created
This commit is contained in:
28
Dockerfile
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28
Dockerfile
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# Используем образ ROCm с предустановленным PyTorch
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FROM rocm/pytorch:rocm6.4.1_ubuntu22.04_py3.10_pytorch_release_2.6.0
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# Устанавливаем рабочую директорию в контейнере
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WORKDIR /app
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# Устанавливаем системные зависимости
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RUN apt-get update && apt-get install -y \
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ffmpeg \
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python3-pip \
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&& rm -rf /var/lib/apt/lists/*
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# Устанавливаем зависимости Python
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COPY requirements.txt .
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RUN pip install --no-cache-dir --default-timeout=100 -r requirements.txt
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# Копируем остальные файлы приложения
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COPY . .
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# Открываем порт, на котором будет работать приложение
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EXPOSE 9854
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# Устанавливаем переменные окружения для ROCm
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ENV HSA_OVERRIDE_GFX_VERSION=10.3.0
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ENV PYTORCH_ROCM_ARCH=gfx1030
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# Команда для запуска приложения
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "9854", "--log-level", "debug"]
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192
app.py
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192
app.py
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import logging
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import os
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import subprocess
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import time
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from typing import Dict
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from typing import Optional, Union, List, Tuple
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import whisper
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from fastapi import FastAPI, Depends, HTTPException, UploadFile, File
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from fastapi.security import APIKeyHeader
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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app = FastAPI()
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# API key header
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api_key_header = APIKeyHeader(name="x-api-key")
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def get_keys(): # не бейте меня за это
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keys_file = "keys.txt"
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if not os.path.exists(keys_file):
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# Create a new keys file with a default key
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default_key = os.urandom(32).hex()
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with open(keys_file, "w") as f:
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f.write(default_key + "\n")
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logger.info(f"Created new keys file with default key: {default_key}")
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return [default_key]
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else:
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# Read keys from the existing file
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with open(keys_file, "r") as f:
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keys = [line.strip() for line in f if line.strip()]
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logger.info(f"Loaded {len(keys)} keys from file")
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logger.debug(f"Keys: {keys}")
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if not keys:
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raise ValueError("No keys found in keys.txt")
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return keys
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def convert_audio(input_path: str, output_path: str, speed: float = 1.25):
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"""
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Convert audio to compatible format and speed up
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"""
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try:
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command = [
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'ffmpeg', '-i', input_path,
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'-filter:a', f'atempo={speed}',
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'-ar', '16000',
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'-ac', '1',
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'-c:a', 'pcm_s16le',
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output_path,
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'-y'
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]
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logger.debug(f"Running FFmpeg command: {' '.join(command)}")
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subprocess.run(command, check=True, capture_output=True)
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return True
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except subprocess.CalledProcessError as e:
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logger.error(f"FFmpeg conversion failed: {e.stderr.decode()}")
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return False
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class TranscriptionMetrics:
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def __init__(self):
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self.start_time = time.time()
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self.end_time = None
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self.text_length = 0
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self.audio_duration = 0
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def stop(self, text: str, audio_duration: float):
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self.end_time = time.time()
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self.text_length = len(text)
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self.audio_duration = audio_duration
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def get_metrics(self) -> Dict[str, float]:
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processing_time = self.end_time - self.start_time
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return {
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"processing_time_seconds": round(processing_time, 2),
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"characters_per_second": round(self.text_length / processing_time, 2),
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"audio_realtime_ratio": round(self.audio_duration / processing_time, 2),
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"audio_duration": round(self.audio_duration, 2),
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"text_length": self.text_length
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}
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def get_audio_duration(file_path: str) -> float:
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"""Get audio duration using ffprobe"""
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cmd = [
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'ffprobe',
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'-v', 'quiet',
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'-show_entries', 'format=duration',
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'-of', 'default=noprint_wrappers=1:nokey=1',
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file_path
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]
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try:
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output = subprocess.check_output(cmd).decode().strip()
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return float(output)
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except:
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return 0.0
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@app.post("/transcribe")
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async def transcribe_audio(
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file: UploadFile = File(...),
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token: str = Depends(api_key_header),
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model_name: str = "medium",
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verbose: Optional[bool] = None,
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temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
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compression_ratio_threshold: Optional[float] = 2.4,
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logprob_threshold: Optional[float] = -1.0,
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no_speech_threshold: Optional[float] = 0.6,
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condition_on_previous_text: bool = True,
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initial_prompt: Optional[str] = None,
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word_timestamps: bool = False,
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prepend_punctuations: str = "\"'\"¿([{-",
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append_punctuations: str = "\"\'.。,，!！?？:：\")]}、",
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clip_timestamps: Union[str, List[float]] = "0",
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hallucination_silence_threshold: Optional[float] = None
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):
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# Token validation
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if token not in get_keys():
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logger.warning(f"Invalid token attempt: {token}")
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raise HTTPException(status_code=403, detail="Forbidden")
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logger.info(f"Processing file: {file.filename} with model: {model_name}")
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metrics = TranscriptionMetrics()
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# Save uploaded file
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temp_input_path = f"/tmp/input_{file.filename}"
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temp_output_path = f"/tmp/converted_{file.filename}.wav"
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try:
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with open(temp_input_path, "wb") as f:
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f.write(await file.read())
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# Convert audio if needed
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logger.debug("Converting audio file")
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if not convert_audio(temp_input_path, temp_output_path):
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raise HTTPException(status_code=400, detail="Audio conversion failed")
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# Get audio duration before speed up
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original_duration = get_audio_duration(temp_input_path)
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# Load model
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logger.debug(f"Loading model: {model_name}")
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model = whisper.load_model(model_name, device="cuda")
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# Transcribe
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logger.info("Starting transcription")
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result = model.transcribe(
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temp_output_path,
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verbose=verbose,
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temperature=temperature,
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compression_ratio_threshold=compression_ratio_threshold,
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logprob_threshold=logprob_threshold,
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no_speech_threshold=no_speech_threshold,
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condition_on_previous_text=condition_on_previous_text,
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initial_prompt=initial_prompt,
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word_timestamps=word_timestamps,
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prepend_punctuations=prepend_punctuations,
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append_punctuations=append_punctuations,
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clip_timestamps=clip_timestamps,
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hallucination_silence_threshold=hallucination_silence_threshold
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)
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# Calculate metrics
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metrics.stop(result["text"], original_duration)
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logger.info(f"Transcription metrics: {metrics.get_metrics()}")
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# Add metrics to result
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result["metrics"] = metrics.get_metrics()
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return result
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except Exception as e:
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logger.error(f"Transcription failed: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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finally:
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# Cleanup temporary files
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if os.path.exists(temp_input_path):
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os.remove(temp_input_path)
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if os.path.exists(temp_output_path):
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os.remove(temp_output_path)
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def main():
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import uvicorn
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get_keys()
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uvicorn.run(app, host="0.0.0.0")
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if __name__ == "__main__":
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main()
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@@ -1,9 +0,0 @@
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server:
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host: "0.0.0.0"
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port: 8000
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ui: true
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whisper:
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model_name: "turbo"
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device: "cuda"
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compute_type: "int8"
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45
converter.py
45
converter.py
@@ -1,45 +0,0 @@
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import ffmpeg
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import os
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import tempfile
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import shutil
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def is_valid_format(file_path: str) -> bool:
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"""Проверяет, является ли аудиофайл 16kHz моно WAV."""
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try:
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probe = ffmpeg.probe(file_path)
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audio_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'audio'), None)
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if audio_stream is None:
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return False
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return (
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audio_stream.get('codec_name') == 'pcm_s16le' and
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audio_stream.get('channels') == 1 and
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audio_stream.get('sample_rate') == '16000'
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)
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except ffmpeg.Error:
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return False
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def convert_to_wav(input_file_path: str) -> tuple[str, bool]:
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"""
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Конвертирует аудиофайл в 16kHz моно WAV.
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Возвращает путь к сконвертированному файлу и флаг, указывающий, была ли выполнена конвертация.
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Если файл уже в нужном формате, возвращает исходный путь и False.
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"""
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if is_valid_format(input_file_path):
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return input_file_path, False
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output_file_path = tempfile.mktemp(suffix=".wav")
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try:
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ffmpeg.input(input_file_path).output(
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output_file_path,
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acodec='pcm_s16le',
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ac=1,
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ar='16k'
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).run(capture_stdout=True, capture_stderr=True)
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return output_file_path, True
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except ffmpeg.Error as e:
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if os.path.exists(output_file_path):
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os.remove(output_file_path)
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raise e
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198
main.py
198
main.py
@@ -1,198 +0,0 @@
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import os
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import tempfile
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import sys
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import yaml
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from typing import Optional, List, Union, Tuple, Iterable
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from fastapi import FastAPI, UploadFile, File, Depends
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from pydantic import BaseModel
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from fastapi.responses import HTMLResponse
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from faster_whisper import WhisperModel
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from converter import convert_to_wav
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with open("config.yaml", 'r') as f:
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config = yaml.safe_load(f)
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app = FastAPI()
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w_config = config['whisper']
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class TranscriptionOptions(BaseModel):
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language: Optional[str] = w_config.get('language')
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task: str = w_config.get('task', 'transcribe')
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beam_size: int = w_config.get('beam_size', 5)
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best_of: int = w_config.get('best_of', 5)
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patience: float = w_config.get('patience', 1.0)
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length_penalty: float = w_config.get('length_penalty', 1.0)
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repetition_penalty: float = w_config.get('repetition_penalty', 1.0)
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no_repeat_ngram_size: int = w_config.get('no_repeat_ngram_size', 0)
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temperature: Union[float, List[float], Tuple[float, ...]] = w_config.get('temperature', [0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
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log_progress: bool = w_config.get('log_progress', False)
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compression_ratio_threshold: Optional[float] = w_config.get('compression_ratio_threshold', 2.4)
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log_prob_threshold: Optional[float] = w_config.get('log_prob_threshold', -1.0)
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no_speech_threshold: Optional[float] = w_config.get('no_speech_threshold', 0.6)
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condition_on_previous_text: bool = w_config.get('condition_on_previous_text', True)
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prompt_reset_on_temperature: float = w_config.get('prompt_reset_on_temperature', 0.5)
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initial_prompt: Optional[Union[str, Iterable[int]]] = w_config.get('initial_prompt')
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prefix: Optional[str] = w_config.get('prefix')
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suppress_blank: bool = w_config.get('suppress_blank', True)
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suppress_tokens: Optional[List[int]] = w_config.get('suppress_tokens', [-1])
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without_timestamps: bool = w_config.get('without_timestamps', False)
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max_initial_timestamp: float = w_config.get('max_initial_timestamp', 1.0)
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word_timestamps: bool = w_config.get('word_timestamps', False)
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prepend_punctuations: str = w_config.get('prepend_punctuations', '"\'“¿([{-')
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append_punctuations: str = w_config.get('append_punctuations', '"\'.。,，!！?？:：”)]}、')
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vad_filter: bool = w_config.get('vad_filter', False)
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vad_parameters: Optional[dict] = w_config.get('vad_parameters')
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max_new_tokens: Optional[int] = w_config.get('max_new_tokens')
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chunk_length: Optional[int] = w_config.get('chunk_length')
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clip_timestamps: Union[str, List[float]] = w_config.get('clip_timestamps', "0")
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hallucination_silence_threshold: Optional[float] = w_config.get('hallucination_silence_threshold')
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hotwords: Optional[str] = w_config.get('hotwords')
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language_detection_threshold: Optional[float] = w_config.get('language_detection_threshold')
|
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language_detection_segments: int = w_config.get('language_detection_segments', 1)
|
||||
|
||||
class WhisperTranscriber:
|
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def __init__(self, model_name, device, compute_type):
|
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self.model = WhisperModel(model_name, device=device, compute_type=compute_type)
|
||||
|
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def transcribe(self, audio_file_path: str, options: dict) -> str:
|
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segments, _ = self.model.transcribe(audio_file_path, **options)
|
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transcription = " ".join([segment.text for segment in segments])
|
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return transcription
|
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|
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transcriber = WhisperTranscriber(
|
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model_name=w_config['model_name'],
|
||||
device=w_config['device'],
|
||||
compute_type=w_config['compute_type']
|
||||
)
|
||||
|
||||
@app.post("/transcribe")
|
||||
async def transcribe_audio(file: UploadFile = File(...), options: TranscriptionOptions = Depends()):
|
||||
temp_audio_file_path = None
|
||||
converted_file_path = None
|
||||
was_converted = False
|
||||
try:
|
||||
with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as temp_audio_file:
|
||||
temp_audio_file.write(await file.read())
|
||||
temp_audio_file_path = temp_audio_file.name
|
||||
|
||||
converted_file_path, was_converted = convert_to_wav(temp_audio_file_path)
|
||||
|
||||
transcription = transcriber.transcribe(converted_file_path, options.dict(exclude_none=True))
|
||||
|
||||
return {"transcription": transcription}
|
||||
finally:
|
||||
if temp_audio_file_path and os.path.exists(temp_audio_file_path):
|
||||
os.remove(temp_audio_file_path)
|
||||
if was_converted and converted_file_path and os.path.exists(converted_file_path):
|
||||
os.remove(converted_file_path)
|
||||
|
||||
def create_ui():
|
||||
return '''
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Whisper Transcription</title>
|
||||
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet">
|
||||
<style>
|
||||
body {
|
||||
background-color: #f8f9fa;
|
||||
}
|
||||
.container {
|
||||
max-width: 700px;
|
||||
}
|
||||
#transcriptionOutput {
|
||||
white-space: pre-wrap;
|
||||
word-wrap: break-word;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container mt-5">
|
||||
<div class="card">
|
||||
<div class="card-body">
|
||||
<h1 class="card-title text-center mb-4">Upload Audio for Transcription</h1>
|
||||
<div class="mb-3">
|
||||
<input class="form-control" type="file" id="audioFile" accept="audio/*">
|
||||
</div>
|
||||
<div class="d-grid">
|
||||
<button class="btn btn-primary" onclick="transcribeAudio()">
|
||||
<span class="spinner-border spinner-border-sm d-none" role="status" aria-hidden="true" id="spinner"></span>
|
||||
Transcribe
|
||||
</button>
|
||||
</div>
|
||||
<h2 class="mt-4">Transcription:</h2>
|
||||
<div class="p-3 bg-light rounded">
|
||||
<pre id="transcriptionOutput"></pre>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
async function transcribeAudio() {
|
||||
const fileInput = document.getElementById('audioFile');
|
||||
const file = fileInput.files[0];
|
||||
if (!file) {
|
||||
alert("Please select a file first.");
|
||||
return;
|
||||
}
|
||||
|
||||
const formData = new FormData();
|
||||
formData.append('file', file);
|
||||
|
||||
const outputElement = document.getElementById('transcriptionOutput');
|
||||
const spinner = document.getElementById('spinner');
|
||||
const transcribeButton = document.querySelector('button');
|
||||
|
||||
outputElement.innerText = '';
|
||||
spinner.classList.remove('d-none');
|
||||
transcribeButton.disabled = true;
|
||||
|
||||
|
||||
try {
|
||||
const response = await fetch('/transcribe', {
|
||||
method: 'POST',
|
||||
body: formData
|
||||
});
|
||||
|
||||
if (response.ok) {
|
||||
const result = await response.json();
|
||||
if (result.transcription) {
|
||||
outputElement.innerText = result.transcription;
|
||||
} else if (result.error) {
|
||||
outputElement.innerText = 'Error: ' + result.error;
|
||||
}
|
||||
} else {
|
||||
const errorText = await response.text();
|
||||
outputElement.innerText = 'Error: ' + response.statusText + ' - ' + errorText;
|
||||
}
|
||||
} catch (error) {
|
||||
outputElement.innerText = 'An error occurred: ' + error;
|
||||
} finally {
|
||||
spinner.classList.add('d-none');
|
||||
transcribeButton.disabled = false;
|
||||
}
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
'''
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
s_config = config['server']
|
||||
|
||||
if s_config['ui'] or "--ui" in sys.argv:
|
||||
@app.get("/", response_class=HTMLResponse)
|
||||
async def read_root():
|
||||
return create_ui()
|
||||
|
||||
uvicorn.run(
|
||||
app,
|
||||
host=s_config['host'],
|
||||
port=s_config['port']
|
||||
)
|
||||
@@ -1,6 +1,6 @@
|
||||
fastapi
|
||||
uvicorn[standard]
|
||||
python-multipart
|
||||
faster-whisper
|
||||
openai-whisper
|
||||
ffmpeg-python
|
||||
PyYAML
|
||||
|
||||
Reference in New Issue
Block a user