Taffy-Bert / preprocess_text.py
XzJosh's picture
Upload 180 files
1cf1e13
raw
history blame
5.22 kB
import json
from collections import defaultdict
from random import shuffle
from typing import Optional
import os
from tqdm import tqdm
import click
from text.cleaner import clean_text
from config import config
from infer import latest_version
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):
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"生成训练集和验证集时发生错误!, 详细信息:\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"重复音频文本:{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"没有找到对应的音频:{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"总重复音频数:{countSame},总未找到的音频数:{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("训练集和验证集生成完成!")
if __name__ == "__main__":
preprocess()