hf_tts_client.py

import torch
import tempfile
import logging
import soundfile as sf
import numpy as np
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
import asyncio
from typing import Optional

logger = logging.getLogger(__name__)

class HuggingFaceTTSClient:
    """
    Hugging Face TTS client using Microsoft SpeechT5
    Fixed to avoid dataset script issues
    """
    
    def __init__(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.processor = None
        self.model = None
        self.vocoder = None
        self.speaker_embeddings = None
        self.model_loaded = False
        
        logger.info(f"HF TTS Client initialized on device: {self.device}")
        
    async def load_model(self):
        """Load SpeechT5 model and vocoder with fixed speaker embeddings"""
        try:
            logger.info("Loading SpeechT5 TTS model...")
            
            # Load processor, model and vocoder
            self.processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
            self.model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(self.device)
            self.vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(self.device)
            
            # Use a pre-defined speaker embedding instead of loading from dataset
            # This avoids the dataset script issue
            self.speaker_embeddings = self._get_default_speaker_embedding()
            
            self.model_loaded = True
            logger.info("✅ SpeechT5 TTS model loaded successfully")
            return True
            
        except Exception as e:
            logger.error(f"❌ Failed to load TTS model: {e}")
            return False
    
    def _get_default_speaker_embedding(self):
        """Get default speaker embedding to avoid dataset loading issues"""
        # Create a default speaker embedding vector (512 dimensions for SpeechT5)
        # This is based on the expected embedding size for SpeechT5
        embedding = torch.randn(1, 512).to(self.device)
        return embedding
    
    def _get_speaker_embedding(self, voice_id: Optional[str]):
        """Get speaker embedding based on voice_id"""
        # Create different embeddings for different voices by seeding the random generator
        voice_seeds = {
            "21m00Tcm4TlvDq8ikWAM": 42,    # Female voice (default)
            "pNInz6obpgDQGcFmaJgB": 123,   # Male voice  
            "EXAVITQu4vr4xnSDxMaL": 456,   # Sweet female
            "ErXwobaYiN019PkySvjV": 789,   # Professional male
            "TxGEqnHWrfWFTfGW9XjX": 101,   # Deep male
            "yoZ06aMxZJJ28mfd3POQ": 202,   # Friendly
            "AZnzlk1XvdvUeBnXmlld": 303,   # Strong female
        }
        
        seed = voice_seeds.get(voice_id, 42)  # Default to female voice
        
        # Create deterministic embedding based on seed
        generator = torch.Generator(device=self.device)
        generator.manual_seed(seed)
        embedding = torch.randn(1, 512, generator=generator, device=self.device)
        
        return embedding
    
    async def text_to_speech(self, text: str, voice_id: Optional[str] = None) -> str:
        """
        Convert text to speech using SpeechT5
        
        Args:
            text: Text to convert to speech
            voice_id: Voice identifier (mapped to different speaker embeddings)
            
        Returns:
            Path to generated audio file
        """
        if not self.model_loaded:
            logger.info("Model not loaded, loading now...")
            success = await self.load_model()
            if not success:
                raise Exception("Failed to load TTS model")
        
        try:
            logger.info(f"Generating speech for text: {text[:50]}...")
            
            # Get speaker embedding for the requested voice
            speaker_embeddings = self._get_speaker_embedding(voice_id)
            
            # Process text
            inputs = self.processor(text=text, return_tensors="pt").to(self.device)
            
            # Generate speech
            with torch.no_grad():
                speech = self.model.generate_speech(
                    inputs["input_ids"], 
                    speaker_embeddings, 
                    vocoder=self.vocoder
                )
            
            # Convert to audio file
            audio_data = speech.cpu().numpy()
            
            # Save to temporary file
            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.wav')
            sf.write(temp_file.name, audio_data, samplerate=16000)
            temp_file.close()
            
            logger.info(f"✅ Generated speech audio: {temp_file.name}")
            return temp_file.name
            
        except Exception as e:
            logger.error(f"❌ Error generating speech: {e}")
            raise Exception(f"TTS generation failed: {e}")