Рабочий пайплайн: YouTube -> транскрипция -> выжимка -> Postgres

- новый api/: /pipeline/process (yt-dlp, субтитры или Vosk, LLM/экстрактивная
  суммаризация), /videos, /llm; старый код перенесён в api_legacy/
- web/: Flet UI (flet 0.28.3 + flet-web, порт 8550)
- Dockerfile.api: ffmpeg слоем из mwader/static-ffmpeg, pip с кэш-маунтом
- requirements/pyproject: убраны moviepy, imageio-ffmpeg, битый asyncio
- README с инструкцией запуска и загрузкой Vosk-модели

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

api_legacy/lib/diarize_embeddings.py

@@ -0,0 +1,69 @@
+from pathlib import Path
+from typing import List, Dict
+
+def diarize_with_resemblyzer(wav_path: Path, window_s: float = 1.5, hop_s: float = 0.75, distance_threshold: float = 0.6) -> List[Dict]:
+    """Lightweight embedding-based diarization using resemblyzer + sklearn.
+
+    Returns list of segments: {'start': float, 'end': float, 'speaker': 'spk_N'}
+    """
+    try:
+        import numpy as np
+        import librosa
+        from resemblyzer import VoiceEncoder
+        from sklearn.cluster import AgglomerativeClustering
+    except Exception as e:
+        raise RuntimeError(f"Missing dependency for embedding diarization: {e}")
+
+    sr = 16000
+    wav, sr_loaded = librosa.load(str(wav_path), sr=sr)
+    n = wav.shape[0]
+    win = int(window_s * sr)
+    hop = int(hop_s * sr)
+    if n <= win:
+        # short file: embed whole
+        encoder = VoiceEncoder()
+        emb = encoder.embed_utterance(wav)
+        labels = [0]
+        centers = [ (0.0 + n / sr) / 2.0 ]
+        windows = [(0.0, n / sr)]
+    else:
+        encoder = VoiceEncoder()
+        embeddings = []
+        centers = []
+        windows = []
+        for start in range(0, n - win + 1, hop):
+            chunk = wav[start:start+win]
+            try:
+                e = encoder.embed_utterance(chunk)
+            except Exception:
+                # fallback: mean pooling
+                e = np.mean(chunk)
+            embeddings.append(e)
+            t0 = start / sr
+            t1 = (start + win) / sr
+            centers.append((t0 + t1) / 2.0)
+            windows.append((t0, t1))
+
+        X = np.vstack(embeddings)
+        # Agglomerative clustering with distance threshold
+        model = AgglomerativeClustering(n_clusters=None, distance_threshold=distance_threshold, affinity='euclidean', linkage='average')
+        labels = model.fit_predict(X)
+
+    # merge consecutive windows with same label into segments
+    segs = []
+    if len(labels) == 0:
+        return segs
+    cur_label = labels[0]
+    cur_start = windows[0][0]
+    cur_end = windows[0][1]
+    for i, lab in enumerate(labels[1:], start=1):
+        if lab == cur_label:
+            cur_end = windows[i][1]
+        else:
+            segs.append({"start": cur_start, "end": cur_end, "speaker": f"spk_{int(cur_label)+1}"})
+            cur_label = lab
+            cur_start = windows[i][0]
+            cur_end = windows[i][1]
+    segs.append({"start": cur_start, "end": cur_end, "speaker": f"spk_{int(cur_label)+1}"})
+
+    return segs