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import gradio as gr | |
import torch | |
import os | |
import uuid | |
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" | |
# Fetch the Hugging Face token from the environment variable | |
hf_token = os.getenv('HF_TOKEN') | |
print(f"Hugging Face token: {hf_token}") | |
# 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}", | |
"seconds_start": 0, | |
"seconds_total": 30 | |
}] | |
# 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() | |
# Generate a unique filename for the output | |
unique_filename = f"output_{uuid.uuid4().hex}.wav" | |
print(f"Saving audio to file: {unique_filename}") | |
# Save to file | |
torchaudio.save(unique_filename, output, sample_rate) | |
print(f"Audio saved: {unique_filename}") | |
# Return the path to the generated audio file | |
return unique_filename | |
# 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() | |