Spaces:
Running
Running
File size: 1,781 Bytes
72c877e c17b696 72c877e d533c9c 72c877e 02a7301 600e950 02a7301 72c877e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
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.\
This takes about 20 minutes without a GPU, so why not make yourself a \
cup of tea in the meantime?",
inputs=[
gr.Dropdown(label="Model",
choices=[
"teticio/audio-diffusion-256",
"teticio/audio-diffusion-breaks-256",
"teticio/audio-diffusion-instrumental-hiphop-256"
],
value="teticio/audio-diffusion-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)
|