build: first commit
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FROM python:3.12-slim
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# системные зависимости для аудио-декодинга
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RUN apt-get update && \
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apt-get install -y --no-install-recommends ffmpeg curl && \
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rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# NVIDIA runtime libraries (cuBLAS + cuDNN) — ставим через pip,
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# чтобы не тянуть полный CUDA Toolkit в образ
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RUN pip install --no-cache-dir nvidia-cublas-cu12 "nvidia-cudnn-cu12==9.*"
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# LD_LIBRARY_PATH для CTranslate2
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ENV LD_LIBRARY_PATH="/usr/local/lib/python3.12/site-packages/nvidia/cublas/lib:/usr/local/lib/python3.12/site-packages/nvidia/cudnn/lib:${LD_LIBRARY_PATH}"
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COPY app.py .
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EXPOSE 8001
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8001"]
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@@ -0,0 +1,207 @@
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"""
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Whisper microservice: транскрибация аудио через faster-whisper.
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Модель загружается по требованию (lazy load) и выгружается
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через UNLOAD_AFTER секунд простоя. Явные endpoints /load и /unload
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позволяют управлять памятью вручную (например, из n8n).
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"""
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from __future__ import annotations
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import asyncio
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import gc
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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 contextlib import asynccontextmanager
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from pathlib import Path
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from fastapi import FastAPI, File, Form, UploadFile
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from faster_whisper import BatchedInferencePipeline, WhisperModel
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logger = logging.getLogger("whisper-service")
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s: %(message)s")
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# ────────────────────────── config ──────────────────────────
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MODEL_SIZE = os.getenv("WHISPER_MODEL", "large-v3")
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DEVICE = os.getenv("WHISPER_DEVICE", "cuda")
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COMPUTE_TYPE = os.getenv("WHISPER_COMPUTE_TYPE", "float16")
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BATCH_SIZE = int(os.getenv("WHISPER_BATCH_SIZE", "16"))
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UNLOAD_AFTER = int(os.getenv("WHISPER_UNLOAD_AFTER", "300")) # секунд простоя, 0 = никогда
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# ────────────────────────── state ──────────────────────────
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model: BatchedInferencePipeline | None = None
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_model_lock = asyncio.Lock()
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_last_used_at: float = 0.0
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_unload_task: asyncio.Task | None = None
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# ────────────────────────── model helpers ──────────────────────────
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def _load_model_sync() -> BatchedInferencePipeline:
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logger.info(f"Loading '{MODEL_SIZE}' on {DEVICE} ({COMPUTE_TYPE}), batch={BATCH_SIZE}…")
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t0 = time.perf_counter()
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base = WhisperModel(MODEL_SIZE, device=DEVICE, compute_type=COMPUTE_TYPE)
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pipeline = BatchedInferencePipeline(model=base)
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logger.info(f"Model loaded in {time.perf_counter() - t0:.1f}s")
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return pipeline
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def _unload_model_sync() -> None:
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global model
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if model is None:
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return
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del model
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model = None
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gc.collect()
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# CTranslate2 освобождает VRAM при удалении объекта,
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# дополнительный вызов не нужен
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logger.info("Model unloaded, VRAM released")
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async def _ensure_loaded() -> BatchedInferencePipeline:
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global model, _last_used_at, _unload_task
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async with _model_lock:
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if model is None:
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loop = asyncio.get_running_loop()
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model = await loop.run_in_executor(None, _load_model_sync)
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_last_used_at = time.monotonic()
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if UNLOAD_AFTER > 0:
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if _unload_task and not _unload_task.done():
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_unload_task.cancel()
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_unload_task = asyncio.create_task(_auto_unload_after(UNLOAD_AFTER))
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return model
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async def _auto_unload_after(seconds: int) -> None:
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await asyncio.sleep(seconds)
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async with _model_lock:
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if model is not None and (time.monotonic() - _last_used_at) >= seconds:
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logger.info(f"Auto-unloading after {seconds}s of inactivity…")
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_unload_model_sync()
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# ────────────────────────── lifespan ──────────────────────────
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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logger.info(f"Whisper service ready (lazy load, unload_after={UNLOAD_AFTER}s)")
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yield
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async with _model_lock:
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_unload_model_sync()
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app = FastAPI(title="Whisper Service", lifespan=lifespan)
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# ────────────────────────── control endpoints ──────────────────────────
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@app.get("/health")
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async def health():
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return {
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"status": "ok",
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"model": MODEL_SIZE,
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"device": DEVICE,
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"loaded": model is not None,
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"idle_seconds": round(time.monotonic() - _last_used_at, 1) if _last_used_at else None,
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}
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@app.post("/load")
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async def load_model():
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"""Явная загрузка модели в VRAM (перед серией запросов)."""
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await _ensure_loaded()
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return {"status": "loaded", "model": MODEL_SIZE}
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@app.post("/unload")
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async def unload_model():
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"""Явная выгрузка модели из VRAM (освобождаем память для Ollama)."""
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global _unload_task
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async with _model_lock:
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if _unload_task and not _unload_task.done():
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_unload_task.cancel()
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_unload_model_sync()
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return {"status": "unloaded"}
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# ────────────────────────── transcribe ──────────────────────────
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@app.post("/transcribe")
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async def transcribe(
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file: UploadFile = File(...),
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language: str | None = Form(None),
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initial_prompt: str | None = Form(None),
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beam_size: int = Form(5),
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batch_size: int = Form(BATCH_SIZE),
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word_timestamps: bool = Form(True),
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vad_filter: bool = Form(True),
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):
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"""Транскрибация загруженного аудиофайла."""
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language = language if language and language not in ("string", "") else None
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initial_prompt = initial_prompt if initial_prompt and initial_prompt not in ("string", "") else None
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suffix = Path(file.filename).suffix if file.filename else ".wav"
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with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
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content = await file.read()
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tmp.write(content)
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tmp_path = tmp.name
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try:
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pipeline = await _ensure_loaded()
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t0 = time.perf_counter()
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logger.info(
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f"Received: {file.filename} ({len(content)} bytes), "
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f"language={language}, model={MODEL_SIZE}, batch_size={batch_size}"
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)
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segments_iter, info = pipeline.transcribe(
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tmp_path,
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language=language,
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initial_prompt=initial_prompt,
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beam_size=beam_size,
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batch_size=batch_size,
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word_timestamps=word_timestamps,
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vad_filter=vad_filter,
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)
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t1 = time.perf_counter()
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logger.info(
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f"Transcribe call returned in {t1 - t0:.1f}s. "
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f"Language: {info.language} ({info.language_probability:.2f}), "
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f"duration: {info.duration:.1f}s"
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)
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segments = []
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for seg in segments_iter:
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seg_data = {
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"start": round(seg.start, 3),
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"end": round(seg.end, 3),
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"text": seg.text.strip(),
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}
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if word_timestamps and seg.words:
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seg_data["words"] = [
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{"start": round(w.start, 3), "end": round(w.end, 3), "word": w.word}
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for w in seg.words
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]
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segments.append(seg_data)
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elapsed = time.perf_counter() - t0
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logger.info(
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f"Done: {file.filename} — {info.duration:.1f}s audio, "
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f"{len(segments)} segments, {elapsed:.1f}s total "
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f"(RTF={elapsed / info.duration:.3f})"
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)
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return {
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"language": info.language,
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"language_probability": round(info.language_probability, 3),
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"duration": round(info.duration, 3),
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"processing_time": round(elapsed, 3),
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"segments": segments,
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}
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finally:
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Path(tmp_path).unlink(missing_ok=True)
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@@ -0,0 +1,4 @@
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faster-whisper==1.2.1
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fastapi==0.115.*
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uvicorn[standard]==0.34.*
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python-multipart==0.0.*
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