preprocess_text.py

import json
import os
import sys
from collections import defaultdict
from random import shuffle
from typing import Optional

import click
from tqdm import tqdm

from config import config
from text.cleaner import clean_text

preprocess_text_config = config.preprocess_text_config


@click.command()
@click.option(
    "--transcription-path",
    default=preprocess_text_config.transcription_path,
    type=click.Path(exists=True, file_okay=True, dir_okay=False),
)
@click.option("--cleaned-path", default=preprocess_text_config.cleaned_path)
@click.option("--train-path", default=preprocess_text_config.train_path)
@click.option("--val-path", default=preprocess_text_config.val_path)
@click.option(
    "--config-path",
    default=preprocess_text_config.config_path,
    type=click.Path(exists=True, file_okay=True, dir_okay=False),
)
@click.option("--val-per-lang", default=preprocess_text_config.val_per_lang)
@click.option("--max-val-total", default=preprocess_text_config.max_val_total)
@click.option("--clean/--no-clean", default=preprocess_text_config.clean)
@click.option("-y", "--yml_config")
def preprocess(
    transcription_path: str,
    cleaned_path: Optional[str],
    train_path: str,
    val_path: str,
    config_path: str,
    val_per_lang: int,
    max_val_total: int,
    clean: bool,
    yml_config: str,  # 这个不要删
):
    if cleaned_path == "" or cleaned_path is None:
        cleaned_path = transcription_path + ".cleaned"

    if clean:
        with open(cleaned_path, "w", encoding="utf-8") as out_file:
            with open(transcription_path, "r", encoding="utf-8") as trans_file:
                lines = trans_file.readlines()
                # print(lines, ' ', len(lines))
                if len(lines) != 0:
                    for line in tqdm(lines, file=sys.stdout):
                        try:
                            utt, spk, language, text = line.strip().split("|")
                            norm_text, phones, tones, word2ph = clean_text(
                                text, language
                            )
                            out_file.write(
                                "{}|{}|{}|{}|{}|{}|{}\n".format(
                                    utt,
                                    spk,
                                    language,
                                    norm_text,
                                    " ".join(phones),
                                    " ".join([str(i) for i in tones]),
                                    " ".join([str(i) for i in word2ph]),
                                )
                            )
                        except Exception as e:
                            print(line)
                            print(
                                f"An error occurred while generating the training set and validation set! Details:\n{e}"
                            )

    transcription_path = cleaned_path
    spk_utt_map = defaultdict(list)
    spk_id_map = {}
    current_sid = 0

    with open(transcription_path, "r", encoding="utf-8") as f:
        audioPaths = set()
        countSame = 0
        countNotFound = 0
        for line in f.readlines():
            utt, spk, language, text, phones, tones, word2ph = line.strip().split("|")
            if utt in audioPaths:
                # 过滤数据集错误：相同的音频匹配多个文本，导致后续bert出问题
                print(f"Same audio matches multiple texts: {line}")
                countSame += 1
                continue
            if not os.path.isfile(utt):
                # 过滤数据集错误：不存在对应音频
                print(f"Audio not found: {utt}")
                countNotFound += 1
                continue
            audioPaths.add(utt)
            spk_utt_map[language].append(line)
            if spk not in spk_id_map.keys():
                spk_id_map[spk] = current_sid
                current_sid += 1
        print(
            f"Total repeated audios: {countSame}, Total number of audio not found: {countNotFound}"
        )

    train_list = []
    val_list = []

    for spk, utts in spk_utt_map.items():
        shuffle(utts)
        val_list += utts[:val_per_lang]
        train_list += utts[val_per_lang:]

    shuffle(val_list)
    if len(val_list) > max_val_total:
        train_list += val_list[max_val_total:]
        val_list = val_list[:max_val_total]

    with open(train_path, "w", encoding="utf-8") as f:
        for line in train_list:
            f.write(line)

    with open(val_path, "w", encoding="utf-8") as f:
        for line in val_list:
            f.write(line)

    json_config = json.load(open(config_path, encoding="utf-8"))
    json_config["data"]["spk2id"] = spk_id_map
    json_config["data"]["n_speakers"] = len(spk_id_map)
    # 新增写入：写入训练版本、数据集路径
    # json_config["version"] = latest_version
    json_config["data"]["training_files"] = os.path.normpath(train_path).replace(
        "\\", "/"
    )
    json_config["data"]["validation_files"] = os.path.normpath(val_path).replace(
        "\\", "/"
    )
    with open(config_path, "w", encoding="utf-8") as f:
        json.dump(json_config, f, indent=2, ensure_ascii=False)
    print("Training set and validation set generation from texts is complete!")


if __name__ == "__main__":
    preprocess()