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Michael Hu
commited on
Commit
·
2d176f4
1
Parent(s):
501f0b5
fix runtime dia model issue
Browse files- utils/tts_dia.py +56 -33
- utils/tts_dummy.py +23 -1
utils/tts_dia.py
CHANGED
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@@ -44,7 +44,11 @@ def _get_model() -> Dia:
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# Log model details
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logger.info(f"Dia model loaded successfully")
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logger.info(f"Model type: {type(_model).__name__}")
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-
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except ImportError as import_err:
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logger.error(f"Import error loading Dia model: {import_err}")
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logger.error(f"This may indicate missing dependencies")
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@@ -74,34 +78,42 @@ def generate_speech(text: str, language: str = "zh") -> str:
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logger.info(f"Generating speech for text length: {len(text)}")
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logger.info(f"Text content (first 50 chars): {text[:50]}...")
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try:
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# Generate audio
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logger.info("Starting audio generation with Dia model")
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@@ -125,11 +137,17 @@ def generate_speech(text: str, language: str = "zh") -> str:
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logger.error(f"Runtime error during generation: {rt_err}")
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if "CUDA out of memory" in str(rt_err):
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logger.error("CUDA out of memory error - consider reducing batch size or model size")
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except Exception as gen_err:
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logger.error(f"Error during audio generation: {gen_err}")
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logger.error(f"Error type: {type(gen_err).__name__}")
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end_time = time.time()
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generation_time = end_time - start_time
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@@ -164,7 +182,10 @@ def generate_speech(text: str, language: str = "zh") -> str:
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except Exception as save_err:
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logger.error(f"Error saving audio file: {save_err}")
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logger.error(f"Error type: {type(save_err).__name__}")
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return output_path
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else:
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@@ -194,5 +215,7 @@ def generate_speech(text: str, language: str = "zh") -> str:
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elif isinstance(e, FileNotFoundError):
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logger.error(f"File not found - check if model files exist and are accessible")
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#
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-
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# Log model details
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logger.info(f"Dia model loaded successfully")
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logger.info(f"Model type: {type(_model).__name__}")
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# Check if model has parameters method (PyTorch models do, but Dia might not)
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if hasattr(_model, 'parameters'):
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logger.info(f"Model device: {next(_model.parameters()).device}")
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else:
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logger.info("Model device: Device information not available for Dia model")
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except ImportError as import_err:
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logger.error(f"Import error loading Dia model: {import_err}")
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logger.error(f"This may indicate missing dependencies")
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logger.info(f"Generating speech for text length: {len(text)}")
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logger.info(f"Text content (first 50 chars): {text[:50]}...")
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# Create output directory if it doesn't exist
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output_dir = "temp/outputs"
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logger.info(f"Ensuring output directory exists: {output_dir}")
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try:
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os.makedirs(output_dir, exist_ok=True)
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logger.info(f"Output directory ready: {output_dir}")
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except PermissionError as perm_err:
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logger.error(f"Permission error creating output directory: {perm_err}")
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# Fall back to dummy TTS
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logger.info("Falling back to dummy TTS due to directory creation error")
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from utils.tts_dummy import generate_speech as dummy_generate_speech
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return dummy_generate_speech(text, language)
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except Exception as dir_err:
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logger.error(f"Error creating output directory: {dir_err}")
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# Fall back to dummy TTS
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logger.info("Falling back to dummy TTS due to directory creation error")
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from utils.tts_dummy import generate_speech as dummy_generate_speech
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return dummy_generate_speech(text, language)
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# Generate unique output path
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timestamp = int(time.time())
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output_path = f"{output_dir}/output_{timestamp}.wav"
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logger.info(f"Output will be saved to: {output_path}")
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# Get the model
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logger.info("Retrieving Dia model instance")
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try:
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model = _get_model()
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logger.info("Successfully retrieved Dia model instance")
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except Exception as model_err:
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logger.error(f"Failed to get Dia model: {model_err}")
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logger.error(f"Error type: {type(model_err).__name__}")
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# Fall back to dummy TTS
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logger.info("Falling back to dummy TTS due to model loading error")
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from utils.tts_dummy import generate_speech as dummy_generate_speech
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return dummy_generate_speech(text, language)
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# Generate audio
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logger.info("Starting audio generation with Dia model")
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logger.error(f"Runtime error during generation: {rt_err}")
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if "CUDA out of memory" in str(rt_err):
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logger.error("CUDA out of memory error - consider reducing batch size or model size")
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# Fall back to dummy TTS
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logger.info("Falling back to dummy TTS due to runtime error during generation")
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from utils.tts_dummy import generate_speech as dummy_generate_speech
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return dummy_generate_speech(text, language)
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except Exception as gen_err:
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logger.error(f"Error during audio generation: {gen_err}")
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logger.error(f"Error type: {type(gen_err).__name__}")
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# Fall back to dummy TTS
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logger.info("Falling back to dummy TTS due to error during generation")
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from utils.tts_dummy import generate_speech as dummy_generate_speech
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return dummy_generate_speech(text, language)
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end_time = time.time()
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generation_time = end_time - start_time
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except Exception as save_err:
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logger.error(f"Error saving audio file: {save_err}")
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logger.error(f"Error type: {type(save_err).__name__}")
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# Fall back to dummy TTS
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logger.info("Falling back to dummy TTS due to error saving audio file")
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from utils.tts_dummy import generate_speech as dummy_generate_speech
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return dummy_generate_speech(text, language)
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return output_path
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else:
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elif isinstance(e, FileNotFoundError):
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logger.error(f"File not found - check if model files exist and are accessible")
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# Fall back to dummy TTS
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logger.info("Falling back to dummy TTS due to unhandled exception")
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from utils.tts_dummy import generate_speech as dummy_generate_speech
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return dummy_generate_speech(text, language)
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utils/tts_dummy.py
CHANGED
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def generate_speech(text: str, language: str = "zh") -> str:
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"""Public interface for TTS generation"""
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def generate_speech(text: str, language: str = "zh") -> str:
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"""Public interface for TTS generation"""
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import os
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import numpy as np
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import soundfile as sf
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import time
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# Create output directory if it doesn't exist
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output_dir = "temp/outputs"
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os.makedirs(output_dir, exist_ok=True)
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# Generate a unique filename
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timestamp = int(time.time())
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output_path = f"{output_dir}/dummy_{timestamp}.wav"
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# Generate a simple sine wave as dummy audio
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sample_rate = 24000
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duration = 2.0 # seconds
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t = np.linspace(0, duration, int(sample_rate * duration), False)
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tone = np.sin(2 * np.pi * 440 * t) * 0.3
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# Save the audio file
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sf.write(output_path, tone, sample_rate)
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return output_path
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