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PaddleSpeech/paddlespeech/t2s/exps/voice_cloning/tacotron2_ge2e/preprocess_transcription.py

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import pickle
import re
from pathlib import Path
import tqdm
import yaml
zh_pattern = re.compile("[\u4e00-\u9fa5]")
_tones = {'<pad>', '<s>', '</s>', '0', '1', '2', '3', '4', '5'}
_pauses = {'%', '$'}
_initials = {
'b',
'p',
'm',
'f',
'd',
't',
'n',
'l',
'g',
'k',
'h',
'j',
'q',
'x',
'zh',
'ch',
'sh',
'r',
'z',
'c',
's',
}
_finals = {
'ii',
'iii',
'a',
'o',
'e',
'ea',
'ai',
'ei',
'ao',
'ou',
'an',
'en',
'ang',
'eng',
'er',
'i',
'ia',
'io',
'ie',
'iai',
'iao',
'iou',
'ian',
'ien',
'iang',
'ieng',
'u',
'ua',
'uo',
'uai',
'uei',
'uan',
'uen',
'uang',
'ueng',
'v',
've',
'van',
'ven',
'veng',
}
_ernized_symbol = {'&r'}
_specials = {'<pad>', '<unk>', '<s>', '</s>'}
_phones = _initials | _finals | _ernized_symbol | _specials | _pauses
def is_zh(word):
global zh_pattern
match = zh_pattern.search(word)
return match is not None
def ernized(syllable):
return syllable[:2] != "er" and syllable[-2] == 'r'
def convert(syllable):
# expansion of o -> uo
syllable = re.sub(r"([bpmf])o$", r"\1uo", syllable)
# syllable = syllable.replace("bo", "buo").replace("po", "puo").replace("mo", "muo").replace("fo", "fuo")
# expansion for iong, ong
syllable = syllable.replace("iong", "veng").replace("ong", "ueng")
# expansion for ing, in
syllable = syllable.replace("ing", "ieng").replace("in", "ien")
# expansion for un, ui, iu
syllable = syllable.replace("un", "uen").replace("ui",
"uei").replace("iu", "iou")
# rule for variants of i
syllable = syllable.replace("zi", "zii").replace("ci", "cii").replace("si", "sii")\
.replace("zhi", "zhiii").replace("chi", "chiii").replace("shi", "shiii")\
.replace("ri", "riii")
# rule for y preceding i, u
syllable = syllable.replace("yi", "i").replace("yu", "v").replace("y", "i")
# rule for w
syllable = syllable.replace("wu", "u").replace("w", "u")
# rule for v following j, q, x
syllable = syllable.replace("ju", "jv").replace("qu",
"qv").replace("xu", "xv")
return syllable
def split_syllable(syllable: str):
"""Split a syllable in pinyin into a list of phones and a list of tones.
Initials have no tone, represented by '0', while finals have tones from
'1,2,3,4,5'.
e.g.
zhang -> ['zh', 'ang'], ['0', '1']
"""
if syllable in _pauses:
# syllable, tone
return [syllable], ['0']
tone = syllable[-1]
syllable = convert(syllable[:-1])
phones = []
tones = []
global _initials
if syllable[:2] in _initials:
phones.append(syllable[:2])
tones.append('0')
phones.append(syllable[2:])
tones.append(tone)
elif syllable[0] in _initials:
phones.append(syllable[0])
tones.append('0')
phones.append(syllable[1:])
tones.append(tone)
else:
phones.append(syllable)
tones.append(tone)
return phones, tones
def load_aishell3_transcription(line: str):
sentence_id, pinyin, text = line.strip().split("|")
syllables = pinyin.strip().split()
results = []
for syllable in syllables:
if syllable in _pauses:
results.append(syllable)
elif not ernized(syllable):
results.append(syllable)
else:
results.append(syllable[:-2] + syllable[-1])
results.append('&r5')
phones = []
tones = []
for syllable in results:
p, t = split_syllable(syllable)
phones.extend(p)
tones.extend(t)
for p in phones:
assert p in _phones, p
return {
"sentence_id": sentence_id,
"text": text,
"syllables": results,
"phones": phones,
"tones": tones
}
def process_aishell3(dataset_root, output_dir):
dataset_root = Path(dataset_root).expanduser()
output_dir = Path(output_dir).expanduser()
output_dir.mkdir(parents=True, exist_ok=True)
prosody_label_path = dataset_root / "label_train-set.txt"
with open(prosody_label_path, 'rt') as f:
lines = [line.strip() for line in f]
records = lines[5:]
processed_records = []
for record in tqdm.tqdm(records):
new_record = load_aishell3_transcription(record)
processed_records.append(new_record)
print(new_record)
with open(output_dir / "metadata.pickle", 'wb') as f:
pickle.dump(processed_records, f)
with open(output_dir / "metadata.yaml", 'wt', encoding="utf-8") as f:
yaml.safe_dump(
processed_records, f, default_flow_style=None, allow_unicode=True)
print("metadata done!")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Preprocess transcription of AiShell3 and save them in a compact file(yaml and pickle)."
)
parser.add_argument(
"--input",
type=str,
default="~/datasets/aishell3/train",
help="path of the training dataset,(contains a label_train-set.txt).")
parser.add_argument(
"--output",
type=str,
help="the directory to save the processed transcription."
"If not provided, it would be the same as the input.")
args = parser.parse_args()
if args.output is None:
args.output = args.input
process_aishell3(args.input, args.output)