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