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Running
on
Zero
Running
on
Zero
File size: 2,151 Bytes
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import torch
import torchaudio
import spaces
from einops import rearrange
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond
import gradio as gr
import os
from huggingface_hub import login
hf_token = os.getenv('HF_TOKEN')
login(token=hf_token,add_to_git_credential=True)
# Define the function to generate audio
@spaces.GPU(duration=120)
def generate_audio(prompt, bpm, seconds_total):
device = "cuda" if torch.cuda.is_available() else "cpu"
# 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"{bpm} BPM {prompt}",
"seconds_start": 0,
"seconds_total": seconds_total
}]
# Generate stereo audio
output = generate_diffusion_cond(
model,
steps=100,
cfg_scale=7,
conditioning=conditioning,
sample_size=sample_size,
sigma_min=0.3,
sigma_max=500,
sampler_type="dpmpp-3m-sde",
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()
output_path = "output.wav"
torchaudio.save(output_path, output, sample_rate)
return output_path
# Define the Gradio interface
iface = gr.Interface(
fn=generate_audio,
inputs=[
gr.Textbox(label="Prompt", placeholder="Enter the description of the audio (e.g., tech house drum loop)"),
gr.Number(label="BPM", value=128),
gr.Number(label="Duration (seconds)", value=30)
],
outputs=gr.Audio(label="Generated Audio"),
title="Stable Audio Generation",
description="Generate audio based on a text prompt using stable audio tools.",
)
# Launch the interface
iface.launch()
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