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"""
Main Pipeline Module
This module provides the main SpeechTranslator class that orchestrates
the entire speech translation workflow with voice cloning.
"""
import logging
import time
from typing import Dict, List, Optional, Union, Any, Callable
from pathlib import Path
import json
from ..speech_recognition.whisper_recognizer import SpeechRecognizer, create_speech_recognizer
from ..translation.translator import TranslationService, create_translation_service
from ..voice_cloning.voice_cloner import VoiceCloner, create_voice_cloner
from ..audio_processing.processor import AudioProcessor, AudioValidator
from ..config import (
WHISPER_MODEL_SIZE, DEFAULT_TRANSLATION_SERVICE, TTS_MODEL,
SUPPORTED_LANGUAGES, SAMPLE_RATE
)
class SpeechTranslator:
"""Main speech translation system with voice cloning."""
def __init__(
self,
speech_model: str = WHISPER_MODEL_SIZE,
translation_engine: str = DEFAULT_TRANSLATION_SERVICE,
tts_model: str = TTS_MODEL,
device: str = "auto",
progress_callback: Optional[Callable] = None
):
"""
Initialize the speech translator.
Args:
speech_model: Whisper model size for speech recognition
translation_engine: Translation engine ('google' or 'local')
tts_model: TTS model for voice cloning
device: Device to run models on
progress_callback: Optional callback for progress updates
"""
self.speech_model = speech_model
self.translation_engine = translation_engine
self.tts_model = tts_model
self.device = device
self.progress_callback = progress_callback
# Initialize components
self.speech_recognizer = None
self.translation_service = None
self.voice_cloner = None
self.audio_processor = AudioProcessor()
self.audio_validator = AudioValidator(self.audio_processor)
self.logger = logging.getLogger(__name__)
# Processing statistics
self.stats = {
'total_processed': 0,
'successful_translations': 0,
'failed_translations': 0,
'total_processing_time': 0.0
}
def initialize(self, load_models: bool = True) -> None:
"""
Initialize all components.
Args:
load_models: Whether to load models immediately
"""
try:
self.logger.info("Initializing Speech Translation System...")
# Initialize speech recognizer
self._update_progress("Loading speech recognition model...")
self.speech_recognizer = SpeechRecognizer(
model_size=self.speech_model,
device=self.device
)
if load_models:
self.speech_recognizer.load_model()
# Initialize translation service
self._update_progress("Initializing translation service...")
self.translation_service = TranslationService(
primary_engine=self.translation_engine,
fallback_engine="google" if self.translation_engine != "google" else None
)
# Initialize voice cloner
self._update_progress("Loading voice cloning model...")
self.voice_cloner = VoiceCloner(
model_name=self.tts_model,
device=self.device
)
if load_models:
self.voice_cloner.load_model()
self._update_progress("Initialization complete!")
self.logger.info("Speech Translation System initialized successfully")
except Exception as e:
self.logger.error(f"Initialization failed: {str(e)}")
raise RuntimeError(f"System initialization failed: {str(e)}")
def translate_audio(
self,
input_audio: Union[str, Path],
source_lang: Optional[str] = None,
target_lang: str = "en",
voice_sample: Optional[Union[str, Path]] = None,
speaker_name: Optional[str] = None,
output_path: Optional[Union[str, Path]] = None,
return_intermediate: bool = False,
**kwargs
) -> Dict[str, Any]:
"""
Translate audio with voice cloning.
Args:
input_audio: Path to input audio file
source_lang: Source language (auto-detected if None)
target_lang: Target language code
voice_sample: Path to voice sample for cloning
speaker_name: Name of registered speaker (alternative to voice_sample)
output_path: Path for output audio file
return_intermediate: Whether to return intermediate results
**kwargs: Additional parameters for each component
Returns:
Dictionary with translation results and generated audio
"""
if not self.speech_recognizer or not self.translation_service or not self.voice_cloner:
self.initialize()
start_time = time.time()
try:
self.logger.info(f"Starting audio translation: {input_audio}")
# Step 1: Validate input audio
self._update_progress("Validating input audio...")
validation = self.audio_validator.validate_audio_file(input_audio)
if not validation['valid']:
raise ValueError(f"Invalid audio file: {validation['errors']}")
# Step 2: Speech Recognition
self._update_progress("Converting speech to text...")
transcription_result = self.speech_recognizer.transcribe(
input_audio,
language=source_lang,
**kwargs.get('speech_recognition', {})
)
original_text = transcription_result['text']
detected_language = transcription_result['language']
self.logger.info(f"Transcribed text: {original_text[:100]}...")
self.logger.info(f"Detected language: {detected_language}")
# Step 3: Translation
self._update_progress("Translating text...")
translation_result = self.translation_service.translate(
text=original_text,
source_lang=detected_language,
target_lang=target_lang,
**kwargs.get('translation', {})
)
translated_text = translation_result['translated_text']
self.logger.info(f"Translated text: {translated_text[:100]}...")
# Step 4: Voice Cloning Setup
if voice_sample and not speaker_name:
# Register temporary speaker
speaker_name = f"temp_speaker_{int(time.time())}"
self._update_progress("Registering voice sample...")
self.voice_cloner.register_voice(
speaker_name,
[voice_sample],
**kwargs.get('voice_registration', {})
)
elif not speaker_name:
raise ValueError("Either voice_sample or speaker_name must be provided")
# Step 5: Voice Cloning
self._update_progress("Generating speech with cloned voice...")
voice_result = self.voice_cloner.clone_voice(
text=translated_text,
speaker_name=speaker_name,
language=target_lang,
output_path=output_path,
**kwargs.get('voice_cloning', {})
)
# Calculate processing time
processing_time = time.time() - start_time
# Update statistics
self.stats['total_processed'] += 1
self.stats['successful_translations'] += 1
self.stats['total_processing_time'] += processing_time
# Prepare results
result = {
'success': True,
'input_audio': str(input_audio),
'output_audio': voice_result['output_path'],
'original_text': original_text,
'translated_text': translated_text,
'source_language': detected_language,
'target_language': target_lang,
'speaker_name': speaker_name,
'processing_time': processing_time,
'audio_duration': voice_result['duration'],
'model_info': {
'speech_model': self.speech_model,
'translation_engine': self.translation_engine,
'tts_model': self.tts_model
}
}
# Add intermediate results if requested
if return_intermediate:
result['intermediate_results'] = {
'transcription': transcription_result,
'translation': translation_result,
'voice_cloning': voice_result
}
self._update_progress("Translation completed successfully!")
self.logger.info(f"Audio translation completed in {processing_time:.2f}s")
return result
except Exception as e:
self.stats['failed_translations'] += 1
self.logger.error(f"Audio translation failed: {str(e)}")
error_result = {
'success': False,
'error': str(e),
'input_audio': str(input_audio),
'processing_time': time.time() - start_time
}
return error_result
def translate_text_with_voice(
self,
text: str,
source_lang: str,
target_lang: str,
voice_sample: Optional[Union[str, Path]] = None,
speaker_name: Optional[str] = None,
output_path: Optional[Union[str, Path]] = None,
**kwargs
) -> Dict[str, Any]:
"""
Translate text and generate speech with cloned voice.
Args:
text: Text to translate
source_lang: Source language code
target_lang: Target language code
voice_sample: Path to voice sample for cloning
speaker_name: Name of registered speaker
output_path: Path for output audio file
**kwargs: Additional parameters
Returns:
Dictionary with translation and voice cloning results
"""
if not self.translation_service or not self.voice_cloner:
self.initialize()
start_time = time.time()
try:
self.logger.info(f"Starting text translation with voice: {text[:50]}...")
# Step 1: Translation
self._update_progress("Translating text...")
translation_result = self.translation_service.translate(
text=text,
source_lang=source_lang,
target_lang=target_lang,
**kwargs.get('translation', {})
)
translated_text = translation_result['translated_text']
# Step 2: Voice Setup
if voice_sample and not speaker_name:
speaker_name = f"temp_speaker_{int(time.time())}"
self.voice_cloner.register_voice(speaker_name, [voice_sample])
elif not speaker_name:
raise ValueError("Either voice_sample or speaker_name must be provided")
# Step 3: Voice Generation
self._update_progress("Generating speech...")
voice_result = self.voice_cloner.clone_voice(
text=translated_text,
speaker_name=speaker_name,
language=target_lang,
output_path=output_path,
**kwargs.get('voice_cloning', {})
)
processing_time = time.time() - start_time
result = {
'success': True,
'original_text': text,
'translated_text': translated_text,
'source_language': source_lang,
'target_language': target_lang,
'speaker_name': speaker_name,
'output_audio': voice_result['output_path'],
'processing_time': processing_time,
'audio_duration': voice_result['duration']
}
self._update_progress("Text translation completed!")
return result
except Exception as e:
self.logger.error(f"Text translation with voice failed: {str(e)}")
return {
'success': False,
'error': str(e),
'original_text': text,
'processing_time': time.time() - start_time
}
def batch_translate_audio(
self,
audio_files: List[Union[str, Path]],
source_lang: Optional[str] = None,
target_lang: str = "en",
voice_sample: Optional[Union[str, Path]] = None,
speaker_name: Optional[str] = None,
output_dir: Optional[Union[str, Path]] = None,
**kwargs
) -> Dict[str, Any]:
"""
Batch translate multiple audio files.
Args:
audio_files: List of audio file paths
source_lang: Source language (auto-detected if None)
target_lang: Target language code
voice_sample: Voice sample for cloning
speaker_name: Registered speaker name
output_dir: Output directory for generated files
**kwargs: Additional parameters
Returns:
Dictionary with batch processing results
"""
if not self.speech_recognizer or not self.translation_service or not self.voice_cloner:
self.initialize()
results = []
failed_files = []
if output_dir:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Setup voice if provided
if voice_sample and not speaker_name:
speaker_name = f"batch_speaker_{int(time.time())}"
self.voice_cloner.register_voice(speaker_name, [voice_sample])
self.logger.info(f"Starting batch translation: {len(audio_files)} files")
for i, audio_file in enumerate(audio_files, 1):
try:
self._update_progress(f"Processing file {i}/{len(audio_files)}: {Path(audio_file).name}")
# Generate output path
output_path = None
if output_dir:
filename = Path(audio_file).stem
output_path = output_dir / f"{filename}_translated.wav"
result = self.translate_audio(
input_audio=audio_file,
source_lang=source_lang,
target_lang=target_lang,
speaker_name=speaker_name,
output_path=output_path,
**kwargs
)
results.append(result)
except Exception as e:
self.logger.error(f"Failed to process {audio_file}: {str(e)}")
failed_files.append({
'file': str(audio_file),
'error': str(e)
})
batch_result = {
'total_files': len(audio_files),
'successful': len(results),
'failed': len(failed_files),
'results': results,
'failed_files': failed_files,
'speaker_name': speaker_name,
'target_language': target_lang
}
self.logger.info(f"Batch processing completed. Success: {batch_result['successful']}, "
f"Failed: {batch_result['failed']}")
return batch_result
def register_speaker_voice(
self,
speaker_name: str,
voice_samples: List[Union[str, Path]],
validate: bool = True
) -> Dict[str, Any]:
"""
Register a speaker voice for reuse.
Args:
speaker_name: Unique speaker identifier
voice_samples: List of voice sample file paths
validate: Whether to validate samples
Returns:
Registration result
"""
if not self.voice_cloner:
self.voice_cloner = VoiceCloner(model_name=self.tts_model, device=self.device)
self.voice_cloner.load_model()
return self.voice_cloner.register_voice(speaker_name, voice_samples, validate)
def get_supported_languages(self) -> Dict[str, str]:
"""Get supported languages."""
return SUPPORTED_LANGUAGES
def get_registered_speakers(self) -> List[str]:
"""Get list of registered speakers."""
if not self.voice_cloner:
return []
return self.voice_cloner.get_registered_speakers()
def get_system_info(self) -> Dict[str, Any]:
"""Get system information and status."""
info = {
'configuration': {
'speech_model': self.speech_model,
'translation_engine': self.translation_engine,
'tts_model': self.tts_model,
'device': self.device
},
'components_loaded': {
'speech_recognizer': self.speech_recognizer is not None,
'translation_service': self.translation_service is not None,
'voice_cloner': self.voice_cloner is not None
},
'statistics': self.stats.copy(),
'supported_languages': len(SUPPORTED_LANGUAGES),
'registered_speakers': len(self.get_registered_speakers())
}
# Add component-specific info if loaded
if self.speech_recognizer:
info['speech_recognizer_info'] = self.speech_recognizer.get_model_info()
if self.translation_service:
info['available_translation_engines'] = self.translation_service.get_available_engines()
if self.voice_cloner:
info['voice_cloner_info'] = self.voice_cloner.get_model_info()
return info
def save_session(self, session_path: Union[str, Path]) -> None:
"""Save current session including registered speakers."""
session_path = Path(session_path)
session_path.mkdir(parents=True, exist_ok=True)
# Save system configuration
config_file = session_path / "session_config.json"
config = {
'speech_model': self.speech_model,
'translation_engine': self.translation_engine,
'tts_model': self.tts_model,
'device': self.device,
'statistics': self.stats
}
with open(config_file, 'w') as f:
json.dump(config, f, indent=2)
# Save speaker data if voice cloner is loaded
if self.voice_cloner:
self.voice_cloner.save_speaker_data(session_path / "speakers")
self.logger.info(f"Session saved to: {session_path}")
def load_session(self, session_path: Union[str, Path]) -> None:
"""Load previous session."""
session_path = Path(session_path)
# Load configuration
config_file = session_path / "session_config.json"
if config_file.exists():
with open(config_file, 'r') as f:
config = json.load(f)
self.stats.update(config.get('statistics', {}))
# Load speaker data
speakers_dir = session_path / "speakers"
if speakers_dir.exists() and self.voice_cloner:
self.voice_cloner.load_speaker_data(speakers_dir)
self.logger.info(f"Session loaded from: {session_path}")
def _update_progress(self, message: str) -> None:
"""Update progress via callback if available."""
if self.progress_callback:
self.progress_callback(message)
self.logger.debug(message)
# Convenience functions
def create_speech_translator(
speech_model: str = WHISPER_MODEL_SIZE,
translation_engine: str = DEFAULT_TRANSLATION_SERVICE,
tts_model: str = TTS_MODEL,
device: str = "auto",
initialize: bool = True
) -> SpeechTranslator:
"""
Create and optionally initialize a speech translator.
Args:
speech_model: Whisper model size
translation_engine: Translation engine to use
tts_model: TTS model for voice cloning
device: Device to run on
initialize: Whether to initialize immediately
Returns:
SpeechTranslator instance
"""
translator = SpeechTranslator(
speech_model=speech_model,
translation_engine=translation_engine,
tts_model=tts_model,
device=device
)
if initialize:
translator.initialize()
return translator
def quick_translate_audio(
input_audio: Union[str, Path],
voice_sample: Union[str, Path],
target_lang: str = "en",
output_path: Optional[Union[str, Path]] = None
) -> str:
"""
Quick audio translation for simple use cases.
Args:
input_audio: Input audio file
voice_sample: Voice sample for cloning
target_lang: Target language
output_path: Output file path
Returns:
Path to generated audio file
"""
translator = create_speech_translator()
result = translator.translate_audio(
input_audio=input_audio,
target_lang=target_lang,
voice_sample=voice_sample,
output_path=output_path
)
if result['success']:
return result['output_audio']
else:
raise RuntimeError(f"Translation failed: {result['error']}") |