Spaces:

urjob
/

test

Paused

test / behavior_backend /app /services /processing /speech_service.py

Add backend application and Dockerfile

8ae78b0 over 1 year ago

22 kB

import os
import whisper
import speech_recognition as sr
import logging
import backoff
import subprocess
import time
import torch
import json
from pathlib import Path
from pydub import AudioSegment
from moviepy.editor import VideoFileClip
from typing import Optional, List, Dict, Any

# Fix import paths
try:
    from app.utils.device_utils import device, run_on_device
    from app.utils.logging_utils import time_it, setup_logger
except ImportError:
    # Try relative imports for running from project root
    from behavior_backend.app.utils.device_utils import device, run_on_device
    from behavior_backend.app.utils.logging_utils import time_it, setup_logger

# Configure logging
logger = setup_logger(__name__)

class TranscriptionService:
    """Service for cloud-based speech-to-text operations."""
    
    def __init__(self):
        """Initialize the transcription service."""
        self.recognizer = sr.Recognizer()
        
        # Load credentials
        self.credentials = self._load_credentials()
        
        # Define available recognizers
        self.available_recognizers = {}
        
        # Check which recognizers are available and add them
        if hasattr(self.recognizer, 'recognize_openai_whisper') or hasattr(self.recognizer, 'recognize_whisper_api'):
            self.available_recognizers['openai_whisper'] = self._transcribe_openai_whisper
        
        if hasattr(self.recognizer, 'recognize_google_cloud'):
            self.available_recognizers['google_cloud'] = self._transcribe_google_cloud
        
        if hasattr(self.recognizer, 'recognize_groq'):
            self.available_recognizers['groq'] = self._transcribe_groq
        
        logger.info(f"Available cloud transcription services: {', '.join(self.available_recognizers.keys())}")
    
    def _load_credentials(self):
        """Load all service credentials"""
        creds = {}
        try:
            # Google Cloud - check for credentials file in the project directory
            google_creds_path = os.path.join(os.path.dirname(__file__), "google_credentials.json")
            if os.path.exists(google_creds_path):
                creds['google_cloud'] = google_creds_path
            else:
                # Try environment variable
                creds['google_cloud'] = os.getenv('GOOGLE_APPLICATION_CREDENTIALS')
            
            # Groq API key
            creds['groq'] = os.getenv('GROQ_API_KEY')
            print('----------------------------------------------   ')
            print( "Groq API key:",creds['groq'])
            print('----------------------------------------------')
            # OpenAI API key
            creds['openai'] = os.getenv('OPENAI_API_KEY')
            print('----------------------------------------------')
            print( "OpenAI API key:",creds['openai'])
            print('----------------------------------------------')
        except Exception as e:
            logger.error(f"Error loading credentials: {e}")
        
        return creds
    
    def convert_to_wav(self, input_path):
        """Convert audio/video file to WAV format if needed"""
        input_path = Path(input_path)
        
        if input_path.suffix.lower() == '.wav':
            return str(input_path)
        
        output_path = input_path.with_suffix('.wav')
        logger.info(f"Converting {input_path} to WAV format")
        
        try:
            audio = AudioSegment.from_file(str(input_path))
            audio.export(str(output_path), format="wav")
            logger.info(f"Conversion completed: {output_path}")
            return str(output_path)
        except Exception as e:
            logger.error(f"Error converting file: {e}")
            raise
    
    @backoff.on_exception(
        backoff.expo,
        Exception,
        max_tries=3
    )
    def transcribe(self, audio_file_path, services=None, cleanup=True, language='en'):
        """
        Transcribe audio using multiple services
        
        Args:
            audio_file_path: Path to the audio file
            services: List of services to use for transcription
            cleanup: Whether to clean up temporary files
            language: Language code
            
        Returns:
            Dictionary of transcription results by service
        """
        if services is None:
            services = list(self.available_recognizers.keys())
            
        results = {}
        original_path = Path(audio_file_path)
        
        try:
            wav_path = self.convert_to_wav(audio_file_path)
            
            with sr.AudioFile(wav_path) as source:
                audio = self.recognizer.record(source)
                
            # Try each requested service
            for service in services:
                if service in self.available_recognizers:
                    try:
                        logger.info(f"Starting transcription with {service}")
                        text = self.available_recognizers[service](audio, language)
                        if text:
                            results[service] = text
                            logger.info(f"{service} transcription completed")
                    except Exception as e:
                        logger.error(f"{service} transcription failed: {e}")
                        results[service] = f"Error: {str(e)}"
            
            if cleanup and original_path.suffix.lower() != '.wav' and wav_path != str(original_path):
                os.remove(wav_path)
                logger.info("Cleaned up converted file")
                
            return results
            
        except Exception as e:
            logger.error(f"Transcription process failed: {e}")
            raise
    
    # Individual transcription methods
    def _transcribe_openai_whisper(self, audio, language):
        """Transcribe using OpenAI Whisper API"""
        if not self.credentials.get('openai'):
            raise ValueError("OpenAI API key not found")
        
        # Convert language code if needed (e.g., 'en-US' to 'en')
        whisper_lang = language.split('-')[0] if '-' in language else language
        
        # Try both method names that might be available
        if hasattr(self.recognizer, 'recognize_whisper_api'):
            return self.recognizer.recognize_whisper_api(
                audio, 
                api_key=self.credentials['openai'],
                language=whisper_lang
            )
        elif hasattr(self.recognizer, 'recognize_openai_whisper'):
            return self.recognizer.recognize_openai_whisper(
                audio, 
                api_key=self.credentials['openai'],
                language=whisper_lang
            )
        else:
            raise NotImplementedError("No OpenAI Whisper API recognition method available")
    
    def _transcribe_google_cloud(self, audio, language):
        """Transcribe using Google Cloud Speech-to-Text"""
        if not self.credentials.get('google_cloud'):
            raise ValueError("Google Cloud credentials not found")
        
        return self.recognizer.recognize_google_cloud(
            audio, 
            credentials_json=self.credentials['google_cloud'],
            language=language
        )
    
    def _transcribe_groq(self, audio, language):
        """Transcribe using Groq API"""
        if not self.credentials.get('groq'):
            raise ValueError("Groq API key not found")
        return self.recognizer.recognize_groq(audio)

class SpeechService:
    """Service for speech-to-text operations."""
    
    def __init__(self):
        """Initialize the speech service."""
        self.whisper_model = None
        self.ffmpeg_success = False
        self.cloud_transcription_service = TranscriptionService()
    
    @time_it
    def extract_audio(self, video_path: str) -> str:
        """
        Extract audio from a video file using FFmpeg (primary) or MoviePy (fallback).
        
        Args:
            video_path: Path to the video file
            
        Returns:
            Path to the extracted audio file
        """
        logger.info(f"Extracting audio from {video_path}")
        
        # Create output path
        video_filename = Path(video_path).stem
        audio_path = f"temp_{video_filename}.wav"
        
        # Try FFmpeg approach first
        self.ffmpeg_success = False
        ffmpeg_start_time = time.time()
        
        try:
            logger.info("Attempting audio extraction with FFmpeg...")
            result = subprocess.run([
                'ffmpeg',
                '-i', str(video_path),
                '-acodec', 'pcm_s16le',
                '-ar', '16000',  # 16kHz sample rate
                '-ac', '1',      # Mono channel
                '-y',            # Overwrite output file if it exists
                str(audio_path)
            ], check=True, capture_output=True, text=True)
            
            self.ffmpeg_success = True
            ffmpeg_end_time = time.time()
            ffmpeg_duration = ffmpeg_end_time - ffmpeg_start_time
            logger.info(f"FFmpeg audio extraction successful in {ffmpeg_duration:.4f} seconds")
            
        except (subprocess.CalledProcessError, FileNotFoundError) as e:
            ffmpeg_end_time = time.time()
            ffmpeg_duration = ffmpeg_end_time - ffmpeg_start_time
            logger.warning(f"FFmpeg audio extraction failed after {ffmpeg_duration:.4f} seconds: {str(e)}")
            logger.warning("Falling back to MoviePy for audio extraction...")
            
            # Fallback to MoviePy approach
            moviepy_start_time = time.time()
            try:
                # Extract audio using moviepy
                video = VideoFileClip(video_path)
                video.audio.write_audiofile(audio_path, codec='pcm_s16le', logger=None)
                video.close()  # Explicitly close to free resources
                
                moviepy_end_time = time.time()
                moviepy_duration = moviepy_end_time - moviepy_start_time
                logger.info(f"MoviePy audio extraction successful in {moviepy_duration:.4f} seconds")
                
            except Exception as e:
                moviepy_end_time = time.time()
                moviepy_duration = moviepy_end_time - moviepy_start_time
                logger.error(f"MoviePy audio extraction also failed after {moviepy_duration:.4f} seconds: {str(e)}")
                raise RuntimeError(f"Failed to extract audio from video using both FFmpeg and MoviePy: {str(e)}")
        
        # Verify the audio file exists and has content
        audio_file = Path(audio_path)
        if not audio_file.exists() or audio_file.stat().st_size == 0:
            logger.error(f"Audio extraction produced empty or missing file: {audio_path}")
            raise RuntimeError(f"Audio extraction failed: output file {audio_path} is empty or missing")
            
        logger.info(f"Audio extracted to {audio_path}")
        
        # Log performance comparison if both methods were used
        if not self.ffmpeg_success:
            logger.info(f"Audio extraction performance comparison - FFmpeg: {ffmpeg_duration:.4f}s, MoviePy: {moviepy_duration:.4f}s")
            
        return audio_path
    
    @time_it
    def split_audio(self, audio_path: str, chunk_length_ms: int = 30000) -> List[str]:
        """
        Split audio file into chunks for processing.
        
        Args:
            audio_path: Path to the audio file
            chunk_length_ms: Length of each chunk in milliseconds
            
        Returns:
            List of paths to audio chunks
        """
        logger.info(f"Splitting audio {audio_path} into {chunk_length_ms}ms chunks")
        
        # Load audio
        audio = AudioSegment.from_file(audio_path)
        
        # Create directory for chunks
        chunks_dir = Path("temp_chunks")
        chunks_dir.mkdir(exist_ok=True)
        
        # Split audio into chunks
        chunk_paths = []
        for i, chunk_start in enumerate(range(0, len(audio), chunk_length_ms)):
            chunk_end = min(chunk_start + chunk_length_ms, len(audio))
            chunk = audio[chunk_start:chunk_end]
            
            chunk_path = chunks_dir / f"chunk_{i}.wav"
            chunk.export(chunk_path, format="wav")
            chunk_paths.append(str(chunk_path))
        
        logger.info(f"Split audio into {len(chunk_paths)} chunks")
        return chunk_paths
    
    @run_on_device
    @time_it
    def transcribe_with_whisper(self, audio_path: str, language: str = 'en', device: str = 'cpu') -> str:
        """
        Transcribe audio using Whisper.
        
        Args:
            audio_path: Path to the audio file
            language: Language code
            device: Device to use for processing
            
        Returns:
            Transcribed text
        """
        logger.info(f"Transcribing {audio_path} with Whisper on {device}")
        
        try:
            # Load model if not already loaded or if device has changed
            if self.whisper_model is None or getattr(self, '_current_device', None) != device:
                # Clear existing model if it exists to free memory
                if self.whisper_model is not None:
                    del self.whisper_model
                    import gc
                    gc.collect()
                    torch.cuda.empty_cache() if device == 'cuda' else None
                    
                logger.info(f"Loading Whisper model on {device}")
                # Use tiny model instead of base to reduce memory usage
                self.whisper_model = whisper.load_model("tiny", device=device)
                self._current_device = device
            
            # Convert language code if needed (e.g., 'en-US' to 'en')
            if '-' in language:
                language = language.split('-')[0]
            
            # Transcribe audio with reduced compute settings
            result = self.whisper_model.transcribe(
                audio_path,
                language=language,
                fp16=(device == 'cuda'),  # Use fp16 only on CUDA
                beam_size=3,  # Reduce beam size (default is 5)
                best_of=1     # Reduce number of candidates (default is 5)
            )
            
            return result["text"]
        finally:
            # Force garbage collection after transcription to free memory
            import gc
            gc.collect()
            torch.cuda.empty_cache() if device == 'cuda' else None
    
    @backoff.on_exception(
        backoff.expo,
        Exception,
        max_tries=3
    )
    @time_it
    def transcribe_audio(self, audio_path: str, language: str = 'en', service: str = 'whisper') -> str:
        """
        Transcribe audio file to text.
        
        Args:
            audio_path: Path to the audio file
            language: Language code
            service: Transcription service to use ('whisper', 'groq', 'google_cloud', 'openai_whisper')
            
        Returns:
            Transcribed text
        """
        logger.info(f"Starting transcription of {audio_path} using {service}")
        
        # For cloud-based transcription services
        if service in ['groq', 'google_cloud', 'openai_whisper']:
            # Check if the requested service is available
            if service not in self.cloud_transcription_service.available_recognizers:
                logger.warning(f"Requested service {service} is not available, falling back to whisper")
                service = 'whisper'
        
        # Continue with the existing implementation
        if service in ['groq', 'google_cloud', 'openai_whisper']:
            logger.info(f"Using cloud-based transcription with {service}")
            
            # For long audio files, split into chunks and transcribe each chunk
            if os.path.getsize(audio_path) > 10 * 1024 * 1024:  # 10 MB
                logger.info(f"Audio file is large, splitting into chunks")
                chunk_paths = self.split_audio(audio_path)
                
                # Transcribe each chunk
                transcripts = []
                for chunk_path in chunk_paths:
                    # Transcribe with cloud service
                    results = self.cloud_transcription_service.transcribe(
                        chunk_path,
                        services=[service],
                        language=language
                    )
                    
                    # Get the result for the requested service
                    if service in results and results[service] and not results[service].startswith('Error:'):
                        transcripts.append(results[service])
                    else:
                        logger.warning(f"Failed to transcribe chunk with {service}, falling back to whisper")
                        transcript = self.transcribe_with_whisper(chunk_path, language)
                        transcripts.append(transcript)
                
                # Combine transcripts
                full_transcript = " ".join(transcripts)
                
                # Clean up chunks
                for chunk_path in chunk_paths:
                    os.remove(chunk_path)
                
                return full_transcript
            else:
                # Transcribe directly with cloud service
                results = self.cloud_transcription_service.transcribe(
                    audio_path,
                    services=[service],
                    language=language
                )
                
                # Get the result for the requested service
                if service in results and results[service] and not results[service].startswith('Error:'):
                    return results[service]
                else:
                    logger.warning(f"Failed to transcribe with {service}, falling back to whisper")
                    return self.transcribe_with_whisper(audio_path, language)
        
        # For local whisper transcription (default)
        else:
            # For long audio files, split into chunks and transcribe each chunk
            if os.path.getsize(audio_path) > 10 * 1024 * 1024:  # 10 MB
                logger.info(f"Audio file is large, splitting into chunks")
                chunk_paths = self.split_audio(audio_path)
                
                # Transcribe each chunk
                transcripts = []
                for chunk_path in chunk_paths:
                    transcript = self.transcribe_with_whisper(chunk_path, language)
                    transcripts.append(transcript)
                
                # Combine transcripts
                full_transcript = " ".join(transcripts)
                
                # Clean up chunks
                for chunk_path in chunk_paths:
                    os.remove(chunk_path)
                
                return full_transcript
            else:
                # Transcribe directly
                return self.transcribe_with_whisper(audio_path, language)
    
    @time_it
    def process_video_speech(self, video_path: str, language: str = 'en', service: str = 'whisper') -> str:
        """
        Process speech in a video file.
        
        Args:
            video_path: Path to the video file
            language: Language code
            service: Transcription service to use ('whisper', 'groq', 'google_cloud', 'openai_whisper')
                     If 'whisper' is selected, local Whisper model will be used.
                     If 'groq', 'google_cloud', or 'openai_whisper' are selected, cloud-based transcription will be used.
                     If the requested service is not available, it will fall back to 'whisper'.
            
        Returns:
            Transcribed text
        """
        audio_path = None
        extraction_method = None
        
        # Check if the requested service is available
        if service != 'whisper' and service not in self.cloud_transcription_service.available_recognizers:
            logger.warning(f"Requested service {service} is not available, falling back to whisper")
            service = 'whisper'
        
        try:
            # Extract audio
            start_time = time.time()
            audio_path = self.extract_audio(video_path)
            extraction_time = time.time() - start_time
            
            # Determine which method was used (for logging)
            if self.ffmpeg_success:
                extraction_method = "FFmpeg"
            else:
                extraction_method = "MoviePy"
            
            logger.info(f"Audio extracted using {extraction_method} in {extraction_time:.4f} seconds")
            
            # Transcribe audio
            start_time = time.time()
            transcript = self.transcribe_audio(audio_path, language, service)
            transcription_time = time.time() - start_time
            
            logger.info(f"Audio transcribed in {transcription_time:.4f} seconds")
            logger.info(f"Total speech processing time: {extraction_time + transcription_time:.4f} seconds")
            
            return transcript
        
        except Exception as e:
            logger.error(f"Error in process_video_speech: {str(e)}")
            raise
        
        finally:
            # Clean up
            if audio_path and os.path.exists(audio_path):
                try:
                    os.remove(audio_path)
                    logger.info(f"Temporary audio file {audio_path} removed")
                except Exception as e:
                    logger.warning(f"Failed to remove temporary audio file {audio_path}: {str(e)}")
            
            # Force garbage collection
            import gc
            gc.collect()
            if torch.cuda.is_available():
                torch.cuda.empty_cache() 

	import os
	import whisper
	import speech_recognition as sr
	import logging
	import backoff
	import subprocess
	import time
	import torch
	import json
	from pathlib import Path
	from pydub import AudioSegment
	from moviepy.editor import VideoFileClip
	from typing import Optional, List, Dict, Any

	# Fix import paths
	try:
	from app.utils.device_utils import device, run_on_device
	from app.utils.logging_utils import time_it, setup_logger
	except ImportError:
	# Try relative imports for running from project root
	from behavior_backend.app.utils.device_utils import device, run_on_device
	from behavior_backend.app.utils.logging_utils import time_it, setup_logger

	# Configure logging
	logger = setup_logger(__name__)

	class TranscriptionService:
	"""Service for cloud-based speech-to-text operations."""

	def __init__(self):
	"""Initialize the transcription service."""
	self.recognizer = sr.Recognizer()

	# Load credentials
	self.credentials = self._load_credentials()

	# Define available recognizers
	self.available_recognizers = {}

	# Check which recognizers are available and add them
	if hasattr(self.recognizer, 'recognize_openai_whisper') or hasattr(self.recognizer, 'recognize_whisper_api'):
	self.available_recognizers['openai_whisper'] = self._transcribe_openai_whisper

	if hasattr(self.recognizer, 'recognize_google_cloud'):
	self.available_recognizers['google_cloud'] = self._transcribe_google_cloud

	if hasattr(self.recognizer, 'recognize_groq'):
	self.available_recognizers['groq'] = self._transcribe_groq

	logger.info(f"Available cloud transcription services: {', '.join(self.available_recognizers.keys())}")

	def _load_credentials(self):
	"""Load all service credentials"""
	creds = {}
	try:
	# Google Cloud - check for credentials file in the project directory
	google_creds_path = os.path.join(os.path.dirname(__file__), "google_credentials.json")
	if os.path.exists(google_creds_path):
	creds['google_cloud'] = google_creds_path
	else:
	# Try environment variable
	creds['google_cloud'] = os.getenv('GOOGLE_APPLICATION_CREDENTIALS')

	# Groq API key
	creds['groq'] = os.getenv('GROQ_API_KEY')
	print('---------------------------------------------- ')
	print( "Groq API key:",creds['groq'])
	print('----------------------------------------------')
	# OpenAI API key
	creds['openai'] = os.getenv('OPENAI_API_KEY')
	print('----------------------------------------------')
	print( "OpenAI API key:",creds['openai'])
	print('----------------------------------------------')
	except Exception as e:
	logger.error(f"Error loading credentials: {e}")

	return creds

	def convert_to_wav(self, input_path):
	"""Convert audio/video file to WAV format if needed"""
	input_path = Path(input_path)

	if input_path.suffix.lower() == '.wav':
	return str(input_path)

	output_path = input_path.with_suffix('.wav')
	logger.info(f"Converting {input_path} to WAV format")

	try:
	audio = AudioSegment.from_file(str(input_path))
	audio.export(str(output_path), format="wav")
	logger.info(f"Conversion completed: {output_path}")
	return str(output_path)
	except Exception as e:
	logger.error(f"Error converting file: {e}")
	raise

	@backoff.on_exception(
	backoff.expo,
	Exception,
	max_tries=3
	)
	def transcribe(self, audio_file_path, services=None, cleanup=True, language='en'):
	"""
	Transcribe audio using multiple services

	Args:
	audio_file_path: Path to the audio file
	services: List of services to use for transcription
	cleanup: Whether to clean up temporary files
	language: Language code

	Returns:
	Dictionary of transcription results by service
	"""
	if services is None:
	services = list(self.available_recognizers.keys())

	results = {}
	original_path = Path(audio_file_path)

	try:
	wav_path = self.convert_to_wav(audio_file_path)

	with sr.AudioFile(wav_path) as source:
	audio = self.recognizer.record(source)

	# Try each requested service
	for service in services:
	if service in self.available_recognizers:
	try:
	logger.info(f"Starting transcription with {service}")
	text = self.available_recognizers[service](audio, language)
	if text:
	results[service] = text
	logger.info(f"{service} transcription completed")
	except Exception as e:
	logger.error(f"{service} transcription failed: {e}")
	results[service] = f"Error: {str(e)}"

	if cleanup and original_path.suffix.lower() != '.wav' and wav_path != str(original_path):
	os.remove(wav_path)
	logger.info("Cleaned up converted file")

	return results

	except Exception as e:
	logger.error(f"Transcription process failed: {e}")
	raise

	# Individual transcription methods
	def _transcribe_openai_whisper(self, audio, language):
	"""Transcribe using OpenAI Whisper API"""
	if not self.credentials.get('openai'):
	raise ValueError("OpenAI API key not found")

	# Convert language code if needed (e.g., 'en-US' to 'en')
	whisper_lang = language.split('-')[0] if '-' in language else language

	# Try both method names that might be available
	if hasattr(self.recognizer, 'recognize_whisper_api'):
	return self.recognizer.recognize_whisper_api(
	audio,
	api_key=self.credentials['openai'],
	language=whisper_lang
	)
	elif hasattr(self.recognizer, 'recognize_openai_whisper'):
	return self.recognizer.recognize_openai_whisper(
	audio,
	api_key=self.credentials['openai'],
	language=whisper_lang
	)
	else:
	raise NotImplementedError("No OpenAI Whisper API recognition method available")

	def _transcribe_google_cloud(self, audio, language):
	"""Transcribe using Google Cloud Speech-to-Text"""
	if not self.credentials.get('google_cloud'):
	raise ValueError("Google Cloud credentials not found")

	return self.recognizer.recognize_google_cloud(
	audio,
	credentials_json=self.credentials['google_cloud'],
	language=language
	)

	def _transcribe_groq(self, audio, language):
	"""Transcribe using Groq API"""
	if not self.credentials.get('groq'):
	raise ValueError("Groq API key not found")
	return self.recognizer.recognize_groq(audio)

	class SpeechService:
	"""Service for speech-to-text operations."""

	def __init__(self):
	"""Initialize the speech service."""
	self.whisper_model = None
	self.ffmpeg_success = False
	self.cloud_transcription_service = TranscriptionService()

	@time_it
	def extract_audio(self, video_path: str) -> str:
	"""
	Extract audio from a video file using FFmpeg (primary) or MoviePy (fallback).

	Args:
	video_path: Path to the video file

	Returns:
	Path to the extracted audio file
	"""
	logger.info(f"Extracting audio from {video_path}")

	# Create output path
	video_filename = Path(video_path).stem
	audio_path = f"temp_{video_filename}.wav"

	# Try FFmpeg approach first
	self.ffmpeg_success = False
	ffmpeg_start_time = time.time()

	try:
	logger.info("Attempting audio extraction with FFmpeg...")
	result = subprocess.run([
	'ffmpeg',
	'-i', str(video_path),
	'-acodec', 'pcm_s16le',
	'-ar', '16000', # 16kHz sample rate
	'-ac', '1', # Mono channel
	'-y', # Overwrite output file if it exists
	str(audio_path)
	], check=True, capture_output=True, text=True)

	self.ffmpeg_success = True
	ffmpeg_end_time = time.time()
	ffmpeg_duration = ffmpeg_end_time - ffmpeg_start_time
	logger.info(f"FFmpeg audio extraction successful in {ffmpeg_duration:.4f} seconds")

	except (subprocess.CalledProcessError, FileNotFoundError) as e:
	ffmpeg_end_time = time.time()
	ffmpeg_duration = ffmpeg_end_time - ffmpeg_start_time
	logger.warning(f"FFmpeg audio extraction failed after {ffmpeg_duration:.4f} seconds: {str(e)}")
	logger.warning("Falling back to MoviePy for audio extraction...")

	# Fallback to MoviePy approach
	moviepy_start_time = time.time()
	try:
	# Extract audio using moviepy
	video = VideoFileClip(video_path)
	video.audio.write_audiofile(audio_path, codec='pcm_s16le', logger=None)
	video.close() # Explicitly close to free resources

	moviepy_end_time = time.time()
	moviepy_duration = moviepy_end_time - moviepy_start_time
	logger.info(f"MoviePy audio extraction successful in {moviepy_duration:.4f} seconds")

	except Exception as e:
	moviepy_end_time = time.time()
	moviepy_duration = moviepy_end_time - moviepy_start_time
	logger.error(f"MoviePy audio extraction also failed after {moviepy_duration:.4f} seconds: {str(e)}")
	raise RuntimeError(f"Failed to extract audio from video using both FFmpeg and MoviePy: {str(e)}")

	# Verify the audio file exists and has content
	audio_file = Path(audio_path)
	if not audio_file.exists() or audio_file.stat().st_size == 0:
	logger.error(f"Audio extraction produced empty or missing file: {audio_path}")
	raise RuntimeError(f"Audio extraction failed: output file {audio_path} is empty or missing")

	logger.info(f"Audio extracted to {audio_path}")

	# Log performance comparison if both methods were used
	if not self.ffmpeg_success:
	logger.info(f"Audio extraction performance comparison - FFmpeg: {ffmpeg_duration:.4f}s, MoviePy: {moviepy_duration:.4f}s")

	return audio_path

	@time_it
	def split_audio(self, audio_path: str, chunk_length_ms: int = 30000) -> List[str]:
	"""
	Split audio file into chunks for processing.

	Args:
	audio_path: Path to the audio file
	chunk_length_ms: Length of each chunk in milliseconds

	Returns:
	List of paths to audio chunks
	"""
	logger.info(f"Splitting audio {audio_path} into {chunk_length_ms}ms chunks")

	# Load audio
	audio = AudioSegment.from_file(audio_path)

	# Create directory for chunks
	chunks_dir = Path("temp_chunks")
	chunks_dir.mkdir(exist_ok=True)

	# Split audio into chunks
	chunk_paths = []
	for i, chunk_start in enumerate(range(0, len(audio), chunk_length_ms)):
	chunk_end = min(chunk_start + chunk_length_ms, len(audio))
	chunk = audio[chunk_start:chunk_end]

	chunk_path = chunks_dir / f"chunk_{i}.wav"
	chunk.export(chunk_path, format="wav")
	chunk_paths.append(str(chunk_path))

	logger.info(f"Split audio into {len(chunk_paths)} chunks")
	return chunk_paths

	@run_on_device
	@time_it
	def transcribe_with_whisper(self, audio_path: str, language: str = 'en', device: str = 'cpu') -> str:
	"""
	Transcribe audio using Whisper.

	Args:
	audio_path: Path to the audio file
	language: Language code
	device: Device to use for processing

	Returns:
	Transcribed text
	"""
	logger.info(f"Transcribing {audio_path} with Whisper on {device}")

	try:
	# Load model if not already loaded or if device has changed
	if self.whisper_model is None or getattr(self, '_current_device', None) != device:
	# Clear existing model if it exists to free memory
	if self.whisper_model is not None:
	del self.whisper_model
	import gc
	gc.collect()
	torch.cuda.empty_cache() if device == 'cuda' else None

	logger.info(f"Loading Whisper model on {device}")
	# Use tiny model instead of base to reduce memory usage
	self.whisper_model = whisper.load_model("tiny", device=device)
	self._current_device = device

	# Convert language code if needed (e.g., 'en-US' to 'en')
	if '-' in language:
	language = language.split('-')[0]

	# Transcribe audio with reduced compute settings
	result = self.whisper_model.transcribe(
	audio_path,
	language=language,
	fp16=(device == 'cuda'), # Use fp16 only on CUDA
	beam_size=3, # Reduce beam size (default is 5)
	best_of=1 # Reduce number of candidates (default is 5)
	)

	return result["text"]
	finally:
	# Force garbage collection after transcription to free memory
	import gc
	gc.collect()
	torch.cuda.empty_cache() if device == 'cuda' else None

	@backoff.on_exception(
	backoff.expo,
	Exception,
	max_tries=3
	)
	@time_it
	def transcribe_audio(self, audio_path: str, language: str = 'en', service: str = 'whisper') -> str:
	"""
	Transcribe audio file to text.

	Args:
	audio_path: Path to the audio file
	language: Language code
	service: Transcription service to use ('whisper', 'groq', 'google_cloud', 'openai_whisper')

	Returns:
	Transcribed text
	"""
	logger.info(f"Starting transcription of {audio_path} using {service}")

	# For cloud-based transcription services
	if service in ['groq', 'google_cloud', 'openai_whisper']:
	# Check if the requested service is available
	if service not in self.cloud_transcription_service.available_recognizers:
	logger.warning(f"Requested service {service} is not available, falling back to whisper")
	service = 'whisper'

	# Continue with the existing implementation
	if service in ['groq', 'google_cloud', 'openai_whisper']:
	logger.info(f"Using cloud-based transcription with {service}")

	# For long audio files, split into chunks and transcribe each chunk
	if os.path.getsize(audio_path) > 10 * 1024 * 1024: # 10 MB
	logger.info(f"Audio file is large, splitting into chunks")
	chunk_paths = self.split_audio(audio_path)

	# Transcribe each chunk
	transcripts = []
	for chunk_path in chunk_paths:
	# Transcribe with cloud service
	results = self.cloud_transcription_service.transcribe(
	chunk_path,
	services=[service],
	language=language
	)

	# Get the result for the requested service
	if service in results and results[service] and not results[service].startswith('Error:'):
	transcripts.append(results[service])
	else:
	logger.warning(f"Failed to transcribe chunk with {service}, falling back to whisper")
	transcript = self.transcribe_with_whisper(chunk_path, language)
	transcripts.append(transcript)

	# Combine transcripts
	full_transcript = " ".join(transcripts)

	# Clean up chunks
	for chunk_path in chunk_paths:
	os.remove(chunk_path)

	return full_transcript
	else:
	# Transcribe directly with cloud service
	results = self.cloud_transcription_service.transcribe(
	audio_path,
	services=[service],
	language=language
	)

	# Get the result for the requested service
	if service in results and results[service] and not results[service].startswith('Error:'):
	return results[service]
	else:
	logger.warning(f"Failed to transcribe with {service}, falling back to whisper")
	return self.transcribe_with_whisper(audio_path, language)

	# For local whisper transcription (default)
	else:
	# For long audio files, split into chunks and transcribe each chunk
	if os.path.getsize(audio_path) > 10 * 1024 * 1024: # 10 MB
	logger.info(f"Audio file is large, splitting into chunks")
	chunk_paths = self.split_audio(audio_path)

	# Transcribe each chunk
	transcripts = []
	for chunk_path in chunk_paths:
	transcript = self.transcribe_with_whisper(chunk_path, language)
	transcripts.append(transcript)

	# Combine transcripts
	full_transcript = " ".join(transcripts)

	# Clean up chunks
	for chunk_path in chunk_paths:
	os.remove(chunk_path)

	return full_transcript
	else:
	# Transcribe directly
	return self.transcribe_with_whisper(audio_path, language)

	@time_it
	def process_video_speech(self, video_path: str, language: str = 'en', service: str = 'whisper') -> str:
	"""
	Process speech in a video file.

	Args:
	video_path: Path to the video file
	language: Language code
	service: Transcription service to use ('whisper', 'groq', 'google_cloud', 'openai_whisper')
	If 'whisper' is selected, local Whisper model will be used.
	If 'groq', 'google_cloud', or 'openai_whisper' are selected, cloud-based transcription will be used.
	If the requested service is not available, it will fall back to 'whisper'.

	Returns:
	Transcribed text
	"""
	audio_path = None
	extraction_method = None

	# Check if the requested service is available
	if service != 'whisper' and service not in self.cloud_transcription_service.available_recognizers:
	logger.warning(f"Requested service {service} is not available, falling back to whisper")
	service = 'whisper'

	try:
	# Extract audio
	start_time = time.time()
	audio_path = self.extract_audio(video_path)
	extraction_time = time.time() - start_time

	# Determine which method was used (for logging)
	if self.ffmpeg_success:
	extraction_method = "FFmpeg"
	else:
	extraction_method = "MoviePy"

	logger.info(f"Audio extracted using {extraction_method} in {extraction_time:.4f} seconds")

	# Transcribe audio
	start_time = time.time()
	transcript = self.transcribe_audio(audio_path, language, service)
	transcription_time = time.time() - start_time

	logger.info(f"Audio transcribed in {transcription_time:.4f} seconds")
	logger.info(f"Total speech processing time: {extraction_time + transcription_time:.4f} seconds")

	return transcript

	except Exception as e:
	logger.error(f"Error in process_video_speech: {str(e)}")
	raise

	finally:
	# Clean up
	if audio_path and os.path.exists(audio_path):
	try:
	os.remove(audio_path)
	logger.info(f"Temporary audio file {audio_path} removed")
	except Exception as e:
	logger.warning(f"Failed to remove temporary audio file {audio_path}: {str(e)}")

	# Force garbage collection
	import gc
	gc.collect()
	if torch.cuda.is_available():
	torch.cuda.empty_cache()