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import torch
import torchaudio
from sgmse.model import ScoreModel
import gradio as gr
from sgmse.util.other import pad_spec
# Load the pre-trained model
model = ScoreModel.load_from_checkpoint("pretrained_checkpoints/speech_enhancement/train_vb_29nqe0uh_epoch=115.ckpt")
def enhance_speech(audio_file):
# Load and process the audio file
y, sr = torchaudio.load(audio_file)
T_orig = y.size(1)
# Normalize
norm_factor = y.abs().max()
y = y / norm_factor
# Prepare DNN input
Y = torch.unsqueeze(model._forward_transform(model._stft(y.to(args.device))), 0)
Y = pad_spec(Y, mode=pad_mode)
# Reverse sampling
sampler = model.get_pc_sampler(
'reverse_diffusion', args.corrector, Y.to(args.device), N=args.N,
corrector_steps=args.corrector_steps, snr=args.snr)
sample, _ = sampler()
# Backward transform in time domain
x_hat = model.to_audio(sample.squeeze(), T_orig)
# Renormalize
x_hat = x_hat * norm_factor
# Save the enhanced audio
output_file = 'enhanced_output.wav'
torchaudio.save(output_file, x_hat.cpu().numpy(), sr)
return output_file
# Gradio interface setup
inputs = gr.Audio(label="Input Audio", type="filepath")
outputs = gr.Audio(label="Output Audio", type="filepath")
title = "Speech Enhancement using SGMSE"
description = "This Gradio demo uses the SGMSE model for speech enhancement. Upload your audio file to enhance it."
article = "<p style='text-align: center'><a href='https://huggingface.co/SP-UHH/speech-enhancement-sgmse' target='_blank'>Model Card</a></p>"
gr.Interface(fn=enhance_speech, inputs=inputs, outputs=outputs, title=title, description=description, article=article).launch()
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