main.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
from pathlib import Path
import platform

import gradio as gr
from huggingface_hub import snapshot_download
import numpy as np
import torch

from project_settings import environment, project_path
from toolbox.torchaudio.models.mpnet.inference_mpnet import InferenceMPNet


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--examples_dir",
        # default=(project_path / "data").as_posix(),
        default=(project_path / "data/examples").as_posix(),
        type=str
    )
    parser.add_argument(
        "--models_repo_id",
        default="qgyd2021/vm_sound_classification",
        type=str
    )
    parser.add_argument(
        "--trained_model_dir",
        default=(project_path / "trained_models").as_posix(),
        type=str
    )
    parser.add_argument(
        "--hf_token",
        default=environment.get("hf_token"),
        type=str,
    )
    parser.add_argument(
        "--server_port",
        default=environment.get("server_port", 7860),
        type=int
    )

    args = parser.parse_args()
    return args


denoise_engines = {
    "mpnet": InferenceMPNet(
        pretrained_model_path_or_zip_file=(project_path / "trained_models/mpnet_aishell_20250221.zip").as_posix(),
    ),

}


def when_click_denoise_button(noisy_audio_t, engine: str):
    sample_rate, signal = noisy_audio_t

    noisy_audio = np.array(signal / (1 << 15), dtype=np.float32)

    infer_engine = denoise_engines.get(engine)
    if infer_engine is None:
        raise gr.Error(f"invalid denoise engine: {engine}.")

    try:
        enhanced_audio = infer_engine.enhancement_by_ndarray(noisy_audio)
        enhanced_audio = np.array(enhanced_audio * (1 << 15), dtype=np.int16)
    except Exception as e:
        raise gr.Error(f"enhancement failed, error type: {type(e)}, error text: {str(e)}.")

    enhanced_audio_t = (sample_rate, enhanced_audio)
    return enhanced_audio_t, None


def main():
    args = get_args()

    examples_dir = Path(args.examples_dir)
    trained_model_dir = Path(args.trained_model_dir)

    # download models
    if not trained_model_dir.exists():
        trained_model_dir.mkdir(parents=True, exist_ok=True)
        _ = snapshot_download(
            repo_id=args.models_repo_id,
            local_dir=trained_model_dir.as_posix(),
            token=args.hf_token,
        )

    # choices
    denoise_engine_choices = list(denoise_engines.keys())

    # ui
    with gr.Blocks() as blocks:
        gr.Markdown(value="nx denoise.")
        with gr.Tabs():
            with gr.TabItem("denoise"):
                with gr.Row():
                    with gr.Column(variant="panel", scale=5):
                        dn_noisy_audio = gr.Audio(label="noisy_audio")
                        dn_engine = gr.Dropdown(choices=denoise_engine_choices, value=denoise_engine_choices[0], label="engine")
                        dn_button = gr.Button(variant="primary")
                    with gr.Column(variant="panel", scale=5):
                        dn_enhanced_audio = gr.Audio(label="enhanced_audio")
                        dn_clean_audio = gr.Audio(label="clean_audio")

                dn_button.click(
                    when_click_denoise_button,
                    inputs=[dn_noisy_audio, dn_engine],
                    outputs=[dn_enhanced_audio, dn_clean_audio]
                )

    # http://127.0.0.1:7864/
    blocks.queue().launch(
        share=False if platform.system() == "Windows" else False,
        server_name="127.0.0.1" if platform.system() == "Windows" else "0.0.0.0",
        server_port=args.server_port
    )
    return


if __name__ == "__main__":
    main()