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Running
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Zero
| # Copyright 2025 ByteDance and/or its affiliates. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import multiprocessing as mp | |
| import torch | |
| import os | |
| from functools import partial | |
| import gradio as gr | |
| import traceback | |
| from tts.infer_cli import MegaTTS3DiTInfer, convert_to_wav, cut_wav | |
| import spaces | |
| os.system('huggingface-cli download ByteDance/MegaTTS3 --local-dir ./checkpoints --repo-type model') | |
| CUDA_AVAILABLE = torch.cuda.is_available() | |
| infer_pipe = MegaTTS3DiTInfer(device='cuda' if CUDA_AVAILABLE else 'cpu') | |
| def forward_gpu(file_content, latent_file, inp_text, time_step, p_w, t_w): | |
| resource_context = infer_pipe.preprocess(file_content, latent_file) | |
| wav_bytes = infer_pipe.forward(resource_context, inp_text, time_step=time_step, p_w=p_w, t_w=t_w) | |
| return wav_bytes | |
| def model_worker(input_queue, output_queue, device_id): | |
| while True: | |
| task = input_queue.get() | |
| inp_audio_path, inp_npy_path, inp_text, infer_timestep, p_w, t_w = task | |
| try: | |
| convert_to_wav(inp_audio_path) | |
| wav_path = os.path.splitext(inp_audio_path)[0] + '.wav' | |
| cut_wav(wav_path, max_len=24) | |
| with open(wav_path, 'rb') as file: | |
| file_content = file.read() | |
| wav_bytes = forward_gpu(file_content, inp_npy_path, inp_text, time_step=infer_timestep, p_w=p_w, t_w=t_w) | |
| output_queue.put(wav_bytes) | |
| except Exception as e: | |
| traceback.print_exc() | |
| print(task, str(e)) | |
| output_queue.put(None) | |
| def main(inp_audio, inp_npy, inp_text, infer_timestep, p_w, t_w, processes, input_queue, output_queue): | |
| print("Push task to the inp queue |", inp_audio, inp_npy, inp_text, infer_timestep, p_w, t_w) | |
| input_queue.put((inp_audio, inp_npy, inp_text, infer_timestep, p_w, t_w)) | |
| res = output_queue.get() | |
| if res is not None: | |
| return res | |
| else: | |
| print("") | |
| return None | |
| if __name__ == '__main__': | |
| mp.set_start_method('spawn', force=True) | |
| mp_manager = mp.Manager() | |
| num_workers = 1 | |
| devices = [0] | |
| input_queue = mp_manager.Queue() | |
| output_queue = mp_manager.Queue() | |
| processes = [] | |
| print("Start open workers") | |
| for i in range(num_workers): | |
| p = mp.Process(target=model_worker, args=(input_queue, output_queue, i % len(devices) if devices is not None else None)) | |
| p.start() | |
| processes.append(p) | |
| api_interface = gr.Interface(fn= | |
| partial(main, processes=processes, input_queue=input_queue, | |
| output_queue=output_queue), | |
| inputs=[gr.Audio(type="filepath", label="Upload .wav"), gr.File(type="filepath", label="Upload .npy"), "text", | |
| gr.Number(label="infer timestep", value=32), | |
| gr.Number(label="Intelligibility Weight", value=1.4), | |
| gr.Number(label="Similarity Weight", value=3.0)], outputs=[gr.Audio(label="Synthesized Audio")], | |
| title="MegaTTS3", | |
| description="Upload a speech clip as a reference for timbre, " + | |
| "upload the pre-extracted latent file, "+ | |
| "input the target text, and receive the cloned voice. "+ | |
| "Tip: a generation process should be within 120s (check if your input text are too long). Please use the system gently, as excessive load or languages other than English or Chinese may cause crashes and disrupt access for other users.", concurrency_limit=1) | |
| api_interface.launch(server_name='0.0.0.0', server_port=7860, debug=True) | |
| for p in processes: | |
| p.join() | |