app.py

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
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
from pydub import AudioSegment
from moviepy.editor import VideoFileClip
import traceback
from logging.handlers import RotatingFileHandler
import os
import boto3
from botocore.exceptions import NoCredentialsError
import time
import tempfile

# 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")

# S3 Configuration
S3_BUCKET = os.environ.get("S3_BUCKET")
S3_REGION = os.environ.get("S3_REGION")
S3_ACCESS_KEY_ID = os.environ.get("AWS_ACCESS_KEY_ID")
S3_SECRET_ACCESS_KEY = os.environ.get("AWS_SECRET_ACCESS_KEY")

# Initialize S3 client
s3_client = boto3.client(
    's3',
    aws_access_key_id=S3_ACCESS_KEY_ID,
    aws_secret_access_key=S3_SECRET_ACCESS_KEY,
    region_name=S3_REGION
)

# 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 extract_audio_from_file(input_bytes):
    with tempfile.NamedTemporaryFile(delete=False, suffix='.tmp') as temp_file:
        temp_file.write(input_bytes)
        temp_file_path = temp_file.name

    try:
        # First, try to read as a standard audio file
        audio_array, sample_rate = sf.read(temp_file_path)
        return audio_array, sample_rate
    except Exception:
        try:
            # Try to read as a video file
            video = VideoFileClip(temp_file_path)
            audio = video.audio
            if audio is not None:
                # Extract audio from video
                audio_array = audio.to_soundarray()
                sample_rate = audio.fps
                
                # Convert to mono if stereo
                if len(audio_array.shape) > 1 and audio_array.shape[1] > 1:
                    audio_array = audio_array.mean(axis=1)
                
                # Ensure audio is float32 and normalized
                audio_array = audio_array.astype(np.float32)
                audio_array /= np.max(np.abs(audio_array))
                
                video.close()
                return audio_array, sample_rate
            else:
                raise ValueError("Video file contains no audio")
        except Exception:
            # If video reading fails, try as generic audio with pydub
            try:
                audio = AudioSegment.from_file(temp_file_path)
                audio_array = np.array(audio.get_array_of_samples())
                
                # Convert to float32 and normalize
                audio_array = audio_array.astype(np.float32) / (2**15 if audio.sample_width == 2 else 2**7)
                
                # Convert stereo to mono if necessary
                if audio.channels == 2:
                    audio_array = audio_array.reshape((-1, 2)).mean(axis=1)
                
                return audio_array, audio.frame_rate
            except Exception as e:
                raise ValueError(f"Unsupported file format: {str(e)}")
    finally:
        # Clean up the temporary file
        os.unlink(temp_file_path)

@app.post("/transcribe")
async def transcribe_audio(request: AudioRequest):
    start_time = time.time()
    try:
        input_bytes = base64.b64decode(request.audio)
        audio_array, sample_rate = extract_audio_from_file(input_bytes)

        # 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)
        processing_time = time.time() - start_time
        return JSONResponse(content={"transcription": result, "processing_time_seconds": processing_time})
    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()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred during transcription", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.post("/synthesize")
async def synthesize_speech(request: TTSRequest):
    start_time = time.time()
    logger.info(f"Synthesize request received: text='{request.text}', language='{request.language}', speed={request.speed}")
    try:
        # Extract the ISO code from the full language name
        lang_code = request.language.split()[0].strip()
        
        # Input validation
        if not request.text:
            raise ValueError("Text cannot be empty")
        if lang_code 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(f"Calling synthesize function with lang_code: {lang_code}")
        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")
        
        # Generate a unique filename
        filename = f"synthesized_audio_{int(time.time())}.wav"
        
        # Upload to S3 without ACL
        try:
            s3_client.upload_fileobj(
                buffer, 
                S3_BUCKET, 
                filename, 
                ExtraArgs={'ContentType': 'audio/wav'}
            )
            logger.info(f"File uploaded successfully to S3: {filename}")
            
            # Generate the public URL with the correct format
            url = f"https://s3.{S3_REGION}.amazonaws.com/{S3_BUCKET}/{filename}"
            logger.info(f"Public URL generated: {url}")
            
            processing_time = time.time() - start_time
            return JSONResponse(content={"audio_url": url, "processing_time_seconds": processing_time})
        
        except NoCredentialsError:
            logger.error("AWS credentials not available or invalid")
            raise HTTPException(status_code=500, detail="Could not upload file to S3: Missing or invalid credentials")
        except Exception as e:
            logger.error(f"Failed to upload to S3: {str(e)}")
            raise HTTPException(status_code=500, detail=f"Could not upload file to S3: {str(e)}")

    except ValueError as ve:
        logger.error(f"ValueError in synthesize_speech: {str(ve)}", exc_info=True)
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=400,
            content={"message": "Invalid input", "details": str(ve), "processing_time_seconds": processing_time}
        )
    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()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An unexpected error occurred during speech synthesis", "details": error_details, "processing_time_seconds": processing_time}
        )
    finally:
        logger.info("Synthesize request completed")

@app.post("/identify")
async def identify_language(request: AudioRequest):
    start_time = time.time()
    try:
        input_bytes = base64.b64decode(request.audio)
        audio_array, sample_rate = extract_audio_from_file(input_bytes)
        result = identify(audio_array)
        processing_time = time.time() - start_time
        return JSONResponse(content={"language_identification": result, "processing_time_seconds": processing_time})
    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()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred during language identification", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.get("/asr_languages")
async def get_asr_languages():
    start_time = time.time()
    try:
        processing_time = time.time() - start_time
        return JSONResponse(content={"languages": ASR_LANGUAGES, "processing_time_seconds": processing_time})
    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()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred while fetching ASR languages", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.get("/tts_languages")
async def get_tts_languages():
    start_time = time.time()
    try:
        processing_time = time.time() - start_time
        return JSONResponse(content={"languages": TTS_LANGUAGES, "processing_time_seconds": processing_time})
    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()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred while fetching TTS languages", "details": error_details, "processing_time_seconds": processing_time}
        )