app.py

import os
import shutil
from os import listdir
import gradio as gr

with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown("# RVC V2 - EASY GUI")
    with gr.Row():
        with gr.Tab("Inference"):
            with gr.Row():
                model_name = gr.Textbox(label="Model Name For Inference")
            with gr.Row():
                input_path = gr.Audio(label="Input Audio Path", type="filepath")
            with gr.Row():
                with gr.Accordion("Inference Settings"):
                    pitch = gr.Slider(minimum=-12, maximum=12, step=1, label="Pitch", value=0)
                    f0_method = gr.Dropdown(choices=["rmvpe", "pm", "harvest"], label="f0 Method", value="rmvpe")
                    index_rate = gr.Slider(minimum=0, maximum=1, step=0.01, label="Index Rate", value=0.5)
                    volume_normalization = gr.Slider(minimum=0, maximum=1, step=0.01, label="Volume Normalization", value=0)
                    consonant_protection = gr.Slider(minimum=0, maximum=1, step=0.01, label="Consonant Protection", value=0.5)
            with gr.Row():
                save_as = gr.Textbox(value="/content/RVC/audios/output_audio.wav", label="Output Audio Path")
                
            run_btn = gr.Button("Run Inference")
            with gr.Row():
                output_message = gr.Textbox(label="Output Message",interactive=False)
                output_audio = gr.Audio(label="Output Audio",interactive=False)
            #run_btn.click(run_inference, [model_name, pitch, input_path, f0_method, save_as, index_rate, volume_normalization, consonant_protection], output_message)

        with gr.Tab("Training"):
            with gr.TabItem("Create Index and stuff"):
                model_name = gr.Textbox(label="Model Name (No spaces or symbols)")
                dataset_folder = gr.Textbox(label="Dataset Folder", value="/content/dataset")
                f0method = gr.Dropdown(["pm", "harvest", "rmvpe", "rmvpe_gpu"], label="F0 Method", value="rmvpe_gpu")
                preprocess_btn = gr.Button("Start Preprocessing")
                f0_btn = gr.Button("Extract F0 Feature")
                train_btn = gr.Button("Train Index")
                preprocess_output = gr.Textbox(label="Preprocessing Log")
                f0_output = gr.Textbox(label="F0 Feature Extraction Log")
                train_output = gr.Textbox(label="Training Log")

                #preprocess_btn.click(preprocess_data, inputs=[model_name, dataset_folder], outputs=preprocess_output)
                #f0_btn.click(extract_f0_feature, inputs=[model_name, f0method], outputs=f0_output)
                #train_btn.click(train_index, inputs=[model_name, "v2"], outputs=train_output)
    
demo.launch()