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import gradio as gr
import torch
from PIL import Image
import numpy as np
from spectro import wav_bytes_from_spectrogram_image
from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionImg2ImgPipeline
device = "cpu"
MODEL_ID = "Hyeon2/riffusion-musiccaps"
pipe = StableDiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.float16)
pipe = pipe.to(device)
def predict(prompt, negative_prompt, audio_input, duration):
return classic(prompt, negative_prompt, duration)
def classic(prompt, negative_prompt, duration):
if duration == 5:
width_duration=512
else:
width_duration = 512 + ((int(duration) - 5) * 128)
spec = pipe(prompt, negative_prompt=negative_prompt, height=512, width=width_duration).images[0]
print(spec)
wav = wav_bytes_from_spectrogram_image(spec)
with open("output.wav", "wb") as f:
f.write(wav[0].getbuffer())
return spec, 'output.wav'
title = """
<div style="text-align: center; max-width: 500px; margin: 0 auto;">
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
margin-bottom: 10px;
line-height: 1em;
"
>
<h1 style="font-weight: 600; margin-bottom: 7px;">
Riffusion-Musiccaps real-time music generation
</h1>
</div>
<p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;">
Describe a musical prompt, generate music by getting a spectrogram image & sound.
</p>
</div>
"""
css = '''
#col-container, #col-container-2 {max-width: 510px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
div#record_btn > .mt-6 {
margin-top: 0!important;
}
div#record_btn > .mt-6 button {
width: 100%;
height: 40px;
}
.footer {
margin-bottom: 45px;
margin-top: 10px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
'''
with gr.Blocks(css="style.css") as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
prompt_input = gr.Textbox(placeholder="a cat diva singing in a New York jazz club", label="Musical prompt", elem_id="prompt-in")
audio_input = gr.Audio(sources=["upload"], type="filepath", visible=False)
with gr.Row():
negative_prompt = gr.Textbox(label="Negative prompt")
duration_input = gr.Slider(label="Duration in seconds", minimum=5, maximum=10, step=1, value=8, elem_id="duration-slider")
send_btn = gr.Button(value="Get a new spectrogram!", elem_id="submit-btn")
with gr.Column(elem_id="col-container-2"):
spectrogram_output = gr.Image(label="spectrogram image result", elem_id="img-out")
sound_output = gr.Audio(type='filepath', label="spectrogram sound", elem_id="music-out")
send_btn.click(predict, inputs=[prompt_input, negative_prompt, audio_input, duration_input], outputs=[spectrogram_output, sound_output])
demo.queue(max_size=250).launch(debug=True, ssr_mode=False) |