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import streamlit as st
import torch
import julius
import soundfile as sf
import io
from model import load_model, invert_audio
# Load the model and processor
preloaded = {}
preloaded["model"], preloaded["processor"] = load_model()
model = preloaded["model"]
processor = preloaded["processor"]
st.title("Audio Inversion with HuggingFace & Streamlit")
uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "flac"])
if uploaded_file:
# Play the uploaded audio
st.audio(uploaded_file, format="audio/wav")
# Read the audio file
audio, sr = sf.read(io.BytesIO(uploaded_file.getvalue()))
# Convert audio to tensor
audio_tensor = torch.tensor(audio).float()
with st.spinner("Inverting audio..."):
# Invert the audio using the modified function
inverted_audio_tensor = invert_audio(model, processor, audio_tensor, sr)
# Convert tensor back to numpy
inverted_audio_np = inverted_audio_tensor.numpy()
# Convert numpy audio to bytes and play
with io.BytesIO() as out_io:
sf.write(out_io, inverted_audio_np, sr, format="wav")
st.audio(out_io.getvalue(), format="audio/wav")
# Offer a download button for the inverted audio
if st.button("Download Inverted Audio"):
with io.BytesIO() as out_io:
sf.write(out_io, inverted_audio_np, sr, format="wav")
st.download_button("Download Inverted Audio", data=out_io.getvalue(), file_name="inverted_output.wav", mime="audio/wav")
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