mirror of
https://github.com/R0m1k3/LiveFlow.git
synced 2026-10-11 17:27:19 +02:00
Michael
284011428a
- Nouveau module diarizer.py : EmbeddingModel (SpeechBrain ECAPA-TDNN) + SpeakerDiarizer par session - Backend : chargement modèle au lifespan, migration DB (colonne speaker), worker enrichi, exports avec locuteur - Frontend : badges locuteurs colorés (8 couleurs), toggle activation, copie avec [Locuteur X] - Config : DIARIZATION=on/off, DIARIZATION_THRESHOLD=0.70, PyTorch CPU-only dans Dockerfile - Rétrocompatible : désactivé par défaut (DIARIZATION=off)
102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
"""Découpage du flux micro en segments de parole via WebRTC VAD.
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Le client envoie du PCM 16 bits mono 16 kHz. On analyse des trames de 30 ms :
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un segment démarre quand la parole domine la fenêtre récente (avec un
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pré-roll pour ne pas couper le début de phrase) et se termine après un
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silence prolongé ou une durée maximale.
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"""
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from collections import deque
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from dataclasses import dataclass
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import webrtcvad
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SAMPLE_RATE = 16000
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FRAME_MS = 30
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FRAME_BYTES = SAMPLE_RATE * FRAME_MS // 1000 * 2 # 960 octets
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VAD_AGGRESSIVENESS = 2
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PREROLL_FRAMES = 10 # 300 ms conservées avant le déclenchement
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TRIGGER_RATIO = 0.6 # part de trames "parole" du pré-roll pour démarrer
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SILENCE_END_MS = 700 # silence qui clôt un segment
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MIN_SPEECH_MS = 300 # en dessous, le segment est ignoré (bruit)
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MAX_SEGMENT_S = 25 # coupe forcée pour garder une latence raisonnable
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@dataclass
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class Segment:
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pcm: bytes
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t0: float # secondes depuis le début de la réunion
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t1: float
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speaker: str = "" # identifié par le diarizer (vide si désactivé)
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class SpeechSegmenter:
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def __init__(self):
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self._vad = webrtcvad.Vad(VAD_AGGRESSIVENESS)
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self._pending = bytearray()
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self._ring: deque[tuple[bytes, bool]] = deque(maxlen=PREROLL_FRAMES)
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self._frame_index = 0
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self._triggered = False
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self._segment = bytearray()
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self._segment_start_frame = 0
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self._silence_frames = 0
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self._speech_frames = 0
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def feed(self, data: bytes) -> list[Segment]:
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"""Ajoute de l'audio brut et renvoie les segments terminés."""
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self._pending.extend(data)
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segments = []
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while len(self._pending) >= FRAME_BYTES:
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frame = bytes(self._pending[:FRAME_BYTES])
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del self._pending[:FRAME_BYTES]
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seg = self._process_frame(frame)
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if seg is not None:
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segments.append(seg)
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return segments
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def flush(self) -> Segment | None:
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"""Clôt le segment en cours (fin d'enregistrement)."""
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seg = self._finish_segment() if self._triggered else None
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self._ring.clear()
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self._pending.clear()
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return seg
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def _process_frame(self, frame: bytes) -> Segment | None:
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is_speech = self._vad.is_speech(frame, SAMPLE_RATE)
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self._frame_index += 1
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if not self._triggered:
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self._ring.append((frame, is_speech))
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voiced = sum(1 for _, s in self._ring if s)
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if len(self._ring) == self._ring.maxlen and voiced >= TRIGGER_RATIO * self._ring.maxlen:
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self._triggered = True
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self._segment_start_frame = self._frame_index - len(self._ring)
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self._segment = bytearray(b"".join(f for f, _ in self._ring))
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self._speech_frames = voiced
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self._silence_frames = 0
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self._ring.clear()
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return None
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self._segment.extend(frame)
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if is_speech:
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self._speech_frames += 1
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self._silence_frames = 0
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else:
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self._silence_frames += 1
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too_long = len(self._segment) >= MAX_SEGMENT_S * SAMPLE_RATE * 2
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ended = self._silence_frames * FRAME_MS >= SILENCE_END_MS
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if ended or too_long:
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return self._finish_segment()
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return None
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def _finish_segment(self) -> Segment | None:
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self._triggered = False
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seg, self._segment = self._segment, bytearray()
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if self._speech_frames * FRAME_MS < MIN_SPEECH_MS:
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return None
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t0 = self._segment_start_frame * FRAME_MS / 1000
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t1 = self._frame_index * FRAME_MS / 1000
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return Segment(pcm=bytes(seg), t0=t0, t1=t1)
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