app.py

import os
import json
import numpy as np
import torch
import soundfile as sf
import gradio as gr
from diffusers import DDPMScheduler
from pico_model import PicoDiffusion
from audioldm.variational_autoencoder.autoencoder import AutoencoderKL
from llm_preprocess import get_event, preprocess_gemini, preprocess_gpt
class dotdict(dict):
    """dot.notation access to dictionary attributes"""
    __getattr__ = dict.get
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

class InferRunner:
    def __init__(self, device):
        vae_config = json.load(open("ckpts/ldm/vae_config.json"))
        self.vae = AutoencoderKL(**vae_config).to(device)
        vae_weights = torch.load("ckpts/ldm/pytorch_model_vae.bin", map_location=device)
        self.vae.load_state_dict(vae_weights)

        train_args = dotdict(json.loads(open("ckpts/pico_model/summary.jsonl").readlines()[0]))
        self.pico_model = PicoDiffusion(
            scheduler_name=train_args.scheduler_name, 
            unet_model_config_path=train_args.unet_model_config, 
            snr_gamma=train_args.snr_gamma,
            freeze_text_encoder_ckpt="ckpts/laion_clap/630k-audioset-best.pt",
            diffusion_pt="ckpts/pico_model/diffusion.pt",
        ).eval().to(device)
        self.scheduler = DDPMScheduler.from_pretrained(train_args.scheduler_name, subfolder="scheduler")

device = "cuda" if torch.cuda.is_available() else "cpu"
runner = InferRunner(device)
event_list = get_event()
def infer(caption, num_steps=200, guidance_scale=3.0, audio_len=16000*10):
    with torch.no_grad():
        latents = runner.pico_model.demo_inference(caption, runner.scheduler, num_steps=num_steps, guidance_scale=guidance_scale, num_samples_per_prompt=1, disable_progress=True)
        mel = runner.vae.decode_first_stage(latents)
        wave = runner.vae.decode_to_waveform(mel)[0][:audio_len]
    outpath = f"output.wav"
    sf.write(outpath, wave, samplerate=16000, subtype='PCM_16')
    return outpath

def preprocess(caption):
    output = preprocess_gemini(caption)
    return output, output

def update_textbox(event_name, current_text):
    event = event_name + ' two times.'
    if current_text:
        return current_text.strip('.') + ' then ' + event
    else:
        return event

with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown("## PicoAudio")
    with gr.Row():
        description_text = f"18 events supported:"
        gr.Markdown(description_text)

    
    btn_event = []
    with gr.Row():
        for i in range(6):
            event_name = f"{event_list[i]}"
            btn_event.append(gr.Button(event_name))
    with gr.Row():
        for i in range(6, 12):
            event_name = f"{event_list[i]}"
            btn_event.append(gr.Button(event_name))
    with gr.Row():
        for i in range(12, 18):
            event_name = f"{event_list[i]}"
            btn_event.append(gr.Button(event_name))
            
        
    with gr.Row():
        gr.Markdown("## Step1-Preprocess")
    with gr.Row():
        preprocess_description_text = f"Transfer free-text into timestamp caption via LLM. "+\
            "This demo uses Gemini as the preprocessor. If any errors occur, please try a few more times. "+\
                "We also provide the GPT version consistent with the paper in the file 'Files/llm_reprocessing.py'. You can use your own api_key to modify and run 'Files/inference.py' for local inference."
        gr.Markdown(preprocess_description_text)
    with gr.Row():
        with gr.Column():
            freetext_prompt = gr.Textbox(label="Free-text Prompt: Input your free-text caption here. (e.g. a dog barks three times.)",
                value="a dog barks three times.",)
            with gr.Row():
                preprocess_run_button = gr.Button()
                preprocess_run_clear = gr.ClearButton([freetext_prompt])
            prompt = None
        with gr.Column():
            freetext_prompt_out = gr.Textbox(label="Timestamp Caption: Preprocess output")
    with gr.Row():
        with gr.Column():
            gr.Examples(
                        examples = [["spraying two times then gunshot three times."],
                                    ["a dog barks three times."],
                                    ["cow mooing two times."],],
                        inputs = [freetext_prompt],
                        outputs = [prompt]
                        )
        with gr.Column():
            pass
    

    with gr.Row():
        gr.Markdown("## Step2-Generate")
    with gr.Row():
        generate_description_text = f"Generate audio based on timestamp caption."
        gr.Markdown(generate_description_text)
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Timestamp Caption: Specify your timestamp caption formatted as 'event1 at onset1-offset1_onset2-offset2 and event2 at onset1-offset1'.",
                value="spraying at 0.38-1.176_3.06-3.856 and gunshot at 1.729-3.729_4.367-6.367_7.031-9.031.",)
            with gr.Row():
                generate_run_button = gr.Button()
                generate_run_clear = gr.ClearButton([prompt])
            with gr.Accordion("Advanced options", open=False):
                num_steps = gr.Slider(label="num_steps", minimum=1, maximum=300, value=200, step=1)
                guidance_scale = gr.Slider(label="guidance_scale", minimum=0.1, maximum=8.0, value=3.0, step=0.1)    
        with gr.Column():
            outaudio = gr.Audio()
            
    for i in range(18):
        event_name = f"{event_list[i]}"
        btn_event[i].click(fn=update_textbox, inputs=[gr.State(event_name), freetext_prompt], outputs=freetext_prompt)
    preprocess_run_button.click(fn=preprocess, inputs=[freetext_prompt], outputs=[prompt, freetext_prompt_out])
    generate_run_button.click(fn=infer, inputs=[prompt, num_steps, guidance_scale], outputs=[outaudio])
    

    with gr.Row():
        with gr.Column():
            gr.Examples(
                        examples = [["spraying at 0.38-1.176_3.06-3.856 and gunshot at 1.729-3.729_4.367-6.367_7.031-9.031."],
                                    ["dog_barking at 0.562-2.562_4.25-6.25."],
                                    ["cow_mooing at 0.958-3.582_5.272-7.896."],],
                        inputs = [prompt, num_steps, guidance_scale],
                        outputs = [outaudio]
                        )
        with gr.Column():
            pass
    

demo.launch()
        
    
# description_text = f"18 events: {', '.join(event_list)}"
# prompt = gr.Textbox(label="Prompt: Input your caption formatted as 'event1 at onset1-offset1_onset2-offset2 and event2 at onset1-offset1'.",
#     value="spraying at 0.38-1.176_3.06-3.856 and gunshot at 1.729-3.729_4.367-6.367_7.031-9.031.",)
# outaudio = gr.Audio()
# num_steps = gr.Slider(label="num_steps", minimum=1, maximum=300, value=200, step=1)
# guidance_scale = gr.Slider(label="guidance_scale", minimum=0.1, maximum=8.0, value=3.0, step=0.1)    
# gr_interface = gr.Interface(
        #     fn=infer,
        #     inputs=[prompt, num_steps, guidance_scale], 
        #     outputs=[outaudio],
        #     title="PicoAudio",
        #     description=description_text,
        #     allow_flagging=False,
        #     examples=[
        #         ["spraying at 0.38-1.176_3.06-3.856 and gunshot at 1.729-3.729_4.367-6.367_7.031-9.031."],
        #         ["dog_barking at 0.562-2.562_4.25-6.25."],
        #         ["cow_mooing at 0.958-3.582_5.272-7.896."],
        #     ],
        #     cache_examples="lazy", # Turn on to cache.
        # )
        # gr_interface.queue(10).launch()