app.py

import torch
import torchaudio
import random
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)):
    seed = random.randint(0, 2**63 - 1)
    random.seed(seed)
    torch.manual_seed(seed)

    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()