from io import BytesIO import streamlit as st import soundfile as sf from librosa.util import normalize from librosa.beat import beat_track from audiodiffusion import AudioDiffusion if __name__ == "__main__": st.header("Audio Diffusion") st.markdown("Generate audio using Huggingface diffusers.\ This takes about 20 minutes without a GPU, so why not make yourself a cup of tea in the meantime?" ) model_id = st.selectbox( "Model", ["teticio/audio-diffusion-256", "teticio/audio-diffusion-breaks-256"]) audio_diffusion = AudioDiffusion(model_id=model_id) if st.button("Generate"): st.markdown("Generating...") image, (sample_rate, audio) = audio_diffusion.generate_spectrogram_and_audio() st.image(image, caption="Mel spectrogram") buffer = BytesIO() sf.write(buffer, normalize(audio), sample_rate, format="WAV") st.audio(buffer, format="audio/wav") audio = AudioDiffusion.loop_it(audio, sample_rate) if audio is not None: st.markdown("Loop") buffer = BytesIO() sf.write(buffer, normalize(audio), sample_rate, format="WAV") st.audio(buffer, format="audio/wav")