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import json
import os
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
from common.stdout_wrapper import SAFE_STDOUT
from common.log import logger
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:
for line in tqdm(trans_file, file=SAFE_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:
logger.error(
f"An error occurred while generating the training set and validation set, at line:\n{line}\nDetails:\n{e}"
)
raise
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
logger.warning(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
logger.warning(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
logger.info(
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)
logger.info("Training set and validation set generation from texts is complete!")
if __name__ == "__main__":
preprocess()
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