app.py

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 pathlib import Path
import magic  # For MIME type detection
from pydub import AudioSegment
import traceback
from logging.handlers import RotatingFileHandler

# 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

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Add a file handler
file_handler = RotatingFileHandler('app.log', maxBytes=10000000, backupCount=5)
file_handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)

app = FastAPI(title="MMS: Scaling Speech Technology to 1000+ languages")

# Define request models
class AudioRequest(BaseModel):
    audio: str  # Base64 encoded audio or video data
    language: str

class TTSRequest(BaseModel):
    text: str
    language: str
    speed: float

def detect_mime_type(input_bytes):
    mime = magic.Magic(mime=True)
    return mime.from_buffer(input_bytes)

def extract_audio(input_bytes):
    mime_type = detect_mime_type(input_bytes)
    
    if mime_type.startswith('audio/'):
        return sf.read(io.BytesIO(input_bytes))
    elif mime_type.startswith('video/webm'):
        audio = AudioSegment.from_file(io.BytesIO(input_bytes), format="webm")
        audio_array = np.array(audio.get_array_of_samples())
        sample_rate = audio.frame_rate
        return audio_array, sample_rate
    else:
        raise ValueError(f"Unsupported MIME type: {mime_type}")

@app.post("/transcribe")
async def transcribe_audio(request: AudioRequest):
    try:
        input_bytes = base64.b64decode(request.audio)
        audio_array, sample_rate = extract_audio(input_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)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred during transcription", "details": error_details}
        )

@app.post("/synthesize")
async def synthesize_speech(request: TTSRequest):
    logger.info(f"Synthesize request received: text='{request.text}', language='{request.language}', speed={request.speed}")
    try:
        # Input validation
        logger.info("Validating input parameters")
        if not request.text:
            raise ValueError("Text cannot be empty")
        if request.language not in TTS_LANGUAGES:
            raise ValueError(f"Unsupported language: {request.language}")
        if not 0.5 <= request.speed <= 2.0:
            raise ValueError(f"Speed must be between 0.5 and 2.0, got {request.speed}")
        
        logger.info("Calling synthesize function")
        result, filtered_text = synthesize(request.text, request.language, request.speed)
        logger.info(f"Synthesize function completed. Filtered text: '{filtered_text}'")
        
        if result is None:
            logger.error("Synthesize function returned None")
            raise ValueError("Synthesis failed to produce audio")
        
        sample_rate, audio = result
        logger.info(f"Synthesis result: sample_rate={sample_rate}, audio_shape={audio.shape if isinstance(audio, np.ndarray) else 'not numpy array'}, audio_dtype={audio.dtype if isinstance(audio, np.ndarray) else type(audio)}")
        
        logger.info("Converting audio to numpy array")
        audio = np.array(audio, dtype=np.float32)
        logger.info(f"Converted audio shape: {audio.shape}, dtype: {audio.dtype}")
        
        logger.info("Normalizing audio")
        max_value = np.max(np.abs(audio))
        if max_value == 0:
            logger.warning("Audio array is all zeros")
            raise ValueError("Generated audio is silent (all zeros)")
        audio = audio / max_value
        logger.info(f"Normalized audio range: [{audio.min()}, {audio.max()}]")
        
        logger.info("Converting to int16")
        audio = (audio * 32767).astype(np.int16)
        logger.info(f"Int16 audio shape: {audio.shape}, dtype: {audio.dtype}")
        
        logger.info("Writing audio to buffer")
        buffer = io.BytesIO()
        sf.write(buffer, audio, sample_rate, format='wav')
        buffer.seek(0)
        logger.info(f"Buffer size: {buffer.getbuffer().nbytes} bytes")
        
        logger.info("Preparing FileResponse")
        response = FileResponse(
            buffer,
            media_type="audio/wav",
            headers={"Content-Disposition": "attachment; filename=synthesized_audio.wav"}
        )
        logger.info("FileResponse prepared successfully")
        
        return response

    except ValueError as ve:
        logger.error(f"ValueError in synthesize_speech: {str(ve)}", exc_info=True)
        return JSONResponse(
            status_code=400,
            content={"message": "Invalid input", "details": str(ve)}
        )
    except Exception as e:
        logger.error(f"Unexpected error in synthesize_speech: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "type": type(e).__name__,
            "traceback": traceback.format_exc()
        }
        return JSONResponse(
            status_code=500,
            content={"message": "An unexpected error occurred during speech synthesis", "details": error_details}
        )
    finally:
        logger.info("Synthesize request completed")

@app.post("/identify")
async def identify_language(request: AudioRequest):
    try:
        input_bytes = base64.b64decode(request.audio)
        audio_array, sample_rate = extract_audio(input_bytes)
        result = identify(audio_array)
        return JSONResponse(content={"language_identification": result})
    except Exception as e:
        logger.error(f"Error in identify_language: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred during language identification", "details": error_details}
        )

@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)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred while fetching ASR languages", "details": error_details}
        )

@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)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred while fetching TTS languages", "details": error_details}
        )