add app v1

app.py

@@ -0,0 +1,124 @@
+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)
+
+    while True:
+        task = input_queue.get()
+        inp_audio_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)
+            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 generate_speech(inp_audio, inp_text, infer_timestep, p_w, t_w, processes, input_queue, output_queue):
+    if not inp_audio or not inp_text:
+        gr.Warning("Please provide both reference audio and text to generate.")
+        return None
+    
+    print("Generating speech with:", inp_audio, inp_text, infer_timestep, p_w, t_w)
+    input_queue.put((inp_audio, inp_text, infer_timestep, p_w, t_w))
+    res = output_queue.get()
+    if res is not None:
+        return res
+    else:
+        gr.Warning("Speech generation failed. Please try again.")
+        return None
+
+
+if __name__ == '__main__':
+    mp.set_start_method('spawn', force=True)
+    mp_manager = mp.Manager()
+
+    devices = os.environ.get('CUDA_VISIBLE_DEVICES', '')
+    if devices != '':
+        devices = os.environ.get('CUDA_VISIBLE_DEVICES', '').split(",")
+    else:
+        devices = None
+    
+    num_workers = 1
+    input_queue = mp_manager.Queue()
+    output_queue = mp_manager.Queue()
+    processes = []
+
+    print("Starting 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)
+
+    with gr.Blocks(title="MegaTTS3 Voice Cloning") as demo:
+        gr.Markdown("# MegaTTS3 Voice Cloning")
+        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=partial(generate_speech, processes=processes, input_queue=input_queue, output_queue=output_queue),
+            inputs=[reference_audio, text_input, infer_timestep, p_w, t_w],
+            outputs=[output_audio]
+        )
+
+    demo.launch(server_name='0.0.0.0', server_port=7860, debug=True)
+    
+    for p in processes:
+        p.join()