Spaces:
Sleeping
Sleeping
File size: 5,456 Bytes
5554ef1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
"""
FastAPI REST API for Whisper German ASR
Provides endpoints for audio transcription
"""
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor
import librosa
import numpy as np
from pathlib import Path
import io
from typing import Optional
import logging
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Initialize FastAPI app
app = FastAPI(
title="Whisper German ASR API",
description="REST API for German speech recognition using fine-tuned Whisper model",
version="1.0.0"
)
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Global variables for model
model = None
processor = None
device = None
class TranscriptionResponse(BaseModel):
"""Response model for transcription"""
transcription: str
language: str = "de"
duration: Optional[float] = None
model: str = "whisper-small-german"
class HealthResponse(BaseModel):
"""Response model for health check"""
status: str
model_loaded: bool
device: str
def load_model(model_path: str = "./whisper_test_tuned"):
"""Load the fine-tuned Whisper model"""
global model, processor, device
logger.info(f"Loading model from: {model_path}")
model_path = Path(model_path)
# Check for checkpoint directories
if model_path.is_dir():
checkpoints = list(model_path.glob('checkpoint-*'))
if checkpoints:
latest = max(checkpoints, key=lambda p: int(p.name.split('-')[1]))
model_path = latest
logger.info(f"Using checkpoint: {latest.name}")
model = WhisperForConditionalGeneration.from_pretrained(str(model_path))
processor = WhisperProcessor.from_pretrained("openai/whisper-small")
# Set German language conditioning
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(
language="german",
task="transcribe"
)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
model.eval()
logger.info(f"Model loaded successfully on {device}")
@app.on_event("startup")
async def startup_event():
"""Load model on startup"""
try:
load_model()
except Exception as e:
logger.error(f"Failed to load model on startup: {e}")
# Don't fail startup, allow manual model loading
@app.get("/", response_model=dict)
async def root():
"""Root endpoint"""
return {
"message": "Whisper German ASR API",
"version": "1.0.0",
"endpoints": {
"health": "/health",
"transcribe": "/transcribe (POST)",
"docs": "/docs"
}
}
@app.get("/health", response_model=HealthResponse)
async def health_check():
"""Health check endpoint"""
return HealthResponse(
status="healthy" if model is not None else "model_not_loaded",
model_loaded=model is not None,
device=device if device else "unknown"
)
@app.post("/transcribe", response_model=TranscriptionResponse)
async def transcribe_audio(
file: UploadFile = File(...),
language: str = "de"
):
"""
Transcribe audio file to text
Args:
file: Audio file (wav, mp3, flac, etc.)
language: Language code (default: de for German)
Returns:
TranscriptionResponse with transcription text
"""
if model is None:
raise HTTPException(status_code=503, detail="Model not loaded")
try:
# Read audio file
contents = await file.read()
# Load audio with librosa
audio, sr = librosa.load(io.BytesIO(contents), sr=16000, mono=True)
duration = len(audio) / sr
# Process audio
input_features = processor(
audio,
sampling_rate=16000,
return_tensors="pt"
).input_features.to(device)
# Generate transcription
with torch.no_grad():
predicted_ids = model.generate(
input_features,
max_length=448,
num_beams=5,
early_stopping=True
)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
logger.info(f"Transcribed {file.filename}: {transcription[:50]}...")
return TranscriptionResponse(
transcription=transcription,
language=language,
duration=duration
)
except Exception as e:
logger.error(f"Transcription error: {e}")
raise HTTPException(status_code=500, detail=f"Transcription failed: {str(e)}")
@app.post("/reload-model")
async def reload_model(model_path: str = "./whisper_test_tuned"):
"""Reload the model (admin endpoint)"""
try:
load_model(model_path)
return {"status": "success", "message": "Model reloaded successfully"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to reload model: {str(e)}")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
|