Create app.py

app.py

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