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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()
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