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import gradio as gr | |
from inference import Inference | |
import os | |
def infer(model, f0_method, audio_file): | |
print("****", audio_file) | |
inference = Inference( | |
model_name=model, | |
f0_method=f0_method, | |
source_audio_path=audio_file, | |
output_file_name=os.path.join("./audio-outputs", os.path.basename(audio_file)) | |
) | |
output = inference.run() | |
if 'success' in output and output['success']: | |
return output, output['file'] | |
else: | |
return | |
with gr.Blocks() as app: | |
gr.HTML("<h1> Simple RVC Inference - by Juuxn 💻 </h1>") | |
model_url = gr.Textbox(placeholder="https://huggingface.co/AIVER-SE/BillieEilish/resolve/main/BillieEilish.zip", label="Url del modelo", show_label=True) | |
audio_path = gr.Audio(label="Archivo de audio", show_label=True, type="filepath", ) | |
f0_method = gr.Dropdown(choices=["harvest", "pm", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"], | |
value="harvest", | |
label="Algoritmo", show_label=True) | |
# Salida | |
with gr.Row(): | |
vc_output1 = gr.Textbox(label="Salida") | |
vc_output2 = gr.Audio(label="Audio de salida") | |
btn = gr.Button(value="Convertir") | |
btn.click(infer, inputs=[model_url, f0_method, audio_path], outputs=[vc_output1, vc_output2]) | |
app.queue(concurrency_count=511, max_size=1022).launch(share=True) |