Ethanfel
12ed183f1b
- Remove legacy distance-mode scanning (build_profile, _similarity, etc.) and hand-crafted intensity features — pipeline is now embedding-only - Integrate Microsoft BEATs as embedding option alongside wav2vec2/HuBERT - Add TrainDialog with positive class selector, model picker, video dir fallback, and live training stats - Add TrainWorker QThread with cancel support and proper lifecycle cleanup - Add source_path column to DB for robust source video tracking - Add get_export_folders/get_training_data/get_training_stats to DB - Wire source_path in all export DB writes (_on_clip_done, _on_auto_clip_done) - Cancel scan/train workers in closeEvent to prevent use-after-free crashes - Add setup_env.sh supporting both conda and python venv (CUDA 12.8) - Update requirements.txt with all actual dependencies - Update 8cut_train.py with --positive flag for new DB-driven training Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
395 lines
14 KiB
Python
395 lines
14 KiB
Python
"""Audio scanning — embedding-based classifier for audio event detection."""
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import hashlib
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import os
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import numpy as np
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import librosa
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from .paths import _log
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_SR = 16000 # lower sr = faster
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_WINDOW = 8.0 # seconds
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_MODEL_DIR = os.path.join(os.path.expanduser("~"), ".8cut_models")
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_W2V_CACHE_DIR = os.path.join(os.path.expanduser("~"), ".8cut_cache", "w2v")
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# ---------------------------------------------------------------------------
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# Embedding extraction (lazy-loaded)
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# ---------------------------------------------------------------------------
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_w2v_model = None
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_w2v_device = None
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_w2v_model_name = None
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# Supported embedding models — name → embed_dim
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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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}
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_DEFAULT_EMBED_MODEL = "WAV2VEC2_BASE"
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_BEATS_CHECKPOINT = os.path.join(
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os.path.expanduser("~"), ".cache", "huggingface", "hub",
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"models--lpepino--beats_ckpts", "snapshots",
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"5b53b0404df452a3a607d7e67687227730e5bad1", "BEATs_iter3_plus_AS2M.pt",
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)
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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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if _w2v_model is None or _w2v_model_name != model_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 model_name == "BEATS":
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from .beats_model import BEATs, BEATsConfig
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checkpoint = torch.load(_BEATS_CHECKPOINT, map_location=_w2v_device,
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weights_only=False)
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cfg = BEATsConfig(checkpoint['cfg'])
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_w2v_model = BEATs(cfg)
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_w2v_model.load_state_dict(checkpoint['model'])
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_w2v_model.to(_w2v_device)
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else:
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import torchaudio
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bundle = getattr(torchaudio.pipelines, model_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 = model_name
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_log(f"audio_scan: {model_name} loaded on {_w2v_device}")
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return _w2v_model, _w2v_device
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def _embed_dim(model_name: str | None = None) -> int:
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"""Return embedding dimension for a model name."""
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if model_name is None:
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model_name = _DEFAULT_EMBED_MODEL
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return _EMBED_MODELS.get(model_name, 768)
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def _w2v_cache_path(video_path: str, hop: float, window: float,
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model_name: str | None = None) -> str:
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"""Return cache file path for a video's embeddings (includes model name)."""
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if model_name is None:
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model_name = _DEFAULT_EMBED_MODEL
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abspath = os.path.abspath(video_path)
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mtime = os.path.getmtime(abspath)
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key = f"{abspath}|{mtime}|{hop}|{window}|{model_name}"
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h = hashlib.sha256(key.encode()).hexdigest()[:16]
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return os.path.join(_W2V_CACHE_DIR, f"{h}.npz")
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def _extract_w2v_windows(y: np.ndarray, sr: int = _SR,
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hop: float = 1.0, window: float = _WINDOW,
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video_path: str | None = None,
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cancel_flag: object = None,
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model_name: str | None = None,
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) -> tuple[np.ndarray, np.ndarray]:
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"""Extract embeddings for all sliding windows using a torchaudio model.
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If video_path is given, results are cached to disk for fast re-scans.
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Returns (timestamps, embeddings) where embeddings is (N, D).
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"""
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edim = _embed_dim(model_name)
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# Try loading from cache
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cache_file = None
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if video_path:
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try:
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cache_file = _w2v_cache_path(video_path, hop, window, model_name)
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if os.path.exists(cache_file):
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data = np.load(cache_file)
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_log(f"audio_scan: cache hit ({cache_file})")
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return data["timestamps"], data["embeddings"]
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except Exception as e:
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_log(f"audio_scan: cache read failed: {e}")
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win_samples = int(window * sr)
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hop_samples = int(hop * sr)
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n_windows = max(0, (len(y) - win_samples) // hop_samples + 1)
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if n_windows == 0:
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return np.array([]), np.empty((0, edim))
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import torch
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model, device = _get_w2v_model(model_name)
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is_beats = (model_name or _DEFAULT_EMBED_MODEL) == "BEATS"
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batch_size = 16
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timestamps = np.arange(n_windows) * hop
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embeddings = []
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for batch_start in range(0, n_windows, batch_size):
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if cancel_flag and getattr(cancel_flag, '_cancel', False):
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return np.array([]), np.empty((0, edim))
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batch_end = min(batch_start + batch_size, n_windows)
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chunks = []
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for i in range(batch_start, batch_end):
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start = i * hop_samples
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chunks.append(y[start:start + win_samples])
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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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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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result_ts = timestamps
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result_emb = np.vstack(embeddings)
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# Save to cache
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if cache_file:
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try:
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os.makedirs(_W2V_CACHE_DIR, exist_ok=True)
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np.savez(cache_file, timestamps=result_ts, embeddings=result_emb)
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_log(f"audio_scan: w2v cache saved ({cache_file})")
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except Exception as e:
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_log(f"audio_scan: cache write failed: {e}")
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return result_ts, result_emb
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def _extract_w2v_targeted(y: np.ndarray, sr: int, gt_intense: list[float],
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gt_soft: list[float], tolerance: float = 12.0,
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neg_margin: float = 120.0,
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model_name: str | None = None,
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) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""Extract embeddings only near positives and distant negatives.
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Returns (timestamps, embeddings, labels) where labels: 1=pos, -1=neg, 0=ambig.
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"""
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edim = _embed_dim(model_name)
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duration = len(y) / sr
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win_samples = int(_WINDOW * sr)
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all_gt = list(gt_intense) + list(gt_soft)
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# Positive windows: every second near intense markers
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pos_times = set()
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for gt in gt_intense:
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for offset in range(-int(tolerance), int(tolerance) + 1):
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t = gt + offset
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if 0 <= t <= duration - _WINDOW:
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pos_times.add(int(t))
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# Negative windows: every 4s, far from any marker
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neg_times = set()
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for t in range(0, int(duration - _WINDOW), 4):
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if min((abs(t - g) for g in all_gt), default=9999) > neg_margin:
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neg_times.add(t)
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all_times = sorted(pos_times | neg_times)
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# Filter out windows that go past the end
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valid_times = [t for t in all_times if int(t * sr) + win_samples <= len(y)]
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if not valid_times:
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return np.array([]), np.zeros((0, edim)), np.array([], dtype=int)
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import torch
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model, device = _get_w2v_model(model_name)
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batch_size = 16
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timestamps_list: list[float] = []
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embeddings_list: list[np.ndarray] = []
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is_beats = (model_name or _DEFAULT_EMBED_MODEL) == "BEATS"
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for batch_start in range(0, len(valid_times), batch_size):
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batch_end = min(batch_start + batch_size, len(valid_times))
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chunks = []
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for t in valid_times[batch_start:batch_end]:
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start = int(t * sr)
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chunks.append(y[start:start + win_samples])
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timestamps_list.append(float(t))
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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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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_list.append(batch_emb)
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timestamps = np.array(timestamps_list)
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embeddings = np.vstack(embeddings_list)
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labels = np.zeros(len(timestamps), dtype=int)
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for i, t in enumerate(timestamps):
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di = min((abs(t - g) for g in gt_intense), default=9999)
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da = min((abs(t - g) for g in all_gt), default=9999)
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if di < tolerance:
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labels[i] = 1
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elif da > neg_margin:
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labels[i] = -1
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return timestamps, embeddings, labels
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# ---------------------------------------------------------------------------
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# Classifier mode — train / save / load / scan
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# ---------------------------------------------------------------------------
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def train_classifier(video_infos: list[tuple[str, list[float], list[float]]],
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model_path: str | None = None,
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tolerance: float = 12.0,
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neg_margin: float = 120.0,
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embed_model: str | None = None) -> dict:
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"""Train a classifier from labeled videos.
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Args:
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video_infos: list of (video_path, intense_times, soft_times)
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model_path: if given, save model to this path
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tolerance/neg_margin: labeling parameters
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embed_model: embedding model name (e.g. "HUBERT_BASE", "BEATS"), defaults to WAV2VEC2_BASE
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Returns:
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dict with 'classifier', 'embed_model', and metadata, or None on failure.
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"""
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from sklearn.ensemble import GradientBoostingClassifier
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all_X, all_y = [], []
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for vi, (vpath, gt_intense, gt_soft) in enumerate(video_infos):
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_log(f"audio_scan: training [{vi+1}/{len(video_infos)}] {os.path.basename(vpath)}")
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y, _ = librosa.load(vpath, sr=_SR, mono=True)
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timestamps, embeddings, labels = _extract_w2v_targeted(
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y, _SR, gt_intense, gt_soft, tolerance, neg_margin,
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model_name=embed_model,
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)
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if len(timestamps) == 0:
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continue
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# Per-video z-score normalize
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vid_mean = embeddings.mean(axis=0)
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vid_std = np.maximum(embeddings.std(axis=0), 1e-6)
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normed = (embeddings - vid_mean) / vid_std
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for i in range(len(labels)):
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if labels[i] == 1:
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all_X.append(normed[i])
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all_y.append(1)
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elif labels[i] == -1:
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all_X.append(normed[i])
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all_y.append(0)
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if not all_X:
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_log("audio_scan: no training samples collected")
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return None
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X = np.stack(all_X)
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y_arr = np.array(all_y)
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n_pos = (y_arr == 1).sum()
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n_neg = (y_arr == 0).sum()
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_log(f"audio_scan: training set — {n_pos} positive, {n_neg} negative")
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if n_pos == 0 or n_neg == 0:
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_log(f"audio_scan: need both classes — {n_pos} pos, {n_neg} neg")
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return None
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# Subsample negatives for balance
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rng = np.random.RandomState(42)
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pos_idx = np.where(y_arr == 1)[0]
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neg_idx = np.where(y_arr == 0)[0]
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n_neg_sample = min(len(neg_idx), len(pos_idx) * 3)
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neg_sample = rng.choice(neg_idx, n_neg_sample, replace=False)
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train_idx = np.concatenate([pos_idx, neg_sample])
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rng.shuffle(train_idx)
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clf = GradientBoostingClassifier(
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n_estimators=200, max_depth=5, learning_rate=0.1, random_state=42,
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)
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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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model = {"classifier": clf, "n_features": X.shape[1],
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"embed_model": embed_model or _DEFAULT_EMBED_MODEL}
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if model_path:
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import joblib
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parent = os.path.dirname(model_path)
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if parent:
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os.makedirs(parent, exist_ok=True)
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joblib.dump(model, model_path)
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_log(f"audio_scan: model saved to {model_path}")
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return model
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def load_classifier(model_path: str) -> dict | None:
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"""Load a saved classifier model."""
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if not os.path.exists(model_path):
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return None
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import joblib
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return joblib.load(model_path)
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def default_model_path(profile_name: str = "default") -> str:
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"""Return the default path for a profile's classifier model."""
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return os.path.join(_MODEL_DIR, f"{profile_name}.joblib")
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# ---------------------------------------------------------------------------
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# Scanning
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# ---------------------------------------------------------------------------
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def scan_video(
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video_path: str,
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model: dict = None,
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threshold: float = 0.30,
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hop: float = 1.0,
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window: float = _WINDOW,
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cancel_flag: object = None,
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) -> list[tuple[float, float, float]]:
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"""Scan a video for matching audio regions using a trained classifier.
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Returns list of (start_time, end_time, score) above threshold.
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"""
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if model is None:
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_log("audio_scan: no model provided")
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return []
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_log(f"audio_scan: loading {video_path}")
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y, sr = librosa.load(video_path, sr=_SR, mono=True)
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duration = len(y) / sr
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_log(f"audio_scan: {duration:.1f}s loaded, extracting features...")
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if cancel_flag and getattr(cancel_flag, '_cancel', False):
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return []
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clf = model["classifier"]
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embed_model = model.get("embed_model")
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_log(f"audio_scan: extracting embeddings ({embed_model or 'default'})...")
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timestamps, window_vectors = _extract_w2v_windows(
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y, sr, hop=hop, window=window, video_path=video_path,
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cancel_flag=cancel_flag, model_name=embed_model,
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)
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if len(timestamps) == 0:
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_log("audio_scan: video shorter than window")
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return []
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# Per-video z-score normalize
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vid_mean = window_vectors.mean(axis=0)
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vid_std = np.maximum(window_vectors.std(axis=0), 1e-6)
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normed = (window_vectors - vid_mean) / vid_std
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_log(f"audio_scan: classifying {len(normed)} windows...")
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if cancel_flag and getattr(cancel_flag, '_cancel', False):
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return []
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probs = clf.predict_proba(normed)[:, 1]
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mask = probs >= threshold
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results = [
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(timestamps[i], timestamps[i] + window, float(probs[i]))
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for i in np.nonzero(mask)[0]
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]
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_log(f"audio_scan: {len(results)} regions above threshold {threshold}")
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return results
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