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import os
import torch
import torchaudio
import gradio as gr
import look2hear.models
# Setup environment and model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = look2hear.models.TIGERDNR.from_pretrained("JusperLee/TIGER-DnR", cache_dir="cache")
model.to(device)
model.eval()
# Processing function
def separate_audio(audio_file):
audio, sr = torchaudio.load(audio_file)
audio = audio.to(device)
with torch.no_grad():
all_target_dialog, all_target_effect, all_target_music = model(audio[None])
# Save outputs
dialog_path = "dialog_output.wav"
effect_path = "effect_output.wav"
music_path = "music_output.wav"
torchaudio.save(dialog_path, all_target_dialog.cpu(), sr)
torchaudio.save(effect_path, all_target_effect.cpu(), sr)
torchaudio.save(music_path, all_target_music.cpu(), sr)
return dialog_path, effect_path, music_path
# Gradio UI
demo = gr.Interface(
fn=separate_audio,
inputs=gr.Audio(type="filepath", label="Upload Audio File"),
outputs=[
gr.Audio(label="Dialog", type="filepath"),
gr.Audio(label="Effects", type="filepath"),
gr.Audio(label="Music", type="filepath")
],
title="TIGER-DnR Audio Separator",
description="Upload a mixed audio file to separate it into dialog, effects, and music using the TIGER-DnR model."
)
if __name__ == "__main__":
demo.launch()
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