app.py

import os
import torch
import random
import numpy as np
import gradio as gr
import librosa
import spaces
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, map_location='cpu')['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']

    gt, gt_mask = None, None

    if text == '':
        guidance_scale = None
        print('empyt input')

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

    pred = inference(autoencoder, unet,
                     gt, gt_mask,
                     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


@spaces.GPU
def editing_audio(text, boundary,
                  gt_file, mask_start, mask_length,
                  guidance_scale, guidance_rescale, ddim_steps, eta,
                  random_seed, randomize_seed):
    neg_text = None
    # max_length = 10

    if text == '':
        guidance_scale = None
        print('empyt input')

    mask_end = mask_start + mask_length

    # Load and preprocess ground truth audio
    gt, sr = librosa.load(gt_file, sr=params['autoencoder']['sr'])
    gt = gt / (np.max(np.abs(gt)) + 1e-9)

    audio_length = len(gt) / sr
    mask_start = min(mask_start, audio_length)
    if mask_end > audio_length:
        # outpadding mode
        padding = round((mask_end - audio_length)*params['autoencoder']['sr'])
        gt = np.pad(gt, (0, padding), 'constant')
        audio_length = len(gt) / sr

    output_audio = gt.copy()

    gt = torch.tensor(gt).unsqueeze(0).unsqueeze(1).to(device)
    boundary = min((mask_end - mask_start)/2, boundary)
    # print(boundary)

    # Calculate start and end indices
    start_idx = max(mask_start - boundary, 0)
    end_idx = min(mask_end + boundary, audio_length)
    # print(start_idx)
    # print(end_idx)

    mask_start -= start_idx
    mask_end -= start_idx

    gt = gt[:, :, round(start_idx*params['autoencoder']['sr']):round(end_idx*params['autoencoder']['sr'])]

    # Encode the audio to latent space
    gt_latent = autoencoder(audio=gt)
    B, D, L = gt_latent.shape
    length = L

    gt_mask = torch.zeros(B, D, L).to(device)
    latent_sr = params['autoencoder']['latent_sr']
    gt_mask[:, :, round(mask_start * latent_sr): round(mask_end * latent_sr)] = 1
    gt_mask = gt_mask.bool()

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

    # Perform inference to get the edited latent representation
    pred = inference(autoencoder, unet,
                     gt_latent, gt_mask,
                     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)

    chunk_length = end_idx - start_idx
    pred = pred[:round(chunk_length*params['autoencoder']['sr'])]

    output_audio[round(start_idx*params['autoencoder']['sr']):round(end_idx*params['autoencoder']['sr'])] = pred

    pred = output_audio

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


# Examples (if needed for the demo)
examples = [
    "a dog barking in the distance",
    "light guitar music is playing",
    "a duck quacks as waves crash gently on the shore",
    "footsteps crunch on the forest floor as crickets chirp",
    "a horse clip-clops in a windy rain as thunder cracks in the distance",
]

# Examples (if needed for the demo)
examples_edit = [
    ["a dog barking in the background", 2, 3],
    ["kids playing and laughing nearby", 5, 4],
    ["rock music playing on the street", 8, 6]
]


# 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 and edit audio from text using a diffusion transformer. Adjust advanced settings for more control.
        
            [Learn more about 😈EzAudio](https://haidog-yaqub.github.io/EzAudio-Page/)
        """)


        # Tabs for Generate and Edit
        with gr.Tab("Audio Generation"):
            # Basic Input: Text prompt
            with gr.Row():
                text_input = gr.Textbox(
                    label="Text Prompt",
                    show_label=True,
                    max_lines=2,
                    placeholder="Enter your prompt",
                    container=True,
                    value="a dog barking in the distance",
                    scale=4
                )
                # Run button
                run_button = gr.Button("Generate", scale=1)

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

            # Advanced settings in an Accordion
            with gr.Accordion("Advanced Settings", open=False):
                # Audio Length
                audio_length = gr.Slider(minimum=1, maximum=10, step=1, value=10, label="Audio Length (in seconds)")
                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=100, 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]
            )

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

        with gr.Tab("Audio Editing and Inpainting"):
            # Input: Upload audio file
            with gr.Row():
                gt_file_input = gr.Audio(label="Upload Audio to Edit", type="filepath", value="edit_example.wav")

            # Text prompt for editing
            text_edit_input = gr.Textbox(
                label="Edit Prompt",
                show_label=True,
                max_lines=2,
                placeholder="Describe the edit you wat",
                container=True,
                value="a dog barking in the background",
                scale=4
            )

            # Mask settings
            mask_start = gr.Number(label="Edit Start (seconds)", value=2.0)
            mask_length = gr.Slider(minimum=0.5, maximum=10, step=0.5, value=3, label="Edit Length (seconds)")
            
            edit_explanation = gr.Markdown(value="**Edit Start**: The time when the edit begins. \n\n**Edit Length**: The duration of the segment to be edited. \n\n**Outpainting**: If the edit extends beyond the audio's length, Outpainting Mode will automatically activate.")

            # Run button for editing
            edit_button = gr.Button("Generate", scale=1)

            # Output Component for edited audio
            edited_result = gr.Audio(label="Edited Audio", type="numpy")

            # Advanced settings in an Accordion
            with gr.Accordion("Advanced Settings", open=False):
                # Audio Length (optional for editing, can be auto or user-defined)
                edit_boundary = gr.Slider(minimum=0.5, maximum=4, step=0.5, value=2, label="Edit Boundary (in seconds)")
                edit_guidance_scale = gr.Slider(minimum=1.0, maximum=10, step=0.5, value=3.0, label="Guidance Scale")
                edit_guidance_rescale = gr.Slider(minimum=0.0, maximum=1, step=0.05, value=0.0, label="Guidance Rescale")
                edit_ddim_steps = gr.Slider(minimum=25, maximum=200, step=5, value=50, label="DDIM Steps")
                edit_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="Eta")
                edit_seed = gr.Slider(minimum=0, maximum=100, step=1, value=0, label="Seed")
                edit_randomize_seed = gr.Checkbox(label="Randomize Seed (Disable Seed)", value=True)

            # Examples block
            gr.Examples(
                examples=examples_edit,
                inputs=[text_edit_input, mask_start, mask_length]
            )

            # Define the trigger and input-output linking for editing
            edit_button.click(
                fn=editing_audio,
                inputs=[
                    text_edit_input,
                    edit_boundary,
                    gt_file_input,
                    mask_start,
                    mask_length,
                    edit_guidance_scale,
                    edit_guidance_rescale,
                    edit_ddim_steps,
                    edit_eta,
                    edit_seed,
                    edit_randomize_seed
                ],
                outputs=[edited_result]
            )
            text_edit_input.submit(
                fn=editing_audio,
                inputs=[
                    text_edit_input,
                    edit_boundary,
                    gt_file_input,
                    mask_start,
                    mask_length,
                    edit_guidance_scale,
                    edit_guidance_rescale,
                    edit_ddim_steps,
                    edit_eta,
                    edit_seed,
                    edit_randomize_seed
                ],
                outputs=[edited_result]
            )

    # Launch the Gradio demo
    demo.launch()