minimal_tts_client.py

import torch
import tempfile
import logging
import soundfile as sf
import numpy as np
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
import asyncio
from typing import Optional

logger = logging.getLogger(__name__)

class MinimalTTSClient:
    """
    Minimal TTS client with basic functionality
    Uses only core transformers without complex dependencies
    """
    
    def __init__(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model_loaded = False
        
        logger.info(f"Minimal TTS Client initialized on device: {self.device}")
        
    async def load_model(self):
        """Load a simple TTS model or create mock audio"""
        try:
            logger.info("Setting up minimal TTS...")
            
            # For now, we'll create a mock TTS that generates simple audio
            # This avoids all the complex model loading issues
            self.model_loaded = True
            logger.info("✅ Minimal TTS ready")
            return True
            
        except Exception as e:
            logger.error(f"❌ Failed to load TTS: {e}")
            return False
    
    async def text_to_speech(self, text: str, voice_id: Optional[str] = None) -> str:
        """
        Convert text to speech - for now creates a simple audio file
        """
        if not self.model_loaded:
            logger.info("TTS not loaded, loading now...")
            success = await self.load_model()
            if not success:
                raise Exception("Failed to load TTS")
        
        try:
            logger.info(f"Generating minimal audio for text: {text[:50]}...")
            
            # Create a simple tone/beep as placeholder audio
            # This ensures the system works while we debug TTS issues
            duration = min(len(text) * 0.1, 10.0)  # Max 10 seconds
            sample_rate = 16000
            t = np.linspace(0, duration, int(sample_rate * duration), False)
            
            # Create a simple tone that varies based on text length
            frequency = 440 + (len(text) % 100) * 2  # Vary frequency slightly
            audio_data = 0.1 * np.sin(2 * np.pi * frequency * t)
            
            # Add some variation to make it less monotonous  
            audio_data = audio_data * (1 + 0.3 * np.sin(2 * np.pi * 2 * t))
            
            # Save to temporary file
            temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.wav')
            sf.write(temp_file.name, audio_data, samplerate=sample_rate)
            temp_file.close()
            
            logger.info(f"✅ Generated placeholder audio: {temp_file.name}")
            logger.warning("📢 Using placeholder audio - TTS will be improved in next update")
            return temp_file.name
            
        except Exception as e:
            logger.error(f"❌ Error generating audio: {e}")
            raise Exception(f"Audio generation failed: {e}")