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PaddleSpeech/examples/g2p/zh/local/ignore_sandhi.py

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3.8 KiB

# 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
from pathlib import Path
from typing import List
from typing import Union
def erized(syllable: str) -> bool:
"""Whether the syllable contains erhua effect.
Example
--------
huar -> True
guanr -> True
er -> False
"""
# note: for pinyin, len(syllable) >=2 is always true
# if not: there is something wrong in the data
assert len(syllable) >= 2, f"inavlid syllable {syllable}"
return syllable[:2] != "er" and syllable[-2] == 'r'
def ignore_sandhi(reference: List[str], generated: List[str]) -> List[str]:
"""
Given a sequence of syllables from human annotation(reference),
which makes sandhi explici and a sequence of syllables from some
simple g2p program(generated), which does not consider sandhi,
return a the reference sequence while ignore sandhi.
Example
--------
['lao2', 'hu3'], ['lao3', 'hu3'] -> ['lao3', 'hu3']
"""
i = 0
j = 0
# sandhi ignored in the result while other errors are not included
result = []
while i < len(reference):
if erized(reference[i]):
result.append(reference[i])
i += 1
j += 2
elif reference[i][:-1] == generated[i][:-1] and reference[i][
-1] == '2' and generated[i][-1] == '3':
result.append(generated[i])
i += 1
j += 1
else:
result.append(reference[i])
i += 1
j += 1
assert j == len(
generated
), "length of transcriptions mismatch, There may be some characters that are ignored in the generated transcription."
return result
def convert_transcriptions(reference: Union[str, Path],
generated: Union[str, Path],
output: Union[str, Path]):
with open(reference, 'rt') as f_ref:
with open(generated, 'rt') as f_gen:
with open(output, 'wt') as f_out:
for i, (ref, gen) in enumerate(zip(f_ref, f_gen)):
sentence_id, ref_transcription = ref.strip().split(' ', 1)
_, gen_transcription = gen.strip().split(' ', 1)
try:
result = ignore_sandhi(ref_transcription.split(),
gen_transcription.split())
result = ' '.join(result)
except Exception:
print(
f"sentence_id: {sentence_id} There is some annotation error in the reference or generated transcription. Use the reference."
)
result = ref_transcription
f_out.write(f"{sentence_id} {result}\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="reference transcription but ignore sandhi.")
parser.add_argument(
"--reference",
type=str,
help="path to the reference transcription of baker dataset.")
parser.add_argument(
"--generated", type=str, help="path to the generated transcription.")
parser.add_argument("--output", type=str, help="path to save result.")
args = parser.parse_args()
convert_transcriptions(args.reference, args.generated, args.output)