app.py

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

def run_inference(model_name, pitch, input_path, f0_method, save_as, index_rate, volume_normalization, consonant_protection):
    # Setting paths for model and index files
    model_filename = model_name + '.pth'
    index_temp = 'Index_Temp'
    
    # Ensure Index_Temp exists
    if not os.path.exists(index_temp):
        os.mkdir(index_temp)
        print("Index_Temp folder created.")
    else:
        print("Index_Temp folder found.")
    
    # Copy .index file to Index_Temp
    index_file_path = os.path.join('logs/', model_name, '')
    for file_name in listdir(index_file_path):
        if file_name.startswith('added') and file_name.endswith('.index'):
            shutil.copy(index_file_path + file_name, os.path.join(index_temp, file_name))
            print('Index file copied successfully.')
    
    # Get the .index file
    indexfile_directory = os.getcwd() + '/' + index_temp
    files = os.listdir(indexfile_directory)
    index_filename = files[0] if files else None
    if index_filename is None:
        raise ValueError("Index file not found.")
    
    shutil.rmtree(index_temp)
    
    model_path = "assets/weights/" + model_filename
    index_path = os.path.join('logs', model_name, index_filename)
    
    if not os.path.exists(input_path):
        raise ValueError(f"{input_path} was not found.")
    
    os.environ['index_root'] = os.path.dirname(index_path)
    index_path = os.path.basename(index_path)
    
    os.environ['weight_root'] = os.path.dirname(model_path)
    
    # Run the command
    cmd = f"python tools/cmd/infer_cli.py --f0up_key {pitch} --input_path {input_path} --index_path {index_path} --f0method {f0_method} --opt_path {save_as} --model_name {os.path.basename(model_path)} --index_rate {index_rate} --device 'cuda:0' --is_half True --filter_radius 3 --resample_sr 0 --rms_mix_rate {volume_normalization} --protect {consonant_protection}"
    os.system(f"rm -f {save_as}")
    os.system(cmd)
    
    return f"Inference completed, output saved at {save_as}.", save_as

# Gradio Interface

with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown("# RVC V2 - EASY GUI")
    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")
        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)
        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")
        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)

demo.launch()