import torch
import torchaudio
from einops import rearrange
import gradio as gr
import spaces
import os
import uuid
# Importing the model-related functions
from stable_audio_tools import get_pretrained_model
from stable_audio_tools.inference.generation import generate_diffusion_cond
PAGE_SIZE = 10
FILE_DIR_PATH = "/data"
theme = gr.themes.Base(
font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)
# Load the model outside of the GPU-decorated function
def load_model():
model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
print("Loading model...Done")
return model, model_config
# Function to set up, generate, and process the audio
@spaces.GPU(duration=120) # Allocate GPU only when this function is called
def generate_audio(prompt, sampler_type_dropdown, seconds_total=30, steps=100, cfg_scale=7,sigma_min_slider=0.3,sigma_max_slider=500, progress=gr.Progress(track_tqdm=True)):
print(f"Prompt received: {prompt}")
print(f"Settings: Duration={seconds_total}s, Steps={steps}, CFG Scale={cfg_scale}")
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# Fetch the Hugging Face token from the environment variable
hf_token = os.getenv('HF_TOKEN')
print(f"Hugging Face token: {hf_token}")
# Use pre-loaded model and configuration
model, model_config = load_model()
sample_rate = model_config["sample_rate"]
sample_size = model_config["sample_size"]
print(f"Sample rate: {sample_rate}, Sample size: {sample_size}")
model = model.to(device)
print("Model moved to device.")
# Set up text and timing conditioning
conditioning = [{
"prompt": prompt,
"seconds_start": 0,
"seconds_total": seconds_total
}]
print(f"Conditioning: {conditioning}")
# Generate stereo audio
print("Generating audio...")
output = generate_diffusion_cond(
model,
steps=steps,
cfg_scale=cfg_scale,
conditioning=conditioning,
sample_size=sample_size,
sigma_min=sigma_min_slider,
sigma_max=sigma_max_slider,
sampler_type=sampler_type_dropdown,#"dpmpp-3m-sde",
device=device
)
print("Audio generated.")
# Rearrange audio batch to a single sequence
output = rearrange(output, "b d n -> d (b n)")
print("Audio rearranged.")
# Peak normalize, clip, convert to int16
output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
max_length = sample_rate * seconds_total
if output.shape[1] > max_length:
output = output[:, :max_length]
print(f"Audio trimmed to {seconds_total} seconds.")
# Generate a unique filename for the output
random_uuid = uuid.uuid4().hex
unique_filename = f"/data/output_{random_uuid}.wav"
unique_textfile = f"/data/output_{random_uuid}.txt"
print(f"Saving audio to file: {unique_filename}")
# Save to file
torchaudio.save(unique_filename, output, sample_rate)
print(f"Audio saved: {unique_filename}")
with open(unique_textfile, "w") as file:
file.write(prompt)
# Return the path to the generated audio file
return unique_filename
def list_all_outputs(generation_history):
directory_path = FILE_DIR_PATH
files_in_directory = os.listdir(directory_path)
wav_files = [os.path.join(directory_path, file) for file in files_in_directory if file.endswith('.wav')]
wav_files.sort(key=lambda x: os.path.getmtime(os.path.join(directory_path, x)), reverse=True)
history_list = generation_history.split(',') if generation_history else []
updated_files = [file for file in wav_files if file not in history_list]
updated_history = updated_files + history_list
return ','.join(updated_history), gr.update(visible=True)
def increase_list_size(list_size):
return list_size+PAGE_SIZE
css = '''
#live_gen:before {
content: '';
animation: svelte-z7cif2-pulseStart 1s cubic-bezier(.4,0,.6,1), svelte-z7cif2-pulse 2s cubic-bezier(.4,0,.6,1) 1s infinite;
border: 2px solid var(--color-accent);
background: transparent;
z-index: var(--layer-1);
pointer-events: none;
position: absolute;
height: 100%;
width: 100%;
border-radius: 7px;
}
#live_gen_items{
max-height: 570px;
overflow-y: scroll;
}
'''
examples = [
[
"A serene soundscape of a quiet beach at sunset.", # Text prompt
"dpmpp-2m-sde", # Sampler type
45, # Duration in Seconds
100, # Number of Diffusion Steps
10, # CFG Scale
0.5, # Sigma min
800 # Sigma max
],
[
"clapping crowd", # Text prompt
"dpmpp-3m-sde", # Sampler type
30, # Duration in Seconds
100, # Number of Diffusion Steps
7, # CFG Scale
0.5, # Sigma min
500 # Sigma max
],
[
"A forest ambiance with birds chirping and wind rustling through the leaves.", # Text prompt
"k-dpm-fast", # Sampler type
60, # Duration in Seconds
140, # Number of Diffusion Steps
7.5, # CFG Scale
0.3, # Sigma min
700 # Sigma max
],
[
"A gentle rainfall with distant thunder.", # Text prompt
"dpmpp-3m-sde", # Sampler type
35, # Duration in Seconds
110, # Number of Diffusion Steps
8, # CFG Scale
0.1, # Sigma min
500 # Sigma max
],
[
"A jazz cafe environment with soft music and ambient chatter.", # Text prompt
"k-lms", # Sampler type
25, # Duration in Seconds
90, # Number of Diffusion Steps
6, # CFG Scale
0.4, # Sigma min
650 # Sigma max
],
["Rock beat played in a treated studio, session drumming on an acoustic kit.",
"dpmpp-2m-sde", # Sampler type
30, # Duration in Seconds
100, # Number of Diffusion Steps
7, # CFG Scale
0.3, # Sigma min
500 # Sigma max
]
]
with gr.Blocks(theme=theme, css=css) as demo:
gr.Markdown("# Stable Audio Multiplayer Live")
gr.Markdown("Generate audio with text, share and learn from others how to best prompt this new model")
generation_history = gr.Textbox(visible=False)
list_size = gr.Number(value=PAGE_SIZE, visible=False)
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", placeholder="Enter your text prompt here")
btn_run = gr.Button("Generate")
with gr.Accordion("Parameters", open=True):
with gr.Row():
duration = gr.Slider(0, 47, value=20, step=1, label="Duration in Seconds")
with gr.Accordion("Advanced parameters", open=False):
steps = gr.Slider(10, 150, value=80, step=10, label="Number of Diffusion Steps")
sampler_type = gr.Dropdown(["dpmpp-2m-sde", "dpmpp-3m-sde", "k-heun", "k-lms",
"k-dpmpp-2s-ancestral", "k-dpm-2", "k-dpm-fast"],
label="Sampler type", value="dpmpp-3m-sde")
with gr.Row():
cfg_scale = gr.Slider(1, 15, value=7, step=0.1, label="CFG Scale")
sigma_min = gr.Slider(0.0, 5.0, step=0.01, value=0.3, label="Sigma min")
sigma_max = gr.Slider(0.0, 1000.0, step=0.1, value=500, label="Sigma max")
with gr.Column() as output_list:
output = gr.Audio(type="filepath", label="Generated Audio")
with gr.Column(elem_id="live_gen") as community_list:
gr.Markdown("# Community generations")
with gr.Column(elem_id="live_gen_items"):
@gr.render(inputs=[generation_history, list_size])
def show_output_list(generation_history, list_size):
history_list = generation_history.split(',') if generation_history else []
history_list_latest = history_list[:list_size]
for generation in history_list_latest:
generation_prompt_file = generation.replace('.wav', '.txt')
with open(generation_prompt_file, 'r') as file:
generation_prompt = file.read()
with gr.Group():
gr.Markdown(value=f"### {generation_prompt}")
gr.Audio(value=generation)
load_more = gr.Button("Load more")
load_more.click(fn=increase_list_size, inputs=list_size, outputs=list_size)
gr.Examples(
fn=generate_audio,
examples=examples,
inputs=[prompt, sampler_type, duration, steps, cfg_scale, sigma_min, sigma_max],
outputs=output,
cache_examples="lazy"
)
gr.on(
triggers=[btn_run.click, prompt.submit],
fn=generate_audio,
inputs=[prompt, sampler_type, duration, steps, cfg_scale, sigma_min, sigma_max],
outputs=output
)
demo.load(fn=list_all_outputs, inputs=generation_history, outputs=[generation_history, community_list], every=2)
model, model_config = load_model()
demo.launch()