add dia tts model

utils/tts_dia.py

@@ -0,0 +1,103 @@
+import os
+import time
+import logging
+import torch
+import numpy as np
+import soundfile as sf
+from pathlib import Path
+from typing import Optional
+
+from dia.model import Dia
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Constants
+DEFAULT_SAMPLE_RATE = 44100
+DEFAULT_MODEL_NAME = "nari-labs/Dia-1.6B"
+
+# Global model instance (lazy loaded)
+_model = None
+
+
+def _get_model() -> Dia:
+    """Lazy-load the Dia model to avoid loading it until needed"""
+    global _model
+    if _model is None:
+        logger.info("Loading Dia model...")
+        try:
+            _model = Dia.from_pretrained(DEFAULT_MODEL_NAME, compute_dtype="float16")
+            logger.info("Dia model loaded successfully")
+        except Exception as e:
+            logger.error(f"Error loading Dia model: {e}", exc_info=True)
+            raise
+    return _model
+
+
+def generate_speech(text: str, language: str = "zh") -> str:
+    """Public interface for TTS generation using Dia model
+    
+    Args:
+        text (str): Input text to synthesize
+        language (str): Language code (not used in Dia model, kept for API compatibility)
+        
+    Returns:
+        str: Path to the generated audio file
+    """
+    logger.info(f"Generating speech for text length: {len(text)}")
+    
+    try:
+        # Create output directory if it doesn't exist
+        os.makedirs("temp/outputs", exist_ok=True)
+        
+        # Generate unique output path
+        output_path = f"temp/outputs/output_{int(time.time())}.wav"
+        
+        # Get the model
+        model = _get_model()
+        
+        # Generate audio
+        start_time = time.time()
+        
+        with torch.inference_mode():
+            output_audio_np = model.generate(
+                text,
+                max_tokens=None,  # Use default from model config
+                cfg_scale=3.0,
+                temperature=1.3,
+                top_p=0.95,
+                cfg_filter_top_k=35,
+                use_torch_compile=False,  # Keep False for stability
+                verbose=False
+            )
+        
+        end_time = time.time()
+        logger.info(f"Generation finished in {end_time - start_time:.2f} seconds")
+        
+        # Process the output
+        if output_audio_np is not None:
+            # Apply a slight slowdown for better quality (0.94x speed)
+            speed_factor = 0.94
+            original_len = len(output_audio_np)
+            target_len = int(original_len / speed_factor)
+            
+            if target_len != original_len and target_len > 0:
+                x_original = np.arange(original_len)
+                x_resampled = np.linspace(0, original_len - 1, target_len)
+                output_audio_np = np.interp(x_resampled, x_original, output_audio_np)
+                logger.info(f"Resampled audio from {original_len} to {target_len} samples for {speed_factor:.2f}x speed")
+            
+            # Save the audio file
+            sf.write(output_path, output_audio_np, DEFAULT_SAMPLE_RATE)
+            logger.info(f"Audio saved to {output_path}")
+            
+            return output_path
+        else:
+            logger.warning("Generation produced no output, returning dummy audio")
+            return "temp/outputs/dummy.wav"
+            
+    except Exception as e:
+        logger.error(f"TTS generation failed: {str(e)}", exc_info=True)
+        # Return dummy path in case of error
+        return "temp/outputs/dummy.wav"