app.py

import torch
import torchaudio
from einops import rearrange
import gradio as gr
import spaces
import os
import uuid
import shutil
import gzip
import io

# 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
    
def compress_file(file_path):
    compressed_file_path = file_path + '.gz'
    with open(file_path, 'rb') as f_in:
        with gzip.open(compressed_file_path, 'wb') as f_out:
            shutil.copyfileobj(f_in, f_out)
    return compressed_file_path


# Function to set up, generate, and process the audio
@spaces.GPU(duration=25)  # 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}")
    
    # compressed_filename = compress_file(unique_filename)
    # return compressed_filename

    # # Return the path to the generated audio file
    return unique_filename
    
    # Convert audio tensor to bytes
    # byte_io = io.BytesIO()
    # torchaudio.save(byte_io, output, sample_rate, format="wav")
    # byte_io.seek(0)
    # audio_bytes = byte_io.read()
    # print("Audio converted to bytes.")

    # return audio_bytes


DESCRIPTION = "Welcome to Raptor APIs"


css = """
  #output {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Tab(label="GenAudio"):
        with gr.Row():
            with gr.Column():
                    prompt = gr.Textbox(label="Prompt", placeholder="Enter your text prompt here")
                    duration = gr.Slider(0, 47, value=30, label="Duration in Seconds")
                    steps = gr.Slider(10, 150, value=100, step=10, label="Number of Diffusion Steps")
                    cfg = gr.Slider(1, 15, value=7, step=0.1, label="CFG Scale")
                    btn = gr.Button(value="generate")
                    
            with gr.Column():
                output = gr.Audio(label="audio")
                # output_byte_code = gr.Textbox(label="Byte Code Output")
                btn.click(generate_audio,inputs=[prompt,duration, steps, cfg],outputs=output,api_name="genAudio")

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

# Launch the Interface
demo.launch()