app.py

import os
import torch
import random
# import spaces
import numpy as np
import gradio as gr
import soundfile as sf
from accelerate import Accelerator
from transformers import T5Tokenizer, T5EncoderModel
from diffusers import DDIMScheduler
from src.models.conditioners import MaskDiT
from src.modules.autoencoder_wrapper import Autoencoder
from src.inference import inference
from src.utils import load_yaml_with_includes


# Load model and configs
def load_models(config_name, ckpt_path, vae_path, device):
    params = load_yaml_with_includes(config_name)

    # Load codec model
    autoencoder = Autoencoder(ckpt_path=vae_path,
                              model_type=params['autoencoder']['name'],
                              quantization_first=params['autoencoder']['q_first']).to(device)
    autoencoder.eval()

    # Load text encoder
    tokenizer = T5Tokenizer.from_pretrained(params['text_encoder']['model'])
    text_encoder = T5EncoderModel.from_pretrained(params['text_encoder']['model']).to(device)
    text_encoder.eval()

    # Load main U-Net model
    unet = MaskDiT(**params['model']).to(device)
    unet.load_state_dict(torch.load(ckpt_path)['model'])
    unet.eval()

    accelerator = Accelerator(mixed_precision="fp16")
    unet = accelerator.prepare(unet)

    # Load noise scheduler
    noise_scheduler = DDIMScheduler(**params['diff'])

    latents = torch.randn((1, 128, 128), device=device)
    noise = torch.randn_like(latents)
    timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (1,), device=device)
    _ = noise_scheduler.add_noise(latents, noise, timesteps)

    return autoencoder, unet, tokenizer, text_encoder, noise_scheduler, params


MAX_SEED = np.iinfo(np.int32).max

# Model and config paths
config_name = 'ckpts/ezaudio-xl.yml'
ckpt_path = 'ckpts/s3/ezaudio_s3_xl.pt'
vae_path = 'ckpts/vae/1m.pt'
save_path = 'output/'
os.makedirs(save_path, exist_ok=True)

device = 'cuda' if torch.cuda.is_available() else 'cpu'

autoencoder, unet, tokenizer, text_encoder, noise_scheduler, params = load_models(config_name, ckpt_path, vae_path,
                                                                                  device)


@spaces.GPU
def generate_audio(text, length,
                   guidance_scale, guidance_rescale, ddim_steps, eta,
                   random_seed, randomize_seed):
    neg_text = None
    length = length * params['autoencoder']['latent_sr']

    if randomize_seed:
        random_seed = random.randint(0, MAX_SEED)

    pred = inference(autoencoder, unet, None, None,
                     tokenizer, text_encoder,
                     params, noise_scheduler,
                     text, neg_text,
                     length,
                     guidance_scale, guidance_rescale,
                     ddim_steps, eta, random_seed,
                     device)

    pred = pred.cpu().numpy().squeeze(0).squeeze(0)
    # output_file = f"{save_path}/{text}.wav"
    # sf.write(output_file, pred, samplerate=params['autoencoder']['sr'])

    return params['autoencoder']['sr'], pred


# Examples (if needed for the demo)
examples = [
    "the sound of rain falling softly",
    "a dog barking in the distance",
    "light guitar music is playing",
]

# CSS styling (optional)
css = """
#col-container {
    margin: 0 auto;
    max-width: 1280px;
}
"""

# Gradio Blocks layout
with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
        # EzAudio: High-quality Text-to-Audio Generator
        Generate audio from text using a diffusion transformer. Adjust advanced settings for more control.
        """)

        # Basic Input: Text prompt and Audio Length
        with gr.Row():
            text_input = gr.Textbox(
                label="Text Prompt",
                show_label=False,
                max_lines=2,
                placeholder="Enter your prompt",
                container=False,
                value="a dog barking in the distance"
            )
            length_input = gr.Slider(minimum=1, maximum=10, step=1, value=10, label="Audio Length (in seconds)")

        # Output Component
        result = gr.Audio(label="Result", type="numpy")

        # Advanced settings in an Accordion
        with gr.Accordion("Advanced Settings", open=False):
            guidance_scale = gr.Slider(minimum=1.0, maximum=10, step=0.1, value=5.0, label="Guidance Scale")
            guidance_rescale = gr.Slider(minimum=0.0, maximum=1, step=0.05, value=0.75, label="Guidance Rescale")
            ddim_steps = gr.Slider(minimum=25, maximum=200, step=5, value=50, label="DDIM Steps")
            eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="Eta")
            seed = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, value=0, label="Seed")
            randomize_seed = gr.Checkbox(label="Randomize Seed (Disable Seed)", value=True)

        # Examples block
        gr.Examples(
            examples=examples,
            inputs=[text_input]
        )

        # Run button
        run_button = gr.Button("Generate")

        # Define the trigger and input-output linking
        run_button.click(
            fn=generate_audio,
            inputs=[text_input, length_input, guidance_scale, guidance_rescale, ddim_steps, eta, seed, randomize_seed],
            outputs=[result]
        )

# Launch the Gradio demo
demo.launch()