main.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
from functools import lru_cache
import logging
from pathlib import Path
import platform
import shutil
import zipfile

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

import log
from project_settings import environment, project_path, log_directory
from toolbox.os.command import Command
from toolbox.torchaudio.models.mpnet.inference_mpnet import InferenceMPNet

log.setup_size_rotating(log_directory=log_directory)

logger = logging.getLogger("main")


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/nx_denoise",
        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


def shell(cmd: str):
    return Command.popen(cmd)


denoise_engines = {
    "mpnet-nx-speech-1-epoch": {
        "infer_cls": InferenceMPNet,
        "kwargs": {
            "pretrained_model_path_or_zip_file": (
                        project_path / "trained_models/mpnet-nx-speech-1-epoch.zip").as_posix()
        }
    },
    "mpnet-aishell-1-epoch": {
        "infer_cls": InferenceMPNet,
        "kwargs": {
            "pretrained_model_path_or_zip_file": (project_path / "trained_models/mpnet-aishell-1-epoch.zip").as_posix()
        }
    },
    "mpnet-aishell-11-epoch": {
        "infer_cls": InferenceMPNet,
        "kwargs": {
            "pretrained_model_path_or_zip_file": (project_path / "trained_models/mpnet-aishell-11-epoch.zip").as_posix()
        }
    },
}


@lru_cache(maxsize=3)
def load_denoise_model(infer_cls, **kwargs):
    infer_engine = infer_cls(**kwargs)

    return infer_engine


def when_click_denoise_button(noisy_audio_t, engine: str):
    sample_rate, signal = noisy_audio_t
    logger.info(f"run denoise; engine: {engine}, sample_rate: {sample_rate}, signal dtype: {signal.dtype}, signal shape: {signal.shape}")

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

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

    try:
        infer_cls = infer_engine_param["infer_cls"]
        kwargs = infer_engine_param["kwargs"]
        infer_engine = load_denoise_model(infer_cls=infer_cls, **kwargs)

        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


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())

    # examples
    if not examples_dir.exists():
        example_zip_file = trained_model_dir / "examples.zip"
        with zipfile.ZipFile(example_zip_file.as_posix(), "r") as f_zip:
            out_root = examples_dir
            if out_root.exists():
                shutil.rmtree(out_root.as_posix())
            out_root.mkdir(parents=True, exist_ok=True)
            f_zip.extractall(path=out_root)

    # examples
    examples = list()
    for filename in examples_dir.glob("**/*.wav"):
        examples.append([
            filename.as_posix(),
            denoise_engine_choices[0]
        ])

    # 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_button.click(
                    when_click_denoise_button,
                    inputs=[dn_noisy_audio, dn_engine],
                    outputs=[dn_enhanced_audio]
                )
                gr.Examples(
                    examples=examples,
                    inputs=[dn_noisy_audio, dn_engine],
                    outputs=[dn_enhanced_audio],
                    fn=when_click_denoise_button,
                    # cache_examples=True,
                    # cache_mode="lazy",
                )

            with gr.TabItem("shell"):
                shell_text = gr.Textbox(label="cmd")
                shell_button = gr.Button("run")
                shell_output = gr.Textbox(label="output")

                shell_button.click(
                    shell,
                    inputs=[shell_text,],
                    outputs=[shell_output],
                )

    # 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()