492 lines
15 KiB
Markdown
492 lines
15 KiB
Markdown
# Audio Pipeline Improvements Implementation Plan
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> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
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**Goal:** Improve audio scan accuracy with multi-layer extraction, AST/EAT models, and calibrated classifier.
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**Architecture:** All changes are in `core/audio_scan.py`. The embedding extraction functions gain new model-type branches (AST, EAT, multi-layer). The classifier gets a calibration wrapper. `_EMBED_MODELS` dict and `_get_w2v_model()` are extended. No UI changes needed — new models appear automatically in dropdowns.
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**Tech Stack:** torchaudio (existing), transformers (new dep), timm (new dep), sklearn.calibration (existing dep)
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---
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### Task 1: Add transformers and timm to requirements
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**Files:**
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- Modify: `requirements.txt`
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**Step 1: Add dependencies**
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Add after the `torchaudio` line in `requirements.txt`:
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```
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transformers>=4.30
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timm>=0.9
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```
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**Step 2: Verify install**
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Run: `pip install transformers timm`
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**Step 3: Commit**
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```bash
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git add requirements.txt
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git commit -m "deps: add transformers and timm for AST/EAT models"
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```
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---
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### Task 2: Multi-layer extraction for torchaudio models
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**Files:**
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- Modify: `core/audio_scan.py:50-58` (_EMBED_MODELS dict)
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- Modify: `core/audio_scan.py:96-100` (_embed_dim)
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- Modify: `core/audio_scan.py:189-205` (_extract_w2v_windows batch loop)
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- Modify: `core/audio_scan.py:278-293` (_extract_w2v_targeted batch loop)
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- Test: `tests/test_audio_scan.py`
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**Step 1: Write failing test**
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Add to `tests/test_audio_scan.py`:
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```python
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def test_embed_dim_multi_layer():
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from core.audio_scan import _embed_dim
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# Multi-layer models should report concatenated dimension
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assert _embed_dim("HUBERT_XLARGE_ML") == 5120
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assert _embed_dim("HUBERT_LARGE_ML") == 4096
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assert _embed_dim("HUBERT_BASE_ML") == 3072
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# Single-layer unchanged
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assert _embed_dim("HUBERT_XLARGE") == 1280
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```
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**Step 2: Run test to verify it fails**
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Run: `pytest tests/test_audio_scan.py::test_embed_dim_multi_layer -v`
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Expected: FAIL — `_embed_dim("HUBERT_XLARGE_ML")` returns 768 (default fallback)
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**Step 3: Add multi-layer entries to _EMBED_MODELS**
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In `core/audio_scan.py:50-58`, add after existing entries:
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```python
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_EMBED_MODELS = {
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"WAV2VEC2_BASE": 768,
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"WAV2VEC2_LARGE": 1024,
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"WAV2VEC2_LARGE_LV60K": 1024,
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"HUBERT_BASE": 768,
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"HUBERT_LARGE": 1024,
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"HUBERT_XLARGE": 1280,
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"BEATS": 768,
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# Multi-layer variants (4 quartile layers concatenated)
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"WAV2VEC2_BASE_ML": 3072, # 768 * 4
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"HUBERT_BASE_ML": 3072, # 768 * 4
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"HUBERT_LARGE_ML": 4096, # 1024 * 4
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"HUBERT_XLARGE_ML": 5120, # 1280 * 4
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}
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```
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**Step 4: Run test to verify it passes**
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Run: `pytest tests/test_audio_scan.py::test_embed_dim_multi_layer -v`
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Expected: PASS
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**Step 5: Add helper to resolve base model and layer indices**
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Add after `_embed_dim()` (around line 101):
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```python
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def _ml_config(model_name: str) -> tuple[str, list[int]] | None:
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"""If model_name is a multi-layer variant, return (base_model, layer_indices).
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Returns None for single-layer models.
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Layer indices are 0-based into the list returned by extract_features().
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"""
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if not model_name.endswith("_ML"):
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return None
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base = model_name[:-3] # strip "_ML"
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if base not in _EMBED_MODELS:
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return None
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# torchaudio layer counts: BASE=12, LARGE=24, XLARGE=48
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layer_counts = {
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"WAV2VEC2_BASE": 12, "WAV2VEC2_LARGE": 24, "WAV2VEC2_LARGE_LV60K": 24,
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"HUBERT_BASE": 12, "HUBERT_LARGE": 24, "HUBERT_XLARGE": 48,
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}
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n = layer_counts.get(base)
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if n is None:
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return None
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# Select 4 layers at quartile boundaries (1-indexed quartiles, 0-indexed list)
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indices = [n // 4 - 1, n // 2 - 1, 3 * n // 4 - 1, n - 1]
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return base, indices
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```
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**Step 6: Write test for _ml_config**
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```python
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def test_ml_config():
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from core.audio_scan import _ml_config
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assert _ml_config("HUBERT_XLARGE") is None
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base, layers = _ml_config("HUBERT_XLARGE_ML")
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assert base == "HUBERT_XLARGE"
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assert layers == [11, 23, 35, 47]
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base, layers = _ml_config("HUBERT_BASE_ML")
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assert base == "HUBERT_BASE"
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assert layers == [2, 5, 8, 11]
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```
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Run: `pytest tests/test_audio_scan.py::test_ml_config -v`
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Expected: PASS
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**Step 7: Modify _get_w2v_model to resolve ML base names**
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In `_get_w2v_model()` (line 68), before loading, strip `_ML` suffix:
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```python
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def _get_w2v_model(model_name: str | None = None):
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"""Lazy-load an embedding model. Reloads if model_name differs from cached."""
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global _w2v_model, _w2v_device, _w2v_model_name
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if model_name is None:
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model_name = _DEFAULT_EMBED_MODEL
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# Multi-layer variants use the same base model
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ml = _ml_config(model_name)
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load_name = ml[0] if ml else model_name
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if _w2v_model is None or _w2v_model_name != load_name:
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import torch
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_w2v_device = "cuda" if torch.cuda.is_available() else "cpu"
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if load_name == "BEATS":
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... # existing BEATs code unchanged
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else:
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import torchaudio
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bundle = getattr(torchaudio.pipelines, load_name)
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_w2v_model = bundle.get_model().to(_w2v_device)
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_w2v_model.eval()
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_w2v_model_name = load_name
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_log(f"audio_scan: {load_name} loaded on {_w2v_device}")
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return _w2v_model, _w2v_device
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```
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**Step 8: Modify extraction to use extract_features for ML models**
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In `_extract_w2v_windows` (line 197-204), change the batch inference block:
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```python
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with torch.no_grad():
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waveforms = torch.from_numpy(np.stack(chunks)).float().to(device)
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if is_beats:
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padding_mask = torch.zeros_like(waveforms, dtype=torch.bool)
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features, _ = model.extract_features(waveforms, padding_mask=padding_mask)
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batch_emb = features.mean(dim=1).cpu().numpy()
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elif ml_cfg is not None:
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all_layers, _ = model.extract_features(waveforms)
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selected = [all_layers[i].mean(dim=1) for i in ml_cfg[1]]
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batch_emb = torch.cat(selected, dim=1).cpu().numpy()
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else:
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features, _ = model(waveforms)
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batch_emb = features.mean(dim=1).cpu().numpy()
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embeddings.append(batch_emb)
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```
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Where `ml_cfg = _ml_config(model_name)` is computed once before the loop.
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Apply the same change to `_extract_w2v_targeted` (line 285-292).
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**Step 9: Run all tests**
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Run: `pytest tests/ -v`
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Expected: All pass
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**Step 10: Commit**
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```bash
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git add core/audio_scan.py tests/test_audio_scan.py
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git commit -m "feat: multi-layer extraction for HuBERT/Wav2Vec2 models"
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```
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---
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### Task 3: AST model integration
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**Files:**
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- Modify: `core/audio_scan.py:50-65` (_EMBED_MODELS, add AST entries)
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- Modify: `core/audio_scan.py:68-93` (_get_w2v_model, add AST branch)
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- Modify: `core/audio_scan.py:189-205` (_extract_w2v_windows, add AST branch)
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- Modify: `core/audio_scan.py:278-293` (_extract_w2v_targeted, add AST branch)
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- Test: `tests/test_audio_scan.py`
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**Step 1: Write failing test**
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```python
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def test_embed_dim_ast():
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from core.audio_scan import _embed_dim
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assert _embed_dim("AST") == 768
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assert _embed_dim("AST_ML") == 3072
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```
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Run: `pytest tests/test_audio_scan.py::test_embed_dim_ast -v`
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Expected: FAIL
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**Step 2: Add AST entries to _EMBED_MODELS**
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```python
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"AST": 768,
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"AST_ML": 3072, # 768 * 4
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```
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**Step 3: Add AST to _ml_config layer counts**
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AST has 12 transformer layers + 1 embedding layer = 13 hidden states. Use layers [3, 6, 9, 12] (0-indexed) for quartiles.
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```python
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layer_counts = {
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...existing...
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"AST": 12,
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}
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```
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**Step 4: Add AST feature extractor cache**
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Add module-level globals near existing `_w2v_model`:
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```python
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_ast_feature_extractor = None
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```
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**Step 5: Add AST loading branch in _get_w2v_model**
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```python
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elif load_name == "AST":
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from transformers import ASTModel
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_w2v_model = ASTModel.from_pretrained(
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"MIT/ast-finetuned-audioset-10-10-0.4593"
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).to(_w2v_device)
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global _ast_feature_extractor
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if _ast_feature_extractor is None:
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from transformers import ASTFeatureExtractor
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_ast_feature_extractor = ASTFeatureExtractor.from_pretrained(
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"MIT/ast-finetuned-audioset-10-10-0.4593"
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)
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```
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**Step 6: Add AST inference branch in extraction functions**
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In `_extract_w2v_windows` and `_extract_w2v_targeted`, add a branch for AST models:
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```python
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elif is_ast:
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# AST uses its own feature extractor for mel spectrogram
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inputs = _ast_feature_extractor(
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list(chunks_np), sampling_rate=sr, return_tensors="pt",
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padding=True,
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)
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input_values = inputs.input_values.to(device)
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out = model(input_values, output_hidden_states=ml_cfg is not None)
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if ml_cfg is not None:
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selected = [out.hidden_states[i].mean(dim=1) for i in ml_cfg[1]]
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batch_emb = torch.cat(selected, dim=1).cpu().numpy()
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else:
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batch_emb = out.last_hidden_state.mean(dim=1).cpu().numpy()
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```
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Where `is_ast = (model_name or _DEFAULT_EMBED_MODEL) in ("AST", "AST_ML")` and `chunks_np` is the list of raw numpy audio arrays (not stacked tensor).
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**Step 7: Run all tests**
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Run: `pytest tests/ -v`
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Expected: All pass
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**Step 8: Commit**
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```bash
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git add core/audio_scan.py tests/test_audio_scan.py
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git commit -m "feat: add AST (Audio Spectrogram Transformer) embedding model"
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```
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---
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### Task 4: EAT model integration
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**Files:**
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- Modify: `core/audio_scan.py:50-65` (_EMBED_MODELS, add EAT entry)
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- Modify: `core/audio_scan.py:68-93` (_get_w2v_model, add EAT branch)
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- Modify: `core/audio_scan.py:189-205` (_extract_w2v_windows, add EAT branch)
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- Modify: `core/audio_scan.py:278-293` (_extract_w2v_targeted, add EAT branch)
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- Test: `tests/test_audio_scan.py`
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**Step 1: Write failing test**
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```python
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def test_embed_dim_eat():
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from core.audio_scan import _embed_dim
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assert _embed_dim("EAT") == 768
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```
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**Step 2: Add EAT entry to _EMBED_MODELS**
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```python
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"EAT": 768,
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```
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**Step 3: Add EAT loading branch in _get_w2v_model**
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```python
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elif load_name == "EAT":
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from transformers import AutoModel
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_w2v_model = AutoModel.from_pretrained(
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"worstchan/EAT-base_epoch30_finetune_AS2M",
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trust_remote_code=True,
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).to(_w2v_device)
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```
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**Step 4: Add EAT preprocessing helper**
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Add near `_get_w2v_model`:
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```python
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def _eat_preprocess(chunks: list[np.ndarray], sr: int, device: str) -> torch.Tensor:
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"""Convert raw audio chunks to EAT mel spectrogram input.
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Returns tensor of shape [B, 1, T, 128].
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"""
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import torch
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import torchaudio.compliance.kaldi as kaldi
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TARGET_LEN = 1024 # ~10s at 10ms frame shift
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MEAN, STD = -4.268, 4.569
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mels = []
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for chunk in chunks:
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wav = torch.from_numpy(chunk).unsqueeze(0).float()
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fbank = kaldi.fbank(
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wav, htk_compat=True, sample_frequency=sr, use_energy=False,
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window_type='hanning', num_mel_bins=128, dither=0.0, frame_shift=10,
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)
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# Pad or truncate to TARGET_LEN
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if fbank.shape[0] < TARGET_LEN:
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fbank = torch.nn.functional.pad(fbank, (0, 0, 0, TARGET_LEN - fbank.shape[0]))
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else:
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fbank = fbank[:TARGET_LEN]
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fbank = (fbank - MEAN) / (STD * 2)
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mels.append(fbank)
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return torch.stack(mels).unsqueeze(1).to(device) # [B, 1, T, 128]
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```
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**Step 5: Add EAT inference branch in extraction functions**
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```python
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elif is_eat:
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mel_input = _eat_preprocess(chunks, sr, device)
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features = model.extract_features(mel_input)
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# Mean-pool frame-level tokens (skip CLS at index 0)
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batch_emb = features[:, 1:, :].mean(dim=1).cpu().numpy()
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```
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Where `is_eat = (model_name or _DEFAULT_EMBED_MODEL) == "EAT"`.
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**Step 6: Run all tests**
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Run: `pytest tests/ -v`
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Expected: All pass
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**Step 7: Commit**
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```bash
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git add core/audio_scan.py tests/test_audio_scan.py
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git commit -m "feat: add EAT (Efficient Audio Transformer) embedding model"
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```
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---
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### Task 5: Calibrated classifier
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**Files:**
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- Modify: `core/audio_scan.py:424-429` (train_classifier, wrap clf)
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- Test: `tests/test_audio_scan.py`
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**Step 1: Modify train_classifier**
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After the existing `clf.fit()` call (line 428), add calibration:
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```python
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clf.fit(X[train_idx], y_arr[train_idx])
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_log("audio_scan: classifier trained")
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# Calibrate probabilities for better threshold behavior
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from sklearn.calibration import CalibratedClassifierCV
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if len(train_idx) >= 10:
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cal_clf = CalibratedClassifierCV(clf, cv=min(3, n_pos, n_neg_sample),
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method='isotonic')
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cal_clf.fit(X[train_idx], y_arr[train_idx])
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clf = cal_clf
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_log("audio_scan: classifier calibrated")
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```
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The `cv=min(3, n_pos, n_neg_sample)` guard prevents errors when one class has very few samples.
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**Step 2: Run all tests**
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Run: `pytest tests/ -v`
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Expected: All pass
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**Step 3: Commit**
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```bash
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git add core/audio_scan.py
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git commit -m "feat: calibrate classifier probabilities with isotonic regression"
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```
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---
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### Task 6: Integration test with real model (manual)
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This task is manual — it requires GPU and a real video file.
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**Step 1: Test multi-layer extraction**
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```bash
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python -c "
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from core.audio_scan import _extract_w2v_windows, _embed_dim
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import numpy as np
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y = np.random.randn(16000 * 20).astype(np.float32) * 0.01
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ts, emb = _extract_w2v_windows(y, model_name='HUBERT_XLARGE_ML')
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print(f'HUBERT_XLARGE_ML: {emb.shape}') # expect (13, 5120)
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assert emb.shape[1] == _embed_dim('HUBERT_XLARGE_ML')
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"
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```
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**Step 2: Test AST extraction**
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```bash
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python -c "
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from core.audio_scan import _extract_w2v_windows, _embed_dim
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import numpy as np
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y = np.random.randn(16000 * 20).astype(np.float32) * 0.01
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ts, emb = _extract_w2v_windows(y, model_name='AST')
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print(f'AST: {emb.shape}') # expect (13, 768)
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assert emb.shape[1] == _embed_dim('AST')
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"
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```
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**Step 3: Test EAT extraction**
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```bash
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python -c "
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from core.audio_scan import _extract_w2v_windows, _embed_dim
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import numpy as np
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y = np.random.randn(16000 * 20).astype(np.float32) * 0.01
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ts, emb = _extract_w2v_windows(y, model_name='EAT')
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print(f'EAT: {emb.shape}') # expect (13, 768)
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assert emb.shape[1] == _embed_dim('EAT')
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"
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```
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**Step 4: Test full train+scan cycle**
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Load app, select HUBERT_XLARGE_ML from scan model dropdown, scan a video, train, verify results display.
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**Step 5: Final commit and push**
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```bash
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git push
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```
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