Update app.py
Browse files
app.py
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import
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import torch
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import julius
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import soundfile as sf
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import io
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@@ -12,45 +11,87 @@ preloaded["model"], preloaded["processor"] = load_model()
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model = preloaded["model"]
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processor = preloaded["processor"]
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# If this is the first run, create a new session state attribute for uploaded file
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if 'uploaded_file' not in st.session_state:
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st.session_state.uploaded_file = None
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#
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#
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if uploaded_file is not None:
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st.session_state.uploaded_file = uploaded_file.getvalue() # store content, not the file object
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import gradio as gr
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import torch
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import soundfile as sf
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import io
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model = preloaded["model"]
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processor = preloaded["processor"]
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def gr_invert_audio(input_audio):
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# Extract the file content and sampling rate
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audio, sr = sf.read(input_audio)
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# Convert audio to tensor
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audio_tensor = torch.tensor(audio).float()
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# Invert the audio
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inverted_audio_tensor = invert_audio(model, processor, audio_tensor, sr)
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inverted_audio_np = inverted_audio_tensor.numpy()
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# Save the inverted audio to a temporary file and return it
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with io.BytesIO() as out_io:
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sf.write(out_io, inverted_audio_np, sr, format="wav")
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out_io.seek(0)
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return out_io.read()
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# Gradio interface
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iface = gr.Interface(
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fn=gr_invert_audio,
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inputs=gr.inputs.Audio(type="file", label="Upload an Audio File"),
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outputs=gr.outputs.Audio(label="Inverted Audio"),
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live=True
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)
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iface.launch()
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# import streamlit as st
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# import torch
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# import julius
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# import soundfile as sf
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# import io
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# from model import load_model, invert_audio
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# # Load the model and processor
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# preloaded = {}
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# preloaded["model"], preloaded["processor"] = load_model()
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# model = preloaded["model"]
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# processor = preloaded["processor"]
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# st.title("Audio Inversion with HuggingFace & Streamlit")
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# # If this is the first run, create a new session state attribute for uploaded file
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# if 'uploaded_file' not in st.session_state:
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# st.session_state.uploaded_file = None
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# # Get the uploaded file
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# uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "flac"])
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# # Update the session state only if a new file is uploaded
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# if uploaded_file is not None:
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# st.session_state.uploaded_file = uploaded_file.getvalue() # store content, not the file object
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# if st.session_state.uploaded_file:
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# # Play the uploaded audio
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# audio_byte_content = st.session_state.uploaded_file
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# st.audio(audio_byte_content, format="audio/wav")
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# # Read the audio file
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# audio, sr = sf.read(io.BytesIO(audio_byte_content))
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# # Convert audio to tensor
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# audio_tensor = torch.tensor(audio).float()
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# @st.cache(allow_output_mutation=True, suppress_st_warning=True)
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# def cache_inverted_audio(audio_tensor):
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# return invert_audio(model, processor, audio_tensor, sr)
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# # Use cached result
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# inverted_audio_tensor = cache_inverted_audio(audio_tensor)
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# inverted_audio_np = inverted_audio_tensor.numpy()
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# # Play inverted audio
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# with io.BytesIO() as out_io:
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# sf.write(out_io, inverted_audio_np, sr, format="wav")
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# st.audio(out_io.getvalue(), format="audio/wav")
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# # Offer a download button for the inverted audio
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# if st.button("Download Inverted Audio"):
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# with io.BytesIO() as out_io:
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# sf.write(out_io, inverted_audio_np, sr, format="wav")
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# st.download_button("Download Inverted Audio", data=out_io.getvalue(), file_name="inverted_output.wav", mime="audio/wav")
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