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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/ZUN-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)
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