import argparse import gradio as gr from audiodiffusion import AudioDiffusion def generate_spectrogram_audio_and_loop(model_id): audio_diffusion = AudioDiffusion(model_id=model_id) image, (sample_rate, audio) = audio_diffusion.generate_spectrogram_and_audio() loop = AudioDiffusion.loop_it(audio, sample_rate) if loop is None: loop = audio return image, (sample_rate, audio), (sample_rate, loop) demo = gr.Interface( fn=generate_spectrogram_audio_and_loop, title="Audio Diffusion", description="Generate audio using Huggingface diffusers.\ The models without 'latent' or 'ddim' give better results but take about \ 20 minutes without a GPU. For GPU, you can use \ [colab](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/gradio_app.ipynb) \ to run this app.", inputs=[ gr.Dropdown(label="Model", choices=[ "teticio/audio-diffusion-256", "teticio/audio-diffusion-breaks-256", "teticio/audio-diffusion-instrumental-hiphop-256", "teticio/audio-diffusion-ddim-256", "teticio/latent-audio-diffusion-256", "teticio/latent-audio-diffusion-ddim-256" ], value="teticio/latent-audio-diffusion-ddim-256") ], outputs=[ gr.Image(label="Mel spectrogram", image_mode="L"), gr.Audio(label="Audio"), gr.Audio(label="Loop"), ], allow_flagging="never") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--port", type=int) parser.add_argument("--server", type=int) args = parser.parse_args() demo.launch(server_name=args.server or "0.0.0.0", server_port=args.port)