app.py

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

# 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):
    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
    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

# Setting up the Gradio Interface
interface = gr.Interface(
    fn=generate_audio,
    
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your text prompt here"),
        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"),
        gr.Slider(0, 47, value=30, label="Duration in Seconds"),
        gr.Slider(10, 150, value=100, step=10, label="Number of Diffusion Steps"),
        gr.Slider(1, 15, value=7, step=0.1, label="CFG Scale"),        
        gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.3, label="Sigma min"),
        gr.Slider(minimum=0.0, maximum=1000.0, step=0.1, value=500, label="Sigma max"),

    ],
    outputs=gr.Audio(type="filepath", label="Generated Audio"),
    title="Stable Audio Generator",
    description="Generate variable-length stereo audio at 44.1kHz from text prompts using Stable Audio Open 1.0.",
    examples=[
    [
        "Create 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",  # 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
    ],
    [
        "Simulate 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
    ],
    [
        "Recreate 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
    ],
    [
        "Imagine 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
    ]
    ]
)

# Pre-load the model to avoid multiprocessing issues
model, model_config = load_model()

# Launch the Interface
interface.queue(max_size=10).launch()