feat: integrate training UI, BEATs model, and clean up legacy code
- 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>
This commit is contained in:
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#!/usr/bin/env python3
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"""Calibration — per-video normalized features + classifier."""
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import sys, os, time, warnings
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sys.path.insert(0, os.path.dirname(__file__))
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warnings.filterwarnings("ignore")
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import numpy as np
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import librosa
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from sklearn.ensemble import GradientBoostingClassifier
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from core.audio_scan import _SR, _WINDOW
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_HOP_LENGTH = 1024
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_N_FFT = 2048
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from core.db import ProcessedDB
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PLEX_DIR = "/media/unraid/appdata/plex/download/porn_jav/"
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PROFILE_NAME = "JAV_missionary"
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TOLERANCE = 12.0
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NEG_MARGIN = 120.0
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def extract_rich_features(y, sr=_SR):
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"""Per-frame features: onset, energy, spectral shape, mel bands (22 features)."""
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hop = _HOP_LENGTH
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S = np.abs(librosa.stft(y, n_fft=_N_FFT, hop_length=hop)) ** 2
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rms = librosa.feature.rms(S=S, hop_length=hop)
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cent = librosa.feature.spectral_centroid(S=S, sr=sr)
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bw = librosa.feature.spectral_bandwidth(S=S, sr=sr)
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rolloff = librosa.feature.spectral_rolloff(S=S, sr=sr)
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flatness = librosa.feature.spectral_flatness(S=S)
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zcr = librosa.feature.zero_crossing_rate(y, hop_length=hop)
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onset = librosa.onset.onset_strength(S=librosa.power_to_db(S), sr=sr, hop_length=hop).reshape(1, -1)
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mel_S = librosa.feature.melspectrogram(S=S, sr=sr, hop_length=hop, n_mels=128)
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mel_freqs = librosa.mel_frequencies(n_mels=128, fmin=0, fmax=sr/2)
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bands = [(0, 100), (100, 300), (300, 600), (600, 1200),
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(1200, 2000), (2000, 3500), (3500, 5500), (5500, 8000)]
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band_feats = []
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for flo, fhi in bands:
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mask = (mel_freqs >= flo) & (mel_freqs < fhi)
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if mask.sum() > 0:
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band_feats.append(librosa.power_to_db(mel_S[mask].mean(axis=0, keepdims=True) + 1e-10))
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else:
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band_feats.append(np.zeros((1, mel_S.shape[1])))
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sc = librosa.feature.spectral_contrast(S=S, sr=sr, hop_length=hop)
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min_t = min(rms.shape[1], cent.shape[1], onset.shape[1], sc.shape[1],
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band_feats[0].shape[1])
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return np.vstack([
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rms[:, :min_t], cent[:, :min_t], bw[:, :min_t], rolloff[:, :min_t],
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flatness[:, :min_t], zcr[:, :min_t], onset[:, :min_t],
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] + [b[:, :min_t] for b in band_feats]
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+ [sc[:, :min_t]])
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def compute_window_stats(feat, hop=1.0):
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"""Sliding window mean/std → (timestamps, feature_vectors)."""
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n_feats, T = feat.shape
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fps = _SR / _HOP_LENGTH
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win_frames = int(_WINDOW * fps)
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hop_frames = int(hop * fps)
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if win_frames > T:
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return np.array([]), np.array([])
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cumsum = np.zeros((n_feats, T + 1))
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cumsum[:, 1:] = np.cumsum(feat, axis=1)
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cumsq = np.zeros((n_feats, T + 1))
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cumsq[:, 1:] = np.cumsum(feat ** 2, axis=1)
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starts = np.arange(0, T - win_frames + 1, hop_frames)
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ends = starts + win_frames
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sums = cumsum[:, ends] - cumsum[:, starts]
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sq_sums = cumsq[:, ends] - cumsq[:, starts]
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means = sums / win_frames
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stds = np.sqrt(np.maximum(sq_sums / win_frames - means ** 2, 0) + 1e-10)
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return starts / fps, np.vstack([means, stds]).T
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def label_windows(timestamps, gt_intense, gt_soft):
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all_gt = list(gt_intense) + list(gt_soft)
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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 labels
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def main():
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db = ProcessedDB()
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rows = db._con.execute(
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"SELECT filename, start_time, output_path FROM processed WHERE profile = ?",
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(PROFILE_NAME,),
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).fetchall()
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intense_by_video, soft_by_video = {}, {}
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for fn, st, op in rows:
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if '/mp4_Intense/' in op:
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intense_by_video.setdefault(fn, set()).add(st)
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elif '/mp4_Soft/' in op:
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soft_by_video.setdefault(fn, set()).add(st)
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videos = [fn for fn in intense_by_video
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if os.path.exists(os.path.join(PLEX_DIR, fn))]
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n_vids = int(sys.argv[1]) if len(sys.argv) > 1 else len(videos)
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videos = videos[:n_vids]
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print(f"Processing {len(videos)} videos...")
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all_data_raw = [] # raw features
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all_data_norm = [] # per-video z-scored features
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for vi, vname in enumerate(videos):
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vpath = os.path.join(PLEX_DIR, vname)
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gt_intense = sorted(intense_by_video.get(vname, set()))
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gt_soft = sorted(soft_by_video.get(vname, set()))
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t0 = time.time()
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y, _ = librosa.load(vpath, sr=_SR, mono=True)
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feat = extract_rich_features(y)
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timestamps, window_vectors = compute_window_stats(feat, hop=1.0)
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dt = time.time() - t0
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if len(timestamps) == 0:
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continue
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labels = label_windows(timestamps, gt_intense, gt_soft)
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# Per-video z-score normalization
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vid_mean = window_vectors.mean(axis=0)
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vid_std = window_vectors.std(axis=0)
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vid_std = np.maximum(vid_std, 1e-6)
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normed = (window_vectors - vid_mean) / vid_std
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n_pos = (labels == 1).sum()
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n_neg = (labels == -1).sum()
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print(f" [{vi+1}/{len(videos)}] {vname[:55]} pos={n_pos} neg={n_neg} ({dt:.1f}s)")
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all_data_raw.append((vi, vname, timestamps, window_vectors, labels))
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all_data_norm.append((vi, vname, timestamps, normed, labels))
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# Run CV for both raw and normalized
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for label, data in [("RAW features", all_data_raw),
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("PER-VIDEO NORMALIZED features", all_data_norm)]:
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print(f"\n{'='*70}")
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print(f" {label}")
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print(f"{'='*70}")
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all_y_true, all_y_prob = [], []
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for test_idx in range(len(data)):
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_, vname, _, test_X, test_labels = data[test_idx]
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test_mask = test_labels != 0
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if test_mask.sum() == 0 or (test_labels[test_mask] == 1).sum() == 0:
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continue
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X_test = test_X[test_mask]
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y_test = (test_labels[test_mask] == 1).astype(int)
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X_parts, y_parts = [], []
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for i, (_, _, _, feats, labs) in enumerate(data):
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if i == test_idx:
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continue
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m = labs != 0
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if m.sum() == 0:
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continue
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X_parts.append(feats[m])
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y_parts.append((labs[m] == 1).astype(int))
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if not X_parts:
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continue
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X_train = np.vstack(X_parts)
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y_train = np.concatenate(y_parts)
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pos_idx = np.where(y_train == 1)[0]
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neg_idx = np.where(y_train == 0)[0]
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if len(pos_idx) == 0 or len(neg_idx) == 0:
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continue
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rng = np.random.RandomState(42)
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n_neg = min(len(neg_idx), len(pos_idx) * 3)
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neg_sample = rng.choice(neg_idx, n_neg, replace=False)
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train_idx = np.concatenate([pos_idx, neg_sample])
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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[train_idx], y_train[train_idx])
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probs = clf.predict_proba(X_test)[:, 1]
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tp = ((probs >= 0.5) & (y_test == 1)).sum()
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fp = ((probs >= 0.5) & (y_test == 0)).sum()
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fn_count = ((probs < 0.5) & (y_test == 1)).sum()
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pos_s = probs[y_test == 1].mean() if (y_test == 1).sum() > 0 else 0
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neg_s = probs[y_test == 0].mean() if (y_test == 0).sum() > 0 else 0
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print(f" {vname[:50]:50s} TP={tp:3d} FP={fp:4d} FN={fn_count:3d} pos_p={pos_s:.3f} neg_p={neg_s:.3f}")
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all_y_true.extend(y_test)
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all_y_prob.extend(probs)
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if not all_y_true:
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print(" No test results.")
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continue
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y_true = np.array(all_y_true)
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y_prob = np.array(all_y_prob)
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pos_probs = y_prob[y_true == 1]
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neg_probs = y_prob[y_true == 0]
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if len(pos_probs) > 0 and len(neg_probs) > 0:
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print(f"\n POS: 25%={np.percentile(pos_probs,25):.3f} 50%={np.percentile(pos_probs,50):.3f}"
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f" 75%={np.percentile(pos_probs,75):.3f} max={pos_probs.max():.3f}")
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print(f" NEG: 25%={np.percentile(neg_probs,25):.3f} 50%={np.percentile(neg_probs,50):.3f}"
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f" 75%={np.percentile(neg_probs,75):.3f} max={neg_probs.max():.3f}")
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best_f1, best_thr = 0, 0
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print(f"\n {'thr':>5} {'prec':>6} {'recall':>6} {'TP':>5} {'FP':>5} {'FN':>4} {'F1':>6}")
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for thr in np.arange(0.10, 0.91, 0.05):
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tp = ((y_prob >= thr) & (y_true == 1)).sum()
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fp = ((y_prob >= thr) & (y_true == 0)).sum()
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fn_count = ((y_prob < thr) & (y_true == 1)).sum()
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prec = tp / (tp + fp) if (tp + fp) > 0 else 0
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rec = tp / (tp + fn_count) if (tp + fn_count) > 0 else 0
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f1 = 2 * prec * rec / (prec + rec) if (prec + rec) > 0 else 0
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if f1 > best_f1:
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best_f1, best_thr = f1, thr
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print(f" {thr:.2f} {prec:.4f} {rec:.4f} {tp:5d} {fp:5d} {fn_count:4d} {f1:.4f}")
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print(f"\n Best F1={best_f1:.4f} at thr={best_thr:.2f}")
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# Feature importance
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X_all = np.vstack([f[l != 0] for _, _, _, f, l in data])
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y_all = np.concatenate([(l[l != 0] == 1).astype(int) for _, _, _, _, l in data])
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pos_idx = np.where(y_all == 1)[0]
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neg_idx = np.where(y_all == 0)[0]
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rng = np.random.RandomState(42)
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neg_sub = rng.choice(neg_idx, min(len(neg_idx), len(pos_idx)*3), replace=False)
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clf = GradientBoostingClassifier(n_estimators=200, max_depth=5, learning_rate=0.1, random_state=42)
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clf.fit(X_all[np.concatenate([pos_idx, neg_sub])], y_all[np.concatenate([pos_idx, neg_sub])])
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feat_names = (
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["rms", "centroid", "bw", "rolloff", "flat", "zcr", "onset"]
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+ [f"mel{i}" for i in range(8)]
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+ [f"sc{i}" for i in range(7)]
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)
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stat_names = [f"{f}_m" for f in feat_names] + [f"{f}_s" for f in feat_names]
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imp = clf.feature_importances_
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top = sorted(zip(stat_names, imp), key=lambda x: -x[1])[:10]
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print(f" Top features: {', '.join(f'{n}={v:.3f}' for n, v in top)}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,92 @@
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#!/usr/bin/env python3
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"""Train an audio scan classifier from DB ground truth.
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Usage:
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python 8cut_train.py # default model, auto-detect positive
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python 8cut_train.py --model BEATS # specific embedding model
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python 8cut_train.py --positive mp4_Intense # explicit positive folder
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python 8cut_train.py --positive mp4_Intense --model BEATS # both
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"""
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import sys, os, warnings
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sys.path.insert(0, os.path.dirname(__file__))
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warnings.filterwarnings("ignore")
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from core.audio_scan import train_classifier, default_model_path, _EMBED_MODELS
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from core.db import ProcessedDB
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PROFILE_NAME = "JAV_missionary"
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# Fallback for old DB rows without source_path
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PLEX_DIR = "/media/unraid/appdata/plex/download/porn_jav/"
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def main():
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embed_model = None
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if "--model" in sys.argv:
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idx = sys.argv.index("--model")
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if idx + 1 < len(sys.argv):
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embed_model = sys.argv[idx + 1]
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if embed_model not in _EMBED_MODELS:
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print(f"Unknown model: {embed_model}")
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print(f"Available: {', '.join(_EMBED_MODELS)}")
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sys.exit(1)
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positive_suffix = None
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if "--positive" in sys.argv:
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idx = sys.argv.index("--positive")
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if idx + 1 < len(sys.argv):
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positive_suffix = sys.argv[idx + 1]
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db = ProcessedDB()
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# If --positive given, use the new DB helper
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if positive_suffix:
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video_infos = db.get_training_data(
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PROFILE_NAME, positive_suffix, fallback_video_dir=PLEX_DIR,
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)
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if not video_infos:
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print(f"No training data found for positive='{positive_suffix}'")
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sys.exit(1)
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else:
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# Legacy fallback: classify by folder path pattern
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rows = db._con.execute(
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"SELECT filename, start_time, output_path, source_path"
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" FROM processed WHERE profile = ?",
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(PROFILE_NAME,),
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).fetchall()
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intense_by_video, soft_by_video = {}, {}
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source_by_fn = {}
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for fn, st, op, sp in rows:
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if sp:
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source_by_fn[fn] = sp
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if "/mp4_Intense/" in op or "_Intense/" in op:
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intense_by_video.setdefault(fn, set()).add(st)
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elif "/mp4_Soft/" in op or "_Soft/" in op:
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soft_by_video.setdefault(fn, set()).add(st)
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video_infos = []
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for fn in intense_by_video:
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# Try source_path from DB first, fall back to PLEX_DIR
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vpath = source_by_fn.get(fn) or os.path.join(PLEX_DIR, fn)
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if not os.path.exists(vpath):
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print(f" skip (not found): {fn}")
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continue
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gt_intense = sorted(intense_by_video[fn])
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gt_soft = sorted(soft_by_video.get(fn, set()))
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video_infos.append((vpath, gt_intense, gt_soft))
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label = embed_model or "WAV2VEC2_BASE"
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print(f"Training {label} model on {len(video_infos)} videos...")
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model_path = default_model_path(PROFILE_NAME)
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result = train_classifier(
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video_infos, model_path=model_path, embed_model=embed_model,
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)
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if result is None:
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print("Training failed: no valid samples or missing class balance")
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sys.exit(1)
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print(f"Model saved to {model_path}")
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if __name__ == "__main__":
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main()
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+347
-128
@@ -1,105 +1,359 @@
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"""Audio similarity scanning — MFCC + spectral contrast profile matching."""
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"""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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_N_MFCC = 13 # coefficients 0-12; we drop C0 → 12 usable
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_SR = 16000 # lower sr = faster, no quality loss for style matching
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_HOP_LENGTH = 1024 # STFT hop (~64ms frames at 16kHz)
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_N_FFT = 2048 # STFT window
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_SR = 16000 # lower sr = faster
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_WINDOW = 8.0 # seconds
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_N_FEATURES = 62 # (12 mfcc + 12 delta + 7 sc) * 2 (mean + std)
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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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def _extract_features_from_signal(y: np.ndarray, sr: int = _SR) -> np.ndarray:
|
||||
"""Compute feature matrix (31 x T) from a raw audio signal.
|
||||
_w2v_model = None
|
||||
_w2v_device = None
|
||||
_w2v_model_name = None
|
||||
|
||||
Features per frame: 12 MFCCs (skip C0) + 12 delta MFCCs + 7 spectral contrast.
|
||||
"""
|
||||
S = np.abs(librosa.stft(y, n_fft=_N_FFT, hop_length=_HOP_LENGTH)) ** 2
|
||||
mel_S = librosa.feature.melspectrogram(S=S, sr=sr, hop_length=_HOP_LENGTH)
|
||||
mfcc = librosa.feature.mfcc(S=librosa.power_to_db(mel_S), sr=sr, n_mfcc=_N_MFCC)
|
||||
mfcc = mfcc[1:] # drop C0 (energy) — dominates cosine sim, kills discrimination
|
||||
delta = librosa.feature.delta(mfcc)
|
||||
sc = librosa.feature.spectral_contrast(S=S, sr=sr, hop_length=_HOP_LENGTH)
|
||||
return np.vstack([mfcc, delta, sc]) # (31, T)
|
||||
|
||||
|
||||
def _aggregate(feature_matrix: np.ndarray) -> np.ndarray:
|
||||
"""Collapse a (31, T) feature matrix into a (62,) vector via mean + std."""
|
||||
return np.concatenate([
|
||||
feature_matrix.mean(axis=1),
|
||||
feature_matrix.std(axis=1),
|
||||
])
|
||||
|
||||
|
||||
def _extract_features(path: str, sr: int = _SR) -> np.ndarray:
|
||||
"""Load audio from a file and return a 62-dim feature vector."""
|
||||
y, _ = librosa.load(path, sr=sr, mono=True)
|
||||
feat = _extract_features_from_signal(y, sr)
|
||||
return _aggregate(feat)
|
||||
|
||||
|
||||
def build_profile(clip_paths: list[str]) -> dict | None:
|
||||
"""Extract features from reference clips.
|
||||
|
||||
Returns dict with:
|
||||
- mean_vector: averaged feature vector across all clips (62,)
|
||||
- clip_vectors: list of individual feature vectors
|
||||
Returns None if no clips could be loaded.
|
||||
"""
|
||||
vectors = []
|
||||
for p in clip_paths:
|
||||
try:
|
||||
vec = _extract_features(p)
|
||||
vectors.append(vec)
|
||||
except Exception as e:
|
||||
_log(f"audio_scan: skip {p}: {e}")
|
||||
if not vectors:
|
||||
return None
|
||||
arr = np.stack(vectors)
|
||||
return {
|
||||
"mean_vector": arr.mean(axis=0),
|
||||
"clip_vectors": vectors,
|
||||
# Supported embedding models — name → embed_dim
|
||||
_EMBED_MODELS = {
|
||||
"WAV2VEC2_BASE": 768,
|
||||
"WAV2VEC2_LARGE": 1024,
|
||||
"WAV2VEC2_LARGE_LV60K":1024,
|
||||
"HUBERT_BASE": 768,
|
||||
"HUBERT_LARGE": 1024,
|
||||
"HUBERT_XLARGE": 1280,
|
||||
"BEATS": 768,
|
||||
}
|
||||
_DEFAULT_EMBED_MODEL = "WAV2VEC2_BASE"
|
||||
|
||||
_BEATS_CHECKPOINT = os.path.join(
|
||||
os.path.expanduser("~"), ".cache", "huggingface", "hub",
|
||||
"models--lpepino--beats_ckpts", "snapshots",
|
||||
"5b53b0404df452a3a607d7e67687227730e5bad1", "BEATs_iter3_plus_AS2M.pt",
|
||||
)
|
||||
|
||||
|
||||
def _similarity(a: np.ndarray, b: np.ndarray) -> float:
|
||||
"""Euclidean-distance-based similarity in (0, 1].
|
||||
def _get_w2v_model(model_name: str | None = None):
|
||||
"""Lazy-load an embedding model. Reloads if model_name differs from cached."""
|
||||
global _w2v_model, _w2v_device, _w2v_model_name
|
||||
if model_name is None:
|
||||
model_name = _DEFAULT_EMBED_MODEL
|
||||
if _w2v_model is None or _w2v_model_name != model_name:
|
||||
import torch
|
||||
_w2v_device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
|
||||
1/(1+dist): identical → 1.0, very different → near 0.
|
||||
if model_name == "BEATS":
|
||||
from .beats_model import BEATs, BEATsConfig
|
||||
checkpoint = torch.load(_BEATS_CHECKPOINT, map_location=_w2v_device,
|
||||
weights_only=False)
|
||||
cfg = BEATsConfig(checkpoint['cfg'])
|
||||
_w2v_model = BEATs(cfg)
|
||||
_w2v_model.load_state_dict(checkpoint['model'])
|
||||
_w2v_model.to(_w2v_device)
|
||||
else:
|
||||
import torchaudio
|
||||
bundle = getattr(torchaudio.pipelines, model_name)
|
||||
_w2v_model = bundle.get_model().to(_w2v_device)
|
||||
|
||||
_w2v_model.eval()
|
||||
_w2v_model_name = model_name
|
||||
_log(f"audio_scan: {model_name} loaded on {_w2v_device}")
|
||||
return _w2v_model, _w2v_device
|
||||
|
||||
|
||||
def _embed_dim(model_name: str | None = None) -> int:
|
||||
"""Return embedding dimension for a model name."""
|
||||
if model_name is None:
|
||||
model_name = _DEFAULT_EMBED_MODEL
|
||||
return _EMBED_MODELS.get(model_name, 768)
|
||||
|
||||
|
||||
def _w2v_cache_path(video_path: str, hop: float, window: float,
|
||||
model_name: str | None = None) -> str:
|
||||
"""Return cache file path for a video's embeddings (includes model name)."""
|
||||
if model_name is None:
|
||||
model_name = _DEFAULT_EMBED_MODEL
|
||||
abspath = os.path.abspath(video_path)
|
||||
mtime = os.path.getmtime(abspath)
|
||||
key = f"{abspath}|{mtime}|{hop}|{window}|{model_name}"
|
||||
h = hashlib.sha256(key.encode()).hexdigest()[:16]
|
||||
return os.path.join(_W2V_CACHE_DIR, f"{h}.npz")
|
||||
|
||||
|
||||
def _extract_w2v_windows(y: np.ndarray, sr: int = _SR,
|
||||
hop: float = 1.0, window: float = _WINDOW,
|
||||
video_path: str | None = None,
|
||||
cancel_flag: object = None,
|
||||
model_name: str | None = None,
|
||||
) -> tuple[np.ndarray, np.ndarray]:
|
||||
"""Extract embeddings for all sliding windows using a torchaudio model.
|
||||
|
||||
If video_path is given, results are cached to disk for fast re-scans.
|
||||
Returns (timestamps, embeddings) where embeddings is (N, D).
|
||||
"""
|
||||
return float(1.0 / (1.0 + np.linalg.norm(a - b)))
|
||||
edim = _embed_dim(model_name)
|
||||
|
||||
# Try loading from cache
|
||||
cache_file = None
|
||||
if video_path:
|
||||
try:
|
||||
cache_file = _w2v_cache_path(video_path, hop, window, model_name)
|
||||
if os.path.exists(cache_file):
|
||||
data = np.load(cache_file)
|
||||
_log(f"audio_scan: cache hit ({cache_file})")
|
||||
return data["timestamps"], data["embeddings"]
|
||||
except Exception as e:
|
||||
_log(f"audio_scan: cache read failed: {e}")
|
||||
|
||||
win_samples = int(window * sr)
|
||||
hop_samples = int(hop * sr)
|
||||
n_windows = max(0, (len(y) - win_samples) // hop_samples + 1)
|
||||
|
||||
if n_windows == 0:
|
||||
return np.array([]), np.empty((0, edim))
|
||||
|
||||
import torch
|
||||
model, device = _get_w2v_model(model_name)
|
||||
is_beats = (model_name or _DEFAULT_EMBED_MODEL) == "BEATS"
|
||||
batch_size = 16
|
||||
timestamps = np.arange(n_windows) * hop
|
||||
embeddings = []
|
||||
|
||||
for batch_start in range(0, n_windows, batch_size):
|
||||
if cancel_flag and getattr(cancel_flag, '_cancel', False):
|
||||
return np.array([]), np.empty((0, edim))
|
||||
batch_end = min(batch_start + batch_size, n_windows)
|
||||
chunks = []
|
||||
for i in range(batch_start, batch_end):
|
||||
start = i * hop_samples
|
||||
chunks.append(y[start:start + win_samples])
|
||||
with torch.no_grad():
|
||||
waveforms = torch.from_numpy(np.stack(chunks)).float().to(device)
|
||||
if is_beats:
|
||||
padding_mask = torch.zeros_like(waveforms, dtype=torch.bool)
|
||||
features, _ = model.extract_features(waveforms, padding_mask=padding_mask)
|
||||
else:
|
||||
features, _ = model(waveforms)
|
||||
batch_emb = features.mean(dim=1).cpu().numpy()
|
||||
embeddings.append(batch_emb)
|
||||
|
||||
result_ts = timestamps
|
||||
result_emb = np.vstack(embeddings)
|
||||
|
||||
# Save to cache
|
||||
if cache_file:
|
||||
try:
|
||||
os.makedirs(_W2V_CACHE_DIR, exist_ok=True)
|
||||
np.savez(cache_file, timestamps=result_ts, embeddings=result_emb)
|
||||
_log(f"audio_scan: w2v cache saved ({cache_file})")
|
||||
except Exception as e:
|
||||
_log(f"audio_scan: cache write failed: {e}")
|
||||
|
||||
return result_ts, result_emb
|
||||
|
||||
|
||||
def _extract_w2v_targeted(y: np.ndarray, sr: int, gt_intense: list[float],
|
||||
gt_soft: list[float], tolerance: float = 12.0,
|
||||
neg_margin: float = 120.0,
|
||||
model_name: str | None = None,
|
||||
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
"""Extract embeddings only near positives and distant negatives.
|
||||
|
||||
Returns (timestamps, embeddings, labels) where labels: 1=pos, -1=neg, 0=ambig.
|
||||
"""
|
||||
edim = _embed_dim(model_name)
|
||||
duration = len(y) / sr
|
||||
win_samples = int(_WINDOW * sr)
|
||||
all_gt = list(gt_intense) + list(gt_soft)
|
||||
|
||||
# Positive windows: every second near intense markers
|
||||
pos_times = set()
|
||||
for gt in gt_intense:
|
||||
for offset in range(-int(tolerance), int(tolerance) + 1):
|
||||
t = gt + offset
|
||||
if 0 <= t <= duration - _WINDOW:
|
||||
pos_times.add(int(t))
|
||||
|
||||
# Negative windows: every 4s, far from any marker
|
||||
neg_times = set()
|
||||
for t in range(0, int(duration - _WINDOW), 4):
|
||||
if min((abs(t - g) for g in all_gt), default=9999) > neg_margin:
|
||||
neg_times.add(t)
|
||||
|
||||
all_times = sorted(pos_times | neg_times)
|
||||
# Filter out windows that go past the end
|
||||
valid_times = [t for t in all_times if int(t * sr) + win_samples <= len(y)]
|
||||
|
||||
if not valid_times:
|
||||
return np.array([]), np.zeros((0, edim)), np.array([], dtype=int)
|
||||
|
||||
import torch
|
||||
model, device = _get_w2v_model(model_name)
|
||||
batch_size = 16
|
||||
timestamps_list: list[float] = []
|
||||
embeddings_list: list[np.ndarray] = []
|
||||
|
||||
is_beats = (model_name or _DEFAULT_EMBED_MODEL) == "BEATS"
|
||||
|
||||
for batch_start in range(0, len(valid_times), batch_size):
|
||||
batch_end = min(batch_start + batch_size, len(valid_times))
|
||||
chunks = []
|
||||
for t in valid_times[batch_start:batch_end]:
|
||||
start = int(t * sr)
|
||||
chunks.append(y[start:start + win_samples])
|
||||
timestamps_list.append(float(t))
|
||||
with torch.no_grad():
|
||||
waveforms = torch.from_numpy(np.stack(chunks)).float().to(device)
|
||||
if is_beats:
|
||||
padding_mask = torch.zeros_like(waveforms, dtype=torch.bool)
|
||||
features, _ = model.extract_features(waveforms, padding_mask=padding_mask)
|
||||
else:
|
||||
features, _ = model(waveforms)
|
||||
batch_emb = features.mean(dim=1).cpu().numpy()
|
||||
embeddings_list.append(batch_emb)
|
||||
|
||||
timestamps = np.array(timestamps_list)
|
||||
embeddings = np.vstack(embeddings_list)
|
||||
|
||||
labels = np.zeros(len(timestamps), dtype=int)
|
||||
for i, t in enumerate(timestamps):
|
||||
di = min((abs(t - g) for g in gt_intense), default=9999)
|
||||
da = min((abs(t - g) for g in all_gt), default=9999)
|
||||
if di < tolerance:
|
||||
labels[i] = 1
|
||||
elif da > neg_margin:
|
||||
labels[i] = -1
|
||||
return timestamps, embeddings, labels
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Classifier mode — train / save / load / scan
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def train_classifier(video_infos: list[tuple[str, list[float], list[float]]],
|
||||
model_path: str | None = None,
|
||||
tolerance: float = 12.0,
|
||||
neg_margin: float = 120.0,
|
||||
embed_model: str | None = None) -> dict:
|
||||
"""Train a classifier from labeled videos.
|
||||
|
||||
Args:
|
||||
video_infos: list of (video_path, intense_times, soft_times)
|
||||
model_path: if given, save model to this path
|
||||
tolerance/neg_margin: labeling parameters
|
||||
embed_model: embedding model name (e.g. "HUBERT_BASE", "BEATS"), defaults to WAV2VEC2_BASE
|
||||
|
||||
Returns:
|
||||
dict with 'classifier', 'embed_model', and metadata, or None on failure.
|
||||
"""
|
||||
from sklearn.ensemble import GradientBoostingClassifier
|
||||
|
||||
all_X, all_y = [], []
|
||||
|
||||
for vi, (vpath, gt_intense, gt_soft) in enumerate(video_infos):
|
||||
_log(f"audio_scan: training [{vi+1}/{len(video_infos)}] {os.path.basename(vpath)}")
|
||||
y, _ = librosa.load(vpath, sr=_SR, mono=True)
|
||||
|
||||
timestamps, embeddings, labels = _extract_w2v_targeted(
|
||||
y, _SR, gt_intense, gt_soft, tolerance, neg_margin,
|
||||
model_name=embed_model,
|
||||
)
|
||||
if len(timestamps) == 0:
|
||||
continue
|
||||
# Per-video z-score normalize
|
||||
vid_mean = embeddings.mean(axis=0)
|
||||
vid_std = np.maximum(embeddings.std(axis=0), 1e-6)
|
||||
normed = (embeddings - vid_mean) / vid_std
|
||||
for i in range(len(labels)):
|
||||
if labels[i] == 1:
|
||||
all_X.append(normed[i])
|
||||
all_y.append(1)
|
||||
elif labels[i] == -1:
|
||||
all_X.append(normed[i])
|
||||
all_y.append(0)
|
||||
|
||||
if not all_X:
|
||||
_log("audio_scan: no training samples collected")
|
||||
return None
|
||||
|
||||
X = np.stack(all_X)
|
||||
y_arr = np.array(all_y)
|
||||
n_pos = (y_arr == 1).sum()
|
||||
n_neg = (y_arr == 0).sum()
|
||||
_log(f"audio_scan: training set — {n_pos} positive, {n_neg} negative")
|
||||
|
||||
if n_pos == 0 or n_neg == 0:
|
||||
_log(f"audio_scan: need both classes — {n_pos} pos, {n_neg} neg")
|
||||
return None
|
||||
|
||||
# Subsample negatives for balance
|
||||
rng = np.random.RandomState(42)
|
||||
pos_idx = np.where(y_arr == 1)[0]
|
||||
neg_idx = np.where(y_arr == 0)[0]
|
||||
n_neg_sample = min(len(neg_idx), len(pos_idx) * 3)
|
||||
neg_sample = rng.choice(neg_idx, n_neg_sample, replace=False)
|
||||
train_idx = np.concatenate([pos_idx, neg_sample])
|
||||
rng.shuffle(train_idx)
|
||||
|
||||
clf = GradientBoostingClassifier(
|
||||
n_estimators=200, max_depth=5, learning_rate=0.1, random_state=42,
|
||||
)
|
||||
clf.fit(X[train_idx], y_arr[train_idx])
|
||||
_log("audio_scan: classifier trained")
|
||||
|
||||
model = {"classifier": clf, "n_features": X.shape[1],
|
||||
"embed_model": embed_model or _DEFAULT_EMBED_MODEL}
|
||||
|
||||
if model_path:
|
||||
import joblib
|
||||
parent = os.path.dirname(model_path)
|
||||
if parent:
|
||||
os.makedirs(parent, exist_ok=True)
|
||||
joblib.dump(model, model_path)
|
||||
_log(f"audio_scan: model saved to {model_path}")
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def load_classifier(model_path: str) -> dict | None:
|
||||
"""Load a saved classifier model."""
|
||||
if not os.path.exists(model_path):
|
||||
return None
|
||||
import joblib
|
||||
return joblib.load(model_path)
|
||||
|
||||
|
||||
def default_model_path(profile_name: str = "default") -> str:
|
||||
"""Return the default path for a profile's classifier model."""
|
||||
return os.path.join(_MODEL_DIR, f"{profile_name}.joblib")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Scanning
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def scan_video(
|
||||
video_path: str,
|
||||
profile: dict,
|
||||
mode: str = "average",
|
||||
threshold: float = 0.05,
|
||||
model: dict = None,
|
||||
threshold: float = 0.30,
|
||||
hop: float = 1.0,
|
||||
window: float = _WINDOW,
|
||||
cancel_flag: object = None,
|
||||
) -> list[tuple[float, float, float]]:
|
||||
"""Slide a window across the video audio and score against the profile.
|
||||
"""Scan a video for matching audio regions using a trained classifier.
|
||||
|
||||
Pre-computes STFT once for the whole file, then uses vectorized
|
||||
cumulative-sum sliding window for speed.
|
||||
|
||||
Args:
|
||||
video_path: path to video/audio file
|
||||
profile: dict from build_profile()
|
||||
mode: "average" (compare to mean) or "nearest" (max over all clips)
|
||||
threshold: minimum similarity to include (0-1, default 0.05)
|
||||
hop: step size in seconds
|
||||
window: window size in seconds (default 8s)
|
||||
cancel_flag: object with _cancel bool attribute; checked periodically
|
||||
|
||||
Returns:
|
||||
list of (start_time, end_time, score) for regions above threshold
|
||||
Returns list of (start_time, end_time, score) above threshold.
|
||||
"""
|
||||
if model is None:
|
||||
_log("audio_scan: no model provided")
|
||||
return []
|
||||
|
||||
_log(f"audio_scan: loading {video_path}")
|
||||
y, sr = librosa.load(video_path, sr=_SR, mono=True)
|
||||
duration = len(y) / sr
|
||||
@@ -108,68 +362,33 @@ def scan_video(
|
||||
if cancel_flag and getattr(cancel_flag, '_cancel', False):
|
||||
return []
|
||||
|
||||
# Compute features for the entire file at once (one STFT)
|
||||
feat = _extract_features_from_signal(y, sr) # (31, T)
|
||||
n_feats, T = feat.shape
|
||||
fps = sr / _HOP_LENGTH # frames per second
|
||||
win_frames = int(window * fps)
|
||||
hop_frames = int(hop * fps)
|
||||
clf = model["classifier"]
|
||||
embed_model = model.get("embed_model")
|
||||
|
||||
if win_frames > T:
|
||||
_log(f"audio_scan: extracting embeddings ({embed_model or 'default'})...")
|
||||
timestamps, window_vectors = _extract_w2v_windows(
|
||||
y, sr, hop=hop, window=window, video_path=video_path,
|
||||
cancel_flag=cancel_flag, model_name=embed_model,
|
||||
)
|
||||
if len(timestamps) == 0:
|
||||
_log("audio_scan: video shorter than window")
|
||||
return []
|
||||
|
||||
_log(f"audio_scan: scanning {T} frames, win={win_frames}, hop={hop_frames}")
|
||||
# Per-video z-score normalize
|
||||
vid_mean = window_vectors.mean(axis=0)
|
||||
vid_std = np.maximum(window_vectors.std(axis=0), 1e-6)
|
||||
normed = (window_vectors - vid_mean) / vid_std
|
||||
|
||||
# Vectorized sliding window via cumulative sums
|
||||
cumsum = np.zeros((n_feats, T + 1))
|
||||
cumsum[:, 1:] = np.cumsum(feat, axis=1)
|
||||
cumsq = np.zeros((n_feats, T + 1))
|
||||
cumsq[:, 1:] = np.cumsum(feat ** 2, axis=1)
|
||||
|
||||
starts = np.arange(0, T - win_frames + 1, hop_frames)
|
||||
ends = starts + win_frames
|
||||
|
||||
sums = cumsum[:, ends] - cumsum[:, starts] # (31, n_windows)
|
||||
sq_sums = cumsq[:, ends] - cumsq[:, starts]
|
||||
means = sums / win_frames
|
||||
stds = np.sqrt(np.maximum(sq_sums / win_frames - means ** 2, 0) + 1e-10)
|
||||
|
||||
window_vectors = np.vstack([means, stds]).T # (n_windows, 62)
|
||||
_log(f"audio_scan: classifying {len(normed)} windows...")
|
||||
|
||||
if cancel_flag and getattr(cancel_flag, '_cancel', False):
|
||||
return []
|
||||
|
||||
# Score all windows
|
||||
if mode == "nearest":
|
||||
# Compare each window to every clip vector, take max
|
||||
clip_vecs = np.stack(profile["clip_vectors"]) # (n_clips, 62)
|
||||
results = []
|
||||
# Process in batches to check cancel_flag periodically
|
||||
batch = 500
|
||||
for i in range(0, len(window_vectors), batch):
|
||||
if cancel_flag and getattr(cancel_flag, '_cancel', False):
|
||||
_log("audio_scan: cancelled")
|
||||
return results
|
||||
chunk = window_vectors[i:i + batch]
|
||||
# cdist: (batch, n_clips) distances
|
||||
dists = np.linalg.norm(chunk[:, None, :] - clip_vecs[None, :, :], axis=2)
|
||||
scores = 1.0 / (1.0 + dists.min(axis=1)) # min dist = max similarity
|
||||
for j, score in enumerate(scores):
|
||||
if score >= threshold:
|
||||
idx = i + j
|
||||
start_t = starts[idx] / fps
|
||||
results.append((start_t, start_t + window, float(score)))
|
||||
else:
|
||||
# Average mode: compare to mean vector
|
||||
ref = profile["mean_vector"]
|
||||
dists = np.linalg.norm(window_vectors - ref, axis=1)
|
||||
scores = 1.0 / (1.0 + dists)
|
||||
mask = scores >= threshold
|
||||
probs = clf.predict_proba(normed)[:, 1]
|
||||
mask = probs >= threshold
|
||||
results = [
|
||||
(starts[i] / fps, starts[i] / fps + window, float(scores[i]))
|
||||
(timestamps[i], timestamps[i] + window, float(probs[i]))
|
||||
for i in np.nonzero(mask)[0]
|
||||
]
|
||||
|
||||
_log(f"audio_scan: {len(results)} regions above threshold {threshold}")
|
||||
return results
|
||||
|
||||
@@ -0,0 +1,783 @@
|
||||
# --------------------------------------------------------
|
||||
# BEATs: Audio Pre-Training with Acoustic Tokenizers (https://arxiv.org/abs/2212.09058)
|
||||
# Github source: https://github.com/microsoft/unilm/tree/master/beats
|
||||
# Copyright (c) 2022 Microsoft
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# Based on fairseq code bases
|
||||
# https://github.com/pytorch/fairseq
|
||||
# --------------------------------------------------------
|
||||
|
||||
import math
|
||||
import numpy as np
|
||||
from typing import Dict, Optional, Tuple
|
||||
import torch
|
||||
from torch import Tensor, nn
|
||||
import torch.nn.functional as F
|
||||
from torch.nn import LayerNorm, Parameter
|
||||
from .beats_modules import (
|
||||
GradMultiply,
|
||||
SamePad,
|
||||
get_activation_fn,
|
||||
GLU_Linear,
|
||||
quant_noise,
|
||||
)
|
||||
|
||||
|
||||
class TransformerEncoder(nn.Module):
|
||||
def __init__(self, args):
|
||||
super().__init__()
|
||||
|
||||
self.dropout = args.dropout
|
||||
self.embedding_dim = args.encoder_embed_dim
|
||||
|
||||
self.pos_conv = nn.Conv1d(
|
||||
self.embedding_dim,
|
||||
self.embedding_dim,
|
||||
kernel_size=args.conv_pos,
|
||||
padding=args.conv_pos // 2,
|
||||
groups=args.conv_pos_groups,
|
||||
)
|
||||
dropout = 0
|
||||
std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim))
|
||||
nn.init.normal_(self.pos_conv.weight, mean=0, std=std)
|
||||
nn.init.constant_(self.pos_conv.bias, 0)
|
||||
|
||||
self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2)
|
||||
self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU())
|
||||
|
||||
if hasattr(args, "relative_position_embedding"):
|
||||
self.relative_position_embedding = args.relative_position_embedding
|
||||
self.num_buckets = args.num_buckets
|
||||
self.max_distance = args.max_distance
|
||||
else:
|
||||
self.relative_position_embedding = False
|
||||
self.num_buckets = 0
|
||||
self.max_distance = 0
|
||||
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
TransformerSentenceEncoderLayer(
|
||||
embedding_dim=self.embedding_dim,
|
||||
ffn_embedding_dim=args.encoder_ffn_embed_dim,
|
||||
num_attention_heads=args.encoder_attention_heads,
|
||||
dropout=self.dropout,
|
||||
attention_dropout=args.attention_dropout,
|
||||
activation_dropout=args.activation_dropout,
|
||||
activation_fn=args.activation_fn,
|
||||
layer_norm_first=args.layer_norm_first,
|
||||
deep_norm=args.deep_norm,
|
||||
has_relative_attention_bias=self.relative_position_embedding,
|
||||
num_buckets=self.num_buckets,
|
||||
max_distance=self.max_distance,
|
||||
gru_rel_pos=args.gru_rel_pos,
|
||||
encoder_layers=args.encoder_layers,
|
||||
)
|
||||
for i in range(args.encoder_layers)
|
||||
]
|
||||
)
|
||||
if self.relative_position_embedding:
|
||||
for i in range(1, args.encoder_layers):
|
||||
del self.layers[i].self_attn.relative_attention_bias
|
||||
self.layers[i].self_attn.relative_attention_bias = self.layers[0].self_attn.relative_attention_bias
|
||||
|
||||
self.layer_norm_first = args.layer_norm_first
|
||||
self.layer_norm = LayerNorm(self.embedding_dim)
|
||||
self.layerdrop = args.encoder_layerdrop
|
||||
|
||||
self.apply(init_bert_params)
|
||||
|
||||
if args.deep_norm:
|
||||
deep_norm_beta = math.pow(8 * args.encoder_layers, -1 / 4)
|
||||
for i in range(args.encoder_layers):
|
||||
nn.init.xavier_normal_(self.layers[i].self_attn.k_proj.weight, gain=1)
|
||||
nn.init.xavier_normal_(self.layers[i].self_attn.v_proj.weight, gain=deep_norm_beta)
|
||||
nn.init.xavier_normal_(self.layers[i].self_attn.q_proj.weight, gain=1)
|
||||
nn.init.xavier_normal_(self.layers[i].self_attn.out_proj.weight, gain=deep_norm_beta)
|
||||
nn.init.xavier_normal_(self.layers[i].fc1.weight, gain=deep_norm_beta)
|
||||
nn.init.xavier_normal_(self.layers[i].fc2.weight, gain=deep_norm_beta)
|
||||
|
||||
self.layer_wise_gradient_decay_ratio = getattr(args, "layer_wise_gradient_decay_ratio", 1)
|
||||
|
||||
def forward(self, x, padding_mask=None, layer=None):
|
||||
x, layer_results = self.extract_features(x, padding_mask, layer)
|
||||
|
||||
if self.layer_norm_first and layer is None:
|
||||
x = self.layer_norm(x)
|
||||
|
||||
return x, layer_results
|
||||
|
||||
def extract_features(self, x, padding_mask=None, tgt_layer=None):
|
||||
|
||||
if padding_mask is not None:
|
||||
x[padding_mask] = 0
|
||||
|
||||
x_conv = self.pos_conv(x.transpose(1, 2))
|
||||
x_conv = x_conv.transpose(1, 2)
|
||||
x = x + x_conv
|
||||
|
||||
if not self.layer_norm_first:
|
||||
x = self.layer_norm(x)
|
||||
|
||||
x = F.dropout(x, p=self.dropout, training=self.training)
|
||||
|
||||
# B x T x C -> T x B x C
|
||||
x = x.transpose(0, 1)
|
||||
|
||||
layer_results = []
|
||||
z = None
|
||||
if tgt_layer is not None:
|
||||
layer_results.append((x, z))
|
||||
r = None
|
||||
pos_bias = None
|
||||
for i, layer in enumerate(self.layers):
|
||||
if self.layer_wise_gradient_decay_ratio != 1.0:
|
||||
x = GradMultiply.apply(x, self.layer_wise_gradient_decay_ratio)
|
||||
dropout_probability = np.random.random()
|
||||
if not self.training or (dropout_probability > self.layerdrop):
|
||||
x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False, pos_bias=pos_bias)
|
||||
if tgt_layer is not None:
|
||||
layer_results.append((x, z))
|
||||
if i == tgt_layer:
|
||||
r = x
|
||||
break
|
||||
|
||||
if r is not None:
|
||||
x = r
|
||||
|
||||
# T x B x C -> B x T x C
|
||||
x = x.transpose(0, 1)
|
||||
|
||||
return x, layer_results
|
||||
|
||||
|
||||
class TransformerSentenceEncoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
embedding_dim: float = 768,
|
||||
ffn_embedding_dim: float = 3072,
|
||||
num_attention_heads: float = 8,
|
||||
dropout: float = 0.1,
|
||||
attention_dropout: float = 0.1,
|
||||
activation_dropout: float = 0.1,
|
||||
activation_fn: str = "relu",
|
||||
layer_norm_first: bool = False,
|
||||
deep_norm: bool = False,
|
||||
has_relative_attention_bias: bool = False,
|
||||
num_buckets: int = 0,
|
||||
max_distance: int = 0,
|
||||
rescale_init: bool = False,
|
||||
gru_rel_pos: bool = False,
|
||||
encoder_layers: int = 0,
|
||||
) -> None:
|
||||
|
||||
super().__init__()
|
||||
self.embedding_dim = embedding_dim
|
||||
self.dropout = dropout
|
||||
self.activation_dropout = activation_dropout
|
||||
|
||||
self.activation_name = activation_fn
|
||||
self.activation_fn = get_activation_fn(activation_fn)
|
||||
self.self_attn = MultiheadAttention(
|
||||
self.embedding_dim,
|
||||
num_attention_heads,
|
||||
dropout=attention_dropout,
|
||||
self_attention=True,
|
||||
has_relative_attention_bias=has_relative_attention_bias,
|
||||
num_buckets=num_buckets,
|
||||
max_distance=max_distance,
|
||||
rescale_init=rescale_init,
|
||||
gru_rel_pos=gru_rel_pos,
|
||||
)
|
||||
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(self.activation_dropout)
|
||||
self.dropout3 = nn.Dropout(dropout)
|
||||
|
||||
self.layer_norm_first = layer_norm_first
|
||||
|
||||
self.self_attn_layer_norm = LayerNorm(self.embedding_dim)
|
||||
|
||||
if self.activation_name == "glu":
|
||||
self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish")
|
||||
else:
|
||||
self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
|
||||
self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
|
||||
|
||||
self.final_layer_norm = LayerNorm(self.embedding_dim)
|
||||
|
||||
self.deep_norm = deep_norm
|
||||
if self.deep_norm:
|
||||
self.deep_norm_alpha = math.pow(2 * encoder_layers, 1 / 4)
|
||||
else:
|
||||
self.deep_norm_alpha = 1
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
self_attn_mask: torch.Tensor = None,
|
||||
self_attn_padding_mask: torch.Tensor = None,
|
||||
need_weights: bool = False,
|
||||
pos_bias=None
|
||||
):
|
||||
residual = x
|
||||
|
||||
if self.layer_norm_first:
|
||||
x = self.self_attn_layer_norm(x)
|
||||
x, attn, pos_bias = self.self_attn(
|
||||
query=x,
|
||||
key=x,
|
||||
value=x,
|
||||
key_padding_mask=self_attn_padding_mask,
|
||||
need_weights=False,
|
||||
attn_mask=self_attn_mask,
|
||||
position_bias=pos_bias
|
||||
)
|
||||
x = self.dropout1(x)
|
||||
x = residual + x
|
||||
|
||||
residual = x
|
||||
x = self.final_layer_norm(x)
|
||||
if self.activation_name == "glu":
|
||||
x = self.fc1(x)
|
||||
else:
|
||||
x = self.activation_fn(self.fc1(x))
|
||||
x = self.dropout2(x)
|
||||
x = self.fc2(x)
|
||||
x = self.dropout3(x)
|
||||
x = residual + x
|
||||
else:
|
||||
x, attn, pos_bias = self.self_attn(
|
||||
query=x,
|
||||
key=x,
|
||||
value=x,
|
||||
key_padding_mask=self_attn_padding_mask,
|
||||
need_weights=need_weights,
|
||||
attn_mask=self_attn_mask,
|
||||
position_bias=pos_bias
|
||||
)
|
||||
|
||||
x = self.dropout1(x)
|
||||
x = residual * self.deep_norm_alpha + x
|
||||
|
||||
x = self.self_attn_layer_norm(x)
|
||||
|
||||
residual = x
|
||||
if self.activation_name == "glu":
|
||||
x = self.fc1(x)
|
||||
else:
|
||||
x = self.activation_fn(self.fc1(x))
|
||||
x = self.dropout2(x)
|
||||
x = self.fc2(x)
|
||||
x = self.dropout3(x)
|
||||
x = residual * self.deep_norm_alpha + x
|
||||
x = self.final_layer_norm(x)
|
||||
|
||||
return x, attn, pos_bias
|
||||
|
||||
|
||||
class MultiheadAttention(nn.Module):
|
||||
"""Multi-headed attention.
|
||||
|
||||
See "Attention Is All You Need" for more details.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim,
|
||||
num_heads,
|
||||
kdim=None,
|
||||
vdim=None,
|
||||
dropout=0.0,
|
||||
bias=True,
|
||||
add_bias_kv=False,
|
||||
add_zero_attn=False,
|
||||
self_attention=False,
|
||||
encoder_decoder_attention=False,
|
||||
q_noise=0.0,
|
||||
qn_block_size=8,
|
||||
has_relative_attention_bias=False,
|
||||
num_buckets=32,
|
||||
max_distance=128,
|
||||
gru_rel_pos=False,
|
||||
rescale_init=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.kdim = kdim if kdim is not None else embed_dim
|
||||
self.vdim = vdim if vdim is not None else embed_dim
|
||||
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
|
||||
|
||||
self.num_heads = num_heads
|
||||
self.dropout_module = nn.Dropout(dropout)
|
||||
|
||||
self.has_relative_attention_bias = has_relative_attention_bias
|
||||
self.num_buckets = num_buckets
|
||||
self.max_distance = max_distance
|
||||
if self.has_relative_attention_bias:
|
||||
self.relative_attention_bias = nn.Embedding(num_buckets, num_heads)
|
||||
|
||||
self.head_dim = embed_dim // num_heads
|
||||
self.q_head_dim = self.head_dim
|
||||
self.k_head_dim = self.head_dim
|
||||
assert (
|
||||
self.head_dim * num_heads == self.embed_dim
|
||||
), "embed_dim must be divisible by num_heads"
|
||||
self.scaling = self.head_dim ** -0.5
|
||||
|
||||
self.self_attention = self_attention
|
||||
self.encoder_decoder_attention = encoder_decoder_attention
|
||||
|
||||
assert not self.self_attention or self.qkv_same_dim, (
|
||||
"Self-attention requires query, key and " "value to be of the same size"
|
||||
)
|
||||
|
||||
k_bias = True
|
||||
if rescale_init:
|
||||
k_bias = False
|
||||
|
||||
k_embed_dim = embed_dim
|
||||
q_embed_dim = embed_dim
|
||||
|
||||
self.k_proj = quant_noise(
|
||||
nn.Linear(self.kdim, k_embed_dim, bias=k_bias), q_noise, qn_block_size
|
||||
)
|
||||
self.v_proj = quant_noise(
|
||||
nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size
|
||||
)
|
||||
self.q_proj = quant_noise(
|
||||
nn.Linear(embed_dim, q_embed_dim, bias=bias), q_noise, qn_block_size
|
||||
)
|
||||
|
||||
self.out_proj = quant_noise(
|
||||
nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size
|
||||
)
|
||||
|
||||
if add_bias_kv:
|
||||
self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
|
||||
self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
|
||||
else:
|
||||
self.bias_k = self.bias_v = None
|
||||
|
||||
self.add_zero_attn = add_zero_attn
|
||||
|
||||
self.gru_rel_pos = gru_rel_pos
|
||||
if self.gru_rel_pos:
|
||||
self.grep_linear = nn.Linear(self.q_head_dim, 8)
|
||||
self.grep_a = nn.Parameter(torch.ones(1, num_heads, 1, 1))
|
||||
|
||||
self.reset_parameters()
|
||||
|
||||
def reset_parameters(self):
|
||||
if self.qkv_same_dim:
|
||||
# Empirically observed the convergence to be much better with
|
||||
# the scaled initialization
|
||||
nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
|
||||
nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
|
||||
nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
|
||||
else:
|
||||
nn.init.xavier_uniform_(self.k_proj.weight)
|
||||
nn.init.xavier_uniform_(self.v_proj.weight)
|
||||
nn.init.xavier_uniform_(self.q_proj.weight)
|
||||
|
||||
nn.init.xavier_uniform_(self.out_proj.weight)
|
||||
if self.out_proj.bias is not None:
|
||||
nn.init.constant_(self.out_proj.bias, 0.0)
|
||||
if self.bias_k is not None:
|
||||
nn.init.xavier_normal_(self.bias_k)
|
||||
if self.bias_v is not None:
|
||||
nn.init.xavier_normal_(self.bias_v)
|
||||
if self.has_relative_attention_bias:
|
||||
nn.init.xavier_normal_(self.relative_attention_bias.weight)
|
||||
|
||||
def _relative_positions_bucket(self, relative_positions, bidirectional=True):
|
||||
num_buckets = self.num_buckets
|
||||
max_distance = self.max_distance
|
||||
relative_buckets = 0
|
||||
|
||||
if bidirectional:
|
||||
num_buckets = num_buckets // 2
|
||||
relative_buckets += (relative_positions > 0).to(torch.long) * num_buckets
|
||||
relative_positions = torch.abs(relative_positions)
|
||||
else:
|
||||
relative_positions = -torch.min(relative_positions, torch.zeros_like(relative_positions))
|
||||
|
||||
max_exact = num_buckets // 2
|
||||
is_small = relative_positions < max_exact
|
||||
|
||||
relative_postion_if_large = max_exact + (
|
||||
torch.log(relative_positions.float() / max_exact)
|
||||
/ math.log(max_distance / max_exact)
|
||||
* (num_buckets - max_exact)
|
||||
).to(torch.long)
|
||||
relative_postion_if_large = torch.min(
|
||||
relative_postion_if_large, torch.full_like(relative_postion_if_large, num_buckets - 1)
|
||||
)
|
||||
|
||||
relative_buckets += torch.where(is_small, relative_positions, relative_postion_if_large)
|
||||
return relative_buckets
|
||||
|
||||
def compute_bias(self, query_length, key_length):
|
||||
context_position = torch.arange(query_length, dtype=torch.long)[:, None]
|
||||
memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
|
||||
relative_position = memory_position - context_position
|
||||
relative_position_bucket = self._relative_positions_bucket(
|
||||
relative_position,
|
||||
bidirectional=True
|
||||
)
|
||||
relative_position_bucket = relative_position_bucket.to(self.relative_attention_bias.weight.device)
|
||||
values = self.relative_attention_bias(relative_position_bucket)
|
||||
values = values.permute([2, 0, 1])
|
||||
return values
|
||||
|
||||
def forward(
|
||||
self,
|
||||
query,
|
||||
key: Optional[Tensor],
|
||||
value: Optional[Tensor],
|
||||
key_padding_mask: Optional[Tensor] = None,
|
||||
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
|
||||
need_weights: bool = True,
|
||||
static_kv: bool = False,
|
||||
attn_mask: Optional[Tensor] = None,
|
||||
before_softmax: bool = False,
|
||||
need_head_weights: bool = False,
|
||||
position_bias: Optional[Tensor] = None
|
||||
) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
|
||||
"""Input shape: Time x Batch x Channel
|
||||
|
||||
Args:
|
||||
key_padding_mask (ByteTensor, optional): mask to exclude
|
||||
keys that are pads, of shape `(batch, src_len)`, where
|
||||
padding elements are indicated by 1s.
|
||||
need_weights (bool, optional): return the attention weights,
|
||||
averaged over heads (default: False).
|
||||
attn_mask (ByteTensor, optional): typically used to
|
||||
implement causal attention, where the mask prevents the
|
||||
attention from looking forward in time (default: None).
|
||||
before_softmax (bool, optional): return the raw attention
|
||||
weights and values before the attention softmax.
|
||||
need_head_weights (bool, optional): return the attention
|
||||
weights for each head. Implies *need_weights*. Default:
|
||||
return the average attention weights over all heads.
|
||||
"""
|
||||
if need_head_weights:
|
||||
need_weights = True
|
||||
|
||||
is_tpu = query.device.type == "xla"
|
||||
|
||||
tgt_len, bsz, embed_dim = query.size()
|
||||
src_len = tgt_len
|
||||
assert embed_dim == self.embed_dim
|
||||
assert list(query.size()) == [tgt_len, bsz, embed_dim]
|
||||
if key is not None:
|
||||
src_len, key_bsz, _ = key.size()
|
||||
if not torch.jit.is_scripting():
|
||||
assert key_bsz == bsz
|
||||
assert value is not None
|
||||
assert src_len, bsz == value.shape[:2]
|
||||
|
||||
if self.has_relative_attention_bias and position_bias is None:
|
||||
position_bias = self.compute_bias(tgt_len, src_len)
|
||||
position_bias = position_bias.unsqueeze(0).repeat(bsz, 1, 1, 1).view(bsz * self.num_heads, tgt_len, src_len)
|
||||
|
||||
if incremental_state is not None:
|
||||
saved_state = self._get_input_buffer(incremental_state)
|
||||
if saved_state is not None and "prev_key" in saved_state:
|
||||
# previous time steps are cached - no need to recompute
|
||||
# key and value if they are static
|
||||
if static_kv:
|
||||
assert self.encoder_decoder_attention and not self.self_attention
|
||||
key = value = None
|
||||
else:
|
||||
saved_state = None
|
||||
|
||||
if self.self_attention:
|
||||
q = self.q_proj(query)
|
||||
k = self.k_proj(query)
|
||||
v = self.v_proj(query)
|
||||
elif self.encoder_decoder_attention:
|
||||
# encoder-decoder attention
|
||||
q = self.q_proj(query)
|
||||
if key is None:
|
||||
assert value is None
|
||||
k = v = None
|
||||
else:
|
||||
k = self.k_proj(key)
|
||||
v = self.v_proj(key)
|
||||
|
||||
else:
|
||||
assert key is not None and value is not None
|
||||
q = self.q_proj(query)
|
||||
k = self.k_proj(key)
|
||||
v = self.v_proj(value)
|
||||
q *= self.scaling
|
||||
alpha = 32
|
||||
q *= 1 / alpha
|
||||
|
||||
if self.bias_k is not None:
|
||||
assert self.bias_v is not None
|
||||
k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
|
||||
v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
|
||||
if attn_mask is not None:
|
||||
attn_mask = torch.cat(
|
||||
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
|
||||
)
|
||||
if key_padding_mask is not None:
|
||||
key_padding_mask = torch.cat(
|
||||
[
|
||||
key_padding_mask,
|
||||
key_padding_mask.new_zeros(key_padding_mask.size(0), 1),
|
||||
],
|
||||
dim=1,
|
||||
)
|
||||
|
||||
q = (
|
||||
q.contiguous()
|
||||
.view(tgt_len, bsz * self.num_heads, self.q_head_dim)
|
||||
.transpose(0, 1)
|
||||
)
|
||||
if k is not None:
|
||||
k = (
|
||||
k.contiguous()
|
||||
.view(-1, bsz * self.num_heads, self.k_head_dim)
|
||||
.transpose(0, 1)
|
||||
)
|
||||
if v is not None:
|
||||
v = (
|
||||
v.contiguous()
|
||||
.view(-1, bsz * self.num_heads, self.head_dim)
|
||||
.transpose(0, 1)
|
||||
)
|
||||
|
||||
if saved_state is not None:
|
||||
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
|
||||
if "prev_key" in saved_state:
|
||||
_prev_key = saved_state["prev_key"]
|
||||
assert _prev_key is not None
|
||||
prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
|
||||
if static_kv:
|
||||
k = prev_key
|
||||
else:
|
||||
assert k is not None
|
||||
k = torch.cat([prev_key, k], dim=1)
|
||||
src_len = k.size(1)
|
||||
if "prev_value" in saved_state:
|
||||
_prev_value = saved_state["prev_value"]
|
||||
assert _prev_value is not None
|
||||
prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
|
||||
if static_kv:
|
||||
v = prev_value
|
||||
else:
|
||||
assert v is not None
|
||||
v = torch.cat([prev_value, v], dim=1)
|
||||
prev_key_padding_mask: Optional[Tensor] = None
|
||||
if "prev_key_padding_mask" in saved_state:
|
||||
prev_key_padding_mask = saved_state["prev_key_padding_mask"]
|
||||
assert k is not None and v is not None
|
||||
key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
|
||||
key_padding_mask=key_padding_mask,
|
||||
prev_key_padding_mask=prev_key_padding_mask,
|
||||
batch_size=bsz,
|
||||
src_len=k.size(1),
|
||||
static_kv=static_kv,
|
||||
)
|
||||
|
||||
saved_state["prev_key"] = k.view(bsz, self.num_heads, -1, self.head_dim)
|
||||
saved_state["prev_value"] = v.view(bsz, self.num_heads, -1, self.head_dim)
|
||||
saved_state["prev_key_padding_mask"] = key_padding_mask
|
||||
# In this branch incremental_state is never None
|
||||
assert incremental_state is not None
|
||||
incremental_state = self._set_input_buffer(incremental_state, saved_state)
|
||||
assert k is not None
|
||||
assert k.size(1) == src_len
|
||||
|
||||
# This is part of a workaround to get around fork/join parallelism
|
||||
# not supporting Optional types.
|
||||
if key_padding_mask is not None and key_padding_mask.dim() == 0:
|
||||
key_padding_mask = None
|
||||
|
||||
if key_padding_mask is not None:
|
||||
assert key_padding_mask.size(0) == bsz
|
||||
assert key_padding_mask.size(1) == src_len
|
||||
|
||||
if self.add_zero_attn:
|
||||
assert v is not None
|
||||
src_len += 1
|
||||
k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
|
||||
v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
|
||||
if attn_mask is not None:
|
||||
attn_mask = torch.cat(
|
||||
[attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1
|
||||
)
|
||||
if key_padding_mask is not None:
|
||||
key_padding_mask = torch.cat(
|
||||
[
|
||||
key_padding_mask,
|
||||
torch.zeros(key_padding_mask.size(0), 1).type_as(
|
||||
key_padding_mask
|
||||
),
|
||||
],
|
||||
dim=1,
|
||||
)
|
||||
|
||||
attn_weights = torch.bmm(q, k.transpose(1, 2))
|
||||
attn_weights = (attn_weights - attn_weights.max(dim=-1, keepdim=True)[0]) * alpha
|
||||
attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
|
||||
|
||||
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
|
||||
|
||||
if attn_mask is not None:
|
||||
attn_mask = attn_mask.unsqueeze(0)
|
||||
attn_weights += attn_mask
|
||||
|
||||
if key_padding_mask is not None:
|
||||
# don't attend to padding symbols
|
||||
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
||||
if not is_tpu:
|
||||
attn_weights = attn_weights.masked_fill(
|
||||
key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
|
||||
float("-inf"),
|
||||
)
|
||||
else:
|
||||
attn_weights = attn_weights.transpose(0, 2)
|
||||
attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
|
||||
attn_weights = attn_weights.transpose(0, 2)
|
||||
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
||||
|
||||
if before_softmax:
|
||||
return attn_weights, v, position_bias
|
||||
|
||||
if position_bias is not None:
|
||||
attn_mask_rel_pos = position_bias
|
||||
if self.gru_rel_pos == 1:
|
||||
query_layer = q.view(bsz, self.num_heads, tgt_len, self.q_head_dim) * alpha / self.scaling
|
||||
_B, _H, _L, __ = query_layer.size()
|
||||
gate_a, gate_b = torch.sigmoid(self.grep_linear(query_layer).view(
|
||||
_B, _H, _L, 2, 4).sum(-1, keepdim=False)).chunk(2, dim=-1)
|
||||
gate_a_1 = gate_a * (gate_b * self.grep_a - 1.0) + 2.0
|
||||
attn_mask_rel_pos = gate_a_1.view(bsz * self.num_heads, tgt_len, 1) * position_bias
|
||||
|
||||
attn_mask_rel_pos = attn_mask_rel_pos.view(attn_weights.size())
|
||||
|
||||
attn_weights = attn_weights + attn_mask_rel_pos
|
||||
|
||||
attn_weights_float = F.softmax(
|
||||
attn_weights, dim=-1
|
||||
)
|
||||
attn_weights = attn_weights_float.type_as(attn_weights)
|
||||
attn_probs = self.dropout_module(attn_weights)
|
||||
|
||||
assert v is not None
|
||||
attn = torch.bmm(attn_probs, v)
|
||||
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
|
||||
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
|
||||
attn = self.out_proj(attn)
|
||||
attn_weights: Optional[Tensor] = None
|
||||
if need_weights:
|
||||
attn_weights = attn_weights_float.view(
|
||||
bsz, self.num_heads, tgt_len, src_len
|
||||
).transpose(1, 0)
|
||||
if not need_head_weights:
|
||||
# average attention weights over heads
|
||||
attn_weights = attn_weights.mean(dim=0)
|
||||
|
||||
return attn, attn_weights, position_bias
|
||||
|
||||
@staticmethod
|
||||
def _append_prev_key_padding_mask(
|
||||
key_padding_mask: Optional[Tensor],
|
||||
prev_key_padding_mask: Optional[Tensor],
|
||||
batch_size: int,
|
||||
src_len: int,
|
||||
static_kv: bool,
|
||||
) -> Optional[Tensor]:
|
||||
# saved key padding masks have shape (bsz, seq_len)
|
||||
if prev_key_padding_mask is not None and static_kv:
|
||||
new_key_padding_mask = prev_key_padding_mask
|
||||
elif prev_key_padding_mask is not None and key_padding_mask is not None:
|
||||
new_key_padding_mask = torch.cat(
|
||||
[prev_key_padding_mask.float(), key_padding_mask.float()], dim=1
|
||||
)
|
||||
# During incremental decoding, as the padding token enters and
|
||||
# leaves the frame, there will be a time when prev or current
|
||||
# is None
|
||||
elif prev_key_padding_mask is not None:
|
||||
if src_len > prev_key_padding_mask.size(1):
|
||||
filler = torch.zeros(
|
||||
(batch_size, src_len - prev_key_padding_mask.size(1)),
|
||||
device=prev_key_padding_mask.device,
|
||||
)
|
||||
new_key_padding_mask = torch.cat(
|
||||
[prev_key_padding_mask.float(), filler.float()], dim=1
|
||||
)
|
||||
else:
|
||||
new_key_padding_mask = prev_key_padding_mask.float()
|
||||
elif key_padding_mask is not None:
|
||||
if src_len > key_padding_mask.size(1):
|
||||
filler = torch.zeros(
|
||||
(batch_size, src_len - key_padding_mask.size(1)),
|
||||
device=key_padding_mask.device,
|
||||
)
|
||||
new_key_padding_mask = torch.cat(
|
||||
[filler.float(), key_padding_mask.float()], dim=1
|
||||
)
|
||||
else:
|
||||
new_key_padding_mask = key_padding_mask.float()
|
||||
else:
|
||||
new_key_padding_mask = prev_key_padding_mask
|
||||
return new_key_padding_mask
|
||||
|
||||
def _get_input_buffer(
|
||||
self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
|
||||
) -> Dict[str, Optional[Tensor]]:
|
||||
result = self.get_incremental_state(incremental_state, "attn_state")
|
||||
if result is not None:
|
||||
return result
|
||||
else:
|
||||
empty_result: Dict[str, Optional[Tensor]] = {}
|
||||
return empty_result
|
||||
|
||||
def _set_input_buffer(
|
||||
self,
|
||||
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
|
||||
buffer: Dict[str, Optional[Tensor]],
|
||||
):
|
||||
return self.set_incremental_state(incremental_state, "attn_state", buffer)
|
||||
|
||||
def apply_sparse_mask(self, attn_weights, tgt_len: int, src_len: int, bsz: int):
|
||||
return attn_weights
|
||||
|
||||
|
||||
def init_bert_params(module):
|
||||
"""
|
||||
Initialize the weights specific to the BERT Model.
|
||||
This overrides the default initializations depending on the specified arguments.
|
||||
1. If normal_init_linear_weights is set then weights of linear
|
||||
layer will be initialized using the normal distribution and
|
||||
bais will be set to the specified value.
|
||||
2. If normal_init_embed_weights is set then weights of embedding
|
||||
layer will be initialized using the normal distribution.
|
||||
3. If normal_init_proj_weights is set then weights of
|
||||
in_project_weight for MultiHeadAttention initialized using
|
||||
the normal distribution (to be validated).
|
||||
"""
|
||||
|
||||
def normal_(data):
|
||||
# with FSDP, module params will be on CUDA, so we cast them back to CPU
|
||||
# so that the RNG is consistent with and without FSDP
|
||||
data.copy_(
|
||||
data.cpu().normal_(mean=0.0, std=0.02).to(data.device)
|
||||
)
|
||||
|
||||
if isinstance(module, nn.Linear):
|
||||
normal_(module.weight.data)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
if isinstance(module, nn.Embedding):
|
||||
normal_(module.weight.data)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
if isinstance(module, MultiheadAttention):
|
||||
normal_(module.q_proj.weight.data)
|
||||
normal_(module.k_proj.weight.data)
|
||||
normal_(module.v_proj.weight.data)
|
||||
@@ -0,0 +1,179 @@
|
||||
# --------------------------------------------------------
|
||||
# BEATs: Audio Pre-Training with Acoustic Tokenizers (https://arxiv.org/abs/2212.09058)
|
||||
# Github source: https://github.com/microsoft/unilm/tree/master/beats
|
||||
# Copyright (c) 2022 Microsoft
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# Based on fairseq code bases
|
||||
# https://github.com/pytorch/fairseq
|
||||
# --------------------------------------------------------
|
||||
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn import LayerNorm
|
||||
import torchaudio.compliance.kaldi as ta_kaldi
|
||||
|
||||
from .beats_backbone import (
|
||||
TransformerEncoder,
|
||||
)
|
||||
|
||||
import logging
|
||||
from typing import Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BEATsConfig:
|
||||
def __init__(self, cfg=None):
|
||||
self.input_patch_size: int = -1 # path size of patch embedding
|
||||
self.embed_dim: int = 512 # patch embedding dimension
|
||||
self.conv_bias: bool = False # include bias in conv encoder
|
||||
|
||||
self.encoder_layers: int = 12 # num encoder layers in the transformer
|
||||
self.encoder_embed_dim: int = 768 # encoder embedding dimension
|
||||
self.encoder_ffn_embed_dim: int = 3072 # encoder embedding dimension for FFN
|
||||
self.encoder_attention_heads: int = 12 # num encoder attention heads
|
||||
self.activation_fn: str = "gelu" # activation function to use
|
||||
|
||||
self.layer_wise_gradient_decay_ratio: float = 1.0 # ratio for layer-wise gradient decay
|
||||
self.layer_norm_first: bool = False # apply layernorm first in the transformer
|
||||
self.deep_norm: bool = False # apply deep_norm first in the transformer
|
||||
|
||||
# dropouts
|
||||
self.dropout: float = 0.1 # dropout probability for the transformer
|
||||
self.attention_dropout: float = 0.1 # dropout probability for attention weights
|
||||
self.activation_dropout: float = 0.0 # dropout probability after activation in FFN
|
||||
self.encoder_layerdrop: float = 0.0 # probability of dropping a tarnsformer layer
|
||||
self.dropout_input: float = 0.0 # dropout to apply to the input (after feat extr)
|
||||
|
||||
# positional embeddings
|
||||
self.conv_pos: int = 128 # number of filters for convolutional positional embeddings
|
||||
self.conv_pos_groups: int = 16 # number of groups for convolutional positional embedding
|
||||
|
||||
# relative position embedding
|
||||
self.relative_position_embedding: bool = False # apply relative position embedding
|
||||
self.num_buckets: int = 320 # number of buckets for relative position embedding
|
||||
self.max_distance: int = 1280 # maximum distance for relative position embedding
|
||||
self.gru_rel_pos: bool = False # apply gated relative position embedding
|
||||
|
||||
# label predictor
|
||||
self.finetuned_model: bool = False # whether the model is a fine-tuned model.
|
||||
self.predictor_dropout: float = 0.1 # dropout probability for the predictor
|
||||
self.predictor_class: int = 527 # target class number for the predictor
|
||||
|
||||
if cfg is not None:
|
||||
self.update(cfg)
|
||||
|
||||
def update(self, cfg: dict):
|
||||
self.__dict__.update(cfg)
|
||||
|
||||
|
||||
class BEATs(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
cfg: BEATsConfig,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
logger.info(f"BEATs Config: {cfg.__dict__}")
|
||||
|
||||
self.cfg = cfg
|
||||
|
||||
self.embed = cfg.embed_dim
|
||||
self.post_extract_proj = (
|
||||
nn.Linear(self.embed, cfg.encoder_embed_dim)
|
||||
if self.embed != cfg.encoder_embed_dim
|
||||
else None
|
||||
)
|
||||
|
||||
self.input_patch_size = cfg.input_patch_size
|
||||
self.patch_embedding = nn.Conv2d(1, self.embed, kernel_size=self.input_patch_size, stride=self.input_patch_size,
|
||||
bias=cfg.conv_bias)
|
||||
|
||||
self.dropout_input = nn.Dropout(cfg.dropout_input)
|
||||
|
||||
assert not cfg.deep_norm or not cfg.layer_norm_first
|
||||
self.encoder = TransformerEncoder(cfg)
|
||||
self.layer_norm = LayerNorm(self.embed)
|
||||
|
||||
if cfg.finetuned_model:
|
||||
self.predictor_dropout = nn.Dropout(cfg.predictor_dropout)
|
||||
self.predictor = nn.Linear(cfg.encoder_embed_dim, cfg.predictor_class)
|
||||
else:
|
||||
self.predictor = None
|
||||
|
||||
def forward_padding_mask(
|
||||
self,
|
||||
features: torch.Tensor,
|
||||
padding_mask: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
extra = padding_mask.size(1) % features.size(1)
|
||||
if extra > 0:
|
||||
padding_mask = padding_mask[:, :-extra]
|
||||
padding_mask = padding_mask.view(
|
||||
padding_mask.size(0), features.size(1), -1
|
||||
)
|
||||
padding_mask = padding_mask.all(-1)
|
||||
return padding_mask
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
source: torch.Tensor,
|
||||
fbank_mean: float = 15.41663,
|
||||
fbank_std: float = 6.55582,
|
||||
) -> torch.Tensor:
|
||||
fbanks = []
|
||||
for waveform in source:
|
||||
waveform = waveform.unsqueeze(0) * 2 ** 15
|
||||
fbank = ta_kaldi.fbank(waveform, num_mel_bins=128, sample_frequency=16000, frame_length=25, frame_shift=10)
|
||||
fbanks.append(fbank)
|
||||
fbank = torch.stack(fbanks, dim=0)
|
||||
fbank = (fbank - fbank_mean) / (2 * fbank_std)
|
||||
return fbank
|
||||
|
||||
def extract_features(
|
||||
self,
|
||||
source: torch.Tensor,
|
||||
padding_mask: Optional[torch.Tensor] = None,
|
||||
fbank_mean: float = 15.41663,
|
||||
fbank_std: float = 6.55582,
|
||||
):
|
||||
fbank = self.preprocess(source, fbank_mean=fbank_mean, fbank_std=fbank_std)
|
||||
|
||||
if padding_mask is not None:
|
||||
padding_mask = self.forward_padding_mask(fbank, padding_mask)
|
||||
|
||||
fbank = fbank.unsqueeze(1)
|
||||
features = self.patch_embedding(fbank)
|
||||
features = features.reshape(features.shape[0], features.shape[1], -1)
|
||||
features = features.transpose(1, 2)
|
||||
features = self.layer_norm(features)
|
||||
|
||||
if padding_mask is not None:
|
||||
padding_mask = self.forward_padding_mask(features, padding_mask)
|
||||
|
||||
if self.post_extract_proj is not None:
|
||||
features = self.post_extract_proj(features)
|
||||
|
||||
x = self.dropout_input(features)
|
||||
|
||||
x, layer_results = self.encoder(
|
||||
x,
|
||||
padding_mask=padding_mask,
|
||||
)
|
||||
|
||||
if self.predictor is not None:
|
||||
x = self.predictor_dropout(x)
|
||||
logits = self.predictor(x)
|
||||
|
||||
if padding_mask is not None and padding_mask.any():
|
||||
logits[padding_mask] = 0
|
||||
logits = logits.sum(dim=1)
|
||||
logits = logits / (~padding_mask).sum(dim=1).unsqueeze(-1).expand_as(logits)
|
||||
else:
|
||||
logits = logits.mean(dim=1)
|
||||
|
||||
lprobs = torch.sigmoid(logits)
|
||||
|
||||
return lprobs, padding_mask
|
||||
else:
|
||||
return x, padding_mask
|
||||
@@ -0,0 +1,219 @@
|
||||
# --------------------------------------------------------
|
||||
# BEATs: Audio Pre-Training with Acoustic Tokenizers (https://arxiv.org/abs/2212.09058)
|
||||
# Github source: https://github.com/microsoft/unilm/tree/master/beats
|
||||
# Copyright (c) 2022 Microsoft
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# Based on fairseq code bases
|
||||
# https://github.com/pytorch/fairseq
|
||||
# --------------------------------------------------------
|
||||
|
||||
import math
|
||||
import warnings
|
||||
import torch
|
||||
from torch import Tensor, nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class GradMultiply(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, x, scale):
|
||||
ctx.scale = scale
|
||||
res = x.new(x)
|
||||
return res
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad):
|
||||
return grad * ctx.scale, None
|
||||
|
||||
|
||||
class SamePad(nn.Module):
|
||||
def __init__(self, kernel_size, causal=False):
|
||||
super().__init__()
|
||||
if causal:
|
||||
self.remove = kernel_size - 1
|
||||
else:
|
||||
self.remove = 1 if kernel_size % 2 == 0 else 0
|
||||
|
||||
def forward(self, x):
|
||||
if self.remove > 0:
|
||||
x = x[:, :, : -self.remove]
|
||||
return x
|
||||
|
||||
|
||||
class Swish(nn.Module):
|
||||
def __init__(self):
|
||||
super(Swish, self).__init__()
|
||||
self.act = torch.nn.Sigmoid()
|
||||
|
||||
def forward(self, x):
|
||||
return x * self.act(x)
|
||||
|
||||
|
||||
class GLU_Linear(nn.Module):
|
||||
def __init__(self, input_dim, output_dim, glu_type="sigmoid", bias_in_glu=True):
|
||||
super(GLU_Linear, self).__init__()
|
||||
|
||||
self.glu_type = glu_type
|
||||
self.output_dim = output_dim
|
||||
|
||||
if glu_type == "sigmoid":
|
||||
self.glu_act = torch.nn.Sigmoid()
|
||||
elif glu_type == "swish":
|
||||
self.glu_act = Swish()
|
||||
elif glu_type == "relu":
|
||||
self.glu_act = torch.nn.ReLU()
|
||||
elif glu_type == "gelu":
|
||||
self.glu_act = torch.nn.GELU()
|
||||
|
||||
if bias_in_glu:
|
||||
self.linear = nn.Linear(input_dim, output_dim * 2, True)
|
||||
else:
|
||||
self.linear = nn.Linear(input_dim, output_dim * 2, False)
|
||||
|
||||
def forward(self, x):
|
||||
# to be consistent with GLU_Linear, we assume the input always has the #channel (#dim) in the last dimension of the tensor, so need to switch the dimension first for 1D-Conv case
|
||||
x = self.linear(x)
|
||||
|
||||
if self.glu_type == "bilinear":
|
||||
x = (x[:, :, 0:self.output_dim] * x[:, :, self.output_dim:self.output_dim * 2])
|
||||
else:
|
||||
x = (x[:, :, 0:self.output_dim] * self.glu_act(x[:, :, self.output_dim:self.output_dim * 2]))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def gelu_accurate(x):
|
||||
if not hasattr(gelu_accurate, "_a"):
|
||||
gelu_accurate._a = math.sqrt(2 / math.pi)
|
||||
return (
|
||||
0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
|
||||
)
|
||||
|
||||
|
||||
def gelu(x: torch.Tensor) -> torch.Tensor:
|
||||
return torch.nn.functional.gelu(x.float()).type_as(x)
|
||||
|
||||
|
||||
def get_activation_fn(activation: str):
|
||||
"""Returns the activation function corresponding to `activation`"""
|
||||
|
||||
if activation == "relu":
|
||||
return F.relu
|
||||
elif activation == "gelu":
|
||||
return gelu
|
||||
elif activation == "gelu_fast":
|
||||
warnings.warn(
|
||||
"--activation-fn=gelu_fast has been renamed to gelu_accurate"
|
||||
)
|
||||
return gelu_accurate
|
||||
elif activation == "gelu_accurate":
|
||||
return gelu_accurate
|
||||
elif activation == "tanh":
|
||||
return torch.tanh
|
||||
elif activation == "linear":
|
||||
return lambda x: x
|
||||
elif activation == "glu":
|
||||
return lambda x: x
|
||||
else:
|
||||
raise RuntimeError("--activation-fn {} not supported".format(activation))
|
||||
|
||||
|
||||
def quant_noise(module, p, block_size):
|
||||
"""
|
||||
Wraps modules and applies quantization noise to the weights for
|
||||
subsequent quantization with Iterative Product Quantization as
|
||||
described in "Training with Quantization Noise for Extreme Model Compression"
|
||||
|
||||
Args:
|
||||
- module: nn.Module
|
||||
- p: amount of Quantization Noise
|
||||
- block_size: size of the blocks for subsequent quantization with iPQ
|
||||
|
||||
Remarks:
|
||||
- Module weights must have the right sizes wrt the block size
|
||||
- Only Linear, Embedding and Conv2d modules are supported for the moment
|
||||
- For more detail on how to quantize by blocks with convolutional weights,
|
||||
see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
|
||||
- We implement the simplest form of noise here as stated in the paper
|
||||
which consists in randomly dropping blocks
|
||||
"""
|
||||
|
||||
# if no quantization noise, don't register hook
|
||||
if p <= 0:
|
||||
return module
|
||||
|
||||
# supported modules
|
||||
assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
|
||||
|
||||
# test whether module.weight has the right sizes wrt block_size
|
||||
is_conv = module.weight.ndim == 4
|
||||
|
||||
# 2D matrix
|
||||
if not is_conv:
|
||||
assert (
|
||||
module.weight.size(1) % block_size == 0
|
||||
), "Input features must be a multiple of block sizes"
|
||||
|
||||
# 4D matrix
|
||||
else:
|
||||
# 1x1 convolutions
|
||||
if module.kernel_size == (1, 1):
|
||||
assert (
|
||||
module.in_channels % block_size == 0
|
||||
), "Input channels must be a multiple of block sizes"
|
||||
# regular convolutions
|
||||
else:
|
||||
k = module.kernel_size[0] * module.kernel_size[1]
|
||||
assert k % block_size == 0, "Kernel size must be a multiple of block size"
|
||||
|
||||
def _forward_pre_hook(mod, input):
|
||||
# no noise for evaluation
|
||||
if mod.training:
|
||||
if not is_conv:
|
||||
# gather weight and sizes
|
||||
weight = mod.weight
|
||||
in_features = weight.size(1)
|
||||
out_features = weight.size(0)
|
||||
|
||||
# split weight matrix into blocks and randomly drop selected blocks
|
||||
mask = torch.zeros(
|
||||
in_features // block_size * out_features, device=weight.device
|
||||
)
|
||||
mask.bernoulli_(p)
|
||||
mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
|
||||
|
||||
else:
|
||||
# gather weight and sizes
|
||||
weight = mod.weight
|
||||
in_channels = mod.in_channels
|
||||
out_channels = mod.out_channels
|
||||
|
||||
# split weight matrix into blocks and randomly drop selected blocks
|
||||
if mod.kernel_size == (1, 1):
|
||||
mask = torch.zeros(
|
||||
int(in_channels // block_size * out_channels),
|
||||
device=weight.device,
|
||||
)
|
||||
mask.bernoulli_(p)
|
||||
mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
|
||||
else:
|
||||
mask = torch.zeros(
|
||||
weight.size(0), weight.size(1), device=weight.device
|
||||
)
|
||||
mask.bernoulli_(p)
|
||||
mask = (
|
||||
mask.unsqueeze(2)
|
||||
.unsqueeze(3)
|
||||
.repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
|
||||
)
|
||||
|
||||
# scale weights and apply mask
|
||||
mask = mask.to(
|
||||
torch.bool
|
||||
) # x.bool() is not currently supported in TorchScript
|
||||
s = 1 / (1 - p)
|
||||
mod.weight.data = s * weight.masked_fill(mask, 0)
|
||||
|
||||
module.register_forward_pre_hook(_forward_pre_hook)
|
||||
return module
|
||||
|
||||
+106
-5
@@ -1,3 +1,4 @@
|
||||
import os
|
||||
import sqlite3
|
||||
import threading
|
||||
from datetime import datetime, timezone
|
||||
@@ -7,7 +8,7 @@ from .paths import _log
|
||||
|
||||
|
||||
class ProcessedDB:
|
||||
_SCHEMA_VERSION = 3 # bump when schema changes
|
||||
_SCHEMA_VERSION = 4 # bump when schema changes
|
||||
|
||||
def __init__(self, db_path: str | None = None):
|
||||
if db_path is None:
|
||||
@@ -47,6 +48,7 @@ class ProcessedDB:
|
||||
" clip_count INTEGER NOT NULL DEFAULT 3,"
|
||||
" spread REAL NOT NULL DEFAULT 3.0,"
|
||||
" profile TEXT NOT NULL DEFAULT 'default',"
|
||||
" source_path TEXT NOT NULL DEFAULT '',"
|
||||
" processed_at TEXT NOT NULL"
|
||||
")"
|
||||
)
|
||||
@@ -62,6 +64,7 @@ class ProcessedDB:
|
||||
"clip_count": "INTEGER NOT NULL DEFAULT 3",
|
||||
"spread": "REAL NOT NULL DEFAULT 3.0",
|
||||
"profile": "TEXT NOT NULL DEFAULT 'default'",
|
||||
"source_path": "TEXT NOT NULL DEFAULT ''",
|
||||
}
|
||||
for col, typedef in new_cols.items():
|
||||
if col not in cols:
|
||||
@@ -85,7 +88,7 @@ class ProcessedDB:
|
||||
short_side: int | None = None, portrait_ratio: str = "",
|
||||
crop_center: float = 0.5, fmt: str = "MP4",
|
||||
clip_count: int = 3, spread: float = 3.0,
|
||||
profile: str = "default") -> None:
|
||||
profile: str = "default", source_path: str = "") -> None:
|
||||
if not self._enabled:
|
||||
return
|
||||
with self._lock:
|
||||
@@ -93,11 +96,11 @@ class ProcessedDB:
|
||||
"INSERT INTO processed"
|
||||
" (filename, start_time, output_path, label, category,"
|
||||
" short_side, portrait_ratio, crop_center, format,"
|
||||
" clip_count, spread, profile, processed_at)"
|
||||
" VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
|
||||
" clip_count, spread, profile, source_path, processed_at)"
|
||||
" VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
|
||||
(filename, start_time, output_path, label, category,
|
||||
short_side, portrait_ratio, crop_center, fmt,
|
||||
clip_count, spread, profile,
|
||||
clip_count, spread, profile, source_path,
|
||||
datetime.now(timezone.utc).isoformat()),
|
||||
)
|
||||
self._con.commit()
|
||||
@@ -223,6 +226,104 @@ class ProcessedDB:
|
||||
).fetchall()
|
||||
return [r[0] for r in rows]
|
||||
|
||||
def get_export_folders(self, profile: str = "default") -> list[str]:
|
||||
"""Return distinct export folder names found in output_paths for a profile.
|
||||
|
||||
Export paths follow the structure:
|
||||
.../export_folder/group_dir/clip.mp4
|
||||
The export folder is 2 levels up from the clip file.
|
||||
Returns folder names sorted alphabetically (e.g. ["mp4_Intense", "mp4_Soft"]).
|
||||
"""
|
||||
if not self._enabled:
|
||||
return []
|
||||
rows = self._con.execute(
|
||||
"SELECT DISTINCT output_path FROM processed WHERE profile = ?",
|
||||
(profile,),
|
||||
).fetchall()
|
||||
folder_names: set[str] = set()
|
||||
for (op,) in rows:
|
||||
grandparent = os.path.basename(os.path.dirname(os.path.dirname(op)))
|
||||
if grandparent:
|
||||
folder_names.add(grandparent)
|
||||
return sorted(folder_names)
|
||||
|
||||
def get_training_data(self, profile: str, positive_folder: str,
|
||||
fallback_video_dir: str = "",
|
||||
) -> list[tuple[str, list[float], list[float]]]:
|
||||
"""Build training video_infos from DB data.
|
||||
|
||||
Args:
|
||||
profile: profile name
|
||||
positive_folder: export folder name for positive class (e.g. "mp4_Intense")
|
||||
fallback_video_dir: if source_path is empty, try filename in this dir
|
||||
|
||||
Returns:
|
||||
list of (source_video_path, positive_times, soft_times) per video.
|
||||
Soft times = clips from any other export folder.
|
||||
"""
|
||||
if not self._enabled:
|
||||
return []
|
||||
rows = self._con.execute(
|
||||
"SELECT filename, start_time, output_path, source_path"
|
||||
" FROM processed WHERE profile = ?",
|
||||
(profile,),
|
||||
).fetchall()
|
||||
|
||||
# Collect times by video, split by positive vs other folders
|
||||
pos_by_video: dict[str, set[float]] = {}
|
||||
soft_by_video: dict[str, set[float]] = {}
|
||||
source_by_filename: dict[str, str] = {}
|
||||
|
||||
for fn, st, op, sp in rows:
|
||||
if sp:
|
||||
source_by_filename[fn] = sp
|
||||
grandparent = os.path.basename(os.path.dirname(os.path.dirname(op)))
|
||||
if grandparent == positive_folder:
|
||||
pos_by_video.setdefault(fn, set()).add(st)
|
||||
else:
|
||||
soft_by_video.setdefault(fn, set()).add(st)
|
||||
|
||||
result = []
|
||||
for fn in pos_by_video:
|
||||
sp = source_by_filename.get(fn, "")
|
||||
if not sp or not os.path.exists(sp):
|
||||
# Fallback: try video_dir / filename
|
||||
if fallback_video_dir:
|
||||
sp = os.path.join(fallback_video_dir, fn)
|
||||
if not sp or not os.path.exists(sp):
|
||||
continue
|
||||
gt_pos = sorted(pos_by_video[fn])
|
||||
gt_soft = sorted(soft_by_video.get(fn, set()))
|
||||
result.append((sp, gt_pos, gt_soft))
|
||||
return result
|
||||
|
||||
def get_training_stats(self, profile: str) -> dict[str, dict]:
|
||||
"""Return per-subprofile stats for training readiness display.
|
||||
|
||||
Returns dict mapping subprofile_name → {
|
||||
'videos': number of distinct source videos,
|
||||
'clips': total clip count,
|
||||
}
|
||||
"""
|
||||
if not self._enabled:
|
||||
return {}
|
||||
rows = self._con.execute(
|
||||
"SELECT filename, output_path FROM processed WHERE profile = ?",
|
||||
(profile,),
|
||||
).fetchall()
|
||||
folders = self.get_export_folders(profile)
|
||||
stats: dict[str, dict] = {}
|
||||
for folder_name in folders:
|
||||
videos: set[str] = set()
|
||||
clips = 0
|
||||
for fn, op in rows:
|
||||
grandparent = os.path.basename(os.path.dirname(os.path.dirname(op)))
|
||||
if grandparent == folder_name:
|
||||
videos.add(fn)
|
||||
clips += 1
|
||||
stats[folder_name] = {"videos": len(videos), "clips": clips}
|
||||
return stats
|
||||
|
||||
def hide_file(self, filename: str, profile: str = "default") -> None:
|
||||
if not self._enabled:
|
||||
return
|
||||
|
||||
@@ -15,7 +15,7 @@ from PyQt6.QtWidgets import (
|
||||
QLabel, QPushButton, QLineEdit, QFileDialog,
|
||||
QListWidget, QListWidgetItem, QAbstractItemView, QSplitter, QToolTip,
|
||||
QComboBox, QCheckBox, QSpinBox, QDoubleSpinBox,
|
||||
QMessageBox, QInputDialog,
|
||||
QMessageBox, QInputDialog, QDialog, QDialogButtonBox, QFormLayout,
|
||||
)
|
||||
from PyQt6.QtCore import Qt, QObject, QThread, QTimer, QRect, QSize, pyqtSignal, QSettings
|
||||
from PyQt6.QtGui import QPainter, QColor, QPen, QPixmap, QDragEnterEvent, QDropEvent, QCursor, QFont, QKeySequence, QShortcut
|
||||
@@ -191,12 +191,11 @@ class ScanWorker(QThread):
|
||||
error = pyqtSignal(str)
|
||||
progress = pyqtSignal(str) # status message
|
||||
|
||||
def __init__(self, video_path: str, clip_paths: list[str],
|
||||
mode: str = "average", threshold: float = 0.7):
|
||||
def __init__(self, video_path: str, model: dict,
|
||||
threshold: float = 0.30):
|
||||
super().__init__()
|
||||
self._video_path = video_path
|
||||
self._clip_paths = clip_paths
|
||||
self._mode = mode
|
||||
self._model = model
|
||||
self._threshold = threshold
|
||||
self._cancel = False
|
||||
|
||||
@@ -204,20 +203,12 @@ class ScanWorker(QThread):
|
||||
self._cancel = True
|
||||
|
||||
def run(self):
|
||||
from core.audio_scan import build_profile, scan_video
|
||||
from core.audio_scan import scan_video
|
||||
try:
|
||||
self.progress.emit(f"Building profile from {len(self._clip_paths)} clips...")
|
||||
profile = build_profile(self._clip_paths)
|
||||
if self._cancel:
|
||||
return
|
||||
if profile is None:
|
||||
self.error.emit("No valid reference clips found")
|
||||
return
|
||||
self.progress.emit("Scanning audio...")
|
||||
regions = scan_video(
|
||||
self._video_path, profile,
|
||||
mode=self._mode, threshold=self._threshold,
|
||||
cancel_flag=self,
|
||||
self._video_path, model=self._model,
|
||||
threshold=self._threshold, cancel_flag=self,
|
||||
)
|
||||
if not self._cancel:
|
||||
self.scan_done.emit(regions)
|
||||
@@ -226,6 +217,151 @@ class ScanWorker(QThread):
|
||||
self.error.emit(str(e))
|
||||
|
||||
|
||||
class TrainDialog(QDialog):
|
||||
"""Dialog for configuring and launching classifier training."""
|
||||
|
||||
def __init__(self, db: ProcessedDB, profile: str, video_dir: str = "",
|
||||
parent=None):
|
||||
super().__init__(parent)
|
||||
self.setWindowTitle("Train Classifier")
|
||||
self.setMinimumWidth(400)
|
||||
|
||||
from core.audio_scan import _EMBED_MODELS
|
||||
self._db = db
|
||||
self._profile = profile
|
||||
self._video_dir = video_dir
|
||||
|
||||
layout = QVBoxLayout(self)
|
||||
form = QFormLayout()
|
||||
|
||||
# Positive class selector — lists export folders
|
||||
self._cmb_positive = QComboBox()
|
||||
stats = db.get_training_stats(profile)
|
||||
if not stats:
|
||||
form.addRow("", QLabel("No exported clips found for this profile."))
|
||||
for folder_name, info in stats.items():
|
||||
label = f"{folder_name} ({info['videos']} videos, {info['clips']} clips)"
|
||||
self._cmb_positive.addItem(label, userData=folder_name)
|
||||
form.addRow("Positive class:", self._cmb_positive)
|
||||
|
||||
# Model selector
|
||||
self._cmb_model = QComboBox()
|
||||
for name in _EMBED_MODELS:
|
||||
self._cmb_model.addItem(name)
|
||||
self._cmb_model.setCurrentText("WAV2VEC2_BASE")
|
||||
form.addRow("Model:", self._cmb_model)
|
||||
|
||||
# Video source directory (fallback for old DB rows without source_path)
|
||||
self._txt_video_dir = QLineEdit(video_dir)
|
||||
self._txt_video_dir.setPlaceholderText("Directory containing source videos")
|
||||
self._debounce = QTimer(self)
|
||||
self._debounce.setSingleShot(True)
|
||||
self._debounce.setInterval(400)
|
||||
self._debounce.timeout.connect(self._update_stats)
|
||||
self._txt_video_dir.textChanged.connect(lambda: self._debounce.start())
|
||||
vid_row = QHBoxLayout()
|
||||
vid_row.addWidget(self._txt_video_dir)
|
||||
btn_browse = QPushButton("...")
|
||||
btn_browse.setFixedWidth(30)
|
||||
btn_browse.clicked.connect(self._browse_video_dir)
|
||||
vid_row.addWidget(btn_browse)
|
||||
form.addRow("Video dir:", vid_row)
|
||||
|
||||
layout.addLayout(form)
|
||||
|
||||
# Stats summary
|
||||
self._lbl_stats = QLabel()
|
||||
self._update_stats()
|
||||
self._cmb_positive.currentIndexChanged.connect(self._update_stats)
|
||||
layout.addWidget(self._lbl_stats)
|
||||
|
||||
# Buttons
|
||||
btns = QDialogButtonBox(
|
||||
QDialogButtonBox.StandardButton.Ok | QDialogButtonBox.StandardButton.Cancel
|
||||
)
|
||||
btns.button(QDialogButtonBox.StandardButton.Ok).setText("Train")
|
||||
btns.button(QDialogButtonBox.StandardButton.Ok).setEnabled(
|
||||
self._cmb_positive.count() > 0
|
||||
)
|
||||
btns.accepted.connect(self.accept)
|
||||
btns.rejected.connect(self.reject)
|
||||
layout.addWidget(btns)
|
||||
|
||||
def _browse_video_dir(self):
|
||||
d = QFileDialog.getExistingDirectory(self, "Select video source directory")
|
||||
if d:
|
||||
self._txt_video_dir.setText(d)
|
||||
|
||||
def _update_stats(self):
|
||||
folder = self._cmb_positive.currentData()
|
||||
if not folder:
|
||||
self._lbl_stats.setText("No export folder data available.")
|
||||
return
|
||||
video_infos = self._db.get_training_data(
|
||||
self._profile, folder,
|
||||
fallback_video_dir=self._txt_video_dir.text(),
|
||||
)
|
||||
n_videos = len(video_infos)
|
||||
n_pos = sum(len(gt) for _, gt, _ in video_infos)
|
||||
n_soft = sum(len(s) for _, _, s in video_infos)
|
||||
lines = [f"<b>{n_videos}</b> videos with positive clips"]
|
||||
lines.append(f"<b>{n_pos}</b> positive markers, <b>{n_soft}</b> soft/buffer markers")
|
||||
if n_videos == 0:
|
||||
lines.append("<i>No source videos found. Set Video dir above.</i>")
|
||||
elif n_videos < 3:
|
||||
lines.append("<i>Recommend at least 3 videos for decent results.</i>")
|
||||
self._lbl_stats.setText("<br>".join(lines))
|
||||
|
||||
@property
|
||||
def positive_folder(self) -> str:
|
||||
return self._cmb_positive.currentData() or ""
|
||||
|
||||
@property
|
||||
def embed_model(self) -> str:
|
||||
return self._cmb_model.currentText()
|
||||
|
||||
@property
|
||||
def video_dir(self) -> str:
|
||||
return self._txt_video_dir.text()
|
||||
|
||||
|
||||
class TrainWorker(QThread):
|
||||
"""Trains an audio classifier off the main thread."""
|
||||
train_done = pyqtSignal(str) # emits model path on success
|
||||
error = pyqtSignal(str)
|
||||
progress = pyqtSignal(str) # per-video status
|
||||
|
||||
def __init__(self, video_infos: list, model_path: str,
|
||||
embed_model: str | None = None):
|
||||
super().__init__()
|
||||
self._video_infos = video_infos
|
||||
self._model_path = model_path
|
||||
self._embed_model = embed_model
|
||||
self._cancel = False
|
||||
|
||||
def cancel(self) -> None:
|
||||
self._cancel = True
|
||||
|
||||
def run(self):
|
||||
from core.audio_scan import train_classifier
|
||||
try:
|
||||
self.progress.emit(f"Training on {len(self._video_infos)} videos...")
|
||||
result = train_classifier(
|
||||
self._video_infos,
|
||||
model_path=self._model_path,
|
||||
embed_model=self._embed_model,
|
||||
)
|
||||
if self._cancel:
|
||||
return
|
||||
if result is None:
|
||||
self.error.emit("Training failed: not enough data or missing class balance")
|
||||
else:
|
||||
self.train_done.emit(self._model_path)
|
||||
except Exception as e:
|
||||
if not self._cancel:
|
||||
self.error.emit(str(e))
|
||||
|
||||
|
||||
class TimelineWidget(QWidget):
|
||||
cursor_changed = pyqtSignal(float) # emits position in seconds
|
||||
seek_changed = pyqtSignal(float) # emits seek position (lock mode)
|
||||
@@ -1564,23 +1700,35 @@ class MainWindow(QMainWindow):
|
||||
self._btn_scan.setToolTip("Scan current video for audio segments matching reference clips")
|
||||
self._btn_scan.clicked.connect(self._start_scan)
|
||||
|
||||
self._btn_auto_export = QPushButton("Auto")
|
||||
self._btn_auto_export.setToolTip("Scan + auto-export best 8s clips")
|
||||
self._btn_auto_export.clicked.connect(self._auto_export)
|
||||
|
||||
self._btn_train = QPushButton("Train")
|
||||
self._btn_train.setToolTip("Train audio classifier from exported clips")
|
||||
self._btn_train.clicked.connect(self._open_train_dialog)
|
||||
self._train_worker: TrainWorker | None = None
|
||||
|
||||
self._spn_auto_fuse = QDoubleSpinBox()
|
||||
self._spn_auto_fuse.setDecimals(1)
|
||||
self._spn_auto_fuse.setRange(0.0, 60.0)
|
||||
self._spn_auto_fuse.setSingleStep(1.0)
|
||||
self._spn_auto_fuse.setValue(float(self._settings.value("auto_fuse", "4.0")))
|
||||
self._spn_auto_fuse.setPrefix("Fuse: ")
|
||||
self._spn_auto_fuse.setSuffix("s")
|
||||
self._spn_auto_fuse.setToolTip("Max gap between scan regions to merge into one cluster")
|
||||
self._spn_auto_fuse.valueChanged.connect(
|
||||
lambda v: self._settings.setValue("auto_fuse", str(v))
|
||||
)
|
||||
|
||||
self._sld_threshold = QDoubleSpinBox()
|
||||
self._sld_threshold.setDecimals(2)
|
||||
self._sld_threshold.setRange(0.0, 1.0)
|
||||
self._sld_threshold.setSingleStep(0.01)
|
||||
self._sld_threshold.setValue(0.05)
|
||||
self._sld_threshold.setValue(0.30)
|
||||
self._sld_threshold.setPrefix("Thr: ")
|
||||
self._sld_threshold.setToolTip("Similarity threshold (0=match everything, 1=exact match)")
|
||||
|
||||
self._cmb_scan_mode = QComboBox()
|
||||
self._cmb_scan_mode.addItems(["Average", "Nearest"])
|
||||
self._cmb_scan_mode.setToolTip("Average: compare to mean profile\nNearest: compare to closest clip")
|
||||
|
||||
self._cmb_scan_ref = QComboBox()
|
||||
self._cmb_scan_ref.addItems(["Current Profile", "Custom Folder"])
|
||||
self._cmb_scan_ref.currentIndexChanged.connect(self._on_scan_ref_changed)
|
||||
self._scan_folder: str = ""
|
||||
|
||||
self._scan_worker: ScanWorker | None = None
|
||||
|
||||
cpu_count = os.cpu_count() or 2
|
||||
@@ -1716,9 +1864,10 @@ class MainWindow(QMainWindow):
|
||||
settings_row.addWidget(self._chk_rand_square)
|
||||
settings_row.addWidget(self._chk_track)
|
||||
settings_row.addWidget(self._btn_scan)
|
||||
settings_row.addWidget(self._btn_auto_export)
|
||||
settings_row.addWidget(self._spn_auto_fuse)
|
||||
settings_row.addWidget(self._sld_threshold)
|
||||
settings_row.addWidget(self._cmb_scan_mode)
|
||||
settings_row.addWidget(self._cmb_scan_ref)
|
||||
settings_row.addWidget(self._btn_train)
|
||||
settings_row.addStretch()
|
||||
self._lbl_status = QLabel()
|
||||
self._lbl_status.setStyleSheet("color: #888; font-size: 11px;")
|
||||
@@ -2503,16 +2652,6 @@ class MainWindow(QMainWindow):
|
||||
return
|
||||
self._step_cursor(markers[0][0] - self._cursor) # wrap to first
|
||||
|
||||
def _on_scan_ref_changed(self, index: int) -> None:
|
||||
if index == 1: # Custom Folder
|
||||
folder = QFileDialog.getExistingDirectory(self, "Select reference clip folder")
|
||||
if folder:
|
||||
self._scan_folder = folder
|
||||
else:
|
||||
self._cmb_scan_ref.blockSignals(True)
|
||||
self._cmb_scan_ref.setCurrentIndex(0)
|
||||
self._cmb_scan_ref.blockSignals(False)
|
||||
|
||||
def _cleanup_scan_worker(self) -> None:
|
||||
"""Disconnect signals and schedule deletion of old scan worker."""
|
||||
if self._scan_worker is not None:
|
||||
@@ -2540,35 +2679,22 @@ class MainWindow(QMainWindow):
|
||||
# Clean up previous worker
|
||||
self._cleanup_scan_worker()
|
||||
|
||||
# Collect reference clip paths
|
||||
if self._cmb_scan_ref.currentIndex() == 0:
|
||||
# Current profile — all exports across all files in this profile
|
||||
clip_paths = [p for p in self._db.get_all_export_paths(self._profile)
|
||||
if os.path.exists(p)]
|
||||
else:
|
||||
# Custom folder
|
||||
if not self._scan_folder:
|
||||
self._show_status("No reference folder selected")
|
||||
return
|
||||
exts = (".mp4", ".mkv", ".avi", ".mov", ".wav", ".mp3", ".flac")
|
||||
clip_paths = [
|
||||
os.path.join(self._scan_folder, f)
|
||||
for f in sorted(os.listdir(self._scan_folder))
|
||||
if f.lower().endswith(exts)
|
||||
]
|
||||
|
||||
if not clip_paths:
|
||||
self._show_status("No reference clips found")
|
||||
return
|
||||
|
||||
mode = self._cmb_scan_mode.currentText().lower()
|
||||
threshold = self._sld_threshold.value()
|
||||
|
||||
self._btn_scan.setEnabled(False)
|
||||
self._scan_file_path = self._file_path # remember which file we're scanning
|
||||
self._show_status(f"Scanning with {len(clip_paths)} reference clips...")
|
||||
from core.audio_scan import load_classifier, default_model_path
|
||||
model_path = default_model_path(self._profile)
|
||||
model = load_classifier(model_path)
|
||||
|
||||
self._scan_worker = ScanWorker(self._file_path, clip_paths, mode, threshold)
|
||||
if model is None:
|
||||
self._show_status("No trained model — click Train first")
|
||||
return
|
||||
|
||||
self._btn_scan.setEnabled(False)
|
||||
self._scan_file_path = self._file_path
|
||||
self._show_status("Scanning...")
|
||||
self._scan_worker = ScanWorker(
|
||||
self._file_path, model=model, threshold=threshold,
|
||||
)
|
||||
self._scan_worker.scan_done.connect(self._on_scan_done)
|
||||
self._scan_worker.error.connect(self._on_scan_error)
|
||||
self._scan_worker.progress.connect(self._show_status)
|
||||
@@ -2576,6 +2702,7 @@ class MainWindow(QMainWindow):
|
||||
|
||||
def _on_scan_done(self, regions: list) -> None:
|
||||
self._btn_scan.setEnabled(True)
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
# Ignore stale results if the user switched files during scan
|
||||
if self._file_path != getattr(self, '_scan_file_path', None):
|
||||
return
|
||||
@@ -2584,8 +2711,294 @@ class MainWindow(QMainWindow):
|
||||
|
||||
def _on_scan_error(self, msg: str) -> None:
|
||||
self._btn_scan.setEnabled(True)
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
self._show_status(f"Scan error: {msg}")
|
||||
|
||||
# ── Training ────────────────────────────────────────────────
|
||||
|
||||
def _cleanup_train_worker(self) -> None:
|
||||
"""Disconnect signals and schedule deletion of old train worker."""
|
||||
if self._train_worker is not None:
|
||||
try:
|
||||
self._train_worker.train_done.disconnect()
|
||||
self._train_worker.error.disconnect()
|
||||
self._train_worker.progress.disconnect()
|
||||
except TypeError:
|
||||
pass
|
||||
if self._train_worker.isRunning():
|
||||
self._train_worker.cancel()
|
||||
self._train_worker.finished.connect(self._train_worker.deleteLater)
|
||||
else:
|
||||
self._train_worker.deleteLater()
|
||||
self._train_worker = None
|
||||
|
||||
def _open_train_dialog(self):
|
||||
"""Show the training config dialog and start training if accepted."""
|
||||
if self._train_worker and self._train_worker.isRunning():
|
||||
self._show_status("Training already in progress…")
|
||||
return
|
||||
|
||||
# Default video dir: parent of currently loaded file, or saved setting
|
||||
default_dir = ""
|
||||
if self._file_path:
|
||||
default_dir = os.path.dirname(self._file_path)
|
||||
saved_dir = self._settings.value("train_video_dir", default_dir)
|
||||
|
||||
dlg = TrainDialog(self._db, self._profile,
|
||||
video_dir=saved_dir or default_dir, parent=self)
|
||||
if dlg.exec() != QDialog.DialogCode.Accepted:
|
||||
return
|
||||
|
||||
pos_folder = dlg.positive_folder
|
||||
embed_model = dlg.embed_model
|
||||
video_dir = dlg.video_dir
|
||||
if not pos_folder:
|
||||
self._show_status("No positive class selected")
|
||||
return
|
||||
|
||||
# Persist video dir for next time
|
||||
if video_dir:
|
||||
self._settings.setValue("train_video_dir", video_dir)
|
||||
|
||||
video_infos = self._db.get_training_data(
|
||||
self._profile, pos_folder, fallback_video_dir=video_dir,
|
||||
)
|
||||
if not video_infos:
|
||||
self._show_status("No training data found for this subprofile")
|
||||
return
|
||||
|
||||
from core.audio_scan import default_model_path
|
||||
model_path = default_model_path(self._profile)
|
||||
|
||||
self._cleanup_train_worker()
|
||||
self._btn_train.setEnabled(False)
|
||||
self._show_status(f"Training {embed_model} on {len(video_infos)} videos...")
|
||||
|
||||
self._train_worker = TrainWorker(video_infos, model_path, embed_model)
|
||||
self._train_worker.train_done.connect(self._on_train_done)
|
||||
self._train_worker.error.connect(self._on_train_error)
|
||||
self._train_worker.progress.connect(self._show_status)
|
||||
self._train_worker.start()
|
||||
|
||||
def _on_train_done(self, model_path: str):
|
||||
self._btn_train.setEnabled(True)
|
||||
self._show_status(f"Model trained and saved")
|
||||
_log(f"Training complete: {model_path}")
|
||||
|
||||
def _on_train_error(self, msg: str):
|
||||
self._btn_train.setEnabled(True)
|
||||
self._show_status(f"Training error: {msg}")
|
||||
|
||||
# ── Auto-export ─────────────────────────────────────────────
|
||||
|
||||
def _auto_export(self) -> None:
|
||||
"""Scan → NMS → export one 8s clip per selected position."""
|
||||
if not self._file_path:
|
||||
self._show_status("No video loaded")
|
||||
return
|
||||
if self._export_worker and self._export_worker.isRunning():
|
||||
self._show_status("Export already running…")
|
||||
return
|
||||
if self._scan_worker and self._scan_worker.isRunning():
|
||||
self._show_status("Scan already running")
|
||||
return
|
||||
|
||||
self._cleanup_scan_worker()
|
||||
self._btn_auto_export.setEnabled(False)
|
||||
self._btn_scan.setEnabled(False)
|
||||
|
||||
threshold = self._sld_threshold.value()
|
||||
|
||||
from core.audio_scan import load_classifier, default_model_path
|
||||
model_path = default_model_path(self._profile)
|
||||
model = load_classifier(model_path)
|
||||
|
||||
if model is not None:
|
||||
self._scan_file_path = self._file_path
|
||||
self._show_status("Auto: scanning with classifier...")
|
||||
self._scan_worker = ScanWorker(
|
||||
self._file_path, model=model, threshold=threshold,
|
||||
)
|
||||
else:
|
||||
self._show_status("Auto: no trained model — click Train first")
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
self._btn_scan.setEnabled(True)
|
||||
return
|
||||
|
||||
self._scan_worker.scan_done.connect(self._on_auto_scan_done)
|
||||
self._scan_worker.error.connect(self._on_scan_error)
|
||||
self._scan_worker.progress.connect(self._show_status)
|
||||
self._scan_worker.start()
|
||||
|
||||
@staticmethod
|
||||
def _select_export_positions(regions: list[tuple[float, float, float]],
|
||||
min_gap: float = 2.0,
|
||||
cluster_fuse: float = 30.0,
|
||||
) -> list[float]:
|
||||
"""Cluster scan regions, then fill each cluster with clips spaced min_gap apart.
|
||||
|
||||
1. Merge overlapping regions into clusters, fusing clusters <cluster_fuse apart.
|
||||
2. Within each cluster, greedily pick positions by score, min_gap apart.
|
||||
"""
|
||||
if not regions:
|
||||
return []
|
||||
|
||||
# Build clusters — merge overlapping + fuse if gap < cluster_fuse
|
||||
sorted_r = sorted(regions, key=lambda r: r[0])
|
||||
clusters: list[list[tuple[float, float, float]]] = []
|
||||
cur_start, cur_end = sorted_r[0][0], sorted_r[0][1]
|
||||
cur_regions = [sorted_r[0]]
|
||||
|
||||
for start, end, score in sorted_r[1:]:
|
||||
if start - cur_end <= cluster_fuse:
|
||||
cur_end = max(cur_end, end)
|
||||
cur_regions.append((start, end, score))
|
||||
else:
|
||||
clusters.append(cur_regions)
|
||||
cur_start, cur_end = start, end
|
||||
cur_regions = [(start, end, score)]
|
||||
clusters.append(cur_regions)
|
||||
|
||||
# Within each cluster, NMS by score with min_gap
|
||||
picked: list[float] = []
|
||||
for cluster in clusters:
|
||||
by_score = sorted(cluster, key=lambda r: -r[2])
|
||||
cluster_picks: list[float] = []
|
||||
for start, _end, _score in by_score:
|
||||
if all(abs(start - p) >= min_gap for p in cluster_picks):
|
||||
cluster_picks.append(start)
|
||||
picked.extend(cluster_picks)
|
||||
|
||||
return sorted(picked)
|
||||
|
||||
def _on_auto_scan_done(self, regions: list) -> None:
|
||||
self._btn_scan.setEnabled(True)
|
||||
if self._file_path != getattr(self, '_scan_file_path', None):
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
return
|
||||
|
||||
self._timeline.set_scan_regions(regions)
|
||||
|
||||
if not regions:
|
||||
self._show_status("Auto: no regions found")
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
return
|
||||
|
||||
positions = self._select_export_positions(
|
||||
regions, min_gap=2.0, cluster_fuse=self._spn_auto_fuse.value(),
|
||||
)
|
||||
if not positions:
|
||||
self._show_status("Auto: no positions after NMS")
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
return
|
||||
|
||||
# Build export jobs — one 8s clip per position
|
||||
folder = self._txt_folder.text()
|
||||
name = self._txt_name.text() or "clip"
|
||||
fmt = self._cmb_format.currentText()
|
||||
image_sequence = fmt == "WebP sequence"
|
||||
os.makedirs(folder, exist_ok=True)
|
||||
|
||||
# Find starting counter
|
||||
counter = 1
|
||||
while True:
|
||||
if image_sequence:
|
||||
p = build_sequence_dir(folder, name, counter, sub=0)
|
||||
else:
|
||||
p = build_export_path(folder, name, counter, sub=0)
|
||||
if not os.path.exists(p):
|
||||
break
|
||||
counter += 1
|
||||
|
||||
jobs = []
|
||||
self._auto_export_positions = [] # stash for DB writes
|
||||
for start_t in positions:
|
||||
group_dir = os.path.join(folder, f"{name}_{counter:03d}")
|
||||
os.makedirs(group_dir, exist_ok=True)
|
||||
if image_sequence:
|
||||
out = build_sequence_dir(folder, name, counter, sub=0)
|
||||
else:
|
||||
out = build_export_path(folder, name, counter, sub=0)
|
||||
jobs.append((start_t, out, None, 0.5))
|
||||
self._auto_export_positions.append((start_t, counter))
|
||||
counter += 1
|
||||
|
||||
self._show_status(f"Auto: exporting {len(jobs)} clips...")
|
||||
|
||||
short_side = self._spn_resize.value() or None
|
||||
self._export_short_side = short_side
|
||||
self._export_portrait = "Off"
|
||||
self._export_format = fmt
|
||||
self._export_clip_count = 1
|
||||
self._export_spread = 0
|
||||
self._export_folder = folder
|
||||
self._export_folder_suffix = ""
|
||||
|
||||
hw_on = self._chk_hw.isChecked() and self._hw_encoders
|
||||
encoder = self._hw_encoders[0] if hw_on else "libx264"
|
||||
max_workers = min(self._spn_workers.value(), 3) if hw_on else self._spn_workers.value()
|
||||
|
||||
self._export_worker = ExportWorker(
|
||||
self._file_path, jobs,
|
||||
short_side=short_side,
|
||||
image_sequence=image_sequence,
|
||||
max_workers=max_workers,
|
||||
encoder=encoder,
|
||||
)
|
||||
self._export_worker.finished.connect(self._on_auto_clip_done)
|
||||
self._export_worker.all_done.connect(self._on_auto_batch_done)
|
||||
self._export_worker.error.connect(self._on_export_error)
|
||||
self._export_worker.cancelled.connect(self._on_export_cancelled)
|
||||
self._btn_cancel.setEnabled(True)
|
||||
self._btn_export.setEnabled(False)
|
||||
self._set_subprofile_btns_enabled(False)
|
||||
self._export_worker.start()
|
||||
|
||||
def _on_auto_clip_done(self, path: str):
|
||||
"""Record each auto-exported clip to DB."""
|
||||
# Find the start_time for this clip from stashed positions
|
||||
counter_str = os.path.basename(os.path.dirname(path)) # e.g. "clip_042"
|
||||
name = self._txt_name.text() or "clip"
|
||||
start_t = None
|
||||
for t, c in self._auto_export_positions:
|
||||
if counter_str == f"{name}_{c:03d}":
|
||||
start_t = t
|
||||
break
|
||||
|
||||
label = self._txt_label.currentText().strip()
|
||||
category = self._cmb_category.currentText()
|
||||
self._db.add(
|
||||
os.path.basename(self._file_path),
|
||||
start_t or 0.0,
|
||||
path,
|
||||
label=label,
|
||||
category=category,
|
||||
short_side=self._export_short_side,
|
||||
portrait_ratio="",
|
||||
crop_center=0.5,
|
||||
fmt=self._export_format,
|
||||
clip_count=1,
|
||||
spread=0,
|
||||
profile=self._profile,
|
||||
source_path=self._file_path,
|
||||
)
|
||||
upsert_clip_annotation(self._export_folder, path, label)
|
||||
self._show_status(f"Auto: {os.path.basename(path)}")
|
||||
_log(f" auto clip done: {os.path.basename(path)}")
|
||||
|
||||
def _on_auto_batch_done(self):
|
||||
n = len(self._auto_export_positions)
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
self._btn_cancel.setEnabled(False)
|
||||
self._btn_export.setEnabled(True)
|
||||
self._set_subprofile_btns_enabled(True)
|
||||
self._refresh_markers()
|
||||
markers = self._db.get_markers(os.path.basename(self._file_path), self._profile)
|
||||
self._playlist.mark_done(self._file_path, len(markers))
|
||||
self._update_next_label()
|
||||
self._show_status(f"Auto export complete: {n} clips")
|
||||
_log(f"Auto export complete: {n} clips")
|
||||
|
||||
def _jump_to_next_scan_region(self) -> None:
|
||||
regions = sorted(self._timeline._scan_regions, key=lambda r: r[0])
|
||||
if not regions:
|
||||
@@ -2812,6 +3225,7 @@ class MainWindow(QMainWindow):
|
||||
clip_count=self._export_clip_count,
|
||||
spread=self._export_spread,
|
||||
profile=self._profile,
|
||||
source_path=self._file_path,
|
||||
)
|
||||
upsert_clip_annotation(self._export_folder, path, label)
|
||||
self._last_export_path = path
|
||||
@@ -2851,6 +3265,7 @@ class MainWindow(QMainWindow):
|
||||
_log(f"Export error: {msg}")
|
||||
self._btn_cancel.setEnabled(False)
|
||||
self._btn_export.setEnabled(True)
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
self._set_subprofile_btns_enabled(True)
|
||||
self._btn_export.setText("Export")
|
||||
self._btn_export.setStyleSheet("")
|
||||
@@ -2866,6 +3281,7 @@ class MainWindow(QMainWindow):
|
||||
def _on_export_cancelled(self):
|
||||
_log("Export cancelled")
|
||||
self._btn_export.setEnabled(True)
|
||||
self._btn_auto_export.setEnabled(True)
|
||||
self._set_subprofile_btns_enabled(True)
|
||||
self._btn_export.setText("Export")
|
||||
self._btn_export.setStyleSheet("")
|
||||
@@ -2886,6 +3302,9 @@ class MainWindow(QMainWindow):
|
||||
_log("Shutting down…")
|
||||
# Save session playlist for resume.
|
||||
self._settings.setValue("session_files", self._playlist._paths)
|
||||
# Cancel background workers to prevent callbacks into dead objects.
|
||||
self._cleanup_scan_worker()
|
||||
self._cleanup_train_worker()
|
||||
# Stop timers first to prevent callbacks into dead objects.
|
||||
self._preview_timer.stop()
|
||||
self._mpv._render_timer.stop()
|
||||
|
||||
+22
-1
@@ -1,4 +1,25 @@
|
||||
# Core GUI
|
||||
PyQt6>=6.4
|
||||
python-mpv>=1.0
|
||||
pytest>=7.0
|
||||
|
||||
# Audio & ML
|
||||
librosa>=0.10
|
||||
numpy>=1.24
|
||||
scikit-learn>=1.3
|
||||
joblib>=1.3
|
||||
soundfile>=0.12
|
||||
|
||||
# Deep learning (torch installed separately for CUDA support)
|
||||
# torch and torchaudio are installed via --index-url in setup_env.sh
|
||||
torchaudio>=2.0
|
||||
|
||||
# Object detection
|
||||
ultralytics>=8.0
|
||||
|
||||
# Server API
|
||||
fastapi>=0.100
|
||||
pydantic>=2.0
|
||||
uvicorn>=0.23
|
||||
|
||||
# Dev
|
||||
pytest>=7.0
|
||||
|
||||
Executable
+108
@@ -0,0 +1,108 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────────
|
||||
# 8-cut environment setup — supports conda (miniforge) or python venv
|
||||
#
|
||||
# Usage:
|
||||
# ./setup_env.sh # auto-detect (prefers conda if available)
|
||||
# ./setup_env.sh --conda # force conda
|
||||
# ./setup_env.sh --venv # force python venv
|
||||
# ─��────────────────────────────��───────────────────────────────────────
|
||||
|
||||
ENV_NAME="8cut"
|
||||
PYTHON_VERSION="3.12"
|
||||
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
|
||||
VENV_DIR="$SCRIPT_DIR/.venv"
|
||||
|
||||
# CUDA version for PyTorch index URL
|
||||
TORCH_INDEX="https://download.pytorch.org/whl/cu128"
|
||||
|
||||
# ── Parse args ────────────────────────────────────────────────────────
|
||||
|
||||
MODE=""
|
||||
for arg in "$@"; do
|
||||
case "$arg" in
|
||||
--conda) MODE="conda" ;;
|
||||
--venv) MODE="venv" ;;
|
||||
*) echo "Unknown arg: $arg"; exit 1 ;;
|
||||
esac
|
||||
done
|
||||
|
||||
if [ -z "$MODE" ]; then
|
||||
if command -v conda &>/dev/null; then
|
||||
MODE="conda"
|
||||
else
|
||||
MODE="venv"
|
||||
fi
|
||||
echo "Auto-detected mode: $MODE"
|
||||
fi
|
||||
|
||||
# ── Conda setup ─────────────��─────────────────────────────────────────
|
||||
|
||||
setup_conda() {
|
||||
echo "==> Setting up conda environment: $ENV_NAME"
|
||||
|
||||
# Source conda shell hooks if not already active
|
||||
if ! command -v conda &>/dev/null; then
|
||||
echo "conda not found in PATH"
|
||||
exit 1
|
||||
fi
|
||||
eval "$(conda shell.bash hook)"
|
||||
|
||||
if conda env list | grep -qw "$ENV_NAME"; then
|
||||
echo " Environment '$ENV_NAME' already exists, updating..."
|
||||
conda activate "$ENV_NAME"
|
||||
else
|
||||
echo " Creating environment '$ENV_NAME' with Python $PYTHON_VERSION..."
|
||||
conda create -y -n "$ENV_NAME" python="$PYTHON_VERSION"
|
||||
conda activate "$ENV_NAME"
|
||||
fi
|
||||
|
||||
echo " Installing PyTorch + torchaudio (CUDA 12.8)..."
|
||||
pip install torch torchaudio --index-url "$TORCH_INDEX"
|
||||
|
||||
echo " Installing project dependencies..."
|
||||
pip install -r "$SCRIPT_DIR/requirements.txt"
|
||||
|
||||
echo ""
|
||||
echo "Done! Activate with:"
|
||||
echo " conda activate $ENV_NAME"
|
||||
}
|
||||
|
||||
# ── Venv setup ───────��────────────────────────────────────────────────
|
||||
|
||||
setup_venv() {
|
||||
echo "==> Setting up Python venv at: $VENV_DIR"
|
||||
|
||||
if [ ! -d "$VENV_DIR" ]; then
|
||||
python3 -m venv "$VENV_DIR"
|
||||
echo " Created venv"
|
||||
else
|
||||
echo " Venv already exists, updating..."
|
||||
fi
|
||||
|
||||
source "$VENV_DIR/bin/activate"
|
||||
|
||||
echo " Installing PyTorch + torchaudio (CUDA 12.8)..."
|
||||
pip install torch torchaudio --index-url "$TORCH_INDEX"
|
||||
|
||||
echo " Installing project dependencies..."
|
||||
pip install -r "$SCRIPT_DIR/requirements.txt"
|
||||
|
||||
echo ""
|
||||
echo "Done! Activate with:"
|
||||
echo " source $VENV_DIR/bin/activate"
|
||||
}
|
||||
|
||||
# ── Run ───────────────────────────────────────────────────────────────
|
||||
|
||||
case "$MODE" in
|
||||
conda) setup_conda ;;
|
||||
venv) setup_venv ;;
|
||||
esac
|
||||
|
||||
echo ""
|
||||
echo "Verify with:"
|
||||
echo " python -c \"import torch; print('PyTorch', torch.__version__, 'CUDA', torch.version.cuda)\""
|
||||
echo " python -c \"import librosa, torchaudio, sklearn; print('All imports OK')\""
|
||||
+14
-140
@@ -1,154 +1,28 @@
|
||||
import tempfile, os
|
||||
import numpy as np
|
||||
from core.audio_scan import build_profile, _extract_features, scan_video, _similarity
|
||||
from core.audio_scan import scan_video, load_classifier, default_model_path
|
||||
|
||||
|
||||
def _make_wav(path: str, duration: float = 8.0, sr: int = 16000, freq: float = 440.0):
|
||||
"""Create a short sine-wave WAV file for testing."""
|
||||
def test_scan_video_no_model_returns_empty():
|
||||
"""scan_video with no model should return empty list."""
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as vid:
|
||||
import soundfile as sf
|
||||
t = np.linspace(0, duration, int(sr * duration), endpoint=False)
|
||||
audio = 0.5 * np.sin(2 * np.pi * freq * t)
|
||||
sf.write(path, audio, sr)
|
||||
|
||||
|
||||
def test_extract_features_returns_62d_vector():
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
|
||||
_make_wav(f.name)
|
||||
try:
|
||||
vec = _extract_features(f.name)
|
||||
assert vec.shape == (62,)
|
||||
assert not np.isnan(vec).any()
|
||||
finally:
|
||||
os.unlink(f.name)
|
||||
|
||||
|
||||
def test_build_profile_single_clip():
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
|
||||
_make_wav(f.name)
|
||||
try:
|
||||
profile = build_profile([f.name])
|
||||
assert "mean_vector" in profile
|
||||
assert "clip_vectors" in profile
|
||||
assert profile["mean_vector"].shape == (62,)
|
||||
assert len(profile["clip_vectors"]) == 1
|
||||
finally:
|
||||
os.unlink(f.name)
|
||||
|
||||
|
||||
def test_build_profile_multiple_clips():
|
||||
paths = []
|
||||
try:
|
||||
for i in range(3):
|
||||
f = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
|
||||
_make_wav(f.name, freq=440 + i * 200)
|
||||
paths.append(f.name)
|
||||
f.close()
|
||||
|
||||
profile = build_profile(paths)
|
||||
assert len(profile["clip_vectors"]) == 3
|
||||
assert profile["mean_vector"].shape == (62,)
|
||||
finally:
|
||||
for p in paths:
|
||||
os.unlink(p)
|
||||
|
||||
|
||||
def test_build_profile_skips_missing_files():
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
|
||||
_make_wav(f.name)
|
||||
try:
|
||||
profile = build_profile([f.name, "/no/such/file.wav"])
|
||||
assert len(profile["clip_vectors"]) == 1
|
||||
finally:
|
||||
os.unlink(f.name)
|
||||
|
||||
|
||||
def test_build_profile_empty_returns_none():
|
||||
result = build_profile([])
|
||||
assert result is None
|
||||
|
||||
|
||||
def test_similarity_identical_is_one():
|
||||
a = np.array([1.0, 2.0, 3.0])
|
||||
assert abs(_similarity(a, a) - 1.0) < 1e-9
|
||||
|
||||
|
||||
def test_similarity_distant_is_low():
|
||||
a = np.zeros(62)
|
||||
b = np.ones(62) * 100
|
||||
assert _similarity(a, b) < 0.01
|
||||
|
||||
|
||||
def test_scan_video_finds_matching_region():
|
||||
"""A video made of the same sine wave as the reference should match."""
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as ref:
|
||||
_make_wav(ref.name, duration=8.0)
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as vid:
|
||||
_make_wav(vid.name, duration=20.0)
|
||||
try:
|
||||
profile = build_profile([ref.name])
|
||||
regions = scan_video(vid.name, profile, mode="average", threshold=0.01, hop=1.0)
|
||||
assert len(regions) > 0
|
||||
for start, end, score in regions:
|
||||
assert abs((end - start) - 8.0) < 0.1
|
||||
assert score >= 0.01
|
||||
finally:
|
||||
os.unlink(ref.name)
|
||||
os.unlink(vid.name)
|
||||
|
||||
|
||||
def test_scan_video_nearest_mode():
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as ref:
|
||||
_make_wav(ref.name, duration=8.0)
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as vid:
|
||||
_make_wav(vid.name, duration=20.0)
|
||||
try:
|
||||
profile = build_profile([ref.name])
|
||||
regions = scan_video(vid.name, profile, mode="nearest", threshold=0.01, hop=1.0)
|
||||
assert len(regions) > 0
|
||||
finally:
|
||||
os.unlink(ref.name)
|
||||
os.unlink(vid.name)
|
||||
|
||||
|
||||
def test_scan_video_high_threshold_no_match():
|
||||
"""Different frequencies with very high threshold should not match."""
|
||||
import soundfile as sf
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as ref:
|
||||
_make_wav(ref.name, duration=8.0, freq=440)
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as vid:
|
||||
# White noise — very different from sine wave
|
||||
sf.write(vid.name, np.random.randn(16000 * 20).astype(np.float32) * 0.1, 16000)
|
||||
try:
|
||||
profile = build_profile([ref.name])
|
||||
regions = scan_video(vid.name, profile, mode="average", threshold=0.5, hop=1.0)
|
||||
assert len(regions) == 0
|
||||
regions = scan_video(vid.name, model=None)
|
||||
assert regions == []
|
||||
finally:
|
||||
os.unlink(ref.name)
|
||||
os.unlink(vid.name)
|
||||
|
||||
|
||||
def test_scan_video_same_vs_different_discrimination():
|
||||
"""Same-frequency match should score higher than cross-frequency."""
|
||||
import soundfile as sf
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as ref:
|
||||
_make_wav(ref.name, duration=8.0, freq=440)
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as same:
|
||||
_make_wav(same.name, duration=10.0, freq=440)
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as diff:
|
||||
# White noise
|
||||
sf.write(diff.name, np.random.randn(16000 * 10).astype(np.float32) * 0.1, 16000)
|
||||
try:
|
||||
profile = build_profile([ref.name])
|
||||
same_regions = scan_video(same.name, profile, mode="average", threshold=0.0, hop=1.0)
|
||||
diff_regions = scan_video(diff.name, profile, mode="average", threshold=0.0, hop=1.0)
|
||||
# Same-audio scores should be higher than noise scores
|
||||
best_same = max(r[2] for r in same_regions)
|
||||
best_diff = max(r[2] for r in diff_regions)
|
||||
assert best_same > best_diff
|
||||
finally:
|
||||
os.unlink(ref.name)
|
||||
os.unlink(same.name)
|
||||
os.unlink(diff.name)
|
||||
def test_load_classifier_missing_returns_none():
|
||||
assert load_classifier("/no/such/model.joblib") is None
|
||||
|
||||
|
||||
def test_default_model_path_contains_profile():
|
||||
path = default_model_path("test_profile")
|
||||
assert "test_profile" in path
|
||||
assert path.endswith(".joblib")
|
||||
|
||||
|
||||
def test_db_get_all_export_paths():
|
||||
|
||||
Reference in New Issue
Block a user