Spaces:
Sleeping
Sleeping
File size: 1,581 Bytes
c7f2982 |
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 46 47 48 49 50 51 52 53 |
import gradio as gr
import torch
import torchaudio
from einops import rearrange
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond
def gen_music(description):
device = "cuda" if torch.cuda.is_available() else "cpu"
st.title("Generate Music with Stability Audio!!")
# Download model
model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
sample_rate = model_config["sample_rate"]
sample_size = model_config["sample_size"]
model = model.to(device)
# Set up text and timing conditioning
conditioning = [{
"prompt": f"{description.placeholder}",
}]
# Generate stereo audio
output = generate_diffusion_cond(
model,
conditioning=conditioning,
sample_size=sample_size,
device=device
)
# Rearrange audio batch to a single sequence
output = rearrange(output, "b d n -> d (b n)")
# Peak normalize, clip, convert to int16, and save to file
output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
torchaudio.save("output.wav", output, sample_rate)
return "output.wav"
# Define a interface Gradio
description = gr.Textbox(label="Description", placeholder="128 BPM tech house drum loop")
output_path = gr.Audio(label="Generated Music", type="filepath")
gr.Interface(
fn=gen_music,
inputs=[description],
outputs=output_path,
title="StableAudio Music Generation Demo",
).launch()
|