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import uuid
import ffmpeg
import gradio as gr
from pathlib import Path
from denoisers.SpectralGating import SpectralGating
from huggingface_hub import hf_hub_download
from denoisers.demucs import Demucs
import torch
import torchaudio
import yaml
def run_app(model_filename, config_filename):
model_path = hf_hub_download(repo_id="BorisovMaksim/demucs", filename=model_filename)
config_path = hf_hub_download(repo_id="BorisovMaksim/demucs", filename=config_filename)
with open(config_path, 'r') as f:
config = yaml.safe_load(f)
model = Demucs(config['demucs'])
checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint['model_state_dict'])
def denoising_transform(audio):
# Path(__file__).parent.resolve()
src_path = Path("cache_wav/original/{}.wav".format(str(uuid.uuid4())))
tgt_path = Path("cache_wav/denoised/{}.wav".format(str(uuid.uuid4())))
src_path.parent.mkdir(exist_ok=True, parents=True)
tgt_path.parent.mkdir(exist_ok=True, parents=True)
(ffmpeg.input(audio)
.output(src_path.as_posix(), acodec='pcm_s16le', ac=1, ar=22050)
.run()
)
wav, rate = torchaudio.load(audio)
reduced_noise = model.predict(wav)
torchaudio.save(tgt_path, reduced_noise, rate)
return tgt_path
demo = gr.Interface(
fn=denoising_transform,
inputs=gr.Audio(label="Source Audio", source="microphone", type='filepath'),
outputs=gr.Audio(label="Target Audio", type='filepath'),
examples=[
["testing/wavs/p232_071.wav"],
["testing/wavs/p232_284.wav"],
],
title="Denoising"
)
demo.launch()
if __name__ == "__main__":
model_filename = "original_sr/Demucs_original_sr_epoch3.pt"
config_filename = "original_sr/config.yaml"
run_app(model_filename, config_filename)
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