import argparse
import os
import re
from pathlib import Path
from typing import Dict
from typing import List
from typing import Union
DICT_EN = 'tools/aligner/cmudict-0.7b'
DICT_ZH = 'tools/aligner/simple.lexicon'
MODEL_DIR_EN = 'tools/aligner/vctk_model.zip'
MODEL_DIR_ZH = 'tools/aligner/aishell3_model.zip'
MFA_PHONE_EN = 'tools/aligner/vctk_model/meta.yaml'
MFA_PHONE_ZH = 'tools/aligner/aishell3_model/meta.yaml'
MFA_PATH = 'tools/montreal-forced-aligner/bin'
os.environ['PATH'] = MFA_PATH + '/:' + os.environ['PATH']
def check_phone(label_file: Union[str, Path],
pronunciation_phones: Dict[str, str],
mfa_phones: List[str],
am_phones: List[str],
oov_record: str="./oov_info.txt",
lang: str="zh"):
"""Check whether the phoneme corresponding to the audio text content
is in the phoneme list of the pretrained mfa model to ensure that the alignment is normal.
Check whether the phoneme corresponding to the audio text content
is in the phoneme list of the pretrained am model to ensure finetune (normalize) is normal.
Args:
label_file (Union[str, Path]): label file, format: utt_id|phone seq
pronunciation_phones (dict): pronunciation to phones map dict
mfa_phones (list): the phone list of pretrained mfa model
am_phones (list): the phone list of pretrained mfa model
Returns:
oov_words (list): oov words
oov_files (list): utt id list that exist oov
oov_file_words (dict): the oov file and oov phone in this file
"""
oov_words = []
oov_files = []
oov_file_words = {}
with open(label_file, "r") as f:
for line in f.readlines():
utt_id = line.split("|")[0]
transcription = line.strip().split("|")[1]
transcription = re.sub(
r'[：、，；。？！,.:;"?!”’《》【】<=>{}()（）#&@“”^_|…\\]', '',
transcription)
if lang == "en":
transcription = transcription.upper()
flag = 0
temp_oov_words = []
for word in transcription.split(" "):
if word not in pronunciation_phones.keys():
temp_oov_words.append(word)
flag = 1
if word not in oov_words:
oov_words.append(word)
else:
for p in pronunciation_phones[word]:
if p not in mfa_phones or p not in am_phones:
temp_oov_words.append(word)
flag = 1
if word not in oov_words:
oov_words.append(word)
if flag == 1:
oov_files.append(utt_id)
oov_file_words[utt_id] = temp_oov_words
if oov_record is not None:
with open(oov_record, "w") as fw:
fw.write("oov_words: " + str(oov_words) + "\n")
fw.write("oov_files: " + str(oov_files) + "\n")
fw.write("oov_file_words: " + str(oov_file_words) + "\n")
return oov_words, oov_files, oov_file_words
def get_pronunciation_phones(lexicon_file: Union[str, Path]):
pronunciation_phones = {}
with open(lexicon_file, "r") as f2:
for line in f2.readlines():
line_list = line.strip().split(" ")
pronunciation = line_list[0]
if line_list[1] == '':
phones = line_list[2:]
else:
phones = line_list[1:]
pronunciation_phones[pronunciation] = phones
return pronunciation_phones
def get_mfa_phone(mfa_phone_file: Union[str, Path]):
mfa_phones = []
with open(mfa_phone_file, "r") as f:
for line in f.readlines():
if line.startswith("-"):
phone = line.strip().split(" ")[-1]
mfa_phones.append(phone)
return mfa_phones
def get_am_phone(am_phone_file: Union[str, Path]):
am_phones = []
with open(am_phone_file, "r") as f:
for line in f.readlines():
phone = line.strip().split(" ")[0]
am_phones.append(phone)
return am_phones
def get_check_result(label_file: Union[str, Path],
am_phone_file: Union[str, Path],
input_dir: Union[str, Path],
newdir_name: str="newdir",
lang: str="zh"):
"""Check if there is any audio in the input that contains the oov word according to label_file.
Copy audio that does not contain oov word to input_dir / newdir_name.
Generate label file and save to input_dir / newdir_name.
Args:
label_file (Union[str, Path]): input audio label file, format: utt|pronunciation
am_phone_file (Union[str, Path]): pretrained am model phone file
input_dir (Union[str, Path]): input dir
newdir_name (str): directory name saved after checking oov
lang (str): input audio language
"""
if lang == 'en':
lexicon_file = DICT_EN
mfa_phone_file = MFA_PHONE_EN
elif lang == 'zh':
lexicon_file = DICT_ZH
mfa_phone_file = MFA_PHONE_ZH
else:
print('please input right lang!!')
pronunciation_phones = get_pronunciation_phones(lexicon_file)
mfa_phones = get_mfa_phone(mfa_phone_file)
am_phones = get_am_phone(am_phone_file)
oov_words, oov_files, oov_file_words = check_phone(
label_file=label_file,
pronunciation_phones=pronunciation_phones,
mfa_phones=mfa_phones,
am_phones=am_phones,
oov_record="./oov_info.txt",
lang=lang)
input_dir = Path(input_dir).expanduser()
new_dir = input_dir / newdir_name
new_dir.mkdir(parents=True, exist_ok=True)
with open(label_file, "r") as f:
for line in f.readlines():
utt_id = line.split("|")[0]
if utt_id not in oov_files:
transcription = line.split("|")[1].strip()
wav_file = str(input_dir) + "/" + utt_id + ".wav"
new_wav_file = str(new_dir) + "/" + utt_id + ".wav"
os.system("cp %s %s" % (wav_file, new_wav_file))
single_file = str(new_dir) + "/" + utt_id + ".txt"
with open(single_file, "w") as fw:
fw.write(transcription)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Preprocess audio and then extract features.")
parser.add_argument(
"--input_dir",
type=str,
default="./input/csmsc_mini",
help="directory containing audio and label file")
parser.add_argument(
"--pretrained_model_dir",
type=str,
default="./pretrained_models/fastspeech2_aishell3_ckpt_1.1.0",
help="Path to pretrained model")
parser.add_argument(
"--newdir_name",
type=str,
default="newdir",
help="directory name saved after checking oov")
parser.add_argument(
'--lang',
type=str,
default='zh',
choices=['zh', 'en'],
help='Choose input audio language. zh or en')
args = parser.parse_args()
assert args.lang == "zh" or args.lang == "en", "please input right lang! zh or en"
input_dir = Path(args.input_dir).expanduser()
pretrained_model_dir = Path(args.pretrained_model_dir).expanduser()
am_phone_file = pretrained_model_dir / "phone_id_map.txt"
label_file = input_dir / "labels.txt"
get_check_result(
label_file=label_file,
am_phone_file=am_phone_file,
input_dir=input_dir,
newdir_name=args.newdir_name,
lang=args.lang)