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Create app.py
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app.py
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import gradio as gr
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from speechbrain.pretrained import SepformerSeparation as separator
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import torchaudio
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import torch
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import os
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class AudioDenoiser:
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def __init__(self):
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# Initialize the SepFormer model for audio enhancement
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self.model = separator.from_hparams(
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source="speechbrain/sepformer-dns4-16k-enhancement",
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savedir='pretrained_models/sepformer-dns4-16k-enhancement'
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)
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# Create output directory if it doesn't exist
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os.makedirs("enhanced_audio", exist_ok=True)
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def enhance_audio(self, audio_path):
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"""
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Process the input audio file and return the enhanced version
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Args:
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audio_path (str): Path to the input audio file
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Returns:
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str: Path to the enhanced audio file
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"""
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try:
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# Separate and enhance the audio
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est_sources = self.model.separate_file(path=audio_path)
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# Generate output filename
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output_path = os.path.join("enhanced_audio", "enhanced_audio.wav")
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# Save the enhanced audio
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torchaudio.save(
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output_path,
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est_sources[:, :, 0].detach().cpu(),
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16000 # Sample rate
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)
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return output_path
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except Exception as e:
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raise gr.Error(f"Error processing audio: {str(e)}")
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def create_gradio_interface():
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# Initialize the denoiser
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denoiser = AudioDenoiser()
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# Create the Gradio interface
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interface = gr.Interface(
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fn=denoiser.enhance_audio,
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inputs=gr.Audio(
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type="filepath",
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label="Upload Noisy Audio"
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),
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outputs=gr.Audio(
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label="Enhanced Audio"
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),
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title="Audio Denoising using SepFormer",
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description="""
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This application uses the SepFormer model from SpeechBrain to enhance audio quality
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by removing background noise. Upload any noisy audio file to get started.
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""",
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article="""
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This application uses the SepFormer model trained on the DNS4 dataset.
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For more information, visit the [SpeechBrain documentation](https://speechbrain.github.io/).
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"""
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)
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return interface
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if __name__ == "__main__":
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# Create and launch the interface
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demo = create_gradio_interface()
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demo.launch()
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