update head of readme

app.py

@@ -1,94 +0,0 @@
-# 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
-
-
-def model_worker(input_queue, output_queue, device_id):
-    device = None
-    if device_id is not None:
-        device = torch.device(f'cuda:{device_id}')
-    infer_pipe = MegaTTS3DiTInfer(device=device)
-    os.system(f'pkill -f "voidgpu{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=28)
-            with open(wav_path, 'rb') as file:
-                file_content = file.read()
-            resource_context = infer_pipe.preprocess(file_content, latent_file=inp_npy_path)
-            wav_bytes = infer_pipe.forward(resource_context, 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)
-    devices = os.environ.get('CUDA_VISIBLE_DEVICES', '')
-    if devices != '':
-        devices = os.environ.get('CUDA_VISIBLE_DEVICES', '').split(",")
-        for d in devices:
-            os.system(f'pkill -f "voidgpu{d}"')
-    else:
-        devices = None
-    
-    num_workers = 1
-    input_queue = mp.Queue()
-    output_queue = mp.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.", concurrency_limit=1)
-    api_interface.launch(server_name='0.0.0.0', server_port=7929, debug=True)
-    for p in processes:
-        p.join()

readme.md

@@ -1,3 +1,15 @@
+---
+title: {{title}}
+emoji: {{emoji}}
+colorFrom: {{colorFrom}}
+colorTo: {{colorTo}}
+sdk: {{sdk}}
+sdk_version: "{{sdkVersion}}"
+app_file: ./tts/gradio_api.py
+pinned: false
+---
+
+
 <div align="center">
     <h1>
     MegaTTS 3 <img src="./assets/fig/Hi.gif" width="40px">