feat: clicking End column in scan results seeks to last 3s of clip

Time column click still seeks to clip start. End column click seeks
to end - 3s so you can preview the tail of the clip.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

core/audio_scan.py

@@ -45,6 +45,7 @@ os.environ.setdefault("HF_HOME", os.path.join(_DL_CACHE_DIR, "huggingface"))
 _w2v_model = None
 _w2v_device = None
 _w2v_model_name = None
+_ast_feature_extractor = None
 
 # Supported embedding models — name → embed_dim
 _EMBED_MODELS = {
@@ -55,6 +56,16 @@ _EMBED_MODELS = {
     "HUBERT_LARGE":        1024,
     "HUBERT_XLARGE":       1280,
     "BEATS":               768,
+    # Multi-layer variants (4 quartile layers concatenated)
+    "WAV2VEC2_BASE_ML":   3072,   # 768 * 4
+    "HUBERT_BASE_ML":     3072,   # 768 * 4
+    "HUBERT_LARGE_ML":    4096,   # 1024 * 4
+    "HUBERT_XLARGE_ML":   5120,   # 1280 * 4
+    # Transformers-based models
+    "AST":                 768,
+    "AST_ML":             3072,   # 768 * 4
+    "EAT":                 768,
+    "EAT_LARGE":          1024,
 }
 _DEFAULT_EMBED_MODEL = "WAV2VEC2_BASE"
 
@@ -70,11 +81,14 @@ def _get_w2v_model(model_name: str | None = None):
     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:
+    # Multi-layer variants use the same base model weights
+    ml = _ml_config(model_name)
+    load_name = ml[0] if ml else model_name
+    if _w2v_model is None or _w2v_model_name != load_name:
         import torch
         _w2v_device = "cuda" if torch.cuda.is_available() else "cpu"
 
-        if model_name == "BEATS":
+        if load_name == "BEATS":
             from .beats_model import BEATs, BEATsConfig
             checkpoint = torch.load(_BEATS_CHECKPOINT, map_location=_w2v_device,
                                     weights_only=False)
@@ -82,17 +96,63 @@ def _get_w2v_model(model_name: str | None = None):
             _w2v_model = BEATs(cfg)
             _w2v_model.load_state_dict(checkpoint['model'])
             _w2v_model.to(_w2v_device)
+        elif load_name == "AST":
+            from transformers import ASTModel, ASTFeatureExtractor
+            _w2v_model = ASTModel.from_pretrained(
+                "MIT/ast-finetuned-audioset-10-10-0.4593"
+            ).to(_w2v_device)
+            global _ast_feature_extractor
+            _ast_feature_extractor = ASTFeatureExtractor.from_pretrained(
+                "MIT/ast-finetuned-audioset-10-10-0.4593"
+            )
+        elif load_name in ("EAT", "EAT_LARGE"):
+            from transformers import AutoModel
+            eat_repo = ("worstchan/EAT-large_epoch20_finetune_AS2M"
+                        if load_name == "EAT_LARGE"
+                        else "worstchan/EAT-base_epoch30_finetune_AS2M")
+            _w2v_model = AutoModel.from_pretrained(
+                eat_repo, trust_remote_code=True,
+            ).to(_w2v_device)
         else:
             import torchaudio
-            bundle = getattr(torchaudio.pipelines, model_name)
+            bundle = getattr(torchaudio.pipelines, load_name)
             _w2v_model = bundle.get_model().to(_w2v_device)
 
         _w2v_model.eval()
-        _w2v_model_name = model_name
+        _w2v_model_name = load_name
-        _log(f"audio_scan: {model_name} loaded on {_w2v_device}")
+        _log(f"audio_scan: {load_name} loaded on {_w2v_device}")
     return _w2v_model, _w2v_device
 
 
+def _eat_preprocess(chunks: list[np.ndarray], sr: int, device: str):
+    """Convert raw audio chunks to EAT mel spectrogram input.
+
+    Returns tensor of shape [B, 1, T, 128].
+    8s audio at 10ms frame shift produces ~798 frames, zero-padded to 1024.
+    """
+    import torch
+    import torchaudio.compliance.kaldi as kaldi
+
+    TARGET_LEN = 1024
+    MEAN, STD = -4.268, 4.569
+
+    mels = []
+    for chunk in chunks:
+        wav = torch.from_numpy(np.array(chunk)).unsqueeze(0).float()
+        fbank = kaldi.fbank(
+            wav, htk_compat=True, sample_frequency=sr, use_energy=False,
+            window_type='hanning', num_mel_bins=128, dither=0.0, frame_shift=10,
+        )
+        # Pad or truncate to TARGET_LEN
+        if fbank.shape[0] < TARGET_LEN:
+            fbank = torch.nn.functional.pad(fbank, (0, 0, 0, TARGET_LEN - fbank.shape[0]))
+        else:
+            fbank = fbank[:TARGET_LEN]
+        fbank = (fbank - MEAN) / (STD * 2)
+        mels.append(fbank)
+    return torch.stack(mels).unsqueeze(1).to(device)  # [B, 1, T, 128]
+
+
 def _embed_dim(model_name: str | None = None) -> int:
     """Return embedding dimension for a model name."""
     if model_name is None:
@@ -100,6 +160,31 @@ def _embed_dim(model_name: str | None = None) -> int:
     return _EMBED_MODELS.get(model_name, 768)
 
 
+def _ml_config(model_name: str) -> tuple[str, list[int]] | None:
+    """If model_name is a multi-layer variant, return (base_model, layer_indices).
+
+    Returns None for single-layer models.
+    Layer indices are 0-based into the list returned by extract_features().
+    """
+    if not model_name.endswith("_ML"):
+        return None
+    base = model_name[:-3]  # strip "_ML"
+    if base not in _EMBED_MODELS:
+        return None
+    # Layer counts per model family
+    layer_counts = {
+        "WAV2VEC2_BASE": 12, "WAV2VEC2_LARGE": 24, "WAV2VEC2_LARGE_LV60K": 24,
+        "HUBERT_BASE": 12, "HUBERT_LARGE": 24, "HUBERT_XLARGE": 48,
+        "AST": 12,
+    }
+    n = layer_counts.get(base)
+    if n is None:
+        return None
+    # Select 4 layers at quartile boundaries (0-indexed)
+    indices = [n // 4 - 1, n // 2 - 1, 3 * n // 4 - 1, n - 1]
+    return base, indices
+
+
 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)."""
@@ -171,6 +256,9 @@ def _extract_w2v_windows(y: np.ndarray, sr: int = _SR,
     import torch
     model, device = _get_w2v_model(model_name)
     is_beats = (model_name or _DEFAULT_EMBED_MODEL) == "BEATS"
+    is_ast = (model_name or _DEFAULT_EMBED_MODEL) in ("AST", "AST_ML")
+    is_eat = (model_name or _DEFAULT_EMBED_MODEL) in ("EAT", "EAT_LARGE")
+    ml_cfg = _ml_config(model_name or _DEFAULT_EMBED_MODEL)
     # Auto-size batches based on available GPU memory
     batch_size = 16
     if device == "cuda":
@@ -195,13 +283,36 @@ def _extract_w2v_windows(y: np.ndarray, sr: int = _SR,
             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_ast:
-            if is_beats:
+                inputs = _ast_feature_extractor(
-                padding_mask = torch.zeros_like(waveforms, dtype=torch.bool)
+                    list(chunks), sampling_rate=sr, return_tensors="pt",
-                features, _ = model.extract_features(waveforms, padding_mask=padding_mask)
+                    padding=True,
+                )
+                input_values = inputs.input_values.to(device)
+                if ml_cfg is not None:
+                    out = model(input_values, output_hidden_states=True)
+                    selected = [out.hidden_states[i].mean(dim=1) for i in ml_cfg[1]]
+                    batch_emb = torch.cat(selected, dim=1).cpu().numpy()
+                else:
+                    out = model(input_values)
+                    batch_emb = out.last_hidden_state.mean(dim=1).cpu().numpy()
+            elif is_eat:
+                mel_input = _eat_preprocess(chunks, sr, device)
+                features = model.extract_features(mel_input)
+                batch_emb = features[:, 1:, :].mean(dim=1).cpu().numpy()
             else:
-                features, _ = model(waveforms)
+                waveforms = torch.from_numpy(np.stack(chunks)).float().to(device)
-            batch_emb = features.mean(dim=1).cpu().numpy()
+                if is_beats:
+                    padding_mask = torch.zeros_like(waveforms, dtype=torch.bool)
+                    features, _ = model.extract_features(waveforms, padding_mask=padding_mask)
+                    batch_emb = features.mean(dim=1).cpu().numpy()
+                elif ml_cfg is not None:
+                    all_layers, _ = model.extract_features(waveforms)
+                    selected = [all_layers[i].mean(dim=1) for i in ml_cfg[1]]
+                    batch_emb = torch.cat(selected, dim=1).cpu().numpy()
+                else:
+                    features, _ = model(waveforms)
+                    batch_emb = features.mean(dim=1).cpu().numpy()
         embeddings.append(batch_emb)
 
     result_ts = timestamps
@@ -274,6 +385,9 @@ def _extract_w2v_targeted(y: np.ndarray, sr: int, gt_intense: list[float],
     embeddings_list: list[np.ndarray] = []
 
     is_beats = (model_name or _DEFAULT_EMBED_MODEL) == "BEATS"
+    is_ast = (model_name or _DEFAULT_EMBED_MODEL) in ("AST", "AST_ML")
+    is_eat = (model_name or _DEFAULT_EMBED_MODEL) in ("EAT", "EAT_LARGE")
+    ml_cfg = _ml_config(model_name or _DEFAULT_EMBED_MODEL)
 
     for batch_start in range(0, len(valid_times), batch_size):
         batch_end = min(batch_start + batch_size, len(valid_times))
@@ -283,13 +397,36 @@ def _extract_w2v_targeted(y: np.ndarray, sr: int, gt_intense: list[float],
             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_ast:
-            if is_beats:
+                inputs = _ast_feature_extractor(
-                padding_mask = torch.zeros_like(waveforms, dtype=torch.bool)
+                    list(chunks), sampling_rate=sr, return_tensors="pt",
-                features, _ = model.extract_features(waveforms, padding_mask=padding_mask)
+                    padding=True,
+                )
+                input_values = inputs.input_values.to(device)
+                if ml_cfg is not None:
+                    out = model(input_values, output_hidden_states=True)
+                    selected = [out.hidden_states[i].mean(dim=1) for i in ml_cfg[1]]
+                    batch_emb = torch.cat(selected, dim=1).cpu().numpy()
+                else:
+                    out = model(input_values)
+                    batch_emb = out.last_hidden_state.mean(dim=1).cpu().numpy()
+            elif is_eat:
+                mel_input = _eat_preprocess(chunks, sr, device)
+                features = model.extract_features(mel_input)
+                batch_emb = features[:, 1:, :].mean(dim=1).cpu().numpy()
             else:
-                features, _ = model(waveforms)
+                waveforms = torch.from_numpy(np.stack(chunks)).float().to(device)
-            batch_emb = features.mean(dim=1).cpu().numpy()
+                if is_beats:
+                    padding_mask = torch.zeros_like(waveforms, dtype=torch.bool)
+                    features, _ = model.extract_features(waveforms, padding_mask=padding_mask)
+                    batch_emb = features.mean(dim=1).cpu().numpy()
+                elif ml_cfg is not None:
+                    all_layers, _ = model.extract_features(waveforms)
+                    selected = [all_layers[i].mean(dim=1) for i in ml_cfg[1]]
+                    batch_emb = torch.cat(selected, dim=1).cpu().numpy()
+                else:
+                    features, _ = model(waveforms)
+                    batch_emb = features.mean(dim=1).cpu().numpy()
         embeddings_list.append(batch_emb)
 
     timestamps = np.array(timestamps_list)
@@ -428,6 +565,17 @@ def train_classifier(video_infos: list[tuple[str, list[float], list[float]]],
     clf.fit(X[train_idx], y_arr[train_idx])
     _log("audio_scan: classifier trained")
 
+    # Calibrate probabilities for better threshold behavior
+    from sklearn.calibration import CalibratedClassifierCV
+    min_class = min(int(n_pos), int(n_neg_sample))
+    if min_class >= 6:
+        cal_clf = CalibratedClassifierCV(clf, cv=3, method='isotonic')
+        cal_clf.fit(X[train_idx], y_arr[train_idx])
+        clf = cal_clf
+        _log("audio_scan: classifier calibrated (isotonic, 3-fold)")
+    else:
+        _log(f"audio_scan: skipping calibration (min class size {min_class} < 6)")
+
     model = {"classifier": clf, "n_features": X.shape[1],
              "embed_model": embed_model or _DEFAULT_EMBED_MODEL}
 
@@ -589,7 +737,7 @@ def prefetch_audio(video_path: str, embed_model: str | None = None,
 def scan_video(
     video_path: str,
     model: dict = None,
-    threshold: float = 0.30,
+    threshold: float = 0.50,
     hop: float = 1.0,
     window: float = _WINDOW,
     cancel_flag: object = None,

core/db.py

@@ -94,7 +94,8 @@ class ProcessedDB:
             "  score           REAL NOT NULL,"
             "  disabled        INTEGER NOT NULL DEFAULT 0,"
             "  orig_start_time REAL,"
-            "  orig_end_time   REAL"
+            "  orig_end_time   REAL,"
+            "  scan_timestamp  TEXT NOT NULL DEFAULT ''"
             ")"
         )
         # Migrate: add new columns to existing scan_results tables
@@ -106,6 +107,7 @@ class ProcessedDB:
             ("disabled",        "INTEGER NOT NULL DEFAULT 0"),
             ("orig_start_time", "REAL"),
             ("orig_end_time",   "REAL"),
+            ("scan_timestamp",  "TEXT NOT NULL DEFAULT ''"),
         ]:
             if col not in sr_cols:
                 self._con.execute(
@@ -117,18 +119,114 @@ class ProcessedDB:
         )
         self._con.execute(
             "CREATE TABLE IF NOT EXISTS hard_negatives ("
-            "  id          INTEGER PRIMARY KEY AUTOINCREMENT,"
+            "  id           INTEGER PRIMARY KEY AUTOINCREMENT,"
-            "  filename    TEXT NOT NULL,"
+            "  filename     TEXT NOT NULL,"
-            "  profile     TEXT NOT NULL DEFAULT 'default',"
+            "  profile      TEXT NOT NULL DEFAULT 'default',"
-            "  start_time  REAL NOT NULL,"
+            "  start_time   REAL NOT NULL,"
-            "  source_path TEXT NOT NULL DEFAULT ''"
+            "  source_path  TEXT NOT NULL DEFAULT '',"
+            "  source_model TEXT NOT NULL DEFAULT ''"
             ")"
         )
+        # Migrate: add source_model column to existing hard_negatives tables
+        hn_cols = {
+            row[1]
+            for row in self._con.execute("PRAGMA table_info(hard_negatives)").fetchall()
+        }
+        if "source_model" not in hn_cols:
+            self._con.execute(
+                "ALTER TABLE hard_negatives ADD COLUMN source_model TEXT NOT NULL DEFAULT ''"
+            )
         self._con.execute(
             "CREATE INDEX IF NOT EXISTS idx_hardneg_file_profile"
             " ON hard_negatives(filename, profile)"
         )
         self._con.commit()
+        self._migrate_vid_folders()
+
+    def _migrate_vid_folders(self) -> None:
+        """Migrate old clip_NNN group dirs → vid_NNN per-video folders.
+
+        Old layout: export_folder/clip_NNN/clip_NNN_sub.mp4
+        New layout: export_folder/vid_NNN/clip_NNN_sub.mp4
+
+        Rewrites output_path in DB and moves files on disk.
+        """
+        # Check if any rows still use the old clip_NNN parent dir layout
+        row = self._con.execute(
+            "SELECT id FROM processed WHERE output_path LIKE '%/clip_%/%' LIMIT 1"
+        ).fetchone()
+        if not row:
+            return
+
+        _log("Migrating old clip group dirs → vid folders …")
+        rows = self._con.execute(
+            "SELECT id, filename, profile, output_path FROM processed"
+            " ORDER BY profile, filename, output_path"
+        ).fetchall()
+
+        # Assign vid_NNN per (profile, export_folder, filename)
+        vid_map: dict[tuple, str] = {}
+        vid_counters: dict[tuple, int] = {}
+
+        for rid, filename, profile, op in rows:
+            parent = os.path.dirname(op)
+            export_folder = os.path.dirname(parent)
+            key = (profile, export_folder, filename)
+            if key not in vid_map:
+                counter_key = (profile, export_folder)
+                n = vid_counters.get(counter_key, 1)
+                vid_map[key] = f"vid_{n:03d}"
+                vid_counters[counter_key] = n + 1
+
+        updates: list[tuple[str, int]] = []
+        moves: list[tuple[str, str]] = []
+        dirs_to_create: set[str] = set()
+        old_dirs: set[str] = set()
+
+        for rid, filename, profile, op in rows:
+            parent = os.path.dirname(op)
+            parent_name = os.path.basename(parent)
+            # Skip rows already using vid_NNN layout
+            if parent_name.startswith("vid_"):
+                continue
+            export_folder = os.path.dirname(parent)
+            key = (profile, export_folder, filename)
+            vid_name = vid_map[key]
+            new_path = os.path.join(export_folder, vid_name, os.path.basename(op))
+            updates.append((new_path, rid))
+            dirs_to_create.add(os.path.join(export_folder, vid_name))
+            old_dirs.add(parent)
+            if os.path.exists(op):
+                moves.append((op, new_path))
+
+        if not updates:
+            return
+
+        # Create vid directories
+        for d in sorted(dirs_to_create):
+            os.makedirs(d, exist_ok=True)
+
+        # Move files
+        import shutil
+        for old, new in moves:
+            if os.path.exists(old) and not os.path.exists(new):
+                shutil.move(old, new)
+
+        # Update DB
+        self._con.executemany(
+            "UPDATE processed SET output_path = ? WHERE id = ?", updates
+        )
+        self._con.commit()
+
+        # Remove empty old group directories
+        for d in sorted(old_dirs, reverse=True):
+            try:
+                if os.path.isdir(d) and not os.listdir(d):
+                    os.rmdir(d)
+            except OSError:
+                pass
+
+        _log(f"Migrated {len(updates)} rows, moved {len(moves)} files to vid folders")
 
     def add(self, filename: str, start_time: float, output_path: str,
             label: str = "", category: str = "",
@@ -291,20 +389,118 @@ class ProcessedDB:
         ).fetchall()
         return [r[0] for r in rows]
 
-    def get_export_folders(self, profile: str = "default") -> list[str]:
+    def get_max_counter(self, folder: str, name: str) -> int:
+        """Return the highest counter N found in output_paths matching folder/name_NNN*.
+
+        Parses the counter from filenames (e.g. 'clip_035_0.mp4' → 35).
+        *folder* is typically the vid folder.  Returns 0 if no matches exist.
+        """
+        if not self._enabled:
+            return 0
+        prefix = os.path.join(folder, name + "_")
+        rows = self._con.execute(
+            "SELECT DISTINCT output_path FROM processed"
+            " WHERE output_path LIKE ?",
+            (prefix + "%",),
+        ).fetchall()
+        max_n = 0
+        name_prefix = name + "_"
+        for (op,) in rows:
+            stem = os.path.splitext(os.path.basename(op))[0]
+            # stem: "clip_035_0" or "clip_036_a1_0"
+            if not stem.startswith(name_prefix):
+                continue
+            rest = stem[len(name_prefix):]  # "035_0" or "036_a1_0"
+            counter_str = rest.split("_")[0]
+            try:
+                max_n = max(max_n, int(counter_str))
+            except ValueError:
+                pass
+        return max_n
+
+    def delete_scan_exports(self, filename: str, profile: str) -> int:
+        """Delete all scan_export entries for *filename* in *profile*.
+
+        Returns the number of rows deleted.
+        """
+        if not self._enabled:
+            return 0
+        cur = self._con.execute(
+            "DELETE FROM processed"
+            " WHERE filename = ? AND profile = ? AND scan_export = 1",
+            (filename, profile),
+        )
+        self._con.commit()
+        return cur.rowcount
+
+    def get_vid_folder(self, filename: str, profile: str,
+                       export_folder: str) -> str:
+        """Return the vid_NNN folder name for a source video.
+
+        Checks existing DB output_paths first; if the video already has a
+        vid_NNN folder, returns it.  Otherwise assigns max(existing) + 1,
+        also checking disk for orphan vid folders.
+        """
+        if not self._enabled:
+            return "vid_001"
+        # Use the most recent entry (ORDER BY rowid DESC) for determinism
+        # when a file has entries across multiple vid folders.
+        row = self._con.execute(
+            "SELECT output_path FROM processed"
+            " WHERE filename = ? AND profile = ?"
+            " ORDER BY rowid DESC LIMIT 1",
+            (filename, profile),
+        ).fetchone()
+        if row:
+            parent = os.path.basename(os.path.dirname(row[0]))
+            if parent.startswith("vid_"):
+                return parent
+        # Collect max vid_NNN number from DB + disk (never reuse old numbers)
+        max_n = 0
+        rows = self._con.execute(
+            "SELECT DISTINCT output_path FROM processed WHERE profile = ?",
+            (profile,),
+        ).fetchall()
+        for (op,) in rows:
+            p = os.path.basename(os.path.dirname(op))
+            if p.startswith("vid_"):
+                try:
+                    max_n = max(max_n, int(p.split("_")[1]))
+                except (IndexError, ValueError):
+                    pass
+        if os.path.isdir(export_folder):
+            for d in os.listdir(export_folder):
+                if d.startswith("vid_") and os.path.isdir(
+                    os.path.join(export_folder, d)
+                ):
+                    try:
+                        max_n = max(max_n, int(d.split("_")[1]))
+                    except (IndexError, ValueError):
+                        pass
+        return f"vid_{max_n + 1:03d}"
+
+    def get_export_folders(self, profile: str = "default",
+                           include_scan_exports: bool = False) -> 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
+            .../export_folder/vid_NNN/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(
+        if include_scan_exports:
-            "SELECT DISTINCT output_path FROM processed WHERE profile = ?",
+            rows = self._con.execute(
-            (profile,),
+                "SELECT DISTINCT output_path FROM processed WHERE profile = ?",
-        ).fetchall()
+                (profile,),
+            ).fetchall()
+        else:
+            rows = self._con.execute(
+                "SELECT DISTINCT output_path FROM processed"
+                " WHERE profile = ? AND scan_export = 0",
+                (profile,),
+            ).fetchall()
         folder_names: set[str] = set()
         for (op,) in rows:
             grandparent = os.path.basename(os.path.dirname(os.path.dirname(op)))
@@ -316,6 +512,7 @@ class ProcessedDB:
                           negative_folder: str = "",
                           fallback_video_dir: str = "",
                           include_scan_exports: bool = False,
+                          use_hard_negatives: bool = True,
                           ) -> list[tuple[str, list[float], list[float], list[float]]]:
         """Build training video_infos from DB data.
 
@@ -325,6 +522,7 @@ class ProcessedDB:
             negative_folder: export folder name for explicit negatives (optional)
             fallback_video_dir: if source_path is empty, try filename in this dir
             include_scan_exports: if True, include auto-exported scan clips
+            use_hard_negatives: if False, skip hard negatives from scan feedback
 
         Returns:
             list of (source_video_path, positive_times, soft_times, negative_times)
@@ -363,15 +561,16 @@ class ProcessedDB:
                 soft_by_video.setdefault(fn, set()).add(st)
 
         # Include hard negatives from scan feedback
-        hard_rows = self._con.execute(
+        if use_hard_negatives:
-            "SELECT filename, start_time, source_path FROM hard_negatives"
+            hard_rows = self._con.execute(
-            " WHERE profile = ?",
+                "SELECT filename, start_time, source_path FROM hard_negatives"
-            (profile,),
+                " WHERE profile = ?",
-        ).fetchall()
+                (profile,),
-        for fn, st, sp in hard_rows:
+            ).fetchall()
-            neg_by_video.setdefault(fn, set()).add(st)
+            for fn, st, sp in hard_rows:
-            if sp:
+                neg_by_video.setdefault(fn, set()).add(st)
-                source_by_filename.setdefault(fn, sp)
+                if sp:
+                    source_by_filename.setdefault(fn, sp)
 
         # Remove positive times from soft/neg to avoid conflicting labels
         for fn in pos_by_video:
@@ -429,7 +628,7 @@ class ProcessedDB:
                 " WHERE profile = ? AND scan_export = 0",
                 (profile,),
             ).fetchall()
-        folders = self.get_export_folders(profile)
+        folders = self.get_export_folders(profile, include_scan_exports=include_scan_exports)
         stats: dict[str, dict] = {}
         for folder_name in folders:
             videos: set[str] = set()
@@ -440,50 +639,105 @@ class ProcessedDB:
                     videos.add(fn)
                     clips += 1
             stats[folder_name] = {"videos": len(videos), "clips": clips}
-        return stats
+        return {k: v for k, v in stats.items() if v["clips"] > 0}
 
     # ── Scan results ─────────────────────────────────────────────
 
     def save_scan_results(self, filename: str, profile: str, model: str,
-                          regions: list[tuple[float, float, float]]) -> None:
+                          regions: list[tuple[float, float, float]],
-        """Replace scan results for (filename, profile, model) with new regions.
+                          max_versions: int = 5) -> None:
+        """Save scan results as a new version for (filename, profile, model).
 
         regions: list of (start_time, end_time, score).
+        Keeps up to max_versions; oldest are pruned automatically.
         """
         if not self._enabled:
             return
+        ts = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
         with self._lock:
-            self._con.execute(
-                "DELETE FROM scan_results"
-                " WHERE filename = ? AND profile = ? AND model = ?",
-                (filename, profile, model),
-            )
             self._con.executemany(
                 "INSERT INTO scan_results"
                 " (filename, profile, model, start_time, end_time, score,"
-                "  orig_start_time, orig_end_time)"
+                "  orig_start_time, orig_end_time, scan_timestamp)"
-                " VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
+                " VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)",
-                [(filename, profile, model, s, e, sc, s, e) for s, e, sc in regions],
+                [(filename, profile, model, s, e, sc, s, e, ts)
+                 for s, e, sc in regions],
             )
+            # Prune old versions beyond max_versions
+            versions = self._con.execute(
+                "SELECT DISTINCT scan_timestamp FROM scan_results"
+                " WHERE filename = ? AND profile = ? AND model = ?"
+                " ORDER BY scan_timestamp DESC",
+                (filename, profile, model),
+            ).fetchall()
+            if len(versions) > max_versions:
+                old_ts = [v[0] for v in versions[max_versions:]]
+                self._con.execute(
+                    "DELETE FROM scan_results"
+                    " WHERE filename = ? AND profile = ? AND model = ?"
+                    f" AND scan_timestamp IN ({','.join('?' * len(old_ts))})",
+                    (filename, profile, model, *old_ts),
+                )
             self._con.commit()
 
-    def get_scan_results(self, filename: str, profile: str
+    def get_scan_versions(self, filename: str, profile: str, model: str
+                          ) -> list[dict]:
+        """Return list of scan versions for (filename, profile, model).
+
+        Returns [{timestamp, count, max_score}, ...] ordered newest first.
+        """
+        if not self._enabled:
+            return []
+        rows = self._con.execute(
+            "SELECT scan_timestamp, COUNT(*), MAX(score)"
+            " FROM scan_results"
+            " WHERE filename = ? AND profile = ? AND model = ?"
+            "   AND scan_timestamp != ''"
+            " GROUP BY scan_timestamp"
+            " ORDER BY scan_timestamp DESC",
+            (filename, profile, model),
+        ).fetchall()
+        return [{"timestamp": ts, "count": cnt, "max_score": sc}
+                for ts, cnt, sc in rows]
+
+    def get_scan_results(self, filename: str, profile: str,
+                         scan_timestamp: str | None = None
                          ) -> dict[str, list[tuple[int, float, float, float, bool, float, float]]]:
         """Return scan results grouped by model.
 
+        If scan_timestamp is given, returns only that version's rows.
+        Otherwise returns the latest version per model.
+
         Returns {model: [(row_id, start, end, score, disabled, orig_start, orig_end), ...]}
         sorted by start_time.
         """
         if not self._enabled:
             return {}
-        rows = self._con.execute(
+        if scan_timestamp:
-            "SELECT id, model, start_time, end_time, score, disabled,"
+            rows = self._con.execute(
-            "       orig_start_time, orig_end_time"
+                "SELECT id, model, start_time, end_time, score, disabled,"
-            " FROM scan_results"
+                "       orig_start_time, orig_end_time"
-            " WHERE filename = ? AND profile = ?"
+                " FROM scan_results"
-            " ORDER BY model, start_time",
+                " WHERE filename = ? AND profile = ? AND scan_timestamp = ?"
-            (filename, profile),
+                " ORDER BY model, start_time",
-        ).fetchall()
+                (filename, profile, scan_timestamp),
+            ).fetchall()
+        else:
+            # For each model, get rows from the latest timestamp only
+            rows = self._con.execute(
+                "SELECT r.id, r.model, r.start_time, r.end_time, r.score,"
+                "       r.disabled, r.orig_start_time, r.orig_end_time"
+                " FROM scan_results r"
+                " INNER JOIN ("
+                "   SELECT model, MAX(scan_timestamp) AS latest"
+                "   FROM scan_results"
+                "   WHERE filename = ? AND profile = ?"
+                "   GROUP BY model"
+                " ) m ON r.model = m.model AND r.scan_timestamp = m.latest"
+                " WHERE r.filename = ? AND r.profile = ?"
+                " ORDER BY r.model, r.start_time",
+                (filename, profile, filename, profile),
+            ).fetchall()
         result: dict[str, list[tuple[int, float, float, float, bool, float, float]]] = {}
         for row_id, model, s, e, sc, dis, os_, oe in rows:
             # Fall back to current bounds for legacy rows without orig
@@ -546,16 +800,18 @@ class ProcessedDB:
         return {r[0] for r in rows}
 
     def add_hard_negatives(self, filename: str, profile: str,
-                           times: list[float], source_path: str = "") -> None:
+                           times: list[float], source_path: str = "",
+                           source_model: str = "") -> None:
         """Save timestamps as hard-negative training examples."""
         if not self._enabled or not times:
             return
         with self._lock:
             for t in times:
                 self._con.execute(
-                    "INSERT INTO hard_negatives (filename, profile, start_time, source_path)"
+                    "INSERT INTO hard_negatives"
-                    " VALUES (?, ?, ?, ?)",
+                    " (filename, profile, start_time, source_path, source_model)"
-                    (filename, profile, t, source_path),
+                    " VALUES (?, ?, ?, ?, ?)",
+                    (filename, profile, t, source_path, source_model),
                 )
             self._con.commit()
 
@@ -570,6 +826,30 @@ class ProcessedDB:
         ).fetchall()
         return {r[0] for r in rows}
 
+    def get_hard_negatives(self, profile: str) -> list[dict]:
+        """Return all hard negatives for a profile with full details."""
+        if not self._enabled:
+            return []
+        rows = self._con.execute(
+            "SELECT id, filename, start_time, source_path, source_model"
+            " FROM hard_negatives WHERE profile = ?"
+            " ORDER BY filename, start_time",
+            (profile,),
+        ).fetchall()
+        return [{"id": r[0], "filename": r[1], "start_time": r[2],
+                 "source_path": r[3], "source_model": r[4]} for r in rows]
+
+    def delete_hard_negatives_by_ids(self, ids: list[int]) -> None:
+        """Delete hard negatives by row IDs."""
+        if not self._enabled or not ids:
+            return
+        with self._lock:
+            self._con.execute(
+                f"DELETE FROM hard_negatives WHERE id IN ({','.join('?' * len(ids))})",
+                ids,
+            )
+            self._con.commit()
+
     def remove_hard_negatives(self, filename: str, profile: str,
                               times: list[float]) -> None:
         """Remove specific hard-negative timestamps."""

core/ffmpeg.py

@@ -128,7 +128,16 @@ def build_ffmpeg_command(
             os.path.join(output_path, "frame_%04d.webp"),
         ]
     else:
-        cmd += ["-c:v", encoder, "-c:a", "pcm_s16le", output_path]
+        cmd += ["-c:v", encoder]
+        if "nvenc" in encoder:
+            cmd += ["-preset", "p4", "-cq", "28"]
+        elif "vaapi" in encoder:
+            cmd += ["-qp", "28"]
+        elif "qsv" in encoder:
+            cmd += ["-global_quality", "28"]
+        elif "amf" in encoder:
+            cmd += ["-qp_i", "28", "-qp_p", "28"]
+        cmd += ["-c:a", "pcm_s16le", output_path]
     return cmd
 
 

core/paths.py

@@ -24,16 +24,26 @@ def _log(*args) -> None:
     print(f"[8-cut {ts}]", *args, file=sys.stderr)
 
 
-def build_export_path(folder: str, basename: str, counter: int, sub: int | None = None) -> str:
+def build_export_path(folder: str, basename: str, counter: int,
-    group = f"{basename}_{counter:03d}"
+                      sub: int | None = None, tag: str | None = None) -> str:
-    name = f"{group}_{sub}" if sub is not None else group
+    """Build clip output path.  *folder* should be the vid folder (e.g. .../mp4/vid_001)."""
-    return os.path.join(folder, group, name + ".mp4")
+    name = f"{basename}_{counter:03d}"
+    if tag is not None:
+        name = f"{name}_{tag}"
+    if sub is not None:
+        name = f"{name}_{sub}"
+    return os.path.join(folder, name + ".mp4")
 
 
-def build_sequence_dir(folder: str, basename: str, counter: int, sub: int | None = None) -> str:
+def build_sequence_dir(folder: str, basename: str, counter: int,
-    group = f"{basename}_{counter:03d}"
+                       sub: int | None = None, tag: str | None = None) -> str:
-    name = f"{group}_{sub}" if sub is not None else group
+    """Build WebP sequence output dir.  *folder* should be the vid folder."""
-    return os.path.join(folder, group, name)
+    name = f"{basename}_{counter:03d}"
+    if tag is not None:
+        name = f"{name}_{tag}"
+    if sub is not None:
+        name = f"{name}_{sub}"
+    return os.path.join(folder, name)
 
 
 def format_time(seconds: float) -> str:

docs/plans/2026-04-19-audio-pipeline-improvements-design.md

@@ -0,0 +1,98 @@
+# Audio Pipeline Improvements Design
+
+Date: 2026-04-19
+
+## Goal
+
+Improve audio scan classification accuracy, especially for non-speech sounds (suction, gagging, impacts), through three changes:
+
+1. Multi-layer feature extraction from existing HuBERT/Wav2Vec2 models
+2. Two new embedding models: AST (AudioSet-supervised) and EAT (self-supervised + AudioSet finetuned)
+3. Calibrated classifier for better threshold behavior
+
+## 1. Multi-Layer Feature Extraction
+
+### Current behavior
+
+`model(waveforms)` extracts embeddings from the **last transformer layer only**.
+
+### Change
+
+Use `model.extract_features(waveforms)` (torchaudio API) to get all layer outputs. Select layers at quartile boundaries, mean-pool each over time, concatenate.
+
+| Model | Layers | Single-layer dim | Multi-layer dim (4 quartiles) |
+|-------|--------|-------------------|-------------------------------|
+| HUBERT_XLARGE | 48 | 1280 | 5120 |
+| HUBERT_LARGE | 24 | 1024 | 4096 |
+| HUBERT_BASE | 12 | 768 | 3072 |
+| WAV2VEC2_BASE | 12 | 768 | 3072 |
+
+### Implementation
+
+- New entries in `_EMBED_MODELS`: `"HUBERT_XLARGE_ML"` -> 5120, etc.
+- `_extract_w2v_windows`: when model name ends with `_ML`, call `extract_features()` instead of `model()`, select quartile layers, concat
+- Cache key: model name includes `_ML` suffix -> separate cache files
+- No change to classifier or training pipeline (HistGBT handles high-dim fine)
+
+## 2. AST (Audio Spectrogram Transformer)
+
+### What
+
+`MIT/ast-finetuned-audioset-10-10-0.4593` via HuggingFace `transformers`. 86M params, 768-dim, supervised on AudioSet 527 sound classes.
+
+### Integration
+
+- Load: `ASTModel.from_pretrained()` + `ASTFeatureExtractor`
+- Preprocessing: `ASTFeatureExtractor` handles mel spectrogram from 16kHz raw audio
+- Batching: prepare `input_values` per window, stack into batch, forward through model
+- Multi-layer: `output_hidden_states=True` returns 13 layers; `AST_ML` variant concats quartile layers -> 3072-dim
+- Model cached via `_get_w2v_model()` same lazy-load pattern
+
+### Entries
+
+- `"AST"` -> 768
+- `"AST_ML"` -> 3072
+
+## 3. EAT (Efficient Audio Transformer)
+
+### What
+
+`worstchan/EAT-base_epoch30_finetune_AS2M` via HuggingFace with `trust_remote_code=True`. 88M params, 768-dim, self-supervised + AudioSet finetuned.
+
+### Integration
+
+- Load: `AutoModel.from_pretrained(..., trust_remote_code=True)`
+- Preprocessing: manual 128-bin Kaldi fbank mel spectrogram via torchaudio, normalize with EAT constants `(mel - (-4.268)) / (4.569 * 2)`, reshape to `[B, 1, T, 128]`
+- Feature extraction: `model.extract_features(mel)` returns `[B, seq, 768]`; CLS token `[:, 0, :]` for utterance-level, or mean-pool `[:, 1:, :]` for frame-level. Use mean-pool for consistency with other models.
+- Multi-layer: not natively supported, skip for now
+
+### Entry
+
+- `"EAT"` -> 768
+
+## 4. Calibrated Classifier
+
+Wrap `HistGradientBoostingClassifier` in `CalibratedClassifierCV(clf, cv=3, method='isotonic')` after fitting. Gives well-calibrated probabilities -> threshold slider maps more linearly to precision/recall.
+
+One change in `train_classifier()`, no UI changes needed.
+
+## 5. Requirements
+
+Add to `requirements.txt`:
+```
+transformers>=4.30
+timm>=0.9
+```
+
+Both AST and EAT need `transformers`. EAT additionally needs `timm` (used internally by its custom model code). Both setup scripts (`setup_env.sh`, `setup-windows.ps1`) install from `requirements.txt` so no changes needed there.
+
+## Cache Compatibility
+
+- All new model variants get distinct cache keys via model name in the hash
+- Existing caches for HUBERT_XLARGE, BEATs, etc. remain valid and untouched
+- New models create new `.npz` files in the same `cache/w2v/` directory
+
+## UI Changes
+
+- `_EMBED_MODELS` dict additions appear automatically in Train dialog model dropdown and scan model dropdown
+- No other UI changes needed

docs/plans/2026-04-19-audio-pipeline-improvements-implementation.md

@@ -0,0 +1,588 @@
+# Audio Pipeline Improvements Implementation Plan
+
+> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
+
+**Goal:** Improve audio scan accuracy with multi-layer extraction, AST/EAT models, and calibrated classifier.
+
+**Architecture:** All changes are in `core/audio_scan.py`. The embedding extraction functions gain new model-type branches (AST, EAT, multi-layer). The classifier gets a calibration wrapper. `_EMBED_MODELS` dict and `_get_w2v_model()` are extended. No UI changes needed — new models appear automatically in dropdowns.
+
+**Tech Stack:** torchaudio (existing), transformers (new dep), timm (new dep), sklearn.calibration (existing dep)
+
+**Key design notes:**
+- `_get_w2v_model()` resolves `_ML` suffixed names to their base model for loading (e.g. `HUBERT_XLARGE_ML` loads `HUBERT_XLARGE`). Both share the same GPU model — only the extraction path differs (last-layer vs multi-layer). The global `_w2v_model_name` stores the **base** name so switching between `HUBERT_XLARGE` and `HUBERT_XLARGE_ML` does NOT trigger a reload.
+- Cache keys use the **full** model name (including `_ML`), so single-layer and multi-layer caches coexist as separate `.npz` files.
+- AST and EAT are separate model types that do NOT share the torchaudio loading path — they get their own `elif` branches in `_get_w2v_model()`.
+- Both `_extract_w2v_windows` and `_extract_w2v_targeted` need identical changes to their batch inference blocks. Keep them in sync.
+
+---
+
+### Task 1: Add transformers and timm to requirements
+
+**Files:**
+- Modify: `requirements.txt`
+
+**Step 1: Add dependencies**
+
+Add after the `torchaudio` line in `requirements.txt`:
+
+```
+transformers>=4.30
+timm>=0.9
+```
+
+**Step 2: Verify install**
+
+Run: `pip install transformers timm`
+
+**Step 3: Commit**
+
+```bash
+git add requirements.txt
+git commit -m "deps: add transformers and timm for AST/EAT models"
+```
+
+---
+
+### Task 2: Multi-layer extraction for torchaudio models
+
+**Files:**
+- Modify: `core/audio_scan.py:50-58` (_EMBED_MODELS dict)
+- Modify: `core/audio_scan.py:96-100` (_embed_dim)
+- Modify: `core/audio_scan.py:68-93` (_get_w2v_model)
+- Modify: `core/audio_scan.py:189-205` (_extract_w2v_windows batch loop)
+- Modify: `core/audio_scan.py:278-293` (_extract_w2v_targeted batch loop)
+- Test: `tests/test_audio_scan.py`
+
+**Step 1: Write failing test**
+
+Add to `tests/test_audio_scan.py`:
+
+```python
+def test_embed_dim_multi_layer():
+    from core.audio_scan import _embed_dim
+    # Multi-layer models should report concatenated dimension
+    assert _embed_dim("HUBERT_XLARGE_ML") == 5120
+    assert _embed_dim("HUBERT_LARGE_ML") == 4096
+    assert _embed_dim("HUBERT_BASE_ML") == 3072
+    # Single-layer unchanged
+    assert _embed_dim("HUBERT_XLARGE") == 1280
+```
+
+**Step 2: Run test to verify it fails**
+
+Run: `pytest tests/test_audio_scan.py::test_embed_dim_multi_layer -v`
+Expected: FAIL — `_embed_dim("HUBERT_XLARGE_ML")` returns 768 (default fallback)
+
+**Step 3: Add multi-layer entries to _EMBED_MODELS**
+
+In `core/audio_scan.py:50-58`, add after existing entries:
+
+```python
+_EMBED_MODELS = {
+    "WAV2VEC2_BASE":           768,
+    "WAV2VEC2_LARGE":         1024,
+    "WAV2VEC2_LARGE_LV60K":  1024,
+    "HUBERT_BASE":             768,
+    "HUBERT_LARGE":           1024,
+    "HUBERT_XLARGE":          1280,
+    "BEATS":                   768,
+    # Multi-layer variants (4 quartile layers concatenated)
+    "WAV2VEC2_BASE_ML":       3072,   # 768 * 4
+    "HUBERT_BASE_ML":         3072,   # 768 * 4
+    "HUBERT_LARGE_ML":        4096,   # 1024 * 4
+    "HUBERT_XLARGE_ML":       5120,   # 1280 * 4
+}
+```
+
+**Step 4: Run test to verify it passes**
+
+Run: `pytest tests/test_audio_scan.py::test_embed_dim_multi_layer -v`
+Expected: PASS
+
+**Step 5: Add helper to resolve base model and layer indices**
+
+Add after `_embed_dim()` (around line 101):
+
+```python
+def _ml_config(model_name: str) -> tuple[str, list[int]] | None:
+    """If model_name is a multi-layer variant, return (base_model, layer_indices).
+
+    Returns None for single-layer models.
+    Layer indices are 0-based into the list returned by extract_features().
+    """
+    if not model_name.endswith("_ML"):
+        return None
+    base = model_name[:-3]  # strip "_ML"
+    if base not in _EMBED_MODELS:
+        return None
+    # Layer counts per model family
+    layer_counts = {
+        "WAV2VEC2_BASE": 12, "WAV2VEC2_LARGE": 24, "WAV2VEC2_LARGE_LV60K": 24,
+        "HUBERT_BASE": 12, "HUBERT_LARGE": 24, "HUBERT_XLARGE": 48,
+        "AST": 12,
+    }
+    n = layer_counts.get(base)
+    if n is None:
+        return None
+    # Select 4 layers at quartile boundaries (0-indexed)
+    indices = [n // 4 - 1, n // 2 - 1, 3 * n // 4 - 1, n - 1]
+    return base, indices
+```
+
+Note: AST is included in the layer_counts dict here already so Task 3 doesn't need to modify it again.
+
+**Step 6: Write test for _ml_config**
+
+```python
+def test_ml_config():
+    from core.audio_scan import _ml_config
+    assert _ml_config("HUBERT_XLARGE") is None
+    assert _ml_config("BEATS_ML") is None  # BEATS has no ML variant
+    base, layers = _ml_config("HUBERT_XLARGE_ML")
+    assert base == "HUBERT_XLARGE"
+    assert layers == [11, 23, 35, 47]
+    base, layers = _ml_config("HUBERT_BASE_ML")
+    assert base == "HUBERT_BASE"
+    assert layers == [2, 5, 8, 11]
+```
+
+Run: `pytest tests/test_audio_scan.py::test_ml_config -v`
+Expected: PASS
+
+**Step 7: Modify _get_w2v_model to resolve ML base names**
+
+In `_get_w2v_model()` (line 68), the comparison key must use the resolved base name so that `HUBERT_XLARGE` and `HUBERT_XLARGE_ML` share the same loaded model without reloading:
+
+```python
+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
+    # Multi-layer variants use the same base model weights
+    ml = _ml_config(model_name)
+    load_name = ml[0] if ml else model_name
+    if _w2v_model is None or _w2v_model_name != load_name:
+        import torch
+        _w2v_device = "cuda" if torch.cuda.is_available() else "cpu"
+        if load_name == "BEATS":
+            ...  # existing BEATs code unchanged
+        else:
+            import torchaudio
+            bundle = getattr(torchaudio.pipelines, load_name)
+            _w2v_model = bundle.get_model().to(_w2v_device)
+        _w2v_model.eval()
+        _w2v_model_name = load_name
+        _log(f"audio_scan: {load_name} loaded on {_w2v_device}")
+    return _w2v_model, _w2v_device
+```
+
+**Step 8: Modify _extract_w2v_windows batch inference**
+
+In `_extract_w2v_windows`, compute `ml_cfg` **once** before the batch loop (after line 173 `is_beats = ...`):
+
+```python
+    ml_cfg = _ml_config(model_name or _DEFAULT_EMBED_MODEL)
+```
+
+Then replace the batch inference block (lines 197-204):
+
+```python
+        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)
+                batch_emb = features.mean(dim=1).cpu().numpy()
+            elif ml_cfg is not None:
+                all_layers, _ = model.extract_features(waveforms)
+                selected = [all_layers[i].mean(dim=1) for i in ml_cfg[1]]
+                batch_emb = torch.cat(selected, dim=1).cpu().numpy()
+            else:
+                features, _ = model(waveforms)
+                batch_emb = features.mean(dim=1).cpu().numpy()
+        embeddings.append(batch_emb)
+```
+
+**Step 9: Modify _extract_w2v_targeted batch inference (keep in sync)**
+
+In `_extract_w2v_targeted`, add `ml_cfg` computation after line 276 `is_beats = ...`:
+
+```python
+    ml_cfg = _ml_config(model_name or _DEFAULT_EMBED_MODEL)
+```
+
+Then replace the batch inference block (lines 285-292) with the same branching logic as Step 8:
+
+```python
+        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)
+                batch_emb = features.mean(dim=1).cpu().numpy()
+            elif ml_cfg is not None:
+                all_layers, _ = model.extract_features(waveforms)
+                selected = [all_layers[i].mean(dim=1) for i in ml_cfg[1]]
+                batch_emb = torch.cat(selected, dim=1).cpu().numpy()
+            else:
+                features, _ = model(waveforms)
+                batch_emb = features.mean(dim=1).cpu().numpy()
+        embeddings_list.append(batch_emb)
+```
+
+Note: `_extract_w2v_targeted` appends to `embeddings_list` (not `embeddings`).
+
+**Step 10: Run all tests**
+
+Run: `pytest tests/ -v`
+Expected: All pass
+
+**Step 11: Commit**
+
+```bash
+git add core/audio_scan.py tests/test_audio_scan.py
+git commit -m "feat: multi-layer extraction for HuBERT/Wav2Vec2 models"
+```
+
+---
+
+### Task 3: AST model integration
+
+**Files:**
+- Modify: `core/audio_scan.py:50-65` (_EMBED_MODELS, add AST entries)
+- Modify: `core/audio_scan.py:45-47` (add _ast_feature_extractor global)
+- Modify: `core/audio_scan.py:68-93` (_get_w2v_model, add AST loading branch)
+- Modify: `core/audio_scan.py` (_extract_w2v_windows and _extract_w2v_targeted, add AST inference branch)
+- Test: `tests/test_audio_scan.py`
+
+**Step 1: Write failing test**
+
+```python
+def test_embed_dim_ast():
+    from core.audio_scan import _embed_dim
+    assert _embed_dim("AST") == 768
+    assert _embed_dim("AST_ML") == 3072
+```
+
+Run: `pytest tests/test_audio_scan.py::test_embed_dim_ast -v`
+Expected: FAIL
+
+**Step 2: Add AST entries to _EMBED_MODELS**
+
+Add to the dict (after the ML entries):
+
+```python
+    # Transformers-based models
+    "AST":                     768,
+    "AST_ML":                 3072,   # 768 * 4
+```
+
+Run test again — should PASS now.
+
+**Step 3: Add module-level global for AST feature extractor**
+
+Near line 47 (after `_w2v_model_name = None`):
+
+```python
+_ast_feature_extractor = None
+```
+
+**Step 4: Add AST loading branch in _get_w2v_model**
+
+In `_get_w2v_model()`, add an `elif` branch **before** the torchaudio fallback `else`:
+
+```python
+        elif load_name == "AST":
+            from transformers import ASTModel, ASTFeatureExtractor
+            _w2v_model = ASTModel.from_pretrained(
+                "MIT/ast-finetuned-audioset-10-10-0.4593"
+            ).to(_w2v_device)
+            global _ast_feature_extractor
+            _ast_feature_extractor = ASTFeatureExtractor.from_pretrained(
+                "MIT/ast-finetuned-audioset-10-10-0.4593"
+            )
+```
+
+Note: `_ast_feature_extractor` is recreated on every model load (not cached separately) — simple and correct since the feature extractor is lightweight and model reloads are rare.
+
+**Step 5: Add AST inference branch in both extraction functions**
+
+In both `_extract_w2v_windows` and `_extract_w2v_targeted`, compute `is_ast` once before the loop:
+
+```python
+    is_ast = (model_name or _DEFAULT_EMBED_MODEL) in ("AST", "AST_ML")
+```
+
+Then in the batch inference block, add after the `elif ml_cfg` branch and before `else`:
+
+```python
+            elif is_ast:
+                # AST uses its own feature extractor for mel spectrogram
+                inputs = _ast_feature_extractor(
+                    list(chunks), sampling_rate=sr, return_tensors="pt",
+                    padding=True,
+                )
+                input_values = inputs.input_values.to(device)
+                if ml_cfg is not None:
+                    out = model(input_values, output_hidden_states=True)
+                    selected = [out.hidden_states[i].mean(dim=1) for i in ml_cfg[1]]
+                    batch_emb = torch.cat(selected, dim=1).cpu().numpy()
+                else:
+                    out = model(input_values)
+                    batch_emb = out.last_hidden_state.mean(dim=1).cpu().numpy()
+```
+
+Important: `chunks` is already a list of numpy arrays (built in the loop at lines 194-196). Pass it directly as `list(chunks)` — the `ASTFeatureExtractor` accepts a list of numpy arrays and handles batching/padding internally. Verified: `ASTFeatureExtractor([np.array, np.array, ...], sampling_rate=16000, return_tensors="pt", padding=True)` returns `input_values` of shape `[B, 1024, 128]`.
+
+**Step 6: Run all tests**
+
+Run: `pytest tests/ -v`
+Expected: All pass
+
+**Step 7: Commit**
+
+```bash
+git add core/audio_scan.py tests/test_audio_scan.py
+git commit -m "feat: add AST (Audio Spectrogram Transformer) embedding model"
+```
+
+---
+
+### Task 4: EAT model integration
+
+**Files:**
+- Modify: `core/audio_scan.py:50-65` (_EMBED_MODELS, add EAT entry)
+- Modify: `core/audio_scan.py:68-93` (_get_w2v_model, add EAT loading branch)
+- Add: `core/audio_scan.py` (_eat_preprocess helper function)
+- Modify: `core/audio_scan.py` (_extract_w2v_windows and _extract_w2v_targeted, add EAT inference branch)
+- Test: `tests/test_audio_scan.py`
+
+**Step 1: Write failing test**
+
+```python
+def test_embed_dim_eat():
+    from core.audio_scan import _embed_dim
+    assert _embed_dim("EAT") == 768
+```
+
+**Step 2: Add EAT entry to _EMBED_MODELS**
+
+```python
+    "EAT":                     768,
+```
+
+Note: No `EAT_ML` variant — EAT's `extract_features()` does not natively support multi-layer output. Can be added later if needed by monkey-patching.
+
+**Step 3: Add EAT loading branch in _get_w2v_model**
+
+Add after the AST branch, before the torchaudio `else`:
+
+```python
+        elif load_name == "EAT":
+            from transformers import AutoModel
+            _w2v_model = AutoModel.from_pretrained(
+                "worstchan/EAT-base_epoch30_finetune_AS2M",
+                trust_remote_code=True,
+            ).to(_w2v_device)
+```
+
+**Step 4: Add EAT preprocessing helper**
+
+Add as a module-level function near `_get_w2v_model`:
+
+```python
+def _eat_preprocess(chunks: list[np.ndarray], sr: int, device: str):
+    """Convert raw audio chunks to EAT mel spectrogram input.
+
+    Returns tensor of shape [B, 1, T, 128].
+    8s audio at 10ms frame shift produces ~798 frames, zero-padded to 1024.
+    """
+    import torch
+    import torchaudio.compliance.kaldi as kaldi
+
+    TARGET_LEN = 1024
+    MEAN, STD = -4.268, 4.569
+
+    mels = []
+    for chunk in chunks:
+        wav = torch.from_numpy(chunk).unsqueeze(0).float()
+        fbank = kaldi.fbank(
+            wav, htk_compat=True, sample_frequency=sr, use_energy=False,
+            window_type='hanning', num_mel_bins=128, dither=0.0, frame_shift=10,
+        )
+        # Pad or truncate to TARGET_LEN
+        if fbank.shape[0] < TARGET_LEN:
+            fbank = torch.nn.functional.pad(fbank, (0, 0, 0, TARGET_LEN - fbank.shape[0]))
+        else:
+            fbank = fbank[:TARGET_LEN]
+        fbank = (fbank - MEAN) / (STD * 2)
+        mels.append(fbank)
+    return torch.stack(mels).unsqueeze(1).to(device)  # [B, 1, T, 128]
+```
+
+**Step 5: Add EAT inference branch in both extraction functions**
+
+Compute `is_eat` once before the loop:
+
+```python
+    is_eat = (model_name or _DEFAULT_EMBED_MODEL) == "EAT"
+```
+
+Then in the batch inference block, add after the `elif is_ast` branch and before `else`:
+
+```python
+            elif is_eat:
+                mel_input = _eat_preprocess(chunks, sr, device)
+                features = model.extract_features(mel_input)
+                # Mean-pool frame-level tokens (skip CLS at index 0)
+                batch_emb = features[:, 1:, :].mean(dim=1).cpu().numpy()
+```
+
+Important: `model.extract_features()` returns a plain `torch.Tensor` of shape `[B, 513, 768]` (not a tuple). Index 0 is the CLS token, indices 1-512 are frame-level patch embeddings. We mean-pool the frame tokens for consistency with how other models are pooled.
+
+**Step 6: Run all tests**
+
+Run: `pytest tests/ -v`
+Expected: All pass
+
+**Step 7: Commit**
+
+```bash
+git add core/audio_scan.py tests/test_audio_scan.py
+git commit -m "feat: add EAT (Efficient Audio Transformer) embedding model"
+```
+
+---
+
+### Task 5: Calibrated classifier
+
+**Files:**
+- Modify: `core/audio_scan.py:424-429` (train_classifier, wrap clf)
+- Test: `tests/test_audio_scan.py`
+
+**Step 1: Modify train_classifier**
+
+After the existing `clf.fit()` call (line 428), add calibration with a safe guard:
+
+```python
+    clf.fit(X[train_idx], y_arr[train_idx])
+    _log("audio_scan: classifier trained")
+
+    # Calibrate probabilities for better threshold behavior
+    # Requires at least 6 samples per class for stable 3-fold isotonic calibration
+    from sklearn.calibration import CalibratedClassifierCV
+    min_class = min(int(n_pos), int(n_neg_sample))
+    if min_class >= 6:
+        cal_clf = CalibratedClassifierCV(clf, cv=3, method='isotonic')
+        cal_clf.fit(X[train_idx], y_arr[train_idx])
+        clf = cal_clf
+        _log("audio_scan: classifier calibrated (isotonic, 3-fold)")
+    else:
+        _log(f"audio_scan: skipping calibration (min class size {min_class} < 6)")
+```
+
+Why `min_class >= 6`: `CalibratedClassifierCV` uses stratified k-fold internally. With `cv=3`, each fold needs at least 2 samples per class. `min_class >= 6` guarantees this. With fewer samples, the uncalibrated HistGBT probabilities are still reasonable — calibration is an enhancement, not a requirement.
+
+Previous plan bug: `cv=min(3, n_pos, n_neg_sample)` could produce `cv=1` when `n_pos=1`, which raises `ValueError` (minimum is 2). Even `cv=2` with 2 positives causes one fold to have only 1 positive, making isotonic regression unstable. The `>= 6` guard avoids all these edge cases.
+
+**Step 2: Run all tests**
+
+Run: `pytest tests/ -v`
+Expected: All pass
+
+**Step 3: Commit**
+
+```bash
+git add core/audio_scan.py
+git commit -m "feat: calibrate classifier probabilities with isotonic regression"
+```
+
+---
+
+### Task 6: Integration test with real model (manual)
+
+This task is manual — it requires GPU and a real video file.
+
+**Step 1: Test multi-layer extraction**
+
+```bash
+python -c "
+from core.audio_scan import _extract_w2v_windows, _embed_dim
+import numpy as np
+y = np.random.randn(16000 * 20).astype(np.float32) * 0.01
+ts, emb = _extract_w2v_windows(y, model_name='HUBERT_XLARGE_ML')
+print(f'HUBERT_XLARGE_ML: {emb.shape}')  # expect (13, 5120)
+assert emb.shape[1] == _embed_dim('HUBERT_XLARGE_ML')
+print('PASS')
+"
+```
+
+**Step 2: Test AST extraction**
+
+```bash
+python -c "
+from core.audio_scan import _extract_w2v_windows, _embed_dim
+import numpy as np
+y = np.random.randn(16000 * 20).astype(np.float32) * 0.01
+ts, emb = _extract_w2v_windows(y, model_name='AST')
+print(f'AST: {emb.shape}')  # expect (13, 768)
+assert emb.shape[1] == _embed_dim('AST')
+print('PASS')
+"
+```
+
+**Step 3: Test AST multi-layer**
+
+```bash
+python -c "
+from core.audio_scan import _extract_w2v_windows, _embed_dim
+import numpy as np
+y = np.random.randn(16000 * 20).astype(np.float32) * 0.01
+ts, emb = _extract_w2v_windows(y, model_name='AST_ML')
+print(f'AST_ML: {emb.shape}')  # expect (13, 3072)
+assert emb.shape[1] == _embed_dim('AST_ML')
+print('PASS')
+"
+```
+
+**Step 4: Test EAT extraction**
+
+```bash
+python -c "
+from core.audio_scan import _extract_w2v_windows, _embed_dim
+import numpy as np
+y = np.random.randn(16000 * 20).astype(np.float32) * 0.01
+ts, emb = _extract_w2v_windows(y, model_name='EAT')
+print(f'EAT: {emb.shape}')  # expect (13, 768)
+assert emb.shape[1] == _embed_dim('EAT')
+print('PASS')
+"
+```
+
+**Step 5: Test model switching doesn't reload unnecessarily**
+
+```bash
+python -c "
+from core.audio_scan import _get_w2v_model
+import core.audio_scan as m
+# Load HUBERT_XLARGE
+_get_w2v_model('HUBERT_XLARGE')
+name1 = m._w2v_model_name
+# Switch to ML variant — should NOT reload
+_get_w2v_model('HUBERT_XLARGE_ML')
+name2 = m._w2v_model_name
+assert name1 == name2 == 'HUBERT_XLARGE', f'Expected no reload, got {name1} -> {name2}'
+print('PASS: no reload on ML switch')
+"
+```
+
+**Step 6: Test full train+scan cycle in app**
+
+Load app, select each new model from scan model dropdown, scan a video, train, verify results display correctly.
+
+**Step 7: Final commit and push**
+
+```bash
+git push
+```

docs/plans/2026-04-19-comfyui-node-pack-design.md

@@ -0,0 +1,226 @@
+# ComfyUI-8cut Node Pack Design
+
+Date: 2026-04-19
+
+## Goal
+
+Port 8-cut's video scanning, training, review, and export workflow to a ComfyUI node pack. The primary motivation is **remote access** — ComfyUI's web UI allows browser-based operation over the network, and HTML5 `<video>` handles streaming compression natively. No tensor-based image pipeline; videos stay as file paths throughout.
+
+## Architecture
+
+### Approach
+
+Monolithic Review Node + simple pipeline nodes. One central **VideoReview** node embeds the full interactive player/timeline/region table as a large DOM widget. Other nodes (Scan, Train, Export) are headless pipeline nodes that pass lightweight metadata.
+
+### Core reuse
+
+The entire `8-cut/core/` package is Qt-free and reusable as-is:
+- `core/audio_scan.py` — `scan_video()`, `train_classifier()`, `load_classifier()`
+- `core/db.py` — `ProcessedDB` (SQLite, all scan/training/export persistence)
+- `core/ffmpeg.py` — `build_ffmpeg_command()` (clip export)
+- `core/tracking.py` — YOLO-based subject tracking
+- `core/paths.py` — path helpers, `format_time()`
+
+No porting required — these are imported directly.
+
+---
+
+## Node Pack Structure
+
+```
+ComfyUI-8cut/
+  __init__.py                    # NODE_CLASS_MAPPINGS, WEB_DIRECTORY
+  core/                          # symlink or copy of 8-cut/core/
+  data/
+    8cut.db                      # separate SQLite DB (can copy from ~/.8cut.db)
+  models/                        # trained classifiers (.joblib)
+  nodes/
+    load_video.py
+    audio_scan.py
+    video_review.py
+    train_model.py
+    export_clips.py
+  server_routes.py               # custom API routes
+  web/
+    js/
+      video_review.js            # timeline + player + scan panel widget
+```
+
+---
+
+## Custom Types
+
+No tensors anywhere in the pipeline. All data flows as lightweight metadata:
+
+| Type | Python value | Purpose |
+|------|-------------|---------|
+| `VIDEO_PATH` | `str` (absolute path) | Video file reference |
+| `SCAN_REGIONS` | `list[dict]` with start/end/score/model/disabled | Scan output / review edits |
+| `SCAN_MODEL` | `str` (path to .joblib) | Trained classifier |
+
+---
+
+## Nodes
+
+### LoadVideo
+
+| | |
+|---|---|
+| **Input** | `video_path` (STRING, file browser), `profile` (STRING combo from DB profiles) |
+| **Output** | `VIDEO_PATH`, `filename` (STRING) |
+| **Logic** | Validates path exists, returns it. Populates profile combo via API route. |
+
+### AudioScan
+
+| | |
+|---|---|
+| **Input** | `VIDEO_PATH`, `SCAN_MODEL`, `threshold` (FLOAT 0-1), `hop` (FLOAT) |
+| **Output** | `SCAN_REGIONS` |
+| **Logic** | Calls `core.audio_scan.scan_video()` directly. Progress via `PromptServer.send_sync("progress", ...)`. |
+
+### VideoReview (interactive, blocking)
+
+| | |
+|---|---|
+| **Input** | `VIDEO_PATH`, `SCAN_REGIONS` (optional) |
+| **Output** | `SCAN_REGIONS` (edited) |
+| **OUTPUT_NODE** | `True` |
+| **Logic** | Execution pauses here. User interacts via the widget. Clicks "Continue" to pass edited regions downstream. |
+
+The widget layout:
+
+```
++-------------------------------------+
+|  [video player (HTML5 <video>)]     |
+|  +- timeline with scan regions ----+|
+|  |  cursor + region drag/resize    ||
+|  +---------------------------------+|
+|  +- model tabs [EAT_LARGE][HuBERT]+|
+|  | Time   | End    | Score         ||
+|  | 1:23   | 1:31   | 0.92          ||
+|  | 3:45   | 3:53   | 0.87          ||
+|  | [Add Negative] [Export] [Continue]|
+|  +---------------------------------+|
++-------------------------------------+
+```
+
+Widget size: ~640x500px minimum, resizable via LiteGraph.
+
+**Blocking mechanism**: The node's `run()` method blocks on a server-side event/queue. The frontend signals completion via `POST /8cut/review_done/{node_id}`, which unblocks `run()` and returns the edited `SCAN_REGIONS`.
+
+### TrainModel
+
+| | |
+|---|---|
+| **Input** | `profile` (STRING combo), `positive_folder` (STRING combo), `negative_folder` (STRING combo, optional), `embed_model` (STRING combo from `_EMBED_MODELS`), `use_hard_negatives` (BOOL) |
+| **Output** | `SCAN_MODEL` |
+| **Logic** | Queries `db.get_training_data()` to assemble `video_infos`, calls `core.audio_scan.train_classifier()`. Saves to `models/{profile}_{embed_model}.joblib` with version rotation. Progress via ComfyUI progress bar. |
+
+### ExportClips
+
+| | |
+|---|---|
+| **Input** | `VIDEO_PATH`, `SCAN_REGIONS`, `output_folder` (STRING), `short_side` (INT), `format` (combo MP4/WEBM), `spread` (FLOAT), `clip_count` (INT), `fuse_gap` (FLOAT) |
+| **Output** | exported file paths (list) |
+| **Logic** | Region fusion via `_build_export_spans()`, then `core.ffmpeg.build_ffmpeg_command()` per clip. Records each clip in DB via `db.add()`. |
+
+### Typical workflow
+
+```
+[LoadVideo] --> [AudioScan] --> [VideoReview] --> [ExportClips]
+                    ^
+              [TrainModel]
+```
+
+### Training loop (hard negatives round-trip)
+
+1. Scan with existing model -> regions in VideoReview
+2. Review -> mark false positives as negatives (DB)
+3. Train -> new model uses hard negatives
+4. Rescan -> better results
+5. Repeat
+
+---
+
+## API Routes
+
+### Video serving
+
+| Route | Method | Purpose |
+|-------|--------|---------|
+| `/8cut/video` | GET | Serve raw video file via `web.FileResponse`. Query param: `path`. Browser decodes mp4/h264 natively — key for remote streaming. |
+| `/8cut/video_transcode` | GET | Fallback: transcode to webm on-the-fly via ffmpeg `StreamResponse` for browser-incompatible formats (some MKV, odd codecs). |
+
+### Region editing (from VideoReview widget)
+
+| Route | Method | Purpose |
+|-------|--------|---------|
+| `/8cut/toggle_region` | POST | `toggle_scan_result_disabled()` |
+| `/8cut/resize_region` | POST | `update_scan_result()` |
+| `/8cut/delete_region` | POST | `delete_scan_result()` |
+| `/8cut/add_negatives` | POST | `add_hard_negatives()` |
+| `/8cut/scan_versions` | GET | `get_scan_versions()` |
+| `/8cut/review_done/{node_id}` | POST | Unblock the VideoReview node's `run()`, pass final regions |
+
+### Data queries (for combo widget population)
+
+| Route | Method | Purpose |
+|-------|--------|---------|
+| `/8cut/profiles` | GET | `db.get_profiles()` |
+| `/8cut/export_folders` | GET | `db.get_export_folders()` |
+| `/8cut/models` | GET | List available `.joblib` models |
+
+---
+
+## Frontend JS Widget (`web/js/video_review.js`)
+
+Registered via `app.registerExtension()`. Hooks into the VideoReview node's `onNodeCreated` and `onExecuted` callbacks.
+
+### Components
+
+1. **Video player** — HTML5 `<video>` element, src pointed at `/8cut/video?path=...`
+2. **Timeline** — `<canvas>` overlay below the video. Renders:
+   - Scan region rectangles (color-coded by score, red for negatives, gray for disabled)
+   - Cursor line (click to seek)
+   - Drag handles on region edges (resize)
+   - Waveform (optional, fetched via separate route)
+3. **Region table** — HTML table with model tabs. Click row to seek. Columns: Time, End, Score.
+4. **Action buttons** — Add Negative, Export, Continue
+5. **Version combo** — dropdown to switch scan history versions
+
+### Interaction flow
+
+- Widget activates when `onExecuted` fires with scan regions
+- User clicks/drags timeline, edits regions, marks negatives
+- Each edit hits an API route (immediate DB persistence)
+- "Continue" sends `POST /8cut/review_done/{node_id}` with final region state
+- Node's `run()` unblocks, passes `SCAN_REGIONS` downstream
+
+---
+
+## DB
+
+Separate SQLite DB at `ComfyUI-8cut/data/8cut.db`. Uses the existing `ProcessedDB` class unchanged — same schema, same migration code. Users can copy their existing `~/.8cut.db` to carry over scan history, training data, and hard negatives.
+
+---
+
+## Dependencies
+
+Same as 8-cut's `requirements.txt` minus PyQt6/python-mpv:
+- `torch`, `torchaudio`, `torchvision` (from CUDA index)
+- `transformers>=4.30,<5.0`, `timm>=0.9`
+- `librosa`, `scikit-learn`, `joblib`, `soundfile`, `numpy`
+- `ultralytics` (YOLO tracking)
+
+ComfyUI already provides torch. The node pack's install script just needs the audio/ML extras.
+
+---
+
+## Implementation Priority
+
+1. **Node pack skeleton** — structure, `__init__.py`, custom types, API routes for video serving
+2. **LoadVideo + AudioScan** — headless nodes, no widget needed yet
+3. **VideoReview widget (minimal)** — video player + static region display + Continue button
+4. **VideoReview interactivity** — timeline click/drag, region editing, negative marking
+5. **TrainModel + ExportClips** — complete the pipeline
+6. **Polish** — version history, waveform overlay, transcode fallback

docs/plans/2026-04-19-scan-history-negatives-design.md

@@ -0,0 +1,205 @@
+# Scan History & Hard Negative Management — Final Design
+
+Date: 2026-04-19 (implemented on `feat/training-ui`)
+
+## Goal
+
+1. Keep scan result history per `(file, model)` so users can track classifier improvement across training iterations
+2. Make hard negatives manageable — viewable, removable, and optionally disabled per training run
+3. Fix latent bug: `get_export_folders()` doesn't filter by `scan_export`
+
+---
+
+## 1. Ghost Folder Fix
+
+### Bug
+
+`get_export_folders()` queried all `output_path` rows without filtering `scan_export`. Folders that only contained scan-exported clips appeared in training dropdowns with 0 clips.
+
+### Implementation (`core/db.py`)
+
+**`get_export_folders(profile, include_scan_exports=False)`** — new parameter. When `False` (default), the SQL query adds `AND scan_export = 0` to exclude scan-only folders. The `get_training_stats()` method passes this through and also filters its return dict to remove folders with 0 clips:
+
+```python
+return {k: v for k, v in stats.items() if v["clips"] > 0}
+```
+
+### Test
+
+`tests/test_db.py::test_export_folders_excludes_scan_exports` — verifies scan-only folders are excluded by default and included when `include_scan_exports=True`.
+
+---
+
+## 2. Scan Result History
+
+### Schema
+
+Added column to `scan_results`:
+
+```sql
+scan_timestamp TEXT NOT NULL DEFAULT ''
+```
+
+All rows from the same scan share one timestamp string with **microsecond precision** (`%Y%m%d_%H%M%S_%f`, e.g. `"20260419_143022_123456"`). Microsecond precision prevents version collisions on fast successive scans.
+
+Migration adds the column via `ALTER TABLE` for existing databases. Legacy rows keep `scan_timestamp = ''`.
+
+### DB methods (`core/db.py`)
+
+**`save_scan_results(filename, profile, model, regions, max_versions=5)`**
+1. Inserts new rows with current microsecond-precision timestamp
+2. Counts distinct timestamps for this `(filename, profile, model)`
+3. Prunes oldest timestamps beyond `max_versions`
+
+No more DELETE-then-INSERT — all versions coexist in the table.
+
+**`get_scan_versions(filename, profile, model)`**
+Returns `[{timestamp, count, max_score}, ...]` ordered newest first. Filters `scan_timestamp != ''` so legacy rows don't appear as named versions.
+
+**`get_scan_results(filename, profile, scan_timestamp=None)`**
+- With `scan_timestamp`: returns rows matching that exact version
+- Without (default): uses `INNER JOIN` subquery with `MAX(scan_timestamp)` per model to return only the latest version. Legacy rows (empty timestamp) sort before any real timestamp, so they're returned when no versioned scans exist.
+
+### UI (`main.py` — `ScanResultsPanel`)
+
+Each model tab wraps its `QTableWidget` in a container `QWidget` with a `QComboBox` for version selection:
+
+```
+container (QWidget)
+├── cmb_version (QComboBox) — hidden when ≤ 1 version
+└── table (QTableWidget)
+```
+
+**Helper methods** unwrap this container:
+- `_current_table()` — returns `QTableWidget` from active tab (handles both raw table and container)
+- `_tab_table(index)` — same by tab index
+
+**Version combo** is populated by `_populate_version_combos()` after every `load_for_file()` and `add_scan_results()` call. Labels use `datetime.strptime` parsing with try/except fallback for robustness:
+
+```
+2026-04-19 14:30 (12 regions, best: 0.95)
+```
+
+**Version switching** via `_on_version_changed(model, idx)`:
+1. Reads `scan_timestamp` from combo's `userData`
+2. Calls `get_scan_results(filename, profile, scan_timestamp=ts)`
+3. Repopulates the table in-place
+4. **Clears the undo stack** — stale undo entries from a different version would corrupt data
+5. Emits `regions_edited` to refresh the timeline
+
+**Tab switch** connects `tab_changed` signal to `_on_scan_regions_edited` (not just `_update_scan_export_count`), so the timeline updates scan regions when switching model tabs.
+
+### Cache interaction
+
+Embedding cache is per `(file, model)` and doesn't change across scans. History stores classified regions (start, end, score), not embeddings.
+
+### Test
+
+`tests/test_db.py::test_scan_result_history` — saves 3 versions, verifies counts, ordering, and latest-by-default behavior.
+
+---
+
+## 3. Hard Negative Management
+
+### Schema
+
+Added column to `hard_negatives`:
+
+```sql
+source_model TEXT NOT NULL DEFAULT ''
+```
+
+Migration adds the column via `ALTER TABLE` for existing databases.
+
+### DB methods (`core/db.py`)
+
+**`add_hard_negatives(filename, profile, times, source_path="", source_model="")`** — now stores which embedding model produced the scan that led to the negative marking.
+
+**`get_hard_negatives(profile)`** — returns all rows as `[{id, filename, start_time, source_path, source_model}, ...]` for the management dialog.
+
+**`delete_hard_negatives_by_ids(ids)`** — bulk delete by row IDs.
+
+**`get_training_data(..., use_hard_negatives=True)`** — new parameter. When `False`, the hard negatives query is skipped entirely. Non-destructive — negatives remain in DB.
+
+### Source model tracking (`main.py`)
+
+`_on_scan_negatives()` now passes `source_model=self._scan_panel.current_model_name()` when marking negatives from scan results. `current_model_name()` extracts the model name from the active tab text (stripping the count suffix).
+
+### Training toggle (`main.py` — `TrainDialog`)
+
+Checkbox **"Use hard negatives in training"** (default checked) with "Manage..." button in an HBox layout. The toggle:
+- Updates live training stats preview via debounced `_update_stats()`
+- Passes `use_hard_negatives` through `_open_train_dialog()` to `get_training_data()`
+
+### Management dialog (`main.py` — `HardNegativesDialog`)
+
+Accessible from TrainDialog's "Manage..." button. Features:
+
+| Component | Details |
+|-----------|---------|
+| **Filter combo** | `(all)` + each distinct `source_model` found in data |
+| **Summary label** | `<b>N</b> hard negatives` |
+| **Table** | File, Time (`{:.1f}s`), Source Model, hidden ID column |
+| **Delete Selected** | Multi-select aware, skips hidden (filtered) rows |
+| **Clear All** | **Filter-aware**: if a model filter is active, only deletes negatives for that model with an appropriate confirmation message. If `(all)`, deletes everything. |
+| **Close** | Closes dialog, triggers stats refresh in parent TrainDialog |
+
+`blockSignals(True)` guards prevent spurious filter callbacks during `_load()` repopulation.
+
+### Tests
+
+- `test_hard_negatives_source_model` — verifies source_model stored and retrieved
+- `test_training_data_skips_hard_negatives` — verifies `use_hard_negatives=False` excludes them
+- `test_delete_hard_negatives_by_ids` — verifies bulk deletion by ID
+
+---
+
+## 4. Runtime Fixes (discovered during testing)
+
+### EAT/torchvision ABI mismatch
+
+**Problem:** `torchvision` installed from PyPI (CPU build) was incompatible with `torch` from CUDA wheel index, causing `operator torchvision::nms does not exist`.
+
+**Fix:** Added `torchvision` to the explicit torch install line in both setup scripts:
+```bash
+pip install torch torchaudio torchvision --index-url "$TORCH_INDEX"
+```
+
+Also added `--extra-index-url "$TORCH_INDEX"` to the `pip install -r requirements.txt` line to prevent transitive dependencies (timm, ultralytics) from pulling CPU-only torch packages.
+
+Applied to: `setup_env.sh` (both conda and venv paths), `setup-windows.ps1`.
+
+### EAT / transformers 5.x incompatibility
+
+**Problem:** transformers 5.x broke EAT's remote model code (`'EATModel' object has no attribute 'all_tied_weights_keys'`).
+
+**Fix:** Pinned `transformers>=4.30,<5.0` in `requirements.txt`.
+
+### NumPy non-writable array warning
+
+**Problem:** Cached HuBERT/EAT embeddings loaded from disk are read-only numpy arrays. `torch.from_numpy()` on a non-writable array triggers a deprecation warning.
+
+**Fix:** In `core/audio_scan.py`, changed EAT preprocessing to copy the array:
+```python
+wav = torch.from_numpy(np.array(chunk)).unsqueeze(0).float()
+```
+
+### Timeline not updating on tab switch
+
+**Problem:** Switching model tabs in the scan results panel didn't refresh the timeline's highlighted regions because `tab_changed` was only connected to `_update_scan_export_count`.
+
+**Fix:** Connected `tab_changed` to `_on_scan_regions_edited` instead, which handles both timeline refresh and export count update.
+
+---
+
+## File Summary
+
+| File | Changes |
+|------|---------|
+| `core/db.py` | Schema migrations, `get_export_folders` filter, versioned `save_scan_results`, `get_scan_versions`, version-aware `get_scan_results`, `add_hard_negatives` with `source_model`, `get_hard_negatives`, `delete_hard_negatives_by_ids`, `get_training_data` with `use_hard_negatives` |
+| `main.py` | `HardNegativesDialog` class, `TrainDialog` hard neg toggle + manage button, `ScanResultsPanel` container/combo architecture, version combo population and switching, `current_model_name()`, tab-switch timeline fix |
+| `core/audio_scan.py` | `np.array(chunk)` copy for read-only numpy arrays in EAT preprocessing |
+| `requirements.txt` | `transformers>=4.30,<5.0` pin |
+| `setup_env.sh` | `torchvision` in torch install, `--extra-index-url` on requirements install |
+| `setup-windows.ps1` | `torchvision` in torch install, `--extra-index-url` on requirements install, removed skip-if-exists guard |
+| `tests/test_db.py` | 5 tests covering all DB-layer changes |

docs/plans/2026-04-19-scan-history-negatives-implementation.md

@@ -0,0 +1,94 @@
+# Scan History & Hard Negative Management — Implementation Log
+
+> All tasks complete. See the design doc for the final specification.
+
+**Branch:** `feat/training-ui`
+
+---
+
+### Task 1: Fix ghost folder bug in get_export_folders -- DONE
+
+**Commit:** `2614a76 fix: get_export_folders respects scan_export filter`
+
+- `core/db.py` — `get_export_folders(profile, include_scan_exports=False)`: filters `scan_export = 0` by default
+- `core/db.py` — `get_training_stats()`: passes `include_scan_exports` through, filters out 0-clip folders
+- `tests/test_db.py` — `test_export_folders_excludes_scan_exports`
+
+---
+
+### Task 2: Scan result history — schema and DB methods -- DONE
+
+**Commit:** `4fb2ae1 feat: scan result history — keep N versions per (file, model)`
+
+- `core/db.py` — added `scan_timestamp TEXT NOT NULL DEFAULT ''` column with migration
+- `core/db.py` — `save_scan_results()`: versioned insert with microsecond-precision timestamp (`%Y%m%d_%H%M%S_%f`), auto-prunes beyond `max_versions=5`
+- `core/db.py` — `get_scan_versions()`: returns `[{timestamp, count, max_score}, ...]` newest first
+- `core/db.py` — `get_scan_results(scan_timestamp=None)`: `INNER JOIN` subquery with `MAX(scan_timestamp)` for latest-by-default
+- `tests/test_db.py` — `test_scan_result_history`
+
+---
+
+### Task 3: Scan history UI — version selector in ScanResultsPanel -- DONE
+
+**Commit:** `8ed9fbf feat: scan version selector in results panel`
+
+- `main.py` — `_add_tab()`: wraps table in container `QWidget` with version `QComboBox` (hidden when ≤ 1 version)
+- `main.py` — `_current_table()` / `_tab_table(idx)`: unwrap container to get `QTableWidget`
+- `main.py` — `_populate_version_combos()`: queries `get_scan_versions()`, formats labels with `datetime.strptime` + try/except fallback
+- `main.py` — `_on_version_changed()`: reloads table from specific version, clears undo stack, emits `regions_edited`
+- `main.py` — `current_model_name()`: extracts model name from tab text
+
+---
+
+### Task 4: Hard negatives — schema and training toggle -- DONE
+
+**Commit:** `edc5784 feat: hard negative source_model tracking, training toggle`
+
+- `core/db.py` — added `source_model TEXT NOT NULL DEFAULT ''` column to `hard_negatives` with migration
+- `core/db.py` — `add_hard_negatives(source_model="")`: stores originating model
+- `core/db.py` — `get_hard_negatives(profile)`: returns full rows as list of dicts
+- `core/db.py` — `delete_hard_negatives_by_ids(ids)`: bulk delete by row IDs
+- `core/db.py` — `get_training_data(use_hard_negatives=True)`: conditionally skips hard negatives query
+- `main.py` — `TrainDialog`: "Use hard negatives" checkbox + "Manage..." button in HBox layout
+- `main.py` — `_on_scan_negatives()`: passes `source_model=self._scan_panel.current_model_name()`
+- `tests/test_db.py` — `test_hard_negatives_source_model`, `test_training_data_skips_hard_negatives`, `test_delete_hard_negatives_by_ids`
+
+---
+
+### Task 5: Hard negatives management dialog -- DONE
+
+**Commit:** `e6db83f feat: hard negatives management dialog with filter and bulk delete`
+
+- `main.py` — `HardNegativesDialog`: table with File/Time/Source Model/hidden ID columns, model filter combo, delete selected, filter-aware clear all, close button
+- Filter-aware "Clear All": respects active model filter, shows appropriate confirmation message
+
+---
+
+### Task 6: Code review fixes -- DONE
+
+**Commit:** `5d45b8d fix: timestamp collision, undo stack invalidation, label parsing, filter-aware clear`
+
+Four issues found during code review:
+1. **Timestamp collision** — second-precision timestamps could merge versions on sub-second calls. Fixed with microsecond precision `%f`
+2. **Undo stack invalidation** — switching scan versions left stale undo entries. Fixed by clearing undo stack in `_on_version_changed()`
+3. **Timestamp label fragile parsing** — hard-coded string slicing. Fixed with `datetime.strptime` + try/except fallback
+4. **Clear All ignoring filter** — deleted all negatives regardless of model filter. Fixed to respect active filter
+
+---
+
+### Runtime fixes (discovered during manual testing)
+
+| Commit | Fix |
+|--------|-----|
+| `a3c657c` | Install `torchvision` from CUDA wheel index (was pulling CPU build from PyPI) |
+| `3c3b1d7` | Remove "skip if torch exists" guard in Windows setup so re-runs fix broken envs |
+| `fd043f4` | Pin `transformers>=4.30,<5.0` — EAT remote model code incompatible with transformers 5.x |
+| `7d6fee9` | Copy read-only numpy array before `torch.from_numpy()` in EAT preprocessing |
+| `bd345ab` | Connect `tab_changed` to `_on_scan_regions_edited` so timeline refreshes on tab switch |
+| `d8b3972` | Add `--extra-index-url` to `pip install -r requirements.txt` in both setup scripts |
+
+---
+
+### Test results
+
+All 68 tests pass (5 new DB tests + 63 existing).

requirements.txt

@@ -13,6 +13,8 @@ soundfile>=0.12
 # or manually: pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128
 torch>=2.0
 torchaudio>=2.0
+transformers>=4.30,<5.0  # EAT remote model code incompatible with transformers 5.x
+timm>=0.9
 
 # Object detection
 ultralytics>=8.0

setup-windows.ps1

@@ -22,25 +22,23 @@ if (Test-Path (Join-Path $venvDir "Scripts\python.exe")) {
 & "$venvDir\Scripts\Activate.ps1"
 
 # ── PyTorch ───────────────────────────────────────────────
-$hasTorch = python -c "import torch" 2>&1
+# Detect NVIDIA GPU via nvidia-smi
-if ($LASTEXITCODE -eq 0) {
+$hasNvidia = Get-Command nvidia-smi -ErrorAction SilentlyContinue
-    Write-Host "`nPyTorch already installed, skipping." -ForegroundColor Green
+if ($hasNvidia) {
+    $torchIndex = "https://download.pytorch.org/whl/cu128"
+    Write-Host "`nNVIDIA GPU detected — using CUDA 12.8 PyTorch index" -ForegroundColor Green
 } else {
-    # Detect NVIDIA GPU via nvidia-smi
+    $torchIndex = "https://download.pytorch.org/whl/cpu"
-    $hasNvidia = Get-Command nvidia-smi -ErrorAction SilentlyContinue
+    Write-Host "`nNo NVIDIA GPU detected — using CPU-only PyTorch index" -ForegroundColor Yellow
-    if ($hasNvidia) {
-        Write-Host "`nNVIDIA GPU detected — installing PyTorch with CUDA 12.8..." -ForegroundColor Green
-        pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128
-    } else {
-        Write-Host "`nNo NVIDIA GPU detected — installing CPU-only PyTorch..." -ForegroundColor Yellow
-        Write-Host "(Audio scanning will work but will be slower without GPU)" -ForegroundColor Yellow
-        pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
-    }
 }
+# Always install/upgrade torch stack from correct index
+# (pip install is a no-op if already at the right version)
+Write-Host "Installing PyTorch + torchaudio + torchvision..."
+pip install torch torchaudio torchvision --index-url $torchIndex
 
 # ── Python deps ───────────────────────────────────────────
 Write-Host "`nInstalling project dependencies..."
-pip install -r (Join-Path $root "requirements.txt")
+pip install -r (Join-Path $root "requirements.txt") --extra-index-url $torchIndex
 
 # ── libmpv ────────────────────────────────────────────────
 $mpvDll = Join-Path $root "libmpv-2.dll"

setup_env.sh

@@ -66,10 +66,10 @@ setup_conda() {
     fi
 
     echo "  Installing PyTorch + torchaudio (CUDA 12.8)..."
-    pip install torch torchaudio --index-url "$TORCH_INDEX"
+    pip install torch torchaudio torchvision --index-url "$TORCH_INDEX"
 
     echo "  Installing project dependencies..."
-    pip install -r "$SCRIPT_DIR/requirements.txt"
+    pip install -r "$SCRIPT_DIR/requirements.txt" --extra-index-url "$TORCH_INDEX"
 
     echo ""
     echo "Done! Activate with:"
@@ -91,10 +91,10 @@ setup_venv() {
     source "$VENV_DIR/bin/activate"
 
     echo "  Installing PyTorch + torchaudio (CUDA 12.8)..."
-    pip install torch torchaudio --index-url "$TORCH_INDEX"
+    pip install torch torchaudio torchvision --index-url "$TORCH_INDEX"
 
     echo "  Installing project dependencies..."
-    pip install -r "$SCRIPT_DIR/requirements.txt"
+    pip install -r "$SCRIPT_DIR/requirements.txt" --extra-index-url "$TORCH_INDEX"
 
     echo ""
     echo "Done! Activate with:"

tests/test_audio_scan.py

@@ -25,6 +25,39 @@ def test_default_model_path_contains_profile():
     assert path.endswith(".joblib")
 
 
+def test_embed_dim_multi_layer():
+    from core.audio_scan import _embed_dim
+    # Multi-layer models should report concatenated dimension
+    assert _embed_dim("HUBERT_XLARGE_ML") == 5120
+    assert _embed_dim("HUBERT_LARGE_ML") == 4096
+    assert _embed_dim("HUBERT_BASE_ML") == 3072
+    # Single-layer unchanged
+    assert _embed_dim("HUBERT_XLARGE") == 1280
+
+
+def test_ml_config():
+    from core.audio_scan import _ml_config
+    assert _ml_config("HUBERT_XLARGE") is None
+    assert _ml_config("BEATS_ML") is None  # BEATS has no ML variant
+    base, layers = _ml_config("HUBERT_XLARGE_ML")
+    assert base == "HUBERT_XLARGE"
+    assert layers == [11, 23, 35, 47]
+    base, layers = _ml_config("HUBERT_BASE_ML")
+    assert base == "HUBERT_BASE"
+    assert layers == [2, 5, 8, 11]
+
+
+def test_embed_dim_ast():
+    from core.audio_scan import _embed_dim
+    assert _embed_dim("AST") == 768
+    assert _embed_dim("AST_ML") == 3072
+
+
+def test_embed_dim_eat():
+    from core.audio_scan import _embed_dim
+    assert _embed_dim("EAT") == 768
+
+
 def test_db_get_all_export_paths():
     with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
         path = f.name

tests/test_db.py

@@ -0,0 +1,106 @@
+import os
+import tempfile
+
+from core.db import ProcessedDB
+
+
+def test_export_folders_excludes_scan_exports():
+    """Scan-export-only folders should not appear when include_scan_exports=False."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        path = f.name
+    try:
+        db = ProcessedDB(path)
+        # Manual export
+        db.add("a.mp4", 10.0, "/out/mp4_Intense/g1/clip.mp4", profile="test")
+        # Scan export to different folder
+        db.add("a.mp4", 20.0, "/out/mp4_ScanOnly/g1/clip.mp4", profile="test",
+               scan_export=True)
+        folders = db.get_export_folders("test")
+        assert "mp4_Intense" in folders
+        assert "mp4_ScanOnly" not in folders, "scan-only folder should be excluded"
+        # With include_scan_exports=True, both should appear
+        folders_all = db.get_export_folders("test", include_scan_exports=True)
+        assert "mp4_ScanOnly" in folders_all
+    finally:
+        os.unlink(path)
+
+
+def test_scan_result_history():
+    """save_scan_results should keep multiple versions."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        path = f.name
+    try:
+        db = ProcessedDB(path)
+        # Save three versions (microsecond-precision timestamps avoid collisions)
+        db.save_scan_results("v.mp4", "test", "MODEL_A", [(0, 8, 0.9)])
+        db.save_scan_results("v.mp4", "test", "MODEL_A",
+                             [(0, 8, 0.8), (10, 18, 0.7)])
+        db.save_scan_results("v.mp4", "test", "MODEL_A", [(5, 13, 0.95)])
+        versions = db.get_scan_versions("v.mp4", "test", "MODEL_A")
+        assert len(versions) == 3
+        # Most recent first
+        assert versions[0]["count"] == 1   # latest: 1 region
+        assert versions[1]["count"] == 2   # middle: 2 regions
+        assert versions[2]["count"] == 1   # oldest: 1 region
+        # get_scan_results returns latest version by default
+        results = db.get_scan_results("v.mp4", "test")
+        assert len(results.get("MODEL_A", [])) == 1
+    finally:
+        os.unlink(path)
+
+
+def test_hard_negatives_source_model():
+    """Hard negatives should store source_model."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        path = f.name
+    try:
+        db = ProcessedDB(path)
+        db.add_hard_negatives("a.mp4", "test", [10.0, 20.0],
+                              source_path="/a.mp4", source_model="HUBERT_XLARGE")
+        rows = db.get_hard_negatives("test")
+        assert len(rows) == 2
+        assert all(r["source_model"] == "HUBERT_XLARGE" for r in rows)
+    finally:
+        os.unlink(path)
+
+
+def test_training_data_skips_hard_negatives():
+    """get_training_data with use_hard_negatives=False should skip them."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        path = f.name
+    try:
+        db = ProcessedDB(path)
+        # Create a source file that "exists" — use the temp db file itself
+        db.add("a.mp4", 10.0, "/out/folder/g/clip.mp4", profile="test",
+               source_path=path)
+        db.add_hard_negatives("a.mp4", "test", [500.0], source_path=path)
+        # With hard negatives
+        data_with = db.get_training_data("test", "folder", use_hard_negatives=True)
+        # Without hard negatives
+        data_without = db.get_training_data("test", "folder", use_hard_negatives=False)
+        assert len(data_with) >= 1
+        # The "with" case should have the hard negative time in neg list
+        neg_with = sum(len(vi[3]) for vi in data_with)
+        neg_without = sum(len(vi[3]) for vi in data_without)
+        assert neg_with > neg_without, "hard negatives should be excluded when use_hard_negatives=False"
+    finally:
+        os.unlink(path)
+
+
+def test_delete_hard_negatives_by_ids():
+    """delete_hard_negatives_by_ids should remove specific rows."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        path = f.name
+    try:
+        db = ProcessedDB(path)
+        db.add_hard_negatives("a.mp4", "test", [10.0, 20.0, 30.0],
+                              source_path="/a.mp4")
+        rows = db.get_hard_negatives("test")
+        assert len(rows) == 3
+        # Delete first two
+        db.delete_hard_negatives_by_ids([rows[0]["id"], rows[1]["id"]])
+        remaining = db.get_hard_negatives("test")
+        assert len(remaining) == 1
+        assert remaining[0]["start_time"] == 30.0
+    finally:
+        os.unlink(path)

tests/test_utils.py

@@ -5,21 +5,21 @@ from main import ProcessedDB
 
 
 def test_build_export_path_first():
-    assert build_export_path("/out", "clip", 1) == "/out/clip_001/clip_001.mp4"
+    assert build_export_path("/out", "clip", 1) == "/out/clip_001.mp4"
 
 def test_build_export_path_counter():
-    assert build_export_path("/out", "clip", 42) == "/out/clip_042/clip_042.mp4"
+    assert build_export_path("/out", "clip", 42) == "/out/clip_042.mp4"
 
 def test_build_export_path_deep_counter():
-    assert build_export_path("/out", "shot", 999) == "/out/shot_999/shot_999.mp4"
+    assert build_export_path("/out", "shot", 999) == "/out/shot_999.mp4"
 
 def test_build_export_path_sub():
-    assert build_export_path("/out", "clip", 1, sub=0) == "/out/clip_001/clip_001_0.mp4"
+    assert build_export_path("/out", "clip", 1, sub=0) == "/out/clip_001_0.mp4"
-    assert build_export_path("/out", "clip", 1, sub=2) == "/out/clip_001/clip_001_2.mp4"
+    assert build_export_path("/out", "clip", 1, sub=2) == "/out/clip_001_2.mp4"
 
 def test_build_sequence_dir_sub():
-    assert build_sequence_dir("/out", "clip", 1, sub=0) == "/out/clip_001/clip_001_0"
+    assert build_sequence_dir("/out", "clip", 1, sub=0) == "/out/clip_001_0"
-    assert build_sequence_dir("/out", "clip", 1, sub=1) == "/out/clip_001/clip_001_1"
+    assert build_sequence_dir("/out", "clip", 1, sub=1) == "/out/clip_001_1"
 
 def test_format_time_seconds():
     assert format_time(0.0) == "0:00.0"
@@ -178,10 +178,10 @@ def test_audio_extract_timing():
 
 
 def test_build_sequence_dir_basic():
-    assert build_sequence_dir("/out", "clip", 1) == "/out/clip_001/clip_001"
+    assert build_sequence_dir("/out", "clip", 1) == "/out/clip_001"
 
 def test_build_sequence_dir_counter():
-    assert build_sequence_dir("/out", "clip", 42) == "/out/clip_042/clip_042"
+    assert build_sequence_dir("/out", "clip", 42) == "/out/clip_042"
 
 def test_ffmpeg_command_image_sequence():
     cmd = build_ffmpeg_command("/in/v.mp4", 0.0, "/out/seq_001", image_sequence=True)
@@ -265,13 +265,13 @@ def test_db_get_group_returns_all_sub_clips():
         path = f.name
     try:
         db = ProcessedDB(path)
-        db.add("video.mp4", 10.0, "/out/clip_001/clip_001_0.mp4")
+        db.add("video.mp4", 10.0, "/out/vid_001/clip_001_0.mp4")
-        db.add("video.mp4", 10.0, "/out/clip_001/clip_001_1.mp4")
+        db.add("video.mp4", 10.0, "/out/vid_001/clip_001_1.mp4")
-        db.add("video.mp4", 10.0, "/out/clip_001/clip_001_2.mp4")
+        db.add("video.mp4", 10.0, "/out/vid_001/clip_001_2.mp4")
-        group = db.get_group("/out/clip_001/clip_001_0.mp4")
+        group = db.get_group("/out/vid_001/clip_001_0.mp4")
         assert len(group) == 3
-        assert "/out/clip_001/clip_001_0.mp4" in group
+        assert "/out/vid_001/clip_001_0.mp4" in group
-        assert "/out/clip_001/clip_001_2.mp4" in group
+        assert "/out/vid_001/clip_001_2.mp4" in group
     finally:
         os.unlink(path)
 
@@ -281,10 +281,10 @@ def test_db_get_group_isolates_by_start_time():
         path = f.name
     try:
         db = ProcessedDB(path)
-        db.add("video.mp4", 10.0, "/out/clip_001/clip_001_0.mp4")
+        db.add("video.mp4", 10.0, "/out/vid_001/clip_001_0.mp4")
-        db.add("video.mp4", 10.0, "/out/clip_001/clip_001_1.mp4")
+        db.add("video.mp4", 10.0, "/out/vid_001/clip_001_1.mp4")
-        db.add("video.mp4", 30.0, "/out/clip_002/clip_002_0.mp4")
+        db.add("video.mp4", 30.0, "/out/vid_001/clip_002_0.mp4")
-        group = db.get_group("/out/clip_001/clip_001_0.mp4")
+        group = db.get_group("/out/vid_001/clip_001_0.mp4")
         assert len(group) == 2
     finally:
         os.unlink(path)
@@ -295,10 +295,10 @@ def test_db_delete_group_removes_all():
         path = f.name
     try:
         db = ProcessedDB(path)
-        db.add("video.mp4", 10.0, "/out/clip_001/clip_001_0.mp4")
+        db.add("video.mp4", 10.0, "/out/vid_001/clip_001_0.mp4")
-        db.add("video.mp4", 10.0, "/out/clip_001/clip_001_1.mp4")
+        db.add("video.mp4", 10.0, "/out/vid_001/clip_001_1.mp4")
-        db.add("video.mp4", 30.0, "/out/clip_002/clip_002_0.mp4")
+        db.add("video.mp4", 30.0, "/out/vid_001/clip_002_0.mp4")
-        deleted = db.delete_group("/out/clip_001/clip_001_0.mp4")
+        deleted = db.delete_group("/out/vid_001/clip_001_0.mp4")
         assert len(deleted) == 2
         # clip_002 should still exist
         markers = db.get_markers("video.mp4")