Create app.py
Browse files
app.py
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import streamlit as st
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import torchaudio
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import speechbrain as sb
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from speechbrain.dataio.dataio import read_audio
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from IPython.display import Audio
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from speechbrain.pretrained import SepformerSeparation as separator
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# Load the pretrained model
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model = separator.from_hparams(source="speechbrain/sepformer-whamr-enhancement", savedir='pretrained_models/sepformer-whamr-enhancement')
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# Define the Streamlit app
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def app():
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st.title("Speech Enhancement using Sepformer")
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# Add a file uploader to allow the user to select an audio file
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uploaded_file = st.file_uploader("Choose an audio file", type=["wav"])
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# If an audio file is uploaded, perform speech enhancement and play the results
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if uploaded_file is not None:
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# Load the uploaded audio file
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audio_bytes = uploaded_file.read()
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with open("uploaded_audio.wav", "wb") as f:
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f.write(audio_bytes)
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signal = read_audio("uploaded_audio.wav").squeeze()
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# Perform speech enhancement using the Sepformer model
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enhanced_speech = model.separate_file(path='uploaded_audio.wav')
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enhanced_signal = enhanced_speech[:, :].detach().cpu().squeeze()
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# Play the original and enhanced audio
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st.audio(audio_bytes, format='audio/wav')
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st.audio(enhanced_signal, format='audio/wav')
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# Run the Streamlit app
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if __name__ == '__main__':
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app()
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