so_vits_svc_fork/__main__.py

from __future__ import annotations

import os
from logging import getLogger
from multiprocessing import freeze_support
from pathlib import Path
from typing import Literal

import click
import torch

from so_vits_svc_fork import __version__
from so_vits_svc_fork.utils import get_optimal_device

LOG = getLogger(__name__)

IS_TEST = "test" in Path(__file__).parent.stem
if IS_TEST:
    LOG.debug("Test mode is on.")


class RichHelpFormatter(click.HelpFormatter):
    def __init__(
        self,
        indent_increment: int = 2,
        width: int | None = None,
        max_width: int | None = None,
    ) -> None:
        width = 100
        super().__init__(indent_increment, width, max_width)
        LOG.info(f"Version: {__version__}")


def patch_wrap_text():
    orig_wrap_text = click.formatting.wrap_text

    def wrap_text(
        text,
        width=78,
        initial_indent="",
        subsequent_indent="",
        preserve_paragraphs=False,
    ):
        return orig_wrap_text(
            text.replace("\n", "\n\n"),
            width=width,
            initial_indent=initial_indent,
            subsequent_indent=subsequent_indent,
            preserve_paragraphs=True,
        ).replace("\n\n", "\n")

    click.formatting.wrap_text = wrap_text


patch_wrap_text()

CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"], show_default=True)
click.Context.formatter_class = RichHelpFormatter


@click.group(context_settings=CONTEXT_SETTINGS)
def cli():
    """so-vits-svc allows any folder structure for training data.
    However, the following folder structure is recommended.\n
        When training: dataset_raw/{speaker_name}/**/{wav_name}.{any_format}\n
        When inference: configs/44k/config.json, logs/44k/G_XXXX.pth\n
    If the folder structure is followed, you DO NOT NEED TO SPECIFY model path, config path, etc.
    (The latest model will be automatically loaded.)\n
    To train a model, run pre-resample, pre-config, pre-hubert, train.\n
    To infer a model, run infer.
    """


@cli.command()
@click.option(
    "-c",
    "--config-path",
    type=click.Path(exists=True),
    help="path to config",
    default=Path("./configs/44k/config.json"),
)
@click.option(
    "-m",
    "--model-path",
    type=click.Path(),
    help="path to output dir",
    default=Path("./logs/44k"),
)
@click.option(
    "-t/-nt",
    "--tensorboard/--no-tensorboard",
    default=False,
    type=bool,
    help="launch tensorboard",
)
@click.option(
    "-r",
    "--reset-optimizer",
    default=False,
    type=bool,
    help="reset optimizer",
    is_flag=True,
)
def train(
    config_path: Path,
    model_path: Path,
    tensorboard: bool = False,
    reset_optimizer: bool = False,
):
    """Train model
    If D_0.pth or G_0.pth not found, automatically download from hub."""
    from .train import train

    config_path = Path(config_path)
    model_path = Path(model_path)

    if tensorboard:
        import webbrowser

        from tensorboard import program

        getLogger("tensorboard").setLevel(30)
        tb = program.TensorBoard()
        tb.configure(argv=[None, "--logdir", model_path.as_posix()])
        url = tb.launch()
        webbrowser.open(url)

    train(
        config_path=config_path, model_path=model_path, reset_optimizer=reset_optimizer
    )


@cli.command()
def gui():
    """Opens GUI
    for conversion and realtime inference"""
    from .gui import main

    main()


@cli.command()
@click.argument(
    "input-path",
    type=click.Path(exists=True),
)
@click.option(
    "-o",
    "--output-path",
    type=click.Path(),
    help="path to output dir",
)
@click.option("-s", "--speaker", type=str, default=None, help="speaker name")
@click.option(
    "-m",
    "--model-path",
    type=click.Path(exists=True),
    default=Path("./logs/44k/"),
    help="path to model",
)
@click.option(
    "-c",
    "--config-path",
    type=click.Path(exists=True),
    default=Path("./configs/44k/config.json"),
    help="path to config",
)
@click.option(
    "-k",
    "--cluster-model-path",
    type=click.Path(exists=True),
    default=None,
    help="path to cluster model",
)
@click.option(
    "-re",
    "--recursive",
    type=bool,
    default=False,
    help="Search recursively",
    is_flag=True,
)
@click.option("-t", "--transpose", type=int, default=0, help="transpose")
@click.option(
    "-db", "--db-thresh", type=int, default=-20, help="threshold (DB) (RELATIVE)"
)
@click.option(
    "-fm",
    "--f0-method",
    type=click.Choice(["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"]),
    default="dio",
    help="f0 prediction method",
)
@click.option(
    "-a/-na",
    "--auto-predict-f0/--no-auto-predict-f0",
    type=bool,
    default=True,
    help="auto predict f0",
)
@click.option(
    "-r", "--cluster-infer-ratio", type=float, default=0, help="cluster infer ratio"
)
@click.option("-n", "--noise-scale", type=float, default=0.4, help="noise scale")
@click.option("-p", "--pad-seconds", type=float, default=0.5, help="pad seconds")
@click.option(
    "-d",
    "--device",
    type=str,
    default=get_optimal_device(),
    help="device",
)
@click.option("-ch", "--chunk-seconds", type=float, default=0.5, help="chunk seconds")
@click.option(
    "-ab/-nab",
    "--absolute-thresh/--no-absolute-thresh",
    type=bool,
    default=False,
    help="absolute thresh",
)
@click.option(
    "-mc",
    "--max-chunk-seconds",
    type=float,
    default=40,
    help="maximum allowed single chunk length, set lower if you get out of memory (0 to disable)",
)
def infer(
    # paths
    input_path: Path,
    output_path: Path,
    model_path: Path,
    config_path: Path,
    recursive: bool,
    # svc config
    speaker: str,
    cluster_model_path: Path | None = None,
    transpose: int = 0,
    auto_predict_f0: bool = False,
    cluster_infer_ratio: float = 0,
    noise_scale: float = 0.4,
    f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio",
    # slice config
    db_thresh: int = -40,
    pad_seconds: float = 0.5,
    chunk_seconds: float = 0.5,
    absolute_thresh: bool = False,
    max_chunk_seconds: float = 40,
    device: str | torch.device = get_optimal_device(),
):
    """Inference"""
    from so_vits_svc_fork.inference.main import infer

    if not auto_predict_f0:
        LOG.warning(
            f"auto_predict_f0 = False, transpose = {transpose}. If you want to change the pitch, please set transpose."
            "Generally transpose = 0 does not work because your voice pitch and target voice pitch are different."
        )

    input_path = Path(input_path)
    if output_path is None:
        output_path = input_path.parent / f"{input_path.stem}.out{input_path.suffix}"
    output_path = Path(output_path)
    if input_path.is_dir() and not recursive:
        raise ValueError(
            "input_path is a directory. Use 0re or --recursive to infer recursively."
        )
    model_path = Path(model_path)
    if model_path.is_dir():
        model_path = list(
            sorted(model_path.glob("G_*.pth"), key=lambda x: x.stat().st_mtime)
        )[-1]
        LOG.info(f"Since model_path is a directory, use {model_path}")
    config_path = Path(config_path)
    if cluster_model_path is not None:
        cluster_model_path = Path(cluster_model_path)
    infer(
        # paths
        input_path=input_path,
        output_path=output_path,
        model_path=model_path,
        config_path=config_path,
        recursive=recursive,
        # svc config
        speaker=speaker,
        cluster_model_path=cluster_model_path,
        transpose=transpose,
        auto_predict_f0=auto_predict_f0,
        cluster_infer_ratio=cluster_infer_ratio,
        noise_scale=noise_scale,
        f0_method=f0_method,
        # slice config
        db_thresh=db_thresh,
        pad_seconds=pad_seconds,
        chunk_seconds=chunk_seconds,
        absolute_thresh=absolute_thresh,
        max_chunk_seconds=max_chunk_seconds,
        device=device,
    )


@cli.command()
@click.option(
    "-m",
    "--model-path",
    type=click.Path(exists=True),
    default=Path("./logs/44k/"),
    help="path to model",
)
@click.option(
    "-c",
    "--config-path",
    type=click.Path(exists=True),
    default=Path("./configs/44k/config.json"),
    help="path to config",
)
@click.option(
    "-k",
    "--cluster-model-path",
    type=click.Path(exists=True),
    default=None,
    help="path to cluster model",
)
@click.option("-t", "--transpose", type=int, default=12, help="transpose")
@click.option(
    "-a/-na",
    "--auto-predict-f0/--no-auto-predict-f0",
    type=bool,
    default=True,
    help="auto predict f0 (not recommended for realtime since voice pitch will not be stable)",
)
@click.option(
    "-r", "--cluster-infer-ratio", type=float, default=0, help="cluster infer ratio"
)
@click.option("-n", "--noise-scale", type=float, default=0.4, help="noise scale")
@click.option(
    "-db", "--db-thresh", type=int, default=-30, help="threshold (DB) (ABSOLUTE)"
)
@click.option(
    "-fm",
    "--f0-method",
    type=click.Choice(["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"]),
    default="dio",
    help="f0 prediction method",
)
@click.option("-p", "--pad-seconds", type=float, default=0.02, help="pad seconds")
@click.option("-ch", "--chunk-seconds", type=float, default=0.5, help="chunk seconds")
@click.option(
    "-cr",
    "--crossfade-seconds",
    type=float,
    default=0.01,
    help="crossfade seconds",
)
@click.option(
    "-ab",
    "--additional-infer-before-seconds",
    type=float,
    default=0.2,
    help="additional infer before seconds",
)
@click.option(
    "-aa",
    "--additional-infer-after-seconds",
    type=float,
    default=0.1,
    help="additional infer after seconds",
)
@click.option("-b", "--block-seconds", type=float, default=0.5, help="block seconds")
@click.option(
    "-d",
    "--device",
    type=str,
    default=get_optimal_device(),
    help="device",
)
@click.option("-s", "--speaker", type=str, default=None, help="speaker name")
@click.option("-v", "--version", type=int, default=2, help="version")
@click.option("-i", "--input-device", type=int, default=None, help="input device")
@click.option("-o", "--output-device", type=int, default=None, help="output device")
@click.option(
    "-po",
    "--passthrough-original",
    type=bool,
    default=False,
    is_flag=True,
    help="passthrough original (for latency check)",
)
def vc(
    # paths
    model_path: Path,
    config_path: Path,
    # svc config
    speaker: str,
    cluster_model_path: Path | None,
    transpose: int,
    auto_predict_f0: bool,
    cluster_infer_ratio: float,
    noise_scale: float,
    f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
    # slice config
    db_thresh: int,
    pad_seconds: float,
    chunk_seconds: float,
    # realtime config
    crossfade_seconds: float,
    additional_infer_before_seconds: float,
    additional_infer_after_seconds: float,
    block_seconds: float,
    version: int,
    input_device: int | str | None,
    output_device: int | str | None,
    device: torch.device,
    passthrough_original: bool = False,
) -> None:
    """Realtime inference from microphone"""
    from so_vits_svc_fork.inference.main import realtime

    if auto_predict_f0:
        LOG.warning(
            "auto_predict_f0 = True in realtime inference will cause unstable voice pitch, use with caution"
        )
    else:
        LOG.warning(
            f"auto_predict_f0 = False, transpose = {transpose}. If you want to change the pitch, please change the transpose value."
            "Generally transpose = 0 does not work because your voice pitch and target voice pitch are different."
        )
    model_path = Path(model_path)
    config_path = Path(config_path)
    if cluster_model_path is not None:
        cluster_model_path = Path(cluster_model_path)
    if model_path.is_dir():
        model_path = list(
            sorted(model_path.glob("G_*.pth"), key=lambda x: x.stat().st_mtime)
        )[-1]
        LOG.info(f"Since model_path is a directory, use {model_path}")

    realtime(
        # paths
        model_path=model_path,
        config_path=config_path,
        # svc config
        speaker=speaker,
        cluster_model_path=cluster_model_path,
        transpose=transpose,
        auto_predict_f0=auto_predict_f0,
        cluster_infer_ratio=cluster_infer_ratio,
        noise_scale=noise_scale,
        f0_method=f0_method,
        # slice config
        db_thresh=db_thresh,
        pad_seconds=pad_seconds,
        chunk_seconds=chunk_seconds,
        # realtime config
        crossfade_seconds=crossfade_seconds,
        additional_infer_before_seconds=additional_infer_before_seconds,
        additional_infer_after_seconds=additional_infer_after_seconds,
        block_seconds=block_seconds,
        version=version,
        input_device=input_device,
        output_device=output_device,
        device=device,
        passthrough_original=passthrough_original,
    )


@cli.command()
@click.option(
    "-i",
    "--input-dir",
    type=click.Path(exists=True),
    default=Path("./dataset_raw"),
    help="path to source dir",
)
@click.option(
    "-o",
    "--output-dir",
    type=click.Path(),
    default=Path("./dataset/44k"),
    help="path to output dir",
)
@click.option("-s", "--sampling-rate", type=int, default=44100, help="sampling rate")
@click.option(
    "-n",
    "--n-jobs",
    type=int,
    default=-1,
    help="number of jobs (optimal value may depend on your RAM capacity and audio duration per file)",
)
@click.option("-d", "--top-db", type=float, default=30, help="top db")
@click.option("-f", "--frame-seconds", type=float, default=1, help="frame seconds")
@click.option(
    "-ho", "-hop", "--hop-seconds", type=float, default=0.3, help="hop seconds"
)
def pre_resample(
    input_dir: Path,
    output_dir: Path,
    sampling_rate: int,
    n_jobs: int,
    top_db: int,
    frame_seconds: float,
    hop_seconds: float,
) -> None:
    """Preprocessing part 1: resample"""
    from so_vits_svc_fork.preprocessing.preprocess_resample import preprocess_resample

    input_dir = Path(input_dir)
    output_dir = Path(output_dir)
    preprocess_resample(
        input_dir=input_dir,
        output_dir=output_dir,
        sampling_rate=sampling_rate,
        n_jobs=n_jobs,
        top_db=top_db,
        frame_seconds=frame_seconds,
        hop_seconds=hop_seconds,
    )


from so_vits_svc_fork.preprocessing.preprocess_flist_config import CONFIG_TEMPLATE_DIR


@cli.command()
@click.option(
    "-i",
    "--input-dir",
    type=click.Path(exists=True),
    default=Path("./dataset/44k"),
    help="path to source dir",
)
@click.option(
    "-f",
    "--filelist-path",
    type=click.Path(),
    default=Path("./filelists/44k"),
    help="path to filelist dir",
)
@click.option(
    "-c",
    "--config-path",
    type=click.Path(),
    default=Path("./configs/44k/config.json"),
    help="path to config",
)
@click.option(
    "-t",
    "--config-type",
    type=click.Choice([x.stem for x in CONFIG_TEMPLATE_DIR.rglob("*.json")]),
    default="so-vits-svc-4.0v1",
    help="config type",
)
def pre_config(
    input_dir: Path,
    filelist_path: Path,
    config_path: Path,
    config_type: str,
):
    """Preprocessing part 2: config"""
    from so_vits_svc_fork.preprocessing.preprocess_flist_config import preprocess_config

    input_dir = Path(input_dir)
    filelist_path = Path(filelist_path)
    config_path = Path(config_path)
    preprocess_config(
        input_dir=input_dir,
        train_list_path=filelist_path / "train.txt",
        val_list_path=filelist_path / "val.txt",
        test_list_path=filelist_path / "test.txt",
        config_path=config_path,
        config_name=config_type,
    )


@cli.command()
@click.option(
    "-i",
    "--input-dir",
    type=click.Path(exists=True),
    default=Path("./dataset/44k"),
    help="path to source dir",
)
@click.option(
    "-c",
    "--config-path",
    type=click.Path(exists=True),
    help="path to config",
    default=Path("./configs/44k/config.json"),
)
@click.option(
    "-n",
    "--n-jobs",
    type=int,
    default=None,
    help="number of jobs (optimal value may depend on your VRAM capacity and audio duration per file)",
)
@click.option(
    "-f/-nf",
    "--force-rebuild/--no-force-rebuild",
    type=bool,
    default=True,
    help="force rebuild existing preprocessed files",
)
@click.option(
    "-fm",
    "--f0-method",
    type=click.Choice(["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"]),
    default="dio",
)
def pre_hubert(
    input_dir: Path,
    config_path: Path,
    n_jobs: bool,
    force_rebuild: bool,
    f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"],
) -> None:
    """Preprocessing part 3: hubert
    If the HuBERT model is not found, it will be downloaded automatically."""
    from so_vits_svc_fork.preprocessing.preprocess_hubert_f0 import preprocess_hubert_f0

    input_dir = Path(input_dir)
    config_path = Path(config_path)
    preprocess_hubert_f0(
        input_dir=input_dir,
        config_path=config_path,
        n_jobs=n_jobs,
        force_rebuild=force_rebuild,
        f0_method=f0_method,
    )


@cli.command()
@click.option(
    "-i",
    "--input-dir",
    type=click.Path(exists=True),
    default=Path("./dataset_raw_raw/"),
    help="path to source dir",
)
@click.option(
    "-o",
    "--output-dir",
    type=click.Path(),
    default=Path("./dataset_raw/"),
    help="path to output dir",
)
@click.option(
    "-n",
    "--n-jobs",
    type=int,
    default=-1,
    help="number of jobs (optimal value may depend on your VRAM capacity and audio duration per file)",
)
@click.option("-min", "--min-speakers", type=int, default=2, help="min speakers")
@click.option("-max", "--max-speakers", type=int, default=2, help="max speakers")
@click.option(
    "-t", "--huggingface-token", type=str, default=None, help="huggingface token"
)
@click.option("-s", "--sr", type=int, default=44100, help="sampling rate")
def pre_sd(
    input_dir: Path | str,
    output_dir: Path | str,
    min_speakers: int,
    max_speakers: int,
    huggingface_token: str | None,
    n_jobs: int,
    sr: int,
):
    """Speech diarization using pyannote.audio"""
    if huggingface_token is None:
        huggingface_token = os.environ.get("HUGGINGFACE_TOKEN", None)
    if huggingface_token is None:
        huggingface_token = click.prompt(
            "Please enter your HuggingFace token", hide_input=True
        )
    if os.environ.get("HUGGINGFACE_TOKEN", None) is None:
        LOG.info("You can also set the HUGGINGFACE_TOKEN environment variable.")
    assert huggingface_token is not None
    huggingface_token = huggingface_token.rstrip(" \n\r\t\0")
    if len(huggingface_token) <= 1:
        raise ValueError("HuggingFace token is empty: " + huggingface_token)

    if max_speakers == 1:
        LOG.warning("Consider using pre-split if max_speakers == 1")
    from so_vits_svc_fork.preprocessing.preprocess_speaker_diarization import (
        preprocess_speaker_diarization,
    )

    preprocess_speaker_diarization(
        input_dir=input_dir,
        output_dir=output_dir,
        min_speakers=min_speakers,
        max_speakers=max_speakers,
        huggingface_token=huggingface_token,
        n_jobs=n_jobs,
        sr=sr,
    )


@cli.command()
@click.option(
    "-i",
    "--input-dir",
    type=click.Path(exists=True),
    default=Path("./dataset_raw_raw/"),
    help="path to source dir",
)
@click.option(
    "-o",
    "--output-dir",
    type=click.Path(),
    default=Path("./dataset_raw/"),
    help="path to output dir",
)
@click.option(
    "-n",
    "--n-jobs",
    type=int,
    default=-1,
    help="number of jobs (optimal value may depend on your RAM capacity and audio duration per file)",
)
@click.option(
    "-l",
    "--max-length",
    type=float,
    default=10,
    help="max length of each split in seconds",
)
@click.option("-d", "--top-db", type=float, default=30, help="top db")
@click.option("-f", "--frame-seconds", type=float, default=1, help="frame seconds")
@click.option(
    "-ho", "-hop", "--hop-seconds", type=float, default=0.3, help="hop seconds"
)
@click.option("-s", "--sr", type=int, default=44100, help="sample rate")
def pre_split(
    input_dir: Path | str,
    output_dir: Path | str,
    max_length: float,
    top_db: int,
    frame_seconds: float,
    hop_seconds: float,
    n_jobs: int,
    sr: int,
):
    """Split audio files into multiple files"""
    from so_vits_svc_fork.preprocessing.preprocess_split import preprocess_split

    preprocess_split(
        input_dir=input_dir,
        output_dir=output_dir,
        max_length=max_length,
        top_db=top_db,
        frame_seconds=frame_seconds,
        hop_seconds=hop_seconds,
        n_jobs=n_jobs,
        sr=sr,
    )


@cli.command()
@click.option(
    "-i",
    "--input-dir",
    type=click.Path(exists=True),
    required=True,
    help="path to source dir",
)
@click.option(
    "-o",
    "--output-dir",
    type=click.Path(),
    default=None,
    help="path to output dir",
)
@click.option(
    "-c/-nc",
    "--create-new/--no-create-new",
    type=bool,
    default=True,
    help="create a new folder for the speaker if not exist",
)
def pre_classify(
    input_dir: Path | str,
    output_dir: Path | str | None,
    create_new: bool,
) -> None:
    """Classify multiple audio files into multiple files"""
    from so_vits_svc_fork.preprocessing.preprocess_classify import preprocess_classify

    if output_dir is None:
        output_dir = input_dir
    preprocess_classify(
        input_dir=input_dir,
        output_dir=output_dir,
        create_new=create_new,
    )


@cli.command
def clean():
    """Clean up files, only useful if you are using the default file structure"""
    import shutil

    folders = ["dataset", "filelists", "logs"]
    # if pyip.inputYesNo(f"Are you sure you want to delete files in {folders}?") == "yes":
    if input("Are you sure you want to delete files in {folders}?") in ["yes", "y"]:
        for folder in folders:
            if Path(folder).exists():
                shutil.rmtree(folder)
        LOG.info("Cleaned up files")
    else:
        LOG.info("Aborted")


@cli.command
@click.option(
    "-i",
    "--input-path",
    type=click.Path(exists=True),
    help="model path",
    default=Path("./logs/44k/"),
)
@click.option(
    "-o",
    "--output-path",
    type=click.Path(),
    help="onnx model path to save",
    default=None,
)
@click.option(
    "-c",
    "--config-path",
    type=click.Path(),
    help="config path",
    default=Path("./configs/44k/config.json"),
)
@click.option(
    "-d",
    "--device",
    type=str,
    default="cpu",
    help="device to use",
)
def onnx(
    input_path: Path, output_path: Path, config_path: Path, device: torch.device | str
) -> None:
    """Export model to onnx (currently not working)"""
    raise NotImplementedError("ONNX export is not yet supported")
    input_path = Path(input_path)
    if input_path.is_dir():
        input_path = list(input_path.glob("*.pth"))[0]
    if output_path is None:
        output_path = input_path.with_suffix(".onnx")
    output_path = Path(output_path)
    if output_path.is_dir():
        output_path = output_path / (input_path.stem + ".onnx")
    config_path = Path(config_path)
    device_ = torch.device(device)
    from so_vits_svc_fork.modules.onnx._export import onnx_export

    onnx_export(
        input_path=input_path,
        output_path=output_path,
        config_path=config_path,
        device=device_,
    )


@cli.command
@click.option(
    "-i",
    "--input-dir",
    type=click.Path(exists=True),
    help="dataset directory",
    default=Path("./dataset/44k"),
)
@click.option(
    "-o",
    "--output-path",
    type=click.Path(),
    help="model path to save",
    default=Path("./logs/44k/kmeans.pt"),
)
@click.option("-n", "--n-clusters", type=int, help="number of clusters", default=2000)
@click.option(
    "-m/-nm", "--minibatch/--no-minibatch", default=True, help="use minibatch k-means"
)
@click.option(
    "-b", "--batch-size", type=int, default=4096, help="batch size for minibatch kmeans"
)
@click.option(
    "-p/-np", "--partial-fit", default=False, help="use partial fit (only use with -m)"
)
def train_cluster(
    input_dir: Path,
    output_path: Path,
    n_clusters: int,
    minibatch: bool,
    batch_size: int,
    partial_fit: bool,
) -> None:
    """Train k-means clustering"""
    from .cluster.train_cluster import main

    main(
        input_dir=input_dir,
        output_path=output_path,
        n_clusters=n_clusters,
        verbose=True,
        use_minibatch=minibatch,
        batch_size=batch_size,
        partial_fit=partial_fit,
    )


if __name__ == "__main__":
    freeze_support()
    cli()