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import argparse
from functools import partial
import gradio as gr
import torch
import torchaudio
from resemble_enhance.enhancer.inference import denoise, enhance
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
def _fn(path, solver, nfe, tau, denoising, unlimited):
if path is None:
gr.Warning("Please upload an audio file.")
return None, None
info = torchaudio.info(path)
solver = solver.lower()
nfe = int(nfe)
lambd = 0.9 if denoising else 0.1
dwav, sr = torchaudio.load(path)
dwav = dwav.mean(dim=0)
wav1, new_sr = denoise(dwav, sr, device)
wav2, new_sr = enhance(dwav, sr, device, nfe=nfe, solver=solver, lambd=lambd, tau=tau)
wav1 = wav1.cpu().numpy()
wav2 = wav2.cpu().numpy()
return (new_sr, wav1), (new_sr, wav2)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--unlimited", action="store_true")
args = parser.parse_args()
inputs: list = [
gr.Audio(type="filepath", label="Input Audio"),
gr.Dropdown(
choices=["Midpoint", "RK4", "Euler"],
value="Midpoint",
label="CFM ODE Solver (Midpoint is recommended)",
),
gr.Slider(
minimum=1,
maximum=128,
value=64,
step=1,
label="CFM Number of Function Evaluations (higher values in general yield better quality but may be slower)",
),
gr.Slider(
minimum=0,
maximum=1,
value=0.5,
step=0.01,
label="CFM Prior Temperature (higher values can improve quality but can reduce stability)",
),
gr.Checkbox(
value=False,
label="Denoise Before Enhancement (tick if your audio contains heavy background noise)",
),
]
outputs: list = [
gr.Audio(label="Output Denoised Audio"),
gr.Audio(label="Output Enhanced Audio"),
]
interface = gr.Interface(
fn=partial(_fn, unlimited=args.unlimited),
title="Resemble Enhance",
description="AI-driven audio enhancement for your audio files, powered by Resemble AI.",
inputs=inputs,
outputs=outputs,
)
interface.launch()
if __name__ == "__main__":
main()
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