feat(voxtral): STT-3B via Transformers (torch cu124, kein Treiber-Upgrade)
Backports brachte keinen neueren Treiber (bleibt 550/CUDA 12.4). Statt Upgrade: Voxtral-Mini-3B-2507 via Transformers mit torch 2.6.0+cu124 (F5-Trick) — laeuft auf 550, ~9 GB bf16 auf GPU 1. Chunked wie whisper mit dem adaptiven M0.1-Endpointer, RVS-Protokoll identisch (drop-in). Ersetzt den vLLM-Realtime-4B-Ansatz (der brauchte 16 GB). Compose: ein Container, GPU-1-gepinnt, Profil 'voxtral'. Transformers-API in einer Methode gekapselt (verify-on-run). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 4.8
+147
-231
@@ -1,39 +1,28 @@
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#!/usr/bin/env python3
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"""
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ARIA Voxtral Bridge — Streaming-STT via Voxtral-Mini-4B-Realtime-2602 (vLLM).
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ARIA Voxtral-STT-3B Bridge (Transformers) — Ersatz fuer whisper.
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Zwilling der whisper-Bridge, aber das Transkribieren macht NICHT faster-whisper
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im selben Prozess, sondern der separate vLLM-Realtime-Server (Container
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`voxtral-vllm`) ueber dessen WebSocket-API `/v1/realtime`. Diese Bridge ist
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reine Glue:
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Laeuft auf Treiber 550/CUDA 12.4 via torch cu124 (kein Treiber-Upgrade noetig).
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Modell: Voxtral-Mini-3B-2507 (bf16, ~9 GB) → GPU 1 (12 GB, per Compose gepinnt).
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App ──(RVS: stt_stream_start / stt_audio_chunk / stt_stream_end)──▶ diese Bridge
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diese Bridge ──(WS /v1/realtime: PCM16-b64 append)──▶ voxtral-vllm
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voxtral-vllm ──(transcription.delta / transcription.done)──▶ diese Bridge
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diese Bridge ──(RVS: stt_partial / stt_endpoint / stt_stream_done)──▶ App/aria-bridge
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Arbeitsweise = Zwilling der whisper-Bridge: App schickt live PCM-Chunks; wir
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transkribieren alle ~STREAM_TRANSCRIBE_INTERVAL_MS auf dem Ringbuffer (Partials)
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und feuern stt_endpoint, sobald der ADAPTIVE Endpointer (Rausch-Boden-VAD +
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semantische Stagnation, aus M0.1) "fertig" sagt. RVS-Wire-Protokoll identisch zu
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whisper → drop-in (die App merkt nur bessere Genauigkeit).
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Das RVS-Wire-Protokoll ist IDENTISCH zur whisper-Bridge (drop-in). Das
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Endpointing (wann hat der User aufgehoert zu sprechen) macht diese Bridge
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selbst — mit demselben ADAPTIVEN Rausch-Boden-Endpointer wie whisper (Voxtral
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Realtime liefert laut vLLM-Doku keine eigene VAD/„speaker done"-Semantik, nur
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transcription.delta/.done). Die akustische Energie messen wir auf unserer
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eigenen PCM-Kopie, die semantische Stagnation am Delta-Textwachstum.
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⚠️ HARDWARE: Voxtral-Mini-4B-Realtime-2602 braucht >=16 GB VRAM (BF16). Auf der
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RTX 3060 (12 GB) laeuft es NICHT — erst auf der 24-GB-Karte. Bis dahin
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bleibt die whisper-Bridge aktiv (Profil-gesteuert im docker-compose).
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⚠️ VERIFY-ON-FIRST-RUN: Die exakten vLLM-Realtime-FRAME-Namen (Audio-Append,
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Delta/Done-Event-Typen) sind unten als Konstanten gebuendelt und nach dem
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OpenAI-Realtime-Schema modelliert. Gegen das offizielle vLLM-Realtime-
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Client-Beispiel pruefen und ggf. anpassen — sie stehen bewusst an EINER
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Stelle. Response-Handling ist defensiv (mehrere moegliche Feldnamen).
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⚠️ VERIFY-ON-FIRST-RUN: Die exakte Transformers-Transkriptions-API von Voxtral
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(apply_transcription_request / generate / decode) ist unten in EINER Methode
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(VoxtralRunner._transcribe_blocking) gekapselt und nach dem HF-Modelcard-Muster
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modelliert. Beim ersten echten Lauf gegen die Voxtral-Modelcard pruefen und dort
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anpassen. Alles andere (RVS, Endpointer) ist bewaehrt.
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Env:
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RVS_HOST, RVS_PORT, RVS_TLS, RVS_TLS_FALLBACK, RVS_TOKEN
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VOXTRAL_VLLM_URL Default: ws://voxtral-vllm:8000/v1/realtime
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VOXTRAL_MODEL Default: mistralai/Voxtral-Mini-4B-Realtime-2602
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VOXTRAL_LANGUAGE Default: de
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VOXTRAL_MODEL Default: mistralai/Voxtral-Mini-3B-2507
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VOXTRAL_LANGUAGE Default: de
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VOXTRAL_DEVICE Default: cuda
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STREAM_TRANSCRIBE_INTERVAL_MS Default 1000 (3B ist schwerer als whisper-small)
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"""
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import asyncio
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import base64
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@@ -60,29 +49,18 @@ RVS_TLS = os.getenv("RVS_TLS", "true").lower() == "true"
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RVS_TLS_FALLBACK = os.getenv("RVS_TLS_FALLBACK", "true").lower() == "true"
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RVS_TOKEN = os.getenv("RVS_TOKEN", "").strip()
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VOXTRAL_VLLM_URL = os.getenv("VOXTRAL_VLLM_URL", "ws://voxtral-vllm:8000/v1/realtime")
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VOXTRAL_MODEL = os.getenv("VOXTRAL_MODEL", "mistralai/Voxtral-Mini-4B-Realtime-2602")
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VOXTRAL_MODEL = os.getenv("VOXTRAL_MODEL", "mistralai/Voxtral-Mini-3B-2507")
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VOXTRAL_LANGUAGE = os.getenv("VOXTRAL_LANGUAGE", "de")
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VOXTRAL_DEVICE = os.getenv("VOXTRAL_DEVICE", "cuda")
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# ── vLLM-Realtime-Frames — HIER anpassen falls das Client-Beispiel abweicht ──
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# Senderichtung (wir → vLLM): PCM16-16kHz-mono base64 anhaengen + committen.
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VLLM_SEND_APPEND = "input_audio_buffer.append" # {"type":..., "audio": "<b64>"}
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VLLM_SEND_COMMIT = "input_audio_buffer.commit" # Buffer abschliessen
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VLLM_AUDIO_FIELD = "audio"
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# Empfangsrichtung (vLLM → wir): inkrementeller Text + final. Defensiv geprueft.
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VLLM_DELTA_SUFFIXES = ("transcription.delta",) # msg["type"] endet hierauf
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VLLM_DONE_SUFFIXES = ("transcription.done", "transcription.completed")
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VLLM_DELTA_FIELDS = ("delta", "text", "transcription") # eins davon traegt den Text
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# ── Streaming-/Endpointing-Parameter (analog whisper-Bridge) ──
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STREAM_TRANSCRIBE_INTERVAL_MS = int(os.getenv("STREAM_TRANSCRIBE_INTERVAL_MS", "1000"))
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STREAM_DEFAULT_ENDPOINT_MS = 2400
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STREAM_DEFAULT_HARD_CAP_MS = 60000
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STREAM_DEFAULT_HARD_CAP_MS = 300000
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STREAM_MIN_AUDIO_MS = 600
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STREAM_SESSION_TTL_S = 120
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STREAM_ENERGY_WINDOW_MS = 300
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STREAM_SEMANTIC_BACKUP_FACTOR = 2.0
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# Adaptiver Voice-Schwellwert (siehe whisper-Bridge M0.1): Grenze relativ zum
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# gemessenen Rausch-Boden statt fix — schneidet leises Sprechen nicht ab.
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# Adaptiver Voice-Schwellwert (M0.1): relativ zum gemessenen Rausch-Boden.
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STREAM_VOICE_FACTOR = 2.5
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STREAM_VOICE_RMS_MIN = 0.005
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STREAM_VOICE_RMS_MAX = 0.020
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@@ -91,21 +69,60 @@ STREAM_VOICE_RMS_MAX = 0.020
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def pcm_s16le_to_float32(data: bytes) -> np.ndarray:
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if not data:
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return np.zeros(0, dtype=np.float32)
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arr = np.frombuffer(data, dtype=np.int16).astype(np.float32) / 32768.0
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return arr
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return np.frombuffer(data, dtype=np.int16).astype(np.float32) / 32768.0
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async def _send(ws, mtype: str, payload: dict) -> None:
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try:
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await ws.send(json.dumps({
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"type": mtype,
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"payload": payload,
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"timestamp": int(time.time() * 1000),
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"type": mtype, "payload": payload, "timestamp": int(time.time() * 1000),
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}))
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except Exception as e:
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logger.warning("RVS-Send fehlgeschlagen (%s): %s", mtype, e)
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class VoxtralRunner:
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"""Haelt das Voxtral-Modell (Transformers). transcribe() blockiert → aus dem
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Event-Loop via run_in_executor aufrufen. Ein Lock serialisiert GPU-Zugriffe."""
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def __init__(self) -> None:
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self.model = None
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self.processor = None
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self._lock = asyncio.Lock()
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def load(self) -> None:
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import torch
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from transformers import AutoProcessor, VoxtralForConditionalGeneration
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t0 = time.time()
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logger.info("Lade Voxtral '%s' (device=%s, bf16)…", VOXTRAL_MODEL, VOXTRAL_DEVICE)
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self.processor = AutoProcessor.from_pretrained(VOXTRAL_MODEL)
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self.model = VoxtralForConditionalGeneration.from_pretrained(
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VOXTRAL_MODEL, torch_dtype=torch.bfloat16, device_map=VOXTRAL_DEVICE,
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)
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logger.info("Voxtral geladen in %.1fs", time.time() - t0)
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def _transcribe_blocking(self, audio_f32: np.ndarray, language: str) -> str:
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# ⚠️ VERIFY: exakte Voxtral-Transformers-API gegen die HF-Modelcard.
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import torch
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proc, model = self.processor, self.model
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if proc is None or model is None or audio_f32.size == 0:
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return ""
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inputs = proc.apply_transcription_request(
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language=language, audio=audio_f32, model_id=VOXTRAL_MODEL, sampling_rate=16000,
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)
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inputs = inputs.to(VOXTRAL_DEVICE, dtype=torch.bfloat16)
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=512)
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trimmed = outputs[:, inputs.input_ids.shape[1]:]
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text = proc.batch_decode(trimmed, skip_special_tokens=True)
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return (text[0] if text else "").strip()
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async def transcribe(self, audio_f32: np.ndarray, language: str) -> str:
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loop = asyncio.get_running_loop()
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async with self._lock:
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return await loop.run_in_executor(None, self._transcribe_blocking, audio_f32, language)
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@dataclass
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class StreamSession:
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request_id: str
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@@ -126,28 +143,26 @@ class StreamSession:
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last_chunk_at: float = field(default_factory=time.time)
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last_partial: str = ""
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last_growth_at: float = 0.0
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last_transcribe_at: float = 0.0
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last_voice_at: float = 0.0
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noise_floor: float = 0.0
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closed: bool = False
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endpoint_sent: bool = False
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# vLLM-Realtime-Session
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vllm_ws: object = None
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vllm_reader: object = None
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class SessionManager:
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def __init__(self) -> None:
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def __init__(self, runner: VoxtralRunner) -> None:
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self.runner = runner
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self._sessions: dict[str, StreamSession] = {}
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self._ws = None # RVS
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self._ws = None
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def attach_ws(self, ws) -> None:
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self._ws = ws
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async def start_session(self, payload: dict) -> Optional[StreamSession]:
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request_id = (payload.get("requestId") or "").strip()
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if not request_id:
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logger.warning("stt_stream_start ohne requestId — ignoriert")
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return None
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def start_session(self, payload: dict) -> None:
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rid = (payload.get("requestId") or "").strip()
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if not rid:
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return
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try:
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endpoint_ms = int(payload.get("endpointMs") or STREAM_DEFAULT_ENDPOINT_MS)
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except (TypeError, ValueError):
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@@ -160,8 +175,8 @@ class SessionManager:
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voice_factor = float(payload.get("voiceFactor") or STREAM_VOICE_FACTOR)
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except (TypeError, ValueError):
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voice_factor = STREAM_VOICE_FACTOR
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sess = StreamSession(
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request_id=request_id,
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self._sessions[rid] = StreamSession(
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request_id=rid,
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audio_request_id=payload.get("audioRequestId", "") or "",
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language=payload.get("language") or VOXTRAL_LANGUAGE,
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endpoint_ms=endpoint_ms,
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@@ -173,159 +188,44 @@ class SessionManager:
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location=payload.get("location") or None,
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sample_rate=int(payload.get("sampleRate") or 16000),
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)
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# vLLM-Realtime-Session oeffnen + Reader starten.
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try:
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sess.vllm_ws = await websockets.connect(VOXTRAL_VLLM_URL, max_size=8 * 1024 * 1024)
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await self._vllm_configure(sess)
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sess.vllm_reader = asyncio.create_task(self._vllm_read_loop(sess))
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except Exception as e:
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logger.exception("Stream %s: vLLM-Realtime-Connect fehlgeschlagen: %s",
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request_id[:8], e)
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# ohne Backend keine Transkription → sofort leeres Endpoint melden
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self._sessions[request_id] = sess
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await self._finalize(sess, reason="vllm_unavailable")
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return None
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self._sessions[request_id] = sess
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logger.info("Voxtral-Session offen: id=%s lang=%s endpointMs=%d",
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request_id[:8], sess.language, sess.endpoint_ms)
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return sess
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async def _vllm_configure(self, sess: StreamSession) -> None:
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"""Optionale Session-Konfig an vLLM (Modell/Sprache/temperature=0).
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VERIFY: exaktes session.update-Schema gegen vLLM-Realtime-Beispiel.
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Best-effort — Fehler hier sind nicht fatal."""
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try:
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await sess.vllm_ws.send(json.dumps({
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"type": "session.update",
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"session": {
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"model": VOXTRAL_MODEL,
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"language": sess.language,
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"temperature": 0.0,
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"input_audio_format": "pcm16",
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},
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}))
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except Exception:
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pass
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async def _vllm_read_loop(self, sess: StreamSession) -> None:
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"""Liest transcription.delta/.done vom vLLM-Realtime-Server."""
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ws = sess.vllm_ws
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try:
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async for raw in ws:
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try:
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msg = json.loads(raw)
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except Exception:
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continue
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mtype = str(msg.get("type", ""))
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if any(mtype.endswith(s) for s in VLLM_DELTA_SUFFIXES):
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text = self._extract_text(msg)
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if text:
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await self._on_delta(sess, text)
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elif any(mtype.endswith(s) for s in VLLM_DONE_SUFFIXES):
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text = self._extract_text(msg)
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if text:
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await self._on_delta(sess, text, final=True)
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except Exception:
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logger.debug("Stream %s: vLLM-Reader beendet", sess.request_id[:8])
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@staticmethod
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def _extract_text(msg: dict) -> str:
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for f in VLLM_DELTA_FIELDS:
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v = msg.get(f)
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if isinstance(v, str) and v:
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return v
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return ""
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async def _on_delta(self, sess: StreamSession, text: str, final: bool = False) -> None:
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"""Neuer/finaler Transkript-Text vom vLLM. delta = inkrementell; wir
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haengen an, wenn er den bisherigen Partial verlaengert, sonst ersetzen
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wir (Voxtral kann korrigieren)."""
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if final or text.startswith(sess.last_partial):
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new_full = text if final else text
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else:
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new_full = (sess.last_partial + text).strip()
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new_full = new_full.strip()
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if new_full and new_full != sess.last_partial:
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sess.last_partial = new_full
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sess.last_growth_at = time.time()
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if self._ws is not None:
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await _send(self._ws, "stt_partial", {
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"requestId": sess.request_id,
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"audioRequestId": sess.audio_request_id,
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"text": new_full,
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})
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rid[:8], self._sessions[rid].language, endpoint_ms)
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def feed_chunk(self, payload: dict) -> bool:
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request_id = payload.get("requestId", "")
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sess = self._sessions.get(request_id)
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sess = self._sessions.get(payload.get("requestId", ""))
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if sess is None or sess.closed:
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return False
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pcm_b64 = payload.get("pcm", "")
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if not pcm_b64:
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return True
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try:
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pcm = base64.b64decode(pcm_b64)
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except Exception:
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return True
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sess.pcm_buffer.extend(pcm)
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if pcm_b64:
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try:
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sess.pcm_buffer.extend(base64.b64decode(pcm_b64))
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except Exception:
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pass
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sess.last_chunk_at = time.time()
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# An vLLM weiterreichen (fire-and-forget).
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if sess.vllm_ws is not None:
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asyncio.create_task(self._vllm_append(sess, pcm_b64))
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return True
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async def _vllm_append(self, sess: StreamSession, pcm_b64: str) -> None:
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try:
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await sess.vllm_ws.send(json.dumps({
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"type": VLLM_SEND_APPEND,
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VLLM_AUDIO_FIELD: pcm_b64,
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}))
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except Exception:
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pass
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def end_session(self, request_id: str) -> None:
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sess = self._sessions.get(request_id)
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if sess is not None:
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sess.closed = True
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def drop(self, request_id: str) -> None:
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sess = self._sessions.pop(request_id, None)
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if sess is not None:
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self._teardown_vllm(sess)
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self._sessions.pop(request_id, None)
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def _teardown_vllm(self, sess: StreamSession) -> None:
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try:
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if sess.vllm_reader is not None:
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sess.vllm_reader.cancel()
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except Exception:
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pass
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if sess.vllm_ws is not None:
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asyncio.create_task(self._close_ws(sess.vllm_ws))
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sess.vllm_ws = None
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@staticmethod
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async def _close_ws(ws) -> None:
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try:
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await ws.close()
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except Exception:
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pass
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# ── Endpointer (adaptiv, wie whisper-Bridge M0.1) ──
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def _buffer_duration_ms(self, sess: StreamSession) -> float:
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# ── Endpointer (adaptiv, M0.1) ──
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def _buffer_ms(self, sess: StreamSession) -> float:
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samples = len(sess.pcm_buffer) // 2
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return (samples / sess.sample_rate) * 1000.0 if samples else 0.0
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|
||||
def _tail_rms(self, sess: StreamSession) -> float:
|
||||
win_bytes = int(sess.sample_rate * STREAM_ENERGY_WINDOW_MS / 1000) * 2
|
||||
if win_bytes <= 0:
|
||||
win = int(sess.sample_rate * STREAM_ENERGY_WINDOW_MS / 1000) * 2
|
||||
if win <= 0:
|
||||
return 0.0
|
||||
tail = sess.pcm_buffer[-win_bytes:]
|
||||
tail = sess.pcm_buffer[-win:]
|
||||
if len(tail) < 2:
|
||||
return 0.0
|
||||
arr = pcm_s16le_to_float32(bytes(tail))
|
||||
if arr.size == 0:
|
||||
return 0.0
|
||||
return float(np.sqrt(np.mean(arr * arr)))
|
||||
return float(np.sqrt(np.mean(arr * arr))) if arr.size else 0.0
|
||||
|
||||
def _voice_threshold(self, sess: StreamSession) -> float:
|
||||
nf = sess.noise_floor
|
||||
@@ -343,7 +243,8 @@ class SessionManager:
|
||||
sess.noise_floor = 0.98 * nf + 0.02 * rms
|
||||
|
||||
async def run_endpointer(self) -> None:
|
||||
logger.info("Voxtral-Endpointer gestartet (adaptiver VAD)")
|
||||
logger.info("Voxtral-Endpointer gestartet (adaptiver VAD, interval=%dms)",
|
||||
STREAM_TRANSCRIBE_INTERVAL_MS)
|
||||
while True:
|
||||
await asyncio.sleep(0.2)
|
||||
now = time.time()
|
||||
@@ -351,7 +252,7 @@ class SessionManager:
|
||||
try:
|
||||
await self._tick(sess, now)
|
||||
except Exception:
|
||||
logger.exception("Endpointer-Tick crashed (session=%s)", sid[:8])
|
||||
logger.exception("Tick crashed (session=%s)", sid[:8])
|
||||
for sid, sess in list(self._sessions.items()):
|
||||
if now - sess.last_chunk_at > STREAM_SESSION_TTL_S:
|
||||
logger.info("Stream %s: TTL — drop", sid[:8])
|
||||
@@ -360,14 +261,13 @@ class SessionManager:
|
||||
async def _tick(self, sess: StreamSession, now: float) -> None:
|
||||
if sess.endpoint_sent:
|
||||
return
|
||||
elapsed_ms = (now - sess.started_at) * 1000.0
|
||||
if elapsed_ms > sess.hard_cap_ms and not sess.closed:
|
||||
await self._finalize(sess, reason="hardcap")
|
||||
if (now - sess.started_at) * 1000.0 > sess.hard_cap_ms and not sess.closed:
|
||||
await self._finalize(sess, "hardcap")
|
||||
return
|
||||
if sess.closed:
|
||||
await self._finalize(sess, reason="stream_end")
|
||||
await self._finalize(sess, "stream_end")
|
||||
return
|
||||
if self._buffer_duration_ms(sess) < STREAM_MIN_AUDIO_MS:
|
||||
if self._buffer_ms(sess) < STREAM_MIN_AUDIO_MS:
|
||||
return
|
||||
# adaptive akustische Sprach-Aktivitaet
|
||||
rms = self._tail_rms(sess)
|
||||
@@ -375,45 +275,65 @@ class SessionManager:
|
||||
sess.last_voice_at = now
|
||||
else:
|
||||
self._update_noise_floor(sess, rms)
|
||||
# Endpoint: akustisch (Primaer) oder semantisch (Backstop), sobald Text da
|
||||
# Endpoint-Entscheidung, sobald Text erkannt wurde
|
||||
if sess.last_growth_at > 0.0:
|
||||
acoustic_silence_ms = (now - sess.last_voice_at) * 1000.0 if sess.last_voice_at > 0 else 0.0
|
||||
semantic_silence_ms = (now - sess.last_growth_at) * 1000.0
|
||||
acoustic_done = sess.last_voice_at > 0 and acoustic_silence_ms >= sess.endpoint_ms
|
||||
semantic_done = semantic_silence_ms >= sess.endpoint_ms * STREAM_SEMANTIC_BACKUP_FACTOR
|
||||
if acoustic_done or semantic_done:
|
||||
await self._finalize(sess, reason="endpoint" if acoustic_done else "endpoint_semantic")
|
||||
ac_sil = (now - sess.last_voice_at) * 1000.0 if sess.last_voice_at > 0 else 0.0
|
||||
se_sil = (now - sess.last_growth_at) * 1000.0
|
||||
ac_done = sess.last_voice_at > 0 and ac_sil >= sess.endpoint_ms
|
||||
se_done = se_sil >= sess.endpoint_ms * STREAM_SEMANTIC_BACKUP_FACTOR
|
||||
if ac_done or se_done:
|
||||
await self._finalize(sess, "endpoint" if ac_done else "endpoint_semantic")
|
||||
return
|
||||
# Partial-Transkription (throttled)
|
||||
if (now - sess.last_transcribe_at) * 1000.0 < STREAM_TRANSCRIBE_INTERVAL_MS:
|
||||
return
|
||||
sess.last_transcribe_at = now
|
||||
audio = pcm_s16le_to_float32(bytes(sess.pcm_buffer))
|
||||
try:
|
||||
text = (await self.runner.transcribe(audio, sess.language)).strip()
|
||||
except Exception:
|
||||
logger.exception("Stream %s: Partial-Transcribe crashed", sess.request_id[:8])
|
||||
return
|
||||
if text and text != sess.last_partial:
|
||||
sess.last_partial = text
|
||||
sess.last_growth_at = now
|
||||
if self._ws is not None:
|
||||
await _send(self._ws, "stt_partial", {
|
||||
"requestId": sess.request_id,
|
||||
"audioRequestId": sess.audio_request_id,
|
||||
"text": text,
|
||||
})
|
||||
|
||||
async def _finalize(self, sess: StreamSession, reason: str) -> None:
|
||||
if sess.endpoint_sent:
|
||||
return
|
||||
sess.endpoint_sent = True
|
||||
# vLLM ggf. committen, damit ein letztes transcription.done kommt.
|
||||
if sess.vllm_ws is not None:
|
||||
try:
|
||||
await sess.vllm_ws.send(json.dumps({"type": VLLM_SEND_COMMIT}))
|
||||
await asyncio.sleep(0.15) # kurz auf finalen Delta warten
|
||||
except Exception:
|
||||
pass
|
||||
final_text = sess.last_partial.strip()
|
||||
duration_s = self._buffer_duration_ms(sess) / 1000.0
|
||||
logger.info("Stream %s: FINAL (reason=%s, %.1fs): %r",
|
||||
sess.request_id[:8], reason, duration_s, final_text[:120])
|
||||
audio = pcm_s16le_to_float32(bytes(sess.pcm_buffer))
|
||||
t0 = time.time()
|
||||
try:
|
||||
final_text = (await self.runner.transcribe(audio, sess.language)).strip()
|
||||
except Exception:
|
||||
logger.exception("Stream %s: Final-Transcribe crashed", sess.request_id[:8])
|
||||
final_text = sess.last_partial
|
||||
stt_ms = int((time.time() - t0) * 1000)
|
||||
duration_s = audio.size / 16000.0
|
||||
logger.info("Stream %s: FINAL (reason=%s, %.1fs, %dms): %r",
|
||||
sess.request_id[:8], reason, duration_s, stt_ms, final_text[:120])
|
||||
if self._ws is not None:
|
||||
endpoint_payload = {
|
||||
payload = {
|
||||
"requestId": sess.request_id,
|
||||
"audioRequestId": sess.audio_request_id,
|
||||
"text": final_text,
|
||||
"reason": reason,
|
||||
"durationS": duration_s,
|
||||
"sttMs": 0,
|
||||
"sttMs": stt_ms,
|
||||
"voice": sess.voice,
|
||||
"speed": sess.speed,
|
||||
"interrupted": sess.interrupted,
|
||||
}
|
||||
if sess.location:
|
||||
endpoint_payload["location"] = sess.location
|
||||
await _send(self._ws, "stt_endpoint", endpoint_payload)
|
||||
payload["location"] = sess.location
|
||||
await _send(self._ws, "stt_endpoint", payload)
|
||||
await _send(self._ws, "stt_stream_done", {
|
||||
"requestId": sess.request_id,
|
||||
"audioRequestId": sess.audio_request_id,
|
||||
@@ -447,7 +367,6 @@ async def run_loop(sessions: SessionManager) -> None:
|
||||
sessions.attach_ws(ws)
|
||||
await _broadcast_status(ws, "ready", model=VOXTRAL_MODEL)
|
||||
await _send(ws, "config_request", {"service": "voxtral"})
|
||||
|
||||
async for raw in ws:
|
||||
try:
|
||||
msg = json.loads(raw)
|
||||
@@ -456,35 +375,32 @@ async def run_loop(sessions: SessionManager) -> None:
|
||||
mtype = msg.get("type", "")
|
||||
payload = msg.get("payload", {}) or {}
|
||||
if mtype == "stt_stream_start":
|
||||
asyncio.create_task(sessions.start_session(payload))
|
||||
sessions.start_session(payload)
|
||||
elif mtype == "stt_audio_chunk":
|
||||
sessions.feed_chunk(payload)
|
||||
elif mtype == "stt_stream_end":
|
||||
sessions.end_session(payload.get("requestId", ""))
|
||||
# stt_request (Legacy One-Shot) macht Voxtral hier NICHT —
|
||||
# dafuer bleibt die whisper-Bridge (Fallback).
|
||||
except Exception as e:
|
||||
logger.warning("RVS-Verbindung verloren: %s — retry in %ds", e, retry_s)
|
||||
if use_tls and RVS_TLS_FALLBACK and not tls_fallback_tried:
|
||||
use_tls = False
|
||||
tls_fallback_tried = True
|
||||
logger.info("TLS-Fallback: versuche ws:// (kein TLS)")
|
||||
continue
|
||||
await asyncio.sleep(retry_s)
|
||||
retry_s = min(retry_s * 2, 30)
|
||||
use_tls = RVS_TLS # fuer den naechsten Zyklus zuruecksetzen
|
||||
use_tls = RVS_TLS
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
if not RVS_HOST or not RVS_TOKEN:
|
||||
logger.error("RVS_HOST/RVS_TOKEN fehlen — .env pruefen. Abbruch.")
|
||||
return
|
||||
sessions = SessionManager()
|
||||
logger.info("Voxtral-Bridge startet — vLLM=%s Modell=%s", VOXTRAL_VLLM_URL, VOXTRAL_MODEL)
|
||||
await asyncio.gather(
|
||||
run_loop(sessions),
|
||||
sessions.run_endpointer(),
|
||||
)
|
||||
runner = VoxtralRunner()
|
||||
loop = asyncio.get_running_loop()
|
||||
await loop.run_in_executor(None, runner.load) # Modell laden (blockierend)
|
||||
sessions = SessionManager(runner)
|
||||
logger.info("Voxtral-Bridge startet — Modell=%s", VOXTRAL_MODEL)
|
||||
await asyncio.gather(run_loop(sessions), sessions.run_endpointer())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user