Brummel
6e6cfd77b7
Off-the-shelf Whisper wrappers hardcode faster-whisper defaults that cause cascading hallucinations on real dictations (2/13 runs against ahmetoner v1.9.1). This thin FastAPI service mirrors the /asr interface but fixes condition_on_previous_text=False, temperature=0.0, and vad_filter=True so the Axum server can switch by changing WHISPER_URL.
130 lines
3.9 KiB
Python
130 lines
3.9 KiB
Python
"""Thin FastAPI wrapper around faster-whisper.
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Exposes the same /asr interface as ahmetoner/whisper-asr-webservice so the
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Axum server stays unchanged, but hardcodes anti-hallucination parameters
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that no off-the-shelf wrapper exposes.
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"""
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import json
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import logging
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import os
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import tempfile
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import time
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from typing import Optional
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from fastapi import FastAPI, File, Form, Query, UploadFile
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from fastapi.responses import JSONResponse, PlainTextResponse
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from faster_whisper import WhisperModel
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s %(levelname)s %(message)s",
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)
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log = logging.getLogger("whisper")
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MODEL_NAME = os.getenv("WHISPER_MODEL", "large-v3-turbo")
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COMPUTE_TYPE = os.getenv("WHISPER_COMPUTE_TYPE", "float16")
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DEVICE = os.getenv("WHISPER_DEVICE", "cuda")
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MODELS_DIR = os.getenv("WHISPER_MODELS_DIR", "/models")
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OFFLINE = os.getenv("WHISPER_OFFLINE", "0") == "1"
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log.info("Loading model=%s device=%s compute_type=%s", MODEL_NAME, DEVICE, COMPUTE_TYPE)
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model = WhisperModel(
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MODEL_NAME,
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device=DEVICE,
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compute_type=COMPUTE_TYPE,
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download_root=MODELS_DIR,
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local_files_only=OFFLINE,
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)
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log.info("Model loaded")
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app = FastAPI(title="doctate-whisper", version="0.1.0")
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@app.get("/health")
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def health():
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return {"status": "ok", "model": MODEL_NAME, "device": DEVICE}
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@app.get("/info")
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def info():
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return {
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"model": MODEL_NAME,
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"device": DEVICE,
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"compute_type": COMPUTE_TYPE,
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"offline": OFFLINE,
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}
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@app.post("/asr")
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async def asr(
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audio_file: UploadFile = File(...),
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output: str = Query("txt"),
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language: Optional[str] = Query(None),
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initial_prompt: Optional[str] = Form(None),
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):
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suffix = os.path.splitext(audio_file.filename or "")[1] or ".bin"
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tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
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try:
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tmp.write(await audio_file.read())
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tmp.flush()
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tmp.close()
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t0 = time.monotonic()
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segments_gen, info_obj = model.transcribe(
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tmp.name,
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language=language,
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task="transcribe",
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beam_size=5,
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best_of=5,
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# Fixed anti-hallucination params — the whole point of this service.
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temperature=0.0,
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condition_on_previous_text=False,
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vad_filter=True,
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vad_parameters={"min_silence_duration_ms": 500},
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no_speech_threshold=0.6,
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log_prob_threshold=-1.0,
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compression_ratio_threshold=2.4,
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initial_prompt=initial_prompt,
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)
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segments = list(segments_gen)
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infer_secs = time.monotonic() - t0
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text = "".join(s.text for s in segments).strip()
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worst_logprob = min((s.avg_logprob for s in segments), default=0.0)
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log.info(
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json.dumps({
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"event": "transcribe",
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"duration_audio": info_obj.duration,
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"duration_infer": round(infer_secs, 2),
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"segments": len(segments),
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"worst_logprob": round(worst_logprob, 3),
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"language": info_obj.language,
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})
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)
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if output == "json":
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return JSONResponse({
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"text": text,
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"language": info_obj.language,
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"duration": info_obj.duration,
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"segments": [
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{
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"start": s.start,
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"end": s.end,
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"text": s.text,
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"avg_logprob": s.avg_logprob,
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"compression_ratio": s.compression_ratio,
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"no_speech_prob": s.no_speech_prob,
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"temperature": s.temperature,
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}
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for s in segments
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],
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})
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return PlainTextResponse(text)
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finally:
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try:
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os.unlink(tmp.name)
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except OSError:
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pass
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