolo_backend import YoloOBBBackend + +_DEFAULT_ROOT = ( + Path.home() + / ".tabvision/data/datasets/guitar-transcription-utaustin" + / "tablature_dataset/tablature_dataset" +) + + +def _quad_extent(homography: np.ndarray, w: int, h: int) -> tuple[float, float]: + """Project the canonical unit square through homography and return its bbox extent + as a fraction of (frame_w, frame_h). A real neck is a narrow diagonal strip + (small in at least one axis); a full-frame mis-fire is ~1.0 x ~1.0.""" + corners = np.array([[0, 0, 1], [1, 0, 1], [1, 1, 1], [0, 1, 1]], dtype=np.float64).T + proj = homography @ corners + proj = proj[:2] / proj[2] + dx = (proj[0].max() - proj[0].min()) / w + dy = (proj[1].max() - proj[1].min()) / h + return float(dx), float(dy) + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--root", type=Path, default=_DEFAULT_ROOT) + ap.add_argument("--checkpoint", type=Path, default=None, help="YOLO-OBB .pt (or set env)") + ap.add_argument("--clips", default="0,1,5,10,15,20,24", help="comma clip ids") + ap.add_argument("--per-clip", type=int, default=3, help="frames per clip") + ap.add_argument("--conf", type=float, default=0.25) + args = ap.parse_args(argv) + + ckpt = args.checkpoint or os.environ.get("TABVISION_GUITAR_YOLO_CHECKPOINT") + yolo = YoloOBBBackend(checkpoint_path=ckpt, conf=args.conf, device="cpu") + fb = KeypointFretboardBackend(backend=yolo) + frames_dir = args.root / "tablature_frames" + + print(f"checkpoint: {yolo.checkpoint_path}") + print( + f"{'frame':>8} {'neck':>5} {'conf':>5} {'frets':>5} {'nut':>4} " + f"{'H.conf':>6} {'quad_w%':>7} {'quad_h%':>7}" + ) + neck_hits = 0 + good_quad = 0 + total = 0 + for cid in args.clips.split(","): + paths = sorted(frames_dir.glob(f"{cid}_*.png"), key=lambda p: int(p.stem.split("_")[1])) + for p in paths[:: max(1, len(paths) // args.per_clip)][: args.per_clip]: + frame = cv2.imread(str(p)) + if frame is None: + continue + total += 1 + h, w = frame.shape[:2] + preds = yolo.predict_all(frame) + neck = preds.best_neck() + homography = fb.detect(frame, None) # GuitarBBox unused by keypoint backend + qw, qh = _quad_extent(homography.H, w, h) if homography.confidence > 0 else (0.0, 0.0) + if neck is not None: + neck_hits += 1 + # "good" = localized strip: covers <85% of at least one axis and H is confident + if homography.confidence > 0 and (qw < 0.85 or qh < 0.85): + good_quad += 1 + print( + f"{p.stem:>8} {('Y' if neck else 'n'):>5} " + f"{(neck.confidence if neck else 0):>5.2f} {len(preds.frets):>5} " + f"{('Y' if preds.best_nut() else 'n'):>4} {homography.confidence:>6.2f} " + f"{qw:>7.2f} {qh:>7.2f}" + ) + + print( + f"\nneck detected {neck_hits}/{total} ({neck_hits / max(1, total):.0%}); " + f"localized (non-full-frame) homography {good_quad}/{total} " + f"({good_quad / max(1, total):.0%})" + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tabvision/scripts/train/_cpu_train_guitar_obb.py b/tabvision/scripts/train/_cpu_train_guitar_obb.py new file mode 100644 index 0000000..55ea173 --- /dev/null +++ b/tabvision/scripts/train/_cpu_train_guitar_obb.py @@ -0,0 +1,85 @@ +"""Robust local-CPU YOLO-OBB training run (v1.1 chunk-2). + +Standalone so a background launch survives turn boundaries and any error lands in the log +rather than vanishing. Saves periodic checkpoints (save_period) so a mid-run death costs at +most a few epochs, and copies the best (or newest) weights to the stable runtime path. + +Usage: python -m scripts.train._cpu_train_guitar_obb [--epochs N] [--batch N] [--resume] +""" + +from __future__ import annotations + +import argparse +import shutil +import sys +import traceback +from pathlib import Path + +DATA = r"C:\Users\patri\.tabvision\data\datasets\roboflow-b101-guitar-3-4efcd-v2\data.yaml" +PROJECT = r"C:\Users\patri\.tabvision\data\models\runs" +NAME = "guitar-obb-finetune" +STABLE = Path(r"C:\Users\patri\.tabvision\data\models\guitar-yolo-obb-finetuned.pt") + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--epochs", type=int, default=50) + ap.add_argument("--batch", type=int, default=8) + ap.add_argument("--patience", type=int, default=15) + ap.add_argument("--resume", action="store_true") + args = ap.parse_args() + + from ultralytics import YOLO + + run_dir = Path(PROJECT) / NAME + ckpt_last = run_dir / "weights" / "last.pt" + if args.resume and ckpt_last.exists(): + print(f"[train] resuming from {ckpt_last}", flush=True) + model = YOLO(str(ckpt_last)) + train_kwargs = {"resume": True} + else: + model = YOLO("yolo11n-obb.pt") + train_kwargs = { + "data": DATA, + "epochs": args.epochs, + "batch": args.batch, + "imgsz": 640, + "device": "cpu", + "seed": 0, + "deterministic": True, + "patience": args.patience, + "project": PROJECT, + "name": NAME, + "exist_ok": True, + "verbose": False, + "plots": False, + "workers": 0, + "cache": False, + "save_period": 5, + } + + try: + results = model.train(**train_kwargs) + except Exception: # noqa: BLE001 — capture any failure to the log + print("[train] TRAINING FAILED:\n" + traceback.format_exc(), flush=True) + return 1 + + best = Path(results.save_dir) / "weights" / "best.pt" + src = best if best.exists() else ckpt_last + if src.exists(): + STABLE.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, STABLE) + print(f"[train] DONE. weights {src} -> {STABLE}", flush=True) + try: + mv = model.val(data=DATA, split="val", device="cpu", verbose=False) + print(f"[train] VAL mAP50={mv.box.map50:.4f} mAP50-95={mv.box.map:.4f}", flush=True) + except Exception: # noqa: BLE001 + print("[train] val failed:\n" + traceback.format_exc(), flush=True) + else: + print(f"[train] NO weights found at {best} or {ckpt_last}", flush=True) + return 1 + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tabvision/tabvision/audio/highres.py b/tabvision/tabvision/audio/highres.py index 4d91c72..f7ea66e 100644 --- a/tabvision/tabvision/audio/highres.py +++ b/tabvision/tabvision/audio/highres.py @@ -134,6 +134,7 @@ def _load_model(self): # type: ignore[no-untyped-def] # instrument="guitar" selects the guitar architecture; checkpoint_path # overrides the weights (None → package default guitar-gaps.pth). + _ensure_utf8_stdio_for_dependency_logs() self._model = MidiTranscriptionModel( instrument="guitar", checkpoint_path=checkpoint_path, @@ -183,6 +184,23 @@ def _resample_if_needed(wav: np.ndarray, sr: int, target_sr: int) -> np.ndarray: return resample_poly(wav, up=up, down=down).astype(np.float32, copy=False) +def _ensure_utf8_stdio_for_dependency_logs() -> None: + """Avoid Windows cp1252 crashes from dependency status glyphs.""" + + import sys + + for stream in (sys.stdout, sys.stderr): + reconfigure = getattr(stream, "reconfigure", None) + if reconfigure is None: + continue + encoding = (getattr(stream, "encoding", None) or "").lower().replace("-", "") + if encoding != "utf8": + try: + reconfigure(encoding="utf-8", errors="replace") + except Exception: + pass + + def _parse_midi(midi_path: Path) -> list[AudioEvent]: """Parse a MIDI file produced by hf_midi_transcription into AudioEvents. diff --git a/tabvision/tabvision/eval/manifest_builder.py b/tabvision/tabvision/eval/manifest_builder.py index 98f5dc1..5b3645b 100644 --- a/tabvision/tabvision/eval/manifest_builder.py +++ b/tabvision/tabvision/eval/manifest_builder.py @@ -109,6 +109,42 @@ def scan_guitarset( return entries +def scan_utaustin(root: Path) -> list[ClipEntry]: + """Scan the Kaggle/UT-Austin tablature dataset into eval clip entries. + + Expected layout:: + + /tablature_audio/.wav + /tablature_labels/.npy + /timestamps.csv + + All entries are validation entries because this corpus is NC eval evidence, + not training material for shipped weights. + """ + label_dir = root / "tablature_labels" + audio_dir = root / "tablature_audio" + if not label_dir.is_dir() or not audio_dir.is_dir() or not (root / "timestamps.csv").is_file(): + return [] + + entries: list[ClipEntry] = [] + for label_path in sorted(label_dir.glob("*.npy"), key=lambda p: int(p.stem)): + audio_path = audio_dir / f"{label_path.stem}.wav" + if not audio_path.is_file(): + continue + entries.append( + ClipEntry( + id=f"utaustin/{label_path.stem}", + tier="clean_acoustic_single_line", + source="KaggleUTAustin", + split="validation", + media_path=str(audio_path.resolve()), + annotation_path=str(label_path.resolve()), + annotation_format="utaustin_tablature_npy", + ) + ) + return entries + + GUITAR_TECHS_VALIDATION_PLAYER = "03" # Stretch-goal articulations (SPEC §1.4 → v1.1). Skipped so the clean_electric @@ -394,6 +430,7 @@ def _refuse_synthetic_in_eval_splits(entries: Iterable[ClipEntry]) -> None: def build_manifest( *, guitarset_root: Path | None = None, + utaustin_root: Path | None = None, guitar_techs_root: Path | None = None, splits: tuple[str, ...] | None = None, max_clips_per_tier: int | None = None, @@ -410,6 +447,8 @@ def build_manifest( entries: list[ClipEntry] = [] if guitarset_root is not None: entries.extend(scan_guitarset(guitarset_root, validation_player=validation_player)) + if utaustin_root is not None: + entries.extend(scan_utaustin(utaustin_root)) if guitar_techs_root is not None: entries.extend(scan_guitar_techs(guitar_techs_root)) @@ -438,11 +477,17 @@ def main(argv: list[str] | None = None) -> int: default=None, help="GuitarSet root directory (with annotation/ and audio_mono-mic/)", ) + parser.add_argument( + "--utaustin", + type=Path, + default=None, + help="UT-Austin tablature dataset root (with tablature_audio/ and tablature_labels/)", + ) parser.add_argument( "--guitar-techs", type=Path, default=None, - help="Guitar-TECHS root directory (scanner is currently a stub)", + help="Guitar-TECHS root directory", ) parser.add_argument("--output", type=Path, required=True) parser.add_argument( @@ -482,8 +527,8 @@ def main(argv: list[str] | None = None) -> int: args = parser.parse_args(argv) - if args.guitarset is None and args.guitar_techs is None: - parser.error("specify at least one of --guitarset or --guitar-techs") + if args.guitarset is None and args.utaustin is None and args.guitar_techs is None: + parser.error("specify at least one of --guitarset, --utaustin, or --guitar-techs") splits_filter: tuple[str, ...] | None = None if args.splits: @@ -492,6 +537,7 @@ def main(argv: list[str] | None = None) -> int: try: entries = build_manifest( guitarset_root=args.guitarset, + utaustin_root=args.utaustin, guitar_techs_root=args.guitar_techs, splits=splits_filter, max_clips_per_tier=args.max_clips_per_tier, @@ -504,7 +550,7 @@ def main(argv: list[str] | None = None) -> int: if not entries: print( - "No clips discovered. Check --guitarset / --guitar-techs paths.", + "No clips discovered. Check --guitarset / --utaustin / --guitar-techs paths.", flush=True, ) return 1 @@ -519,6 +565,7 @@ def main(argv: list[str] | None = None) -> int: args.output.write_text( render_toml(entries, header_comment=header, data_root=args.data_root), encoding="utf-8", + newline="\n", ) print(f"Wrote {len(entries)} clips to {args.output}", flush=True) @@ -545,6 +592,7 @@ def main(argv: list[str] | None = None) -> int: "render_toml", "scan_guitar_techs", "scan_guitarset", + "scan_utaustin", "summarise_coverage", ] diff --git a/tabvision/tabvision/eval/parsers/__init__.py b/tabvision/tabvision/eval/parsers/__init__.py index 656e8a8..579ab59 100644 --- a/tabvision/tabvision/eval/parsers/__init__.py +++ b/tabvision/tabvision/eval/parsers/__init__.py @@ -13,7 +13,11 @@ """ # Built-in parsers — importing them registers their FORMAT_NAME. -from tabvision.eval.parsers import guitar_techs_midi, guitarset_jams # noqa: F401 +from tabvision.eval.parsers import ( # noqa: F401 + guitar_techs_midi, + guitarset_jams, + utaustin_tablature_npy, +) from tabvision.eval.parsers.registry import ( ParserFn, clear_parsers, diff --git a/tabvision/tabvision/eval/parsers/utaustin_tablature_npy.py b/tabvision/tabvision/eval/parsers/utaustin_tablature_npy.py new file mode 100644 index 0000000..e36859a --- /dev/null +++ b/tabvision/tabvision/eval/parsers/utaustin_tablature_npy.py @@ -0,0 +1,88 @@ +"""UT-Austin guitar-transcription label parser. + +The Kaggle/UT-Austin tablature dataset stores one ``.npy`` label file per +clip. Each frame has four finger slots with ``[active, fret, string]`` labels; +the companion ``timestamps.csv`` maps frame names to seconds. This parser uses +the same gold derivation as the v1.1 real-video probes: new finger placements +become note onsets, same-string simultaneous placements collapse to the highest +fret, and dataset strings are mapped to TabVision string indices with +``our_string_idx = 6 - their_string``. +""" + +from __future__ import annotations + +import csv +from pathlib import Path + +import numpy as np + +from tabvision.eval.parsers.registry import register_parser +from tabvision.types import GuitarConfig, TabEvent + +FORMAT_NAME = "utaustin_tablature_npy" + + +def _load_timestamps(root: Path) -> dict[str, float]: + with open(root / "timestamps.csv", newline="", encoding="utf-8") as fh: + return {row["frame"]: float(row["timestamp"]) for row in csv.DictReader(fh)} + + +def parse( + annotation_path: str | Path, + cfg: GuitarConfig | None = None, + *, + default_dur: float = 0.3, +) -> list[TabEvent]: + """Parse a UT-Austin ``tablature_labels/.npy`` file into tab events.""" + + label_path = Path(annotation_path) + if cfg is None: + cfg = GuitarConfig() + + root = label_path.parent.parent + timestamps = _load_timestamps(root) + clip_id = label_path.stem + arr = np.load(label_path) + + gold: list[TabEvent] = [] + prev: set[tuple[int, int]] = set() + for frame_index in range(arr.shape[0]): + cur = { + (int(arr[frame_index, finger_index, 1]), int(arr[frame_index, finger_index, 2])) + for finger_index in range(arr.shape[1]) + if arr[frame_index, finger_index].any() + } + + highest_by_string: dict[int, int] = {} + for fret, their_string in cur - prev: + highest_by_string[their_string] = max(fret, highest_by_string.get(their_string, -1)) + + for their_string, fret in sorted(highest_by_string.items()): + string_idx = 6 - their_string + onset_s = timestamps.get(f"{clip_id}_{frame_index}.png") + if ( + onset_s is None + or not (0 <= string_idx < cfg.n_strings) + or not (0 <= fret <= cfg.max_fret) + ): + continue + gold.append( + TabEvent( + onset_s=onset_s, + duration_s=default_dur, + string_idx=string_idx, + fret=fret, + pitch_midi=cfg.tuning_midi[string_idx] + fret, + confidence=1.0, + ) + ) + prev = cur + + gold.sort(key=lambda event: (event.onset_s, event.string_idx, event.fret)) + return gold + + +register_parser(FORMAT_NAME, parse) + + +__all__ = ["FORMAT_NAME", "parse"] diff --git a/tabvision/tabvision/eval/runner.py b/tabvision/tabvision/eval/runner.py index f8009ad..8006e03 100644 --- a/tabvision/tabvision/eval/runner.py +++ b/tabvision/tabvision/eval/runner.py @@ -97,7 +97,7 @@ def _phase_debt(eval_root: str | Path) -> dict[str, object]: "phase_1_5": { "status": "optional_future", "gate": ( - "public/programmatic manifest completeness; hand-annotated user clips " + "public/programmatic manifest completeness; hand-annotated private clips " "removed_from_v1" ), "command": "tabvision-eval --manifest tabvision/data/eval/manifest.toml --check", diff --git a/tabvision/tabvision/fusion/playability.py b/tabvision/tabvision/fusion/playability.py index edc1fc8..8f27a61 100644 --- a/tabvision/tabvision/fusion/playability.py +++ b/tabvision/tabvision/fusion/playability.py @@ -80,6 +80,8 @@ def find_fingering_at(t: float, fingerings: Sequence[FrameFingering]) -> FrameFi best: FrameFingering | None = None best_dt = math.inf for f in fingerings: + if f.homography_confidence <= 0.0: + continue if f.finger_pos_logits is None or f.finger_pos_logits.size == 0: continue if not (f.finger_pos_logits != 0).any(): @@ -122,7 +124,8 @@ def emission_cost( if fingering is not None: marginal = fingering.marginal_string_fret() p = float(marginal[candidate.string_idx, candidate.fret]) - cost += lambda_vision * (-math.log(max(p, VISION_FLOOR))) + vision_weight = lambda_vision * max(0.0, min(1.0, fingering.homography_confidence)) + cost += vision_weight * (-math.log(max(p, VISION_FLOOR))) cost += LOW_FRET_BIAS * candidate.fret if candidate.fret == 0: diff --git a/tabvision/tabvision/fusion/vision_evidence.py b/tabvision/tabvision/fusion/vision_evidence.py new file mode 100644 index 0000000..2655472 --- /dev/null +++ b/tabvision/tabvision/fusion/vision_evidence.py @@ -0,0 +1,209 @@ +"""Utilities for making noisy video evidence safe for fusion. + +The core fusion contract stays simple: callers still pass ``FrameFingering`` +objects. These helpers prepare those objects by choosing a canonical +orientation, voting over nearby frames, and dropping weak evidence before it +can pull a strong audio decode off course. +""" + +from __future__ import annotations + +import math +from collections.abc import Mapping, Sequence +from dataclasses import dataclass, replace + +import numpy as np + +from tabvision.fusion.candidates import candidate_positions +from tabvision.types import AudioEvent, FrameFingering, GuitarConfig + +EPS = 1e-12 +DEFAULT_N_FINGERS = 4 + + +@dataclass(frozen=True) +class Orientation: + """Canonical-axis flips to apply to a fingering posterior.""" + + name: str + flip_string: bool = False + flip_fret: bool = False + + +ORIENTATIONS: tuple[Orientation, ...] = ( + Orientation("none"), + Orientation("flip-fret", flip_fret=True), + Orientation("flip-string", flip_string=True), + Orientation("flip-both", flip_string=True, flip_fret=True), +) +ORIENTATION_BY_NAME: Mapping[str, Orientation] = {o.name: o for o in ORIENTATIONS} + + +def empty_fingering( + t: float, + cfg: GuitarConfig, + *, + n_fingers: int = DEFAULT_N_FINGERS, +) -> FrameFingering: + """Evidence-free fingering at ``t``.""" + + return FrameFingering( + t=t, + finger_pos_logits=np.zeros((n_fingers, cfg.n_strings, cfg.max_fret + 1), dtype=np.float64), + homography_confidence=0.0, + ) + + +def apply_orientation(logits: np.ndarray, orientation: Orientation) -> np.ndarray: + """Return a copy of ``logits`` with canonical axes flipped as requested.""" + + out = np.asarray(logits).copy() + if orientation.flip_string: + out = out[:, ::-1, :] + if orientation.flip_fret: + out = out[:, :, ::-1] + return out + + +def orient_fingering(fingering: FrameFingering, orientation: Orientation) -> FrameFingering: + """Apply ``orientation`` to a ``FrameFingering`` without mutating it.""" + + return replace( + fingering, + finger_pos_logits=apply_orientation(fingering.finger_pos_logits, orientation), + ) + + +def combine_fingerings( + fingerings: Sequence[FrameFingering], + cfg: GuitarConfig, + *, + t: float, +) -> FrameFingering: + """Average nearby frame posteriors into one onset-aligned fingering.""" + + usable = [ + f + for f in fingerings + if f.homography_confidence > 0.0 + and f.finger_pos_logits is not None + and f.finger_pos_logits.size > 0 + and (f.finger_pos_logits != 0).any() + ] + if not usable: + return empty_fingering(t, cfg) + + marginals = np.array([f.marginal_string_fret() for f in usable], dtype=np.float64) + avg = marginals.mean(axis=0) + total = float(avg.sum()) + if not math.isfinite(total) or total <= 0.0: + return empty_fingering(t, cfg, n_fingers=usable[0].finger_pos_logits.shape[0]) + + avg /= total + n_fingers = usable[0].finger_pos_logits.shape[0] + logits = np.full((n_fingers, cfg.n_strings, cfg.max_fret + 1), -30.0, dtype=np.float64) + logits[0] = np.log(np.maximum(avg, EPS)) + confidence = float(np.mean([f.homography_confidence for f in usable])) + return FrameFingering(t=t, finger_pos_logits=logits, homography_confidence=confidence) + + +def candidate_support(event: AudioEvent, fingering: FrameFingering, cfg: GuitarConfig) -> float: + """Probability mass on string/fret candidates compatible with ``event`` pitch.""" + + candidates = candidate_positions(event.pitch_midi, cfg) + if not candidates or fingering.homography_confidence <= 0.0: + return 0.0 + marginal = fingering.marginal_string_fret() + return float(sum(marginal[c.string_idx, c.fret] for c in candidates)) + + +def candidate_probabilities( + event: AudioEvent, fingering: FrameFingering, cfg: GuitarConfig +) -> list[tuple[int, int, float]]: + """Return pitch-compatible candidate probabilities sorted high to low.""" + + candidates = candidate_positions(event.pitch_midi, cfg) + if not candidates or fingering.homography_confidence <= 0.0: + return [] + marginal = fingering.marginal_string_fret() + probs = [(c.string_idx, c.fret, float(marginal[c.string_idx, c.fret])) for c in candidates] + probs.sort(key=lambda row: row[2], reverse=True) + return probs + + +def choose_orientation( + raw_by_event: Sequence[Sequence[FrameFingering]], + events: Sequence[AudioEvent], + cfg: GuitarConfig, +) -> tuple[Orientation, dict[str, float]]: + """Pick the axis orientation whose video mass best agrees with audio pitch. + + This uses only audio pitch candidates, not tab labels, so it is safe for + real clips where ground truth is unavailable. + """ + + scores: dict[str, float] = {} + for orientation in ORIENTATIONS: + score = 0.0 + for event, raw_fingerings in zip(events, raw_by_event, strict=False): + oriented = [orient_fingering(f, orientation) for f in raw_fingerings] + voted = combine_fingerings(oriented, cfg, t=event.onset_s) + support = candidate_support(event, voted, cfg) + score += math.log(max(support, EPS)) * max(0.05, voted.homography_confidence) + scores[orientation.name] = score + best = max(ORIENTATIONS, key=lambda o: scores[o.name]) + return best, scores + + +def gate_fingering_to_audio( + event: AudioEvent, + fingering: FrameFingering, + cfg: GuitarConfig, + *, + min_homography_confidence: float = 0.1, + min_candidate_support: float = 0.02, + min_best_ratio: float = 1.2, +) -> FrameFingering: + """Drop video evidence that is too weak to help this audio event.""" + + if fingering.homography_confidence < min_homography_confidence: + return empty_fingering(fingering.t, cfg, n_fingers=fingering.finger_pos_logits.shape[0]) + + probs = candidate_probabilities(event, fingering, cfg) + if not probs: + return empty_fingering(fingering.t, cfg, n_fingers=fingering.finger_pos_logits.shape[0]) + + support = sum(p for *_sf, p in probs) + if support < min_candidate_support: + return empty_fingering(fingering.t, cfg, n_fingers=fingering.finger_pos_logits.shape[0]) + + default = candidate_positions(event.pitch_midi, cfg)[0] + default_prob = next( + ( + p + for string_idx, fret, p in probs + if string_idx == default.string_idx and fret == default.fret + ), + 0.0, + ) + best_string, best_fret, best_prob = probs[0] + if (best_string, best_fret) != (default.string_idx, default.fret): + if best_prob < max(default_prob * min_best_ratio, EPS): + return empty_fingering(fingering.t, cfg, n_fingers=fingering.finger_pos_logits.shape[0]) + + return fingering + + +__all__ = [ + "Orientation", + "ORIENTATIONS", + "ORIENTATION_BY_NAME", + "apply_orientation", + "candidate_probabilities", + "candidate_support", + "choose_orientation", + "combine_fingerings", + "empty_fingering", + "gate_fingering_to_audio", + "orient_fingering", +] diff --git a/tabvision/tests/unit/test_annotate_storage.py b/tabvision/tests/unit/test_annotate_storage.py index 2de5683..f023692 100644 --- a/tabvision/tests/unit/test_annotate_storage.py +++ b/tabvision/tests/unit/test_annotate_storage.py @@ -12,7 +12,7 @@ def test_clip_id_strips_extensions_and_normalises_special_chars(): - assert storage.clip_id("/path/to/training-01.MOV") == "training-01" + assert storage.clip_id("/path/to/public-clip-01.MOV") == "public-clip-01" assert storage.clip_id("clip with spaces.mp4") == "clip-with-spaces" assert storage.clip_id("foo!!bar.mov") == "foo-bar" diff --git a/tabvision/tests/unit/test_eval_manifest.py b/tabvision/tests/unit/test_eval_manifest.py index b4fd0d4..d836257 100644 --- a/tabvision/tests/unit/test_eval_manifest.py +++ b/tabvision/tests/unit/test_eval_manifest.py @@ -187,3 +187,16 @@ def test_synthetic_source_allowed_in_train_split(tmp_path: Path) -> None: result = validate_manifest(manifest) assert not any(item.code == "SYNTHETIC_IN_EVAL_SPLIT" for item in result.items) + + +def test_guitartechs_highres_smoke_manifest_is_valid() -> None: + manifest = ( + Path(__file__).resolve().parents[2] / "data" / "eval" / "guitartechs_highres_smoke.toml" + ) + + result = validate_manifest(manifest) + + assert result.passed + assert result.clip_count == 1 + assert result.present_tiers == ["clean_electric"] + assert result.clip_ids == ["guitar-techs/P1_chords/midi/midi_Set1_aug"] diff --git a/tabvision/tests/unit/test_manifest_builder.py b/tabvision/tests/unit/test_manifest_builder.py index 895daf7..d70dea0 100644 --- a/tabvision/tests/unit/test_manifest_builder.py +++ b/tabvision/tests/unit/test_manifest_builder.py @@ -16,6 +16,7 @@ render_toml, scan_guitar_techs, scan_guitarset, + scan_utaustin, summarise_coverage, ) @@ -42,6 +43,20 @@ def _make_guitarset_layout( (audio_dir / f"{track_id}_mic.wav").write_bytes(b"") +def _make_utaustin_layout(root: Path, clip_ids: list[str]) -> None: + label_dir = root / "tablature_labels" + audio_dir = root / "tablature_audio" + label_dir.mkdir(parents=True, exist_ok=True) + audio_dir.mkdir(parents=True, exist_ok=True) + (root / "timestamps.csv").write_text( + "frame,timestamp\n" + "\n".join(f"{clip_id}_0.png,0.0" for clip_id in clip_ids) + "\n", + encoding="utf-8", + ) + for clip_id in clip_ids: + (audio_dir / f"{clip_id}.wav").write_bytes(b"") + (label_dir / f"{clip_id}.npy").write_bytes(b"fake-npy") + + def test_scan_guitarset_classifies_comp_and_solo(tmp_path: Path) -> None: _make_guitarset_layout( tmp_path, @@ -109,6 +124,25 @@ def test_scan_guitarset_returns_empty_for_partial_layout(tmp_path: Path) -> None assert scan_guitarset(tmp_path) == [] +def test_scan_utaustin_discovers_audio_label_pairs(tmp_path: Path) -> None: + _make_utaustin_layout(tmp_path, ["0", "1"]) + + entries = scan_utaustin(tmp_path) + + assert [entry.id for entry in entries] == ["utaustin/0", "utaustin/1"] + assert {entry.tier for entry in entries} == {"clean_acoustic_single_line"} + assert {entry.source for entry in entries} == {"KaggleUTAustin"} + assert {entry.split for entry in entries} == {"validation"} + assert {entry.annotation_format for entry in entries} == {"utaustin_tablature_npy"} + + +def test_scan_utaustin_skips_missing_audio(tmp_path: Path) -> None: + _make_utaustin_layout(tmp_path, ["0"]) + (tmp_path / "tablature_audio" / "0.wav").unlink() + + assert scan_utaustin(tmp_path) == [] + + def test_scan_guitar_techs_returns_empty_stub(tmp_path: Path) -> None: """Guitar-TECHS scanner is a stub until the dataset is acquired.""" assert scan_guitar_techs(tmp_path) == [] @@ -340,6 +374,15 @@ def test_build_manifest_splits_filter(tmp_path: Path) -> None: assert len(both) == 2 +def test_build_manifest_includes_utaustin_root(tmp_path: Path) -> None: + _make_utaustin_layout(tmp_path / "utaustin", ["0"]) + + entries = build_manifest(utaustin_root=tmp_path / "utaustin") + + assert [entry.id for entry in entries] == ["utaustin/0"] + assert entries[0].annotation_format == "utaustin_tablature_npy" + + def test_build_manifest_emits_synthetic_train_clip_ok(tmp_path: Path) -> None: """Training-split synthetic clips should pass the in-builder guard.""" # Use a custom ClipEntry-yielding scanner via the public function diff --git a/tabvision/tests/unit/test_melodic_prior.py b/tabvision/tests/unit/test_melodic_prior.py index 81f5369..6fb31cb 100644 --- a/tabvision/tests/unit/test_melodic_prior.py +++ b/tabvision/tests/unit/test_melodic_prior.py @@ -32,7 +32,7 @@ def test_apply_melodic_segment_prior_attaches_candidate_prior(): def test_long_scale_run_prior_prefers_compact_mid_neck_window(): - # E minor 3-note-per-string run from training-14. Low-fret equivalents + # E minor 3-note-per-string run. Low-fret equivalents # exist for most notes, but the compact playable shape is around frets 7-10. pitches = [47, 48, 50, 52, 53, 55, 57, 59, 60, 62, 64, 67, 69, 71, 72, 74] events = [_ev(index * 0.2, pitch) for index, pitch in enumerate(pitches)] diff --git a/tabvision/tests/unit/test_parser_utaustin_tablature_npy.py b/tabvision/tests/unit/test_parser_utaustin_tablature_npy.py new file mode 100644 index 0000000..6f591f0 --- /dev/null +++ b/tabvision/tests/unit/test_parser_utaustin_tablature_npy.py @@ -0,0 +1,40 @@ +"""Tests for the UT-Austin tablature ``.npy`` parser.""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np + +from tabvision.eval.parsers import get_parser +from tabvision.types import GuitarConfig + + +def test_utaustin_parser_converts_new_finger_placements_to_tab_events(tmp_path: Path) -> None: + root = tmp_path + label_dir = root / "tablature_labels" + label_dir.mkdir() + (root / "timestamps.csv").write_text( + "frame,timestamp\n0_0.png,0.0\n0_1.png,0.1\n0_2.png,0.2\n", + encoding="utf-8", + ) + + # Shape: frames, four fingers, [active, fret, their_string]. + # Frame 0 starts a note at their string 6 / fret 3 -> TabVision string 0. + # Frame 1 holds the same note -> no new event. + # Frame 2 adds their string 5 / fret 5 -> TabVision string 1. + labels = np.zeros((3, 4, 3), dtype=np.float32) + labels[0, 0] = [1, 3, 6] + labels[1, 0] = [1, 3, 6] + labels[2, 0] = [1, 3, 6] + labels[2, 1] = [1, 5, 5] + np.save(label_dir / "0.npy", labels) + + parser = get_parser("utaustin_tablature_npy") + events = parser(label_dir / "0.npy", GuitarConfig()) + + assert [(event.onset_s, event.string_idx, event.fret) for event in events] == [ + (0.0, 0, 3), + (0.2, 1, 5), + ] + assert [event.pitch_midi for event in events] == [43, 50] diff --git a/tabvision/tests/unit/test_playability.py b/tabvision/tests/unit/test_playability.py index 745767e..fe7a962 100644 --- a/tabvision/tests/unit/test_playability.py +++ b/tabvision/tests/unit/test_playability.py @@ -195,6 +195,18 @@ def test_find_fingering_at_skips_empty_logits(): assert chosen is real +def test_find_fingering_at_skips_zero_confidence_logits(): + """Nonzero floor logits with zero homography confidence are no evidence.""" + empty = FrameFingering( + t=0.5, + finger_pos_logits=np.full((4, 6, 25), -10.0), + homography_confidence=0.0, + ) + real = _peaked_fingering(t=2.0, target_string=2, target_fret=7) + chosen = find_fingering_at(0.6, [empty, real]) + assert chosen is real + + def test_find_fingering_at_returns_none_when_all_empty(): empty = FrameFingering( t=0.5, diff --git a/tabvision/tests/unit/test_v1_1_audio_alignment_probe.py b/tabvision/tests/unit/test_v1_1_audio_alignment_probe.py new file mode 100644 index 0000000..ff8f8ea --- /dev/null +++ b/tabvision/tests/unit/test_v1_1_audio_alignment_probe.py @@ -0,0 +1,133 @@ +"""Tests for the v1.1 UT-Austin audio alignment probe helpers.""" + +from __future__ import annotations + +import pytest + +from scripts.eval.v1_1_audio_alignment_probe import ( + count_pitch_time_matches, + estimate_audio_alignment, + estimate_global_alignment, + events_from_json, + events_to_json, + score_events, + shift_audio_events, +) +from tabvision.types import AudioEvent, GuitarConfig, TabEvent + + +def _audio(t: float, pitch: int) -> AudioEvent: + return AudioEvent( + onset_s=t, + offset_s=t + 0.2, + pitch_midi=pitch, + velocity=0.8, + confidence=0.9, + ) + + +def _tab(t: float, string_idx: int, fret: int, pitch: int) -> TabEvent: + return TabEvent( + onset_s=t, + duration_s=0.2, + string_idx=string_idx, + fret=fret, + pitch_midi=pitch, + confidence=1.0, + ) + + +def test_estimate_audio_alignment_finds_pitch_and_time_shift() -> None: + gold = [_tab(1.50, 5, 0, 64), _tab(2.00, 5, 1, 65)] + events = [_audio(0.25, 65), _audio(0.75, 66)] + + choice, scores = estimate_audio_alignment( + events, + gold, + tolerance_s=0.05, + max_abs_pitch_shift=2, + max_abs_time_shift_s=2.0, + time_step_s=0.25, + ) + + assert choice.pitch_shift == -1 + assert choice.time_shift_s == pytest.approx(1.25) + assert choice.matches == 2 + assert scores[(-1, 1.25)] == 2 + + +def test_count_pitch_time_matches_is_greedy_one_to_one() -> None: + gold = [_tab(1.0, 5, 0, 64)] + events = [_audio(1.0, 64), _audio(1.01, 64)] + + matches = count_pitch_time_matches( + events, + gold, + pitch_shift=0, + time_shift_s=0.0, + tolerance_s=0.05, + ) + + assert matches == 1 + + +def test_estimate_global_alignment_sums_clip_score_maps() -> None: + gold_a = [_tab(1.50, 5, 0, 64)] + gold_b = [_tab(2.50, 5, 1, 65)] + events_a = [_audio(0.25, 65)] + events_b = [_audio(1.25, 66)] + + _choice_a, scores_a = estimate_audio_alignment( + events_a, + gold_a, + tolerance_s=0.05, + max_abs_pitch_shift=2, + max_abs_time_shift_s=2.0, + time_step_s=0.25, + ) + _choice_b, scores_b = estimate_audio_alignment( + events_b, + gold_b, + tolerance_s=0.05, + max_abs_pitch_shift=2, + max_abs_time_shift_s=2.0, + time_step_s=0.25, + ) + + choice = estimate_global_alignment([scores_a, scores_b]) + + assert choice.pitch_shift == -1 + assert choice.time_shift_s == pytest.approx(1.25) + assert choice.matches == 2 + + +def test_event_cache_json_round_trip_keeps_score_fields() -> None: + events = [_audio(0.25, 65)] + + restored = events_from_json(events_to_json(events)) + + assert restored == events + + +def test_score_events_reports_perfect_when_audio_matches_gold() -> None: + cfg = GuitarConfig() + gold = [_tab(1.0, 5, 0, 64)] + events = [_audio(1.0, 64)] + + score = score_events(events, gold, cfg=cfg, onset_tolerance_s=0.05) + + assert score.onset_f1 == pytest.approx(1.0) + assert score.pitch_f1 == pytest.approx(1.0) + assert score.tab_f1 == pytest.approx(1.0) + assert score.oracle_tab_f1 == pytest.approx(1.0) + + +def test_shift_audio_events_does_not_mutate_original() -> None: + original = [_audio(0.25, 65)] + + shifted = shift_audio_events(original, pitch_shift=-1, time_shift_s=1.25) + + assert shifted[0].pitch_midi == 64 + assert shifted[0].onset_s == pytest.approx(1.50) + assert original[0].pitch_midi == 65 + assert original[0].onset_s == pytest.approx(0.25) diff --git a/tabvision/tests/unit/test_v1_1_second_corpus_probe.py b/tabvision/tests/unit/test_v1_1_second_corpus_probe.py new file mode 100644 index 0000000..d546f9d --- /dev/null +++ b/tabvision/tests/unit/test_v1_1_second_corpus_probe.py @@ -0,0 +1,127 @@ +"""Tests for the v1.1 cached/resumable second-corpus composite-eval runner.""" + +from __future__ import annotations + +import os +from pathlib import Path + +from scripts.eval.v1_1_second_corpus_probe import ( + CachingPredictor, + tabevents_from_json, + tabevents_to_json, +) +from tabvision.types import SessionConfig, TabEvent + + +def _tab(t: float, string_idx: int, fret: int, pitch: int) -> TabEvent: + return TabEvent( + onset_s=t, + duration_s=0.2, + string_idx=string_idx, + fret=fret, + pitch_midi=pitch, + confidence=1.0, + techniques=("slide",), + ) + + +def _make_media(tmp_path: Path, name: str = "clip.wav") -> Path: + media = tmp_path / name + media.write_bytes(b"fake-audio") + return media + + +class _CountingPredictor: + """Fake base predictor: returns a fixed event list and counts invocations.""" + + def __init__(self, events: list[TabEvent]) -> None: + self.events = events + self.calls = 0 + + def __call__(self, media_path: Path, session: SessionConfig) -> list[TabEvent]: + self.calls += 1 + return list(self.events) + + +def test_tabevents_json_round_trip_preserves_all_fields() -> None: + events = [_tab(1.0, 5, 0, 64), _tab(2.0, 4, 3, 53)] + + restored = tabevents_from_json(tabevents_to_json(events)) + + assert restored == events + + +def test_caching_predictor_computes_once_then_reads_cache(tmp_path: Path) -> None: + media = _make_media(tmp_path) + base = _CountingPredictor([_tab(1.0, 5, 0, 64)]) + predictor = CachingPredictor( + base, + cache_dir=tmp_path / "cache", + key_fields={"backend": "highres", "position_prior": "none"}, + ) + + first = predictor(media, SessionConfig()) + second = predictor(media, SessionConfig()) + + assert base.calls == 1 # second call served from disk cache + assert first == second == base.events + assert predictor.cache_misses == 1 + assert predictor.cache_hits == 1 + + +def test_caching_predictor_resumes_with_a_fresh_instance(tmp_path: Path) -> None: + media = _make_media(tmp_path) + cache_dir = tmp_path / "cache" + base_a = _CountingPredictor([_tab(1.0, 5, 0, 64)]) + CachingPredictor(base_a, cache_dir=cache_dir, key_fields={"backend": "highres"})( + media, SessionConfig() + ) + + base_b = _CountingPredictor([_tab(9.0, 0, 9, 99)]) # different output if recomputed + restored = CachingPredictor(base_b, cache_dir=cache_dir, key_fields={"backend": "highres"})( + media, SessionConfig() + ) + + assert base_b.calls == 0 # a brand-new process reuses the on-disk cache + assert restored == base_a.events + + +def test_caching_predictor_recomputes_when_media_changes(tmp_path: Path) -> None: + media = _make_media(tmp_path) + base = _CountingPredictor([_tab(1.0, 5, 0, 64)]) + predictor = CachingPredictor(base, cache_dir=tmp_path / "cache", key_fields={}) + + predictor(media, SessionConfig()) + # Bump mtime so the cached mtime_ns no longer matches. + stat = media.stat() + os.utime(media, ns=(stat.st_atime_ns, stat.st_mtime_ns + 1_000_000)) + predictor(media, SessionConfig()) + + assert base.calls == 2 + + +def test_caching_predictor_keys_on_settings(tmp_path: Path) -> None: + media = _make_media(tmp_path) + base = _CountingPredictor([_tab(1.0, 5, 0, 64)]) + cache_dir = tmp_path / "cache" + + CachingPredictor(base, cache_dir=cache_dir, key_fields={"prior": "none"})( + media, SessionConfig() + ) + CachingPredictor(base, cache_dir=cache_dir, key_fields={"prior": "guitarset-v1"})( + media, SessionConfig() + ) + + assert base.calls == 2 # different settings → different cache entry + + +def test_caching_predictor_refresh_cache_forces_recompute(tmp_path: Path) -> None: + media = _make_media(tmp_path) + base = _CountingPredictor([_tab(1.0, 5, 0, 64)]) + + CachingPredictor(base, cache_dir=tmp_path / "cache", key_fields={})(media, SessionConfig()) + CachingPredictor(base, cache_dir=tmp_path / "cache", key_fields={}, refresh_cache=True)( + media, SessionConfig() + ) + + assert base.calls == 2 diff --git a/tabvision/tests/unit/test_vision_evidence.py b/tabvision/tests/unit/test_vision_evidence.py new file mode 100644 index 0000000..19e26ea --- /dev/null +++ b/tabvision/tests/unit/test_vision_evidence.py @@ -0,0 +1,80 @@ +"""Unit tests for chunk-3 video-evidence robustness helpers.""" + +from __future__ import annotations + +import numpy as np + +from tabvision.fusion.vision_evidence import ( + ORIENTATION_BY_NAME, + candidate_support, + choose_orientation, + combine_fingerings, + gate_fingering_to_audio, +) +from tabvision.types import AudioEvent, FrameFingering, GuitarConfig + + +def _event(pitch: int = 69, t: float = 1.0) -> AudioEvent: + return AudioEvent( + onset_s=t, + offset_s=t + 0.25, + pitch_midi=pitch, + velocity=1.0, + confidence=1.0, + ) + + +def _peak(t: float, string_idx: int, fret: int, cfg: GuitarConfig) -> FrameFingering: + logits = np.full((4, cfg.n_strings, cfg.max_fret + 1), -12.0, dtype=np.float64) + logits[0, string_idx, fret] = 12.0 + return FrameFingering(t=t, finger_pos_logits=logits, homography_confidence=0.9) + + +def test_choose_orientation_uses_audio_pitch_candidates() -> None: + cfg = GuitarConfig() + event = _event(69) + # A4's audio default is high-E fret 5. This raw posterior is inverted on + # both canonical axes, so only flip-both places mass on a pitch-compatible + # string/fret cell. + raw = _peak(event.onset_s, string_idx=0, fret=19, cfg=cfg) + + orientation, scores = choose_orientation([[raw]], [event], cfg) + + assert orientation == ORIENTATION_BY_NAME["flip-both"] + assert scores["flip-both"] > scores["none"] + + +def test_combine_fingerings_votes_nearby_frame_posteriors() -> None: + cfg = GuitarConfig() + event = _event(69) + fingerings = [ + _peak(0.96, string_idx=5, fret=5, cfg=cfg), + _peak(1.02, string_idx=5, fret=5, cfg=cfg), + ] + + voted = combine_fingerings(fingerings, cfg, t=event.onset_s) + + assert voted.t == event.onset_s + assert voted.homography_confidence == 0.9 + assert candidate_support(event, voted, cfg) > 0.5 + + +def test_gate_drops_video_that_does_not_support_audio_pitch() -> None: + cfg = GuitarConfig() + event = _event(69) + wrong_pitch = _peak(event.onset_s, string_idx=0, fret=0, cfg=cfg) + + gated = gate_fingering_to_audio(event, wrong_pitch, cfg) + + assert gated.homography_confidence == 0.0 + assert not gated.finger_pos_logits.any() + + +def test_gate_keeps_decisive_pitch_compatible_video() -> None: + cfg = GuitarConfig() + event = _event(69) + non_default_a4 = _peak(event.onset_s, string_idx=3, fret=14, cfg=cfg) + + gated = gate_fingering_to_audio(event, non_default_a4, cfg) + + assert gated is non_default_a4 diff --git a/tabvision/yolo11n-obb.pt b/tabvision/yolo11n-obb.pt new file mode 100644 index 0000000..314fd1a Binary files /dev/null and b/tabvision/yolo11n-obb.pt differ diff --git a/test-data/existing/sample-video-10-tabs.txt b/test-data/existing/sample-video-10-tabs.txt deleted file mode 100644 index b19a9b4..0000000 --- a/test-data/existing/sample-video-10-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------------|----------------|----------------|--1-2-3-4------| -B|----------------|----------------|--------1-2-3-4-|---------------| -G|----------------|---------------1|-2-3-4----------|---------------| -D|----------------|------1-2-3-4---|----------------|---------------| -A|-------------1-2|-3-4------------|----------------|---------------| -E|----1-2-3-4-----|----------------|----------------|-------------X-| \ No newline at end of file diff --git a/test-data/existing/sample-video-11-tabs.txt b/test-data/existing/sample-video-11-tabs.txt deleted file mode 100644 index 18fdf84..0000000 --- a/test-data/existing/sample-video-11-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|--------0----0--|---0----0-----0-|----0----0-----0|----| -B|------1-----1---|--1----1-----1--|--1-----1----1--|----| -G|-----0-----0----|0-----0----0----|-0----0-----0---|----| -D|---------2------|----2----------2|-----------2----|----| -A|----3----------3|----------3-----|-----3----------|----| -E|----------------|----------------|----------------|--X-| \ No newline at end of file diff --git a/test-data/existing/sample-video-12-tabs.txt b/test-data/existing/sample-video-12-tabs.txt deleted file mode 100644 index ffec946..0000000 --- a/test-data/existing/sample-video-12-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------------|---------3------|--2----3--------| -B|----------------|--------------1-|--3-------------| -G|----2--2--4--2--|7---5-----------|--2-------------| -D|----------------|--------------2-|--0-------------| -A|----------------|---------2----3-|-------2--------| -E|----------------|---------3------|-------3------X-| \ No newline at end of file diff --git a/test-data/existing/sample-video-3-tabs.txt b/test-data/existing/sample-video-3-tabs.txt deleted file mode 100644 index 9440c0f..0000000 --- a/test-data/existing/sample-video-3-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|---------------| -B|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|---------------| -G|----------------|----------------|----------------|----------------|----------------|----------------|------0--3-5---0|-3-65----0--3-5-|--3-0-------0--3|-5---0-3-6-5----|0-3-5---3-0-----|----------------|---------------| -D|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|-------------X-| -A|----------------|-------1-3-----1|-43-------1-3---|1------------1--|3----1--43------|1--3--1---------|----------------|----------------|----------------|----------------|----------------|----------------|---------------| -E|----------------|----3--------3--|--------3-------|--3--------3----|---3----------3-|---------3------|----------------|----------------|----------------|----------------|----------------|----------------|---------------| \ No newline at end of file diff --git a/test-data/existing/sample-video-4-tabs.txt b/test-data/existing/sample-video-4-tabs.txt deleted file mode 100644 index 7262565..0000000 --- a/test-data/existing/sample-video-4-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-----------0----|--2--0--5---4---|------0------2--|0---7--5--------|--0-----12--9--5|---4--2------10-|10--9--5---7--5-|------------| -B|----------------|----------------|----------------|----------------|----------------|----------------|----------------|------------| -G|----------------|----------------|----------------|----------------|----------------|----------------|----------------|------------| -D|----------------|----------------|----------------|----------------|----------------|----------------|----------------|------------| -A|----------------|----------------|----------------|----------------|----------------|----------------|----------------|------------| -E|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------X-| \ No newline at end of file diff --git a/test-data/existing/sample-video-5-tabs.txt b/test-data/existing/sample-video-5-tabs.txt deleted file mode 100644 index eb9e19f..0000000 --- a/test-data/existing/sample-video-5-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----0---2---4---|5---7---9---11--|12--11--9---7---|5---4---2---0---|----| -B|----------------|----------------|----------------|----------------|----| -G|----------------|----------------|----------------|----------------|----| -D|----------------|----------------|----------------|----------------|----| -A|----------------|----------------|----------------|----------------|----| -E|----------------|----------------|----------------|----------------|--X-| \ No newline at end of file diff --git a/test-data/existing/sample-video-6-tabs.txt b/test-data/existing/sample-video-6-tabs.txt deleted file mode 100644 index 9525af7..0000000 --- a/test-data/existing/sample-video-6-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------------|----------------|----------------|----| -B|----1--1--8--8--|10--10-8--6--6--|5---5--3--3--1--|----| -G|----------------|----------------|----------------|----| -D|----------------|----------------|----------------|----| -A|----------------|----------------|----------------|----| -E|----------------|----------------|----------------|--X-| \ No newline at end of file diff --git a/test-data/existing/sample-video-7-tabs.txt b/test-data/existing/sample-video-7-tabs.txt deleted file mode 100644 index b07b9e8..0000000 --- a/test-data/existing/sample-video-7-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----0-----------|----------------|-------0------| -B|-------0--------|----------------|----0---------| -G|----------0-----|----------------|0-------------| -D|-------------0--|-------------0--|--------------| -A|----------------|0---------0-----|--------------| -E|----------------|----0--0--------|------------X-| \ No newline at end of file diff --git a/test-data/existing/sample-video-8-tabs.txt b/test-data/existing/sample-video-8-tabs.txt deleted file mode 100644 index 798e155..0000000 --- a/test-data/existing/sample-video-8-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------------|----------------|--------------| -B|----------------|----------------|--------------| -G|----------------|----------------|------------X-| -D|-------------2--|----------------|2-------------| -A|----2---5----0--|--5----2----5---|0----5--------| -E|----0---3-------|--3----0----3---|-----3--------| \ No newline at end of file diff --git a/test-data/existing/sample-video-9-tabs.txt b/test-data/existing/sample-video-9-tabs.txt deleted file mode 100644 index 3e4ab5c..0000000 --- a/test-data/existing/sample-video-9-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------0-----|----0-----------|--------------| -B|--------1-------|--1----------1--|-------0------| -G|-------0--------|2-----------2---|-----0--------| -D|-----2---------2|----------3-----|----0---------| -A|----3--------0--|--------3-------|--2-----------| -E|----------------|-------1--------|3-----------X-| \ No newline at end of file diff --git a/test-data/existing/sample-video-tabs.txt b/test-data/existing/sample-video-tabs.txt deleted file mode 100644 index cb30b33..0000000 --- a/test-data/existing/sample-video-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-------5-7-----7-|-8-----8-2-----2-|-0---------0-----|-----------------| -B|-----5-----5-----|---5-------3-----|---1---1-----1---|-0-1-1-----------| -G|---5---------5---|-----5-------2---|-----2---------2-|-0-2-2----2------| -D|-7-------6-------|-5-------4-------|-3---------------|-----------------| -A|-----------------|-----------------|-----------------|-2-0-0----0-/8-7-| -E|-----------------|-----------------|-----------------|-----------------| \ No newline at end of file diff --git a/test-data/training-tabs/README.md b/test-data/training-tabs/README.md deleted file mode 100644 index 8d68371..0000000 --- a/test-data/training-tabs/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# Training Tabs for TabVision - -These tabs are designed to be recorded on acoustic and/or electric guitar -for training and fine-tuning the TabVision transcription pipeline. See the -**Instrument** section below — a few tabs reach past the 12th fret and are -marked electric-only. - -## Recording Instructions - -1. Use a webcam at desk, guitar in lap -2. Standard tuning (EADGBE), no capo unless noted -3. Play to a metronome at the BPM indicated -4. Clean tone on electric (no distortion/effects) -5. Keep the guitar neck visible and roughly horizontal -6. Play each note clearly with good separation -7. Record each tab as a separate video file -8. Name files: `training-XX-acoustic.mp4` / `training-XX-electric.mp4` - -## Categories - -| # | Category | Tabs | Focus | -|---|----------|------|-------| -| 01-05 | Position Ambiguity | 5 | Same notes at different positions | -| 06-10 | Chord Varieties | 5 | Open, barre, power, jazz, partial | -| 11-15 | Single-Note Passages | 5 | Scales, arpeggios, chromatic runs | -| 16-20 | Edge Cases | 5 | Jumps, high frets, mixed techniques | - -## Instrument - -Each `-info.txt` has an `Instrument:` line indicating which guitars the tab -is playable on. Any tab reaching past fret 12 is marked **electric** (acoustic -necks typically join the body at fret 12, making higher positions impractical -without a cutaway). All other tabs are **acoustic or electric**. - -Electric-only tabs: **03, 04, 16, 17** (max frets 14, 14, 15, 17). - -## Tab File Format - -Each `-tabs.txt` file is the ground truth in standard ASCII tab format, -compatible with `evaluate_transcription.py`. The companion `-info.txt` has -BPM, description, instrument, and what the tab tests. diff --git a/test-data/training-tabs/training-01-info.txt b/test-data/training-tabs/training-01-info.txt deleted file mode 100644 index 1a6ba35..0000000 --- a/test-data/training-tabs/training-01-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Open vs Fretted Equivalent Notes -Category: Position Ambiguity -Instrument: acoustic or electric -BPM: 72 -Duration: ~20 seconds -Tests: Whether the system correctly identifies the SAME pitch played at two -different positions. Each note is played first open, then fretted equivalent. -E.g., open B string vs G string fret 4, open G vs D string fret 5. -This directly targets the position ambiguity weakness. diff --git a/test-data/training-tabs/training-01-tabs.txt b/test-data/training-tabs/training-01-tabs.txt deleted file mode 100644 index c17efd4..0000000 --- a/test-data/training-tabs/training-01-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|---0-------5----|---0-------5----|----------------|----------------| -B|-----0-------5--|----------------|---0-------5----|----------------| -G|----------------|-----0-------4--|-----0-------4--|---0-------5----| -D|----------------|----------------|----------------|-----0-------5--| -A|----------------|----------------|----------------|----------------| -E|----------------|----------------|----------------|----------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-02-info.txt b/test-data/training-tabs/training-02-info.txt deleted file mode 100644 index 38d2a46..0000000 --- a/test-data/training-tabs/training-02-info.txt +++ /dev/null @@ -1,8 +0,0 @@ -Title: A Minor Melody - Two Positions -Category: Position Ambiguity -Instrument: acoustic or electric -BPM: 80 -Duration: ~20 seconds -Tests: Same melody played entirely in open position (frets 0-3), then -repeated at 5th position (frets 5-8). The pitch detection should produce -identical MIDI notes but the position detection must resolve differently. diff --git a/test-data/training-tabs/training-02-tabs.txt b/test-data/training-tabs/training-02-tabs.txt deleted file mode 100644 index 1bc53b4..0000000 --- a/test-data/training-tabs/training-02-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-0---1---3---1--|-0--------------|-5---6---8---6--|-5--------------| -B|----------------|---1---3---1---0|----------------|---6---8---6---5| -G|----------------|----------------|----------------|----------------| -D|----------------|----------------|----------------|----------------| -A|----------------|----------------|----------------|----------------| -E|----------------|----------------|----------------|----------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-03-info.txt b/test-data/training-tabs/training-03-info.txt deleted file mode 100644 index 4b0fd50..0000000 --- a/test-data/training-tabs/training-03-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Same Pitch, Three Positions -Category: Position Ambiguity -Instrument: electric -BPM: 66 -Duration: ~25 seconds -Tests: Notes that can be played at 3 different string/fret combos. -For example, the note A4 (MIDI 69) can be played at e-string fret 5, -B-string fret 10, or G-string fret 14. Plays each option with pauses. -Extremely hard for audio-only; video hand position is the tiebreaker. diff --git a/test-data/training-tabs/training-03-tabs.txt b/test-data/training-tabs/training-03-tabs.txt deleted file mode 100644 index 94940b1..0000000 --- a/test-data/training-tabs/training-03-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|---5---------5--|------10--------|------14--------|----------------| -B|------5---------|---10------10---|---14-----------|---5------------| -G|---------5------|----------------|------14--------|------5---------| -D|----------------|----------------|----------------|----------5-----| -A|----------------|----------------|----------------|----------------| -E|----------------|----------------|----------------|----------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-04-info.txt b/test-data/training-tabs/training-04-info.txt deleted file mode 100644 index b18fb23..0000000 --- a/test-data/training-tabs/training-04-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: G Major Scale - Three Octaves -Category: Position Ambiguity -Instrument: electric -BPM: 80 -Duration: ~25 seconds -Tests: G major scale ascending through three octaves, each in a fixed -position on the neck. Position 2 (frets 2-5), position 7 (frets 7-10), -position 12 (frets 12-15). Tests whether the system tracks hand position -shifts and resolves each note to the correct region. diff --git a/test-data/training-tabs/training-04-tabs.txt b/test-data/training-tabs/training-04-tabs.txt deleted file mode 100644 index fb3173c..0000000 --- a/test-data/training-tabs/training-04-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------------|----------------|---7---8--10----|--10--12--------| -B|----------------|--------5---3---|--8-------------|----------------| -G|--------2---4---|---5--------4---|----------------|--12--14--------| -D|---2--4---5-----|----------------|----------------|--12--------14--| -A|--2--3------5---|----------------|----------------|--------12--14--| -E|-3-------3------|----------------|----------------|----------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-05-info.txt b/test-data/training-tabs/training-05-info.txt deleted file mode 100644 index 4e9d666..0000000 --- a/test-data/training-tabs/training-05-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Octave Shapes Across the Neck -Category: Position Ambiguity -Instrument: acoustic or electric -BPM: 72 -Duration: ~20 seconds -Tests: Octave pairs (same note, different octave) played on adjacent string -groups. E.g., E2 on low E open + E3 on D string fret 2. This targets the -known octave harmonic filter issue - the system must NOT filter real octaves -as harmonics. Also tests whether 2-note "chords" get correct positions. diff --git a/test-data/training-tabs/training-05-tabs.txt b/test-data/training-tabs/training-05-tabs.txt deleted file mode 100644 index 311b70a..0000000 --- a/test-data/training-tabs/training-05-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------------|----------------|----------------| -B|----------------|------5---------|---8------------| -G|------2---------|---5------7-----|------9---------| -D|---2------4-----|----------------|----------------| -A|----------------|----------------|----------------| -E|-0------2-------|--5------7------|--8------10-----| \ No newline at end of file diff --git a/test-data/training-tabs/training-06-info.txt b/test-data/training-tabs/training-06-info.txt deleted file mode 100644 index a4da46d..0000000 --- a/test-data/training-tabs/training-06-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Open Chord Progression -Category: Chord Varieties -Instrument: acoustic or electric -BPM: 80 -Duration: ~20 seconds -Tests: Standard open chords strummed cleanly, one strum per chord. -G - C - D - Em - Am - D - G. These are the most common chords and -should be high-confidence detections. Tests chord detection with all -6 strings ringing, including open strings (fret 0). diff --git a/test-data/training-tabs/training-06-tabs.txt b/test-data/training-tabs/training-06-tabs.txt deleted file mode 100644 index b6e32ee..0000000 --- a/test-data/training-tabs/training-06-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-3-------0-------2-------0-------0-------2-------3---------------| -B|-0-------1-------3-------0-------1-------3-------0---------------| -G|-0-------0-------2-------0-------2-------2-------0---------------| -D|-0-------2-------0-------2-------2-------0-------0---------------| -A|-2-------3---------------2-------0---------------2---------------| -E|-3-----------------------0-------------------------------3-------| \ No newline at end of file diff --git a/test-data/training-tabs/training-07-info.txt b/test-data/training-tabs/training-07-info.txt deleted file mode 100644 index 07f6941..0000000 --- a/test-data/training-tabs/training-07-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Barre Chord Progression -Category: Chord Varieties -Instrument: acoustic or electric -BPM: 72 -Duration: ~20 seconds -Tests: Full 6-string barre chords at various positions. F major (fret 1), -Bb major (fret 1 A-shape), B minor (fret 2), F# minor (fret 2 E-shape). -No open strings at all. Tests whether the system handles barre positions -where multiple strings share the same fret (the barre finger). diff --git a/test-data/training-tabs/training-07-tabs.txt b/test-data/training-tabs/training-07-tabs.txt deleted file mode 100644 index 64232ab..0000000 --- a/test-data/training-tabs/training-07-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-1-------1-------2-------2-------| -B|-1-------3-------3-------2-------| -G|-2-------3-------4-------2-------| -D|-3-------3-------4-------4-------| -A|-3-------1-------2-------4-------| -E|-1---------------2-------2-------| \ No newline at end of file diff --git a/test-data/training-tabs/training-08-info.txt b/test-data/training-tabs/training-08-info.txt deleted file mode 100644 index fc0e909..0000000 --- a/test-data/training-tabs/training-08-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Power Chords - Root 6 and Root 5 -Category: Chord Varieties -Instrument: acoustic or electric -BPM: 100 -Duration: ~15 seconds -Tests: 2-note and 3-note power chords (root + fifth, optionally + octave). -Played on low E root and A root. These are partial chords (not all 6 strings) -so tests the chord size limiting and muted string detection. Mute unused -strings with fretting hand. Faster tempo tests detection under quicker changes. diff --git a/test-data/training-tabs/training-08-tabs.txt b/test-data/training-tabs/training-08-tabs.txt deleted file mode 100644 index d811da1..0000000 --- a/test-data/training-tabs/training-08-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|----------------|----------------| -B|----------------|----------------| -G|----------------|------4------6--| -D|------2------4--|---4------6-----| -A|---2------4-----|--2------4------| -E|-0------3-------|----------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-09-info.txt b/test-data/training-tabs/training-09-info.txt deleted file mode 100644 index 83d5786..0000000 --- a/test-data/training-tabs/training-09-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Jazz Voicings - Drop 2 and Shell Chords -Category: Chord Varieties -Instrument: acoustic or electric -BPM: 66 -Duration: ~25 seconds -Tests: 3-4 note jazz chord voicings on middle strings (D/G/B/e). These use -unusual intervals and spread-out fingerings. Cmaj7, Dm7, G7, Cmaj7 in -drop-2 voicing. Tests chord detection with notes on non-adjacent strings -and higher frets. Slow tempo for clean articulation. diff --git a/test-data/training-tabs/training-09-tabs.txt b/test-data/training-tabs/training-09-tabs.txt deleted file mode 100644 index b8a4fcf..0000000 --- a/test-data/training-tabs/training-09-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|---0-----------1-------1-------0-------| -B|-----5-------1-------0-------5--------| -G|-------4---2-------0-------4----------| -D|---------2-------0-------3------------| -A|-3-----------5-------3-------3--------| -E|--------------------------------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-10-info.txt b/test-data/training-tabs/training-10-info.txt deleted file mode 100644 index c760912..0000000 --- a/test-data/training-tabs/training-10-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Mixed Chord Types - Open to Barre Transitions -Category: Chord Varieties -Instrument: acoustic or electric -BPM: 84 -Duration: ~20 seconds -Tests: Transitioning between open chords and barre chords in the same -progression. Am (open) - F (barre) - C (open) - G (open) - Bb (barre) - -Am (open). Tests the system's ability to handle anchor switching between -open position and barred positions within a single sequence. diff --git a/test-data/training-tabs/training-10-tabs.txt b/test-data/training-tabs/training-10-tabs.txt deleted file mode 100644 index 03feeef..0000000 --- a/test-data/training-tabs/training-10-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-0-------1-------0-------3-------1-------0-------| -B|-1-------1-------1-------0-------3-------1-------| -G|-2-------2-------0-------0-------3-------2-------| -D|-2-------3-------2-------0-------3-------2-------| -A|-0-------3-------3-------2-------1-------0-------| -E|---------1---------------3-----------------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-11-info.txt b/test-data/training-tabs/training-11-info.txt deleted file mode 100644 index bccc447..0000000 --- a/test-data/training-tabs/training-11-info.txt +++ /dev/null @@ -1,10 +0,0 @@ -Title: Pentatonic Scale - Box 1 (Am) -Category: Single-Note Passages -Instrument: acoustic or electric -BPM: 90 -Duration: ~15 seconds -Tests: A minor pentatonic in the classic box 1 position (frets 5-8). -All notes on one position, ascending then descending. This is a -fundamental pattern that should produce very high accuracy. Tests -whether the melodic segment correction works properly on a single-box -scale run. diff --git a/test-data/training-tabs/training-11-tabs.txt b/test-data/training-tabs/training-11-tabs.txt deleted file mode 100644 index dbe0c65..0000000 --- a/test-data/training-tabs/training-11-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-------------------5---8--|--8---5-------------------| -B|--------------5---8-------|--------8---5-------------| -G|---------5---7------------|-------------7---5--------| -D|----5---7-----------------|------------------7---5---| -A|---5--7-------------------|---------------------7--5-| -E|-5------------------------|------------------------5-| \ No newline at end of file diff --git a/test-data/training-tabs/training-12-info.txt b/test-data/training-tabs/training-12-info.txt deleted file mode 100644 index 6cb9c3f..0000000 --- a/test-data/training-tabs/training-12-info.txt +++ /dev/null @@ -1,10 +0,0 @@ -Title: Chromatic Exercise - All Frets 1-4 -Category: Single-Note Passages -Instrument: acoustic or electric -BPM: 80 -Duration: ~25 seconds -Tests: 1-2-3-4 chromatic exercise on each string, low E through high e. -This is the classic finger independence exercise. Every fret from 1-4 on -every string. Tests accurate detection of half-step intervals and -string-by-string tracking. Known difficulty: cross-string transitions -can confuse the slide correction algorithm. diff --git a/test-data/training-tabs/training-12-tabs.txt b/test-data/training-tabs/training-12-tabs.txt deleted file mode 100644 index d24c3e5..0000000 --- a/test-data/training-tabs/training-12-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|------------------------------------------|-1---2---3---4--| -B|-----------------------------------1---2--|-3---4----------| -G|--------------------------1---2---3---4---|----------------| -D|-----------------1---2---3---4------------|----------------| -A|--------1---2---3---4---------------------|----------------| -E|-1---2--3---4-----------------------------|----------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-13-info.txt b/test-data/training-tabs/training-13-info.txt deleted file mode 100644 index bb272e3..0000000 --- a/test-data/training-tabs/training-13-info.txt +++ /dev/null @@ -1,10 +0,0 @@ -Title: Arpeggio Patterns - Am, C, G, Em -Category: Single-Note Passages -Instrument: acoustic or electric -BPM: 72 -Duration: ~25 seconds -Tests: Fingerpicked arpeggios of common chords. Each chord is arpeggiated -low-to-high (p-i-m-a pattern). This is a KNOWN WEAKNESS - the system -struggles to distinguish arpeggiated chords from scalar runs. The audio -hears sequential notes; the video should show the chord hand shape held -while individual strings are plucked. diff --git a/test-data/training-tabs/training-13-tabs.txt b/test-data/training-tabs/training-13-tabs.txt deleted file mode 100644 index 99cdaa4..0000000 --- a/test-data/training-tabs/training-13-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|--------0------------0----|--------3-----------0-----| -B|------1---------1--------|------0---------0---------| -G|----2---------0----------|----0---------0-----------| -D|--2---------2------------|--0---------2-------------| -A|-0--------3--------------|-2--------2---------------| -E|-------------------------|--3--------0---------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-14-info.txt b/test-data/training-tabs/training-14-info.txt deleted file mode 100644 index 39cc663..0000000 --- a/test-data/training-tabs/training-14-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: Fast Scale Run - E Minor 3-Note-Per-String -Category: Single-Note Passages -Instrument: acoustic or electric -BPM: 140 -Duration: ~15 seconds -Tests: E minor scale using 3-note-per-string pattern (frets 7-10 region), -played at a fast tempo. Tests whether Basic Pitch can separate rapid notes -and whether the fusion engine handles fast sequences without dropping notes -or mis-assigning strings. Ascending only, one pass through. diff --git a/test-data/training-tabs/training-14-tabs.txt b/test-data/training-tabs/training-14-tabs.txt deleted file mode 100644 index a0f1c71..0000000 --- a/test-data/training-tabs/training-14-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-----------------------------7--8-10--| -B|------------------------8-10---------| -G|-------------------7--9-------------| -D|--------------7--9--10-------------- | -A|---------7--8--10--------------------| -E|-7--8-10-----------------------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-15-info.txt b/test-data/training-tabs/training-15-info.txt deleted file mode 100644 index 6f4dc53..0000000 --- a/test-data/training-tabs/training-15-info.txt +++ /dev/null @@ -1,10 +0,0 @@ -Title: String Skipping Melody -Category: Single-Note Passages -Instrument: acoustic or electric -BPM: 84 -Duration: ~20 seconds -Tests: Single notes that skip strings (e.g., low E then G, then B, then D). -This is NOT scalar motion - the intervals are wide and unpredictable. -Tests whether the system correctly handles non-adjacent string assignments -without the melodic segment correction incorrectly "fixing" them onto -one string. diff --git a/test-data/training-tabs/training-15-tabs.txt b/test-data/training-tabs/training-15-tabs.txt deleted file mode 100644 index ce862a3..0000000 --- a/test-data/training-tabs/training-15-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|---5-----------5---|---7-----------7---| -B|-----------3-------|----------5--------| -G|-------4-----------|-------6-----------| -D|-------------------|-------------------| -A|---3-----------3---|---5-----------5---| -E|-------0-----------|-------2-----------| \ No newline at end of file diff --git a/test-data/training-tabs/training-16-info.txt b/test-data/training-tabs/training-16-info.txt deleted file mode 100644 index 77f8c30..0000000 --- a/test-data/training-tabs/training-16-info.txt +++ /dev/null @@ -1,10 +0,0 @@ -Title: Wide Position Jumps -Category: Edge Cases -Instrument: electric -BPM: 66 -Duration: ~25 seconds -Tests: Notes that jump from low frets to high frets within a few beats. -Fret 2 -> fret 12, fret 1 -> fret 9, etc. Tests the hand position anchor -system's ability to handle abrupt shifts. The slide correction algorithm -must NOT merge these into slides - they are discrete jumps. -Play each note cleanly with a brief pause for the hand shift. diff --git a/test-data/training-tabs/training-16-tabs.txt b/test-data/training-tabs/training-16-tabs.txt deleted file mode 100644 index 84c25c6..0000000 --- a/test-data/training-tabs/training-16-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|---2------12------|---1------9--------|---3------15------| -B|------------------|-------------------|------------------| -G|------------------|-------------------|------------------| -D|------------------|-------------------|------------------| -A|------3------10---|------2------12----|------5------14---| -E|------------------|-------------------|------------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-17-info.txt b/test-data/training-tabs/training-17-info.txt deleted file mode 100644 index 27816a4..0000000 --- a/test-data/training-tabs/training-17-info.txt +++ /dev/null @@ -1,9 +0,0 @@ -Title: High Fret Melody (Frets 12-17) -Category: Edge Cases -Instrument: electric -BPM: 72 -Duration: ~20 seconds -Tests: Melody played entirely above the 12th fret on strings 1-3. -This is difficult for the video pipeline because frets are closer together -at the high end, making finger position harder to resolve. Also tests -whether the system handles 2-digit fret numbers correctly in tabs. diff --git a/test-data/training-tabs/training-17-tabs.txt b/test-data/training-tabs/training-17-tabs.txt deleted file mode 100644 index ae68f71..0000000 --- a/test-data/training-tabs/training-17-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-12--13--15--17--|-15--13--12-------|--15--17--15--13-|-12-------------| -B|-----------------|----------13--15-|-----------------|----15--13--12--| -G|-----------------|-----------------|-----------------|------------14--| -D|-----------------|-----------------|-----------------|----------------| -A|-----------------|-----------------|-----------------|----------------| -E|-----------------|-----------------|-----------------|----------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-18-info.txt b/test-data/training-tabs/training-18-info.txt deleted file mode 100644 index 8584621..0000000 --- a/test-data/training-tabs/training-18-info.txt +++ /dev/null @@ -1,11 +0,0 @@ -Title: Hammer-Ons and Pull-Offs -Category: Edge Cases -Instrument: acoustic or electric -BPM: 90 -Duration: ~20 seconds -Tests: Legato passages with hammer-ons (h) and pull-offs (p). The audio -will detect all notes but hammer-ons/pull-offs have different attack -characteristics than picked notes (softer onset). Tests whether the pitch -detector catches these weaker onsets. Play each hammer-on and pull-off -with enough force to be heard clearly. -Note: Tab notation uses h and p but ground truth only tracks fret numbers. diff --git a/test-data/training-tabs/training-18-tabs.txt b/test-data/training-tabs/training-18-tabs.txt deleted file mode 100644 index d8be82b..0000000 --- a/test-data/training-tabs/training-18-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-5--7--8---8--7--5-|--7--8--10--8--7---| -B|-------------------|-------------------| -G|-4--5--7---7--5--4-|--5--7--9---9--7--5| -D|-------------------|-------------------| -A|-5--7--8---8--7--5-|--7--8--10--8--7---| -E|-------------------|-------------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-19-info.txt b/test-data/training-tabs/training-19-info.txt deleted file mode 100644 index 2ab2976..0000000 --- a/test-data/training-tabs/training-19-info.txt +++ /dev/null @@ -1,10 +0,0 @@ -Title: Chord-to-Single-Note Transition -Category: Edge Cases -Instrument: acoustic or electric -BPM: 84 -Duration: ~20 seconds -Tests: Alternating between strummed chords and single-note fills. -Strum Am chord, play a 4-note fill, strum C chord, play a fill, etc. -This tests the system's ability to switch between chord mode and single-note -mode. The pre-filter and post-filter must correctly separate chord events -from single-note events. diff --git a/test-data/training-tabs/training-19-tabs.txt b/test-data/training-tabs/training-19-tabs.txt deleted file mode 100644 index 2a26567..0000000 --- a/test-data/training-tabs/training-19-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-0-----------0-1-3-|-0-----------3-1-0-|-3-----------0-2-3-|-0----------| -B|-1----------------|-1-----------------|-0-----------------|-1----------| -G|-2----------------|-0-----------------|-0-----------------|-2----------| -D|-2----------------|-2-----------------|-0-----------------|-2----------| -A|-0----------------|-3-----------------|-2-----------------|-0----------| -E|------------------|-------------------|-3-----------------|------------| \ No newline at end of file diff --git a/test-data/training-tabs/training-20-info.txt b/test-data/training-tabs/training-20-info.txt deleted file mode 100644 index 55b88cb..0000000 --- a/test-data/training-tabs/training-20-info.txt +++ /dev/null @@ -1,10 +0,0 @@ -Title: Open Strings and Muted Notes -Category: Edge Cases -Instrument: acoustic or electric -BPM: 72 -Duration: ~20 seconds -Tests: Mix of open strings (fret 0) and muted/dead notes (X). Open strings -are common in real playing but have no finger on the fretboard - the video -pipeline must recognize the absence of a finger. Muted notes produce a -percussive sound with no pitch. Both are edge cases for the fusion engine. -Pattern: play open string, play fretted note, play muted strum. diff --git a/test-data/training-tabs/training-20-tabs.txt b/test-data/training-tabs/training-20-tabs.txt deleted file mode 100644 index 7adf131..0000000 --- a/test-data/training-tabs/training-20-tabs.txt +++ /dev/null @@ -1,6 +0,0 @@ -e|-0---3---X---|-0---5---X---|-0---7---X---| -B|-0---3---X---|-0---5---X---|-0---8---X---| -G|-0---4---X---|-0---5---X---|-0---7---X---| -D|-0---5---X---|-0---7---X---|-0---9---X---| -A|-0---5---X---|-0---7---X---|-0---9---X---| -E|-0---3---X---|-0---5---X---|-0---7---X---| \ No newline at end of file