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| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| import soundfile as sf | |
| import numpy as np | |
| from datasets import load_dataset | |
| # Load the TTS pipeline | |
| # Use the appropriate device (CPU or GPU) | |
| # You might need to install torch with CUDA support for GPU acceleration | |
| device = 0 if torch.cuda.is_available() else -1 | |
| pipe = pipeline("text-to-speech", model="microsoft/speecht5_tts", device=device) | |
| # Load the speaker embedding dataset | |
| try: | |
| embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| except Exception as e: | |
| print(f"Error loading embeddings dataset: {e}") | |
| embeddings_dataset = None | |
| # Function to get a speaker embedding (using a default for now) | |
| def get_speaker_embedding(): | |
| # Using a default speaker embedding from the dataset | |
| # You can explore the dataset to find other speaker embeddings | |
| if embeddings_dataset: | |
| # Using a speaker that sounds reasonably clear (e.g., 'p335' which is index 730) from cmu-arctic-xvectors | |
| speaker_embedding = torch.tensor(embeddings_dataset[730]["xvector"]).unsqueeze(0) | |
| return speaker_embedding | |
| else: | |
| # Fallback if dataset loading failed - this might not work well | |
| print("Warning: Using a placeholder speaker embedding. TTS quality may be poor.") | |
| # A random tensor as a placeholder - will likely result in poor quality speech | |
| return torch.randn(1, 512) | |
| def text_to_speech(text): | |
| if not text: | |
| return (None, None) | |
| speaker_embedding = get_speaker_embedding() | |
| if speaker_embedding is not None: | |
| try: | |
| # Generate speech | |
| # The pipeline returns a dictionary with 'audio' and 'sampling_rate' | |
| output = pipe(text, forward_params={"speaker_embeddings": speaker_embedding}) | |
| sampling_rate = output['sampling_rate'] | |
| audio_data = output['audio'] | |
| # Gradio's Audio component expects a tuple (sampling_rate, audio_data) | |
| return (sampling_rate, audio_data) | |
| except Exception as e: | |
| print(f"Error during speech generation: {e}") | |
| return (None, None) | |
| else: | |
| # Return empty output if speaker embedding could not be loaded | |
| return (None, None) | |
| # Create the Gradio interface | |
| interface = gr.Interface( | |
| fn=text_to_speech, | |
| inputs=gr.Textbox(label="Enter text"), | |
| outputs=gr.Audio(label="Generated Speech", autoplay=True), | |
| title="Text-to-Speech with SpeechT5", | |
| description="Enter text to convert it to speech using the microsoft/speecht5_tts model. A default speaker is used." | |
| ) | |
| # Launch the interface | |
| if __name__ == "__main__": | |
| # Set share=True to create a shareable link (useful for demos, but be mindful of security/privacy) | |
| # Set debug=True for more detailed logs | |
| interface.launch(debug=True) | |