Spaces:
Sleeping
Sleeping
File size: 3,570 Bytes
3cf82c2 09ab406 06e4c74 3cf82c2 09ab406 4d56027 00f260b 4d56027 09ab406 3cf82c2 09ab406 06e4c74 4d56027 3cf82c2 aa3c419 4d56027 09ab406 4d56027 7bcf8d7 09ab406 06e4c74 4d56027 09ab406 4d56027 09ab406 4d56027 3cf82c2 |
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 |
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse, FileResponse
from pydantic import BaseModel
import numpy as np
import io
import soundfile as sf
import base64
import logging
import torch
import librosa
from transformers import Wav2Vec2ForCTC, AutoProcessor
from pathlib import Path
# Import functions from other modules
from asr import transcribe, ASR_LANGUAGES
from tts import synthesize, TTS_LANGUAGES
from lid import identify
from asr import ASR_SAMPLING_RATE, transcribe
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = FastAPI(title="MMS: Scaling Speech Technology to 1000+ languages")
# Define request models
class AudioRequest(BaseModel):
audio: str # Base64 encoded audio data
language: str
class TTSRequest(BaseModel):
text: str
language: str
speed: float
@app.post("/transcribe")
async def transcribe_audio(request: AudioRequest):
try:
audio_bytes = base64.b64decode(request.audio)
audio_array, sample_rate = sf.read(io.BytesIO(audio_bytes))
# Convert to mono if stereo
if len(audio_array.shape) > 1:
audio_array = audio_array.mean(axis=1)
# Ensure audio_array is float32
audio_array = audio_array.astype(np.float32)
# Resample if necessary
if sample_rate != ASR_SAMPLING_RATE:
audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=ASR_SAMPLING_RATE)
result = transcribe(audio_array, request.language)
return JSONResponse(content={"transcription": result})
except Exception as e:
logger.error(f"Error in transcribe_audio: {str(e)}")
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@app.post("/synthesize")
async def synthesize_speech(request: TTSRequest):
try:
audio, filtered_text = synthesize(request.text, request.language, request.speed)
# Convert numpy array to bytes
buffer = io.BytesIO()
sf.write(buffer, audio, 22050, format='wav')
buffer.seek(0)
return FileResponse(
buffer,
media_type="audio/wav",
headers={"Content-Disposition": "attachment; filename=synthesized_audio.wav"}
)
except Exception as e:
logger.error(f"Error in synthesize_speech: {str(e)}")
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@app.post("/identify")
async def identify_language(request: AudioRequest):
try:
audio_bytes = base64.b64decode(request.audio)
audio_array, sample_rate = sf.read(io.BytesIO(audio_bytes))
result = identify(audio_array)
return JSONResponse(content={"language_identification": result})
except Exception as e:
logger.error(f"Error in identify_language: {str(e)}")
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@app.get("/asr_languages")
async def get_asr_languages():
try:
return JSONResponse(content=ASR_LANGUAGES)
except Exception as e:
logger.error(f"Error in get_asr_languages: {str(e)}")
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
@app.get("/tts_languages")
async def get_tts_languages():
try:
return JSONResponse(content=TTS_LANGUAGES)
except Exception as e:
logger.error(f"Error in get_tts_languages: {str(e)}")
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
|