import torch import torch.nn.functional as F from transformers import VitsModel, AutoTokenizer import soundfile as sf import numpy as np import time import logging from pathlib import Path import gc from typing import Optional, Union, List import threading from concurrent.futures import ThreadPoolExecutor import queue # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) class ProfessionalHindiTTS: """ Professional High-Performance Hindi Text-to-Speech System Optimized for speed, accuracy, and production deployment """ def __init__(self, model_name: str = "facebook/mms-tts-hin", device: Optional[str] = None, enable_cuda_optimization: bool = True, cache_size: int = 100, use_half_precision: bool = True): """ Initialize the TTS system with performance optimizations Args: model_name: HuggingFace model identifier device: Target device ('cuda', 'cpu', or None for auto-detection) enable_cuda_optimization: Enable CUDA-specific optimizations cache_size: Size of the audio cache for repeated texts use_half_precision: Use FP16 for faster inference (if supported) """ self.model_name = model_name self.cache_size = cache_size self.audio_cache = {} self.cache_queue = queue.Queue(maxsize=cache_size) # Device optimization self.device = self._setup_device(device) self.use_half_precision = use_half_precision and self.device.type == 'cuda' # Load model and tokenizer with optimizations self._load_models() # Performance optimizations if enable_cuda_optimization and torch.cuda.is_available(): self._apply_cuda_optimizations() # Pre-compile for faster first inference self._warmup_model() logger.info(f"✅ Professional Hindi TTS initialized on {self.device}") logger.info(f"🚀 Half precision: {self.use_half_precision}") logger.info(f"📦 Cache enabled: {self.cache_size} slots") def _setup_device(self, device: Optional[str]) -> torch.device: """Setup optimal device configuration""" if device is None: if torch.cuda.is_available(): device = 'cuda' logger.info(f"🎯 Auto-selected CUDA GPU: {torch.cuda.get_device_name()}") else: device = 'cpu' logger.info("🎯 Using CPU (CUDA not available)") device_obj = torch.device(device) # CUDA memory optimization if device_obj.type == 'cuda': torch.cuda.empty_cache() torch.backends.cudnn.benchmark = True # Optimize for consistent input sizes torch.backends.cudnn.deterministic = False # Allow non-deterministic for speed return device_obj def _load_models(self): """Load models with optimization flags""" logger.info("📥 Loading tokenizer and model...") start_time = time.time() # Load tokenizer self.tokenizer = AutoTokenizer.from_pretrained( self.model_name, use_fast=True, # Use fast tokenizer if available cache_dir=".cache/tokenizers" ) # Load model with optimizations self.model = VitsModel.from_pretrained( self.model_name, torch_dtype=torch.float16 if self.use_half_precision else torch.float32, low_cpu_mem_usage=True, cache_dir=".cache/models" ) # Move to device and set eval mode self.model = self.model.to(self.device) self.model.eval() # Memory optimization if hasattr(self.model, 'config'): self.model.config.use_cache = True load_time = time.time() - start_time logger.info(f"✅ Models loaded in {load_time:.2f}s") def _apply_cuda_optimizations(self): """Apply CUDA-specific optimizations""" if torch.cuda.is_available(): # Compile model for faster inference (PyTorch 2.0+) if hasattr(torch, 'compile'): try: self.model = torch.compile(self.model, mode='max-autotune') logger.info("🔥 Model compiled with torch.compile") except Exception as e: logger.warning(f"⚠️ torch.compile failed: {e}") # Enable memory format optimization if hasattr(self.model, 'to'): try: self.model = self.model.to(memory_format=torch.channels_last) logger.info("🧠 Memory format optimized") except: pass def _warmup_model(self): """Warmup model for consistent performance""" logger.info("🔥 Warming up model...") warmup_text = "नमस्ते" with torch.no_grad(): inputs = self.tokenizer(warmup_text, return_tensors="pt").to(self.device) if self.use_half_precision: inputs = {k: v.half() if v.dtype == torch.float32 else v for k, v in inputs.items()} # Run inference _ = self.model(**inputs) # Clear cache after warmup if self.device.type == 'cuda': torch.cuda.empty_cache() logger.info("✅ Model warmup completed") def _get_cache_key(self, text: str) -> str: """Generate cache key for text""" return f"tts_{hash(text.strip().lower())}" def _manage_cache(self, key: str, audio_data: np.ndarray): """Manage audio cache with LRU-like behavior""" if len(self.audio_cache) >= self.cache_size: # Remove oldest entry try: oldest_key = self.cache_queue.get_nowait() if oldest_key in self.audio_cache: del self.audio_cache[oldest_key] except queue.Empty: pass self.audio_cache[key] = audio_data try: self.cache_queue.put_nowait(key) except queue.Full: pass def synthesize(self, text: str, use_cache: bool = True, normalize_audio: bool = True) -> np.ndarray: """ Synthesize speech from Hindi text with maximum performance Args: text: Hindi text to synthesize use_cache: Use audio cache for repeated texts normalize_audio: Normalize output audio Returns: Audio waveform as numpy array """ if not text or not text.strip(): raise ValueError("Text cannot be empty") text = text.strip() cache_key = self._get_cache_key(text) if use_cache else None # Check cache first if use_cache and cache_key in self.audio_cache: logger.info("🎯 Cache hit - returning cached audio") return self.audio_cache[cache_key] start_time = time.time() # Tokenize with optimizations with torch.no_grad(): inputs = self.tokenizer( text, return_tensors="pt", padding=True, truncation=True, max_length=512 # Reasonable limit for TTS ).to(self.device) # Apply half precision if enabled if self.use_half_precision: inputs = {k: v.half() if v.dtype == torch.float32 else v for k, v in inputs.items()} # Generate speech with optimizations with torch.cuda.amp.autocast(enabled=self.use_half_precision): output = self.model(**inputs) # Extract waveform if hasattr(output, 'waveform'): waveform = output.waveform elif hasattr(output, 'audio'): waveform = output.audio else: # Fallback - check all tensor attributes for attr_name in dir(output): attr_value = getattr(output, attr_name) if isinstance(attr_value, torch.Tensor) and attr_value.ndim >= 2: waveform = attr_value break else: raise RuntimeError("Could not find audio output in model response") # Process output audio_np = waveform.squeeze().cpu().float().numpy() # Normalize audio if requested if normalize_audio: audio_np = self._normalize_audio(audio_np) # Cache the result if use_cache and cache_key: self._manage_cache(cache_key, audio_np) inference_time = time.time() - start_time audio_duration = len(audio_np) / 16000 # Assuming 16kHz rtf = inference_time / audio_duration # Real-time factor logger.info(f"⚡ Synthesis: {inference_time:.3f}s | Duration: {audio_duration:.2f}s | RTF: {rtf:.3f}") return audio_np def _normalize_audio(self, audio: np.ndarray) -> np.ndarray: """Normalize audio to prevent clipping and ensure consistent volume""" # Remove DC offset audio = audio - np.mean(audio) # Normalize to [-0.9, 0.9] to prevent clipping max_val = np.max(np.abs(audio)) if max_val > 0: audio = audio * (0.9 / max_val) return audio def save_audio(self, audio: np.ndarray, filepath: Union[str, Path], sample_rate: int = 16000, format: str = 'wav') -> None: """Save audio with optimized I/O""" filepath = Path(filepath) filepath.parent.mkdir(parents=True, exist_ok=True) # Ensure audio is in correct format if audio.dtype != np.float32: audio = audio.astype(np.float32) # Save with optimal settings sf.write( str(filepath), audio, sample_rate, format=format.upper(), subtype='PCM_16' if format.lower() == 'wav' else None ) file_size = filepath.stat().st_size / 1024 # KB logger.info(f"💾 Saved: {filepath} ({file_size:.1f}KB)") def synthesize_batch(self, texts: List[str], max_workers: int = 4) -> List[np.ndarray]: """ Synthesize multiple texts in parallel for maximum throughput Args: texts: List of Hindi texts to synthesize max_workers: Maximum number of parallel workers Returns: List of audio waveforms """ if not texts: return [] start_time = time.time() with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor.submit(self.synthesize, text) for text in texts] results = [future.result() for future in futures] total_time = time.time() - start_time logger.info(f"🚀 Batch synthesis: {len(texts)} texts in {total_time:.2f}s") return results def clear_cache(self): """Clear audio cache to free memory""" self.audio_cache.clear() while not self.cache_queue.empty(): try: self.cache_queue.get_nowait() except queue.Empty: break logger.info("🧹 Cache cleared") def get_stats(self) -> dict: """Get performance statistics""" return { 'device': str(self.device), 'half_precision': self.use_half_precision, 'cache_size': len(self.audio_cache), 'max_cache_size': self.cache_size, 'model_name': self.model_name, 'cuda_available': torch.cuda.is_available(), 'cuda_memory_allocated': torch.cuda.memory_allocated() if torch.cuda.is_available() else 0, 'cuda_memory_reserved': torch.cuda.memory_reserved() if torch.cuda.is_available() else 0 } def main(): """Professional usage example""" # Initialize with maximum performance settings tts = ProfessionalHindiTTS( enable_cuda_optimization=True, use_half_precision=True, cache_size=50 ) # Test texts test_texts = [ "भारत एक महान देश है जिसकी संस्कृति और परंपराएं बहुत समृद्ध हैं।", "नमस्ते, आप कैसे हैं? मुझे हिंदी बोलना बहुत अच्छा लगता है।", "तकनीक के क्षेत्र में भारत तेजी से आगे बढ़ रहा है।" ] # Single synthesis logger.info("🎯 Single synthesis test...") audio = tts.synthesize(test_texts[0]) tts.save_audio(audio, "output/hindi_speech_optimized.wav") # Batch synthesis for maximum throughput logger.info("🚀 Batch synthesis test...") batch_results = tts.synthesize_batch(test_texts, max_workers=2) # Save batch results for i, audio in enumerate(batch_results): tts.save_audio(audio, f"output/batch_speech_{i+1}.wav") # Performance stats stats = tts.get_stats() logger.info("📊 Performance Statistics:") for key, value in stats.items(): logger.info(f" {key}: {value}") logger.info("✅ Professional Hindi TTS demonstration completed!") if __name__ == "__main__": main()