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| import spaces | |
| import torch | |
| import os | |
| os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" | |
| import gradio as gr | |
| import traceback | |
| from huggingface_hub import snapshot_download | |
| from tts.infer_cli import MegaTTS3DiTInfer, convert_to_wav, cut_wav | |
| def download_weights(): | |
| """Download model weights from HuggingFace if not already present.""" | |
| repo_id = "mrfakename/MegaTTS3-VoiceCloning" | |
| weights_dir = "checkpoints" | |
| if not os.path.exists(weights_dir): | |
| print("Downloading model weights from HuggingFace...") | |
| snapshot_download( | |
| repo_id=repo_id, | |
| local_dir=weights_dir, | |
| local_dir_use_symlinks=False | |
| ) | |
| print("Model weights downloaded successfully!") | |
| else: | |
| print("Model weights already exist.") | |
| return weights_dir | |
| # Download weights and initialize model | |
| download_weights() | |
| print("Initializing MegaTTS3 model...") | |
| infer_pipe = MegaTTS3DiTInfer() | |
| print("Model loaded successfully!") | |
| def generate_speech(inp_audio, inp_text, infer_timestep, p_w, t_w): | |
| if not inp_audio or not inp_text: | |
| gr.Warning("Please provide both reference audio and text to generate.") | |
| return None | |
| try: | |
| print(f"Generating speech with: {inp_text}...") | |
| # Convert and prepare audio | |
| convert_to_wav(inp_audio) | |
| wav_path = os.path.splitext(inp_audio)[0] + '.wav' | |
| cut_wav(wav_path, max_len=28) | |
| # Read audio file | |
| with open(wav_path, 'rb') as file: | |
| file_content = file.read() | |
| # Generate speech | |
| resource_context = infer_pipe.preprocess(file_content) | |
| wav_bytes = infer_pipe.forward(resource_context, inp_text, time_step=infer_timestep, p_w=p_w, t_w=t_w) | |
| return wav_bytes | |
| except Exception as e: | |
| traceback.print_exc() | |
| gr.Warning(f"Speech generation failed: {str(e)}") | |
| return None | |
| with gr.Blocks(title="MegaTTS3 Voice Cloning") as demo: | |
| gr.Markdown("# MegaTTS 3 Voice Cloning") | |
| gr.Markdown("MegaTTS 3 is a text-to-speech model trained by ByteDance with exceptional voice cloning capabilities. The original authors did not release the WavVAE encoder, so voice cloning was not publicly available; however, thanks to [@ACoderPassBy](https://modelscope.cn/models/ACoderPassBy/MegaTTS-SFT)'s WavVAE encoder, we can now clone voices with MegaTTS 3!") | |
| gr.Markdown("This is by no means the best voice cloning solution, but it works pretty well for some specific use-cases. Try out multiple and see which one works best for you.") | |
| gr.Markdown("**Please use this Space responsibly and do not abuse it!**") | |
| gr.Markdown("h/t to MysteryShack on Discord for the info about the unofficial WavVAE encoder!") | |
| gr.Markdown("Upload a reference audio clip and enter text to generate speech with the cloned voice.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| reference_audio = gr.Audio( | |
| label="Reference Audio", | |
| type="filepath", | |
| sources=["upload", "microphone"] | |
| ) | |
| text_input = gr.Textbox( | |
| label="Text to Generate", | |
| placeholder="Enter the text you want to synthesize...", | |
| lines=3 | |
| ) | |
| with gr.Accordion("Advanced Options", open=False): | |
| infer_timestep = gr.Number( | |
| label="Inference Timesteps", | |
| value=32, | |
| minimum=1, | |
| maximum=100, | |
| step=1 | |
| ) | |
| p_w = gr.Number( | |
| label="Intelligibility Weight", | |
| value=1.4, | |
| minimum=0.1, | |
| maximum=5.0, | |
| step=0.1 | |
| ) | |
| t_w = gr.Number( | |
| label="Similarity Weight", | |
| value=3.0, | |
| minimum=0.1, | |
| maximum=10.0, | |
| step=0.1 | |
| ) | |
| generate_btn = gr.Button("Generate Speech", variant="primary") | |
| with gr.Column(): | |
| output_audio = gr.Audio(label="Generated Audio") | |
| generate_btn.click( | |
| fn=generate_speech, | |
| inputs=[reference_audio, text_input, infer_timestep, p_w, t_w], | |
| outputs=[output_audio] | |
| ) | |
| if __name__ == '__main__': | |
| demo.launch(server_name='0.0.0.0', server_port=7860, debug=True) |