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


# from huggingface_hub import login

# hf_token = os.getenv('HF_TOKEN')
# login(token=hf_token,add_to_git_credential=True)

# # Load the model outside of the GPU-decorated function
# def load_model():
#     print("Loading model...")
#     model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
#     print("Model loaded successfully.")
#     return model, model_config

# # 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 = load_model()
#     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()

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():
    print("Loading model...")
    model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
    print("Model loaded successfully.")
    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, seconds_total=30, steps=100, cfg_scale=7):
    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=0.3,
        sigma_max=500,
        sampler_type="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()
    print("Audio normalized and converted.")

    # 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.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")
    ],
    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."
)

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

# Launch the Interface
interface.launch()