You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
341 lines
10 KiB
341 lines
10 KiB
# Copyright (c) 2020 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.
|
|
from abc import ABC
|
|
from abc import abstractmethod
|
|
from typing import List
|
|
|
|
import numpy as np
|
|
import paddle
|
|
from g2p_en import G2p
|
|
from g2pM import G2pM
|
|
|
|
from paddlespeech.t2s.frontend.normalizer.normalizer import normalize
|
|
from paddlespeech.t2s.frontend.punctuation import get_punctuations
|
|
from paddlespeech.t2s.frontend.vocab import Vocab
|
|
from paddlespeech.t2s.frontend.zh_normalization.text_normlization import TextNormalizer
|
|
|
|
# discard opencc untill we find an easy solution to install it on windows
|
|
# from opencc import OpenCC
|
|
|
|
__all__ = ["Phonetics", "English", "EnglishCharacter", "Chinese"]
|
|
|
|
|
|
class Phonetics(ABC):
|
|
@abstractmethod
|
|
def __call__(self, sentence):
|
|
pass
|
|
|
|
@abstractmethod
|
|
def phoneticize(self, sentence):
|
|
pass
|
|
|
|
@abstractmethod
|
|
def numericalize(self, phonemes):
|
|
pass
|
|
|
|
|
|
class English(Phonetics):
|
|
""" Normalize the input text sequence and convert into pronunciation id sequence.
|
|
"""
|
|
|
|
def __init__(self, phone_vocab_path=None):
|
|
self.backend = G2p()
|
|
self.phonemes = list(self.backend.phonemes)
|
|
self.punctuations = get_punctuations("en")
|
|
self.vocab = Vocab(self.phonemes + self.punctuations)
|
|
self.vocab_phones = {}
|
|
self.punc = "：，；。？！“”‘’':,;.?!"
|
|
self.text_normalizer = TextNormalizer()
|
|
if phone_vocab_path:
|
|
with open(phone_vocab_path, 'rt') as f:
|
|
phn_id = [line.strip().split() for line in f.readlines()]
|
|
for phn, id in phn_id:
|
|
self.vocab_phones[phn] = int(id)
|
|
|
|
def phoneticize(self, sentence):
|
|
""" Normalize the input text sequence and convert it into pronunciation sequence.
|
|
Parameters
|
|
-----------
|
|
sentence: str
|
|
The input text sequence.
|
|
Returns
|
|
----------
|
|
List[str]
|
|
The list of pronunciation sequence.
|
|
"""
|
|
start = self.vocab.start_symbol
|
|
end = self.vocab.end_symbol
|
|
phonemes = ([] if start is None else [start]) \
|
|
+ self.backend(sentence) \
|
|
+ ([] if end is None else [end])
|
|
phonemes = [item for item in phonemes if item in self.vocab.stoi]
|
|
return phonemes
|
|
|
|
def _p2id(self, phonemes: List[str]) -> np.array:
|
|
# replace unk phone with sp
|
|
phonemes = [
|
|
phn if (phn in self.vocab_phones and phn not in self.punc) else "sp"
|
|
for phn in phonemes
|
|
]
|
|
phone_ids = [self.vocab_phones[item] for item in phonemes]
|
|
return np.array(phone_ids, np.int64)
|
|
|
|
def get_input_ids(self, sentence: str,
|
|
merge_sentences: bool=False) -> paddle.Tensor:
|
|
result = {}
|
|
sentences = self.text_normalizer._split(sentence, lang="en")
|
|
phones_list = []
|
|
temp_phone_ids = []
|
|
for sentence in sentences:
|
|
phones = self.phoneticize(sentence)
|
|
# remove start_symbol and end_symbol
|
|
phones = phones[1:-1]
|
|
phones = [phn for phn in phones if not phn.isspace()]
|
|
phones_list.append(phones)
|
|
|
|
if merge_sentences:
|
|
merge_list = sum(phones_list, [])
|
|
# rm the last 'sp' to avoid the noise at the end
|
|
# cause in the training data, no 'sp' in the end
|
|
if merge_list[-1] == 'sp':
|
|
merge_list = merge_list[:-1]
|
|
phones_list = []
|
|
phones_list.append(merge_list)
|
|
|
|
for part_phones_list in phones_list:
|
|
phone_ids = self._p2id(part_phones_list)
|
|
phone_ids = paddle.to_tensor(phone_ids)
|
|
temp_phone_ids.append(phone_ids)
|
|
result["phone_ids"] = temp_phone_ids
|
|
return result
|
|
|
|
def numericalize(self, phonemes):
|
|
""" Convert pronunciation sequence into pronunciation id sequence.
|
|
Parameters
|
|
-----------
|
|
phonemes: List[str]
|
|
The list of pronunciation sequence.
|
|
Returns
|
|
----------
|
|
List[int]
|
|
The list of pronunciation id sequence.
|
|
"""
|
|
ids = [
|
|
self.vocab.lookup(item) for item in phonemes
|
|
if item in self.vocab.stoi
|
|
]
|
|
return ids
|
|
|
|
def reverse(self, ids):
|
|
""" Reverse the list of pronunciation id sequence to a list of pronunciation sequence.
|
|
Parameters
|
|
-----------
|
|
ids: List[int]
|
|
The list of pronunciation id sequence.
|
|
Returns
|
|
----------
|
|
List[str]
|
|
The list of pronunciation sequence.
|
|
"""
|
|
return [self.vocab.reverse(i) for i in ids]
|
|
|
|
def __call__(self, sentence):
|
|
""" Convert the input text sequence into pronunciation id sequence.
|
|
Parameters
|
|
-----------
|
|
sentence: str
|
|
The input text sequence.
|
|
Returns
|
|
----------
|
|
List[str]
|
|
The list of pronunciation id sequence.
|
|
"""
|
|
return self.numericalize(self.phoneticize(sentence))
|
|
|
|
@property
|
|
def vocab_size(self):
|
|
""" Vocab size.
|
|
"""
|
|
return len(self.vocab)
|
|
|
|
|
|
class EnglishCharacter(Phonetics):
|
|
""" Normalize the input text sequence and convert it into character id sequence.
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.backend = G2p()
|
|
self.graphemes = list(self.backend.graphemes)
|
|
self.punctuations = get_punctuations("en")
|
|
self.vocab = Vocab(self.graphemes + self.punctuations)
|
|
|
|
def phoneticize(self, sentence):
|
|
""" Normalize the input text sequence.
|
|
Parameters
|
|
-----------
|
|
sentence: str
|
|
The input text sequence.
|
|
Returns
|
|
----------
|
|
str
|
|
A text sequence after normalize.
|
|
"""
|
|
words = normalize(sentence)
|
|
return words
|
|
|
|
def numericalize(self, sentence):
|
|
""" Convert a text sequence into ids.
|
|
Parameters
|
|
-----------
|
|
sentence: str
|
|
The input text sequence.
|
|
Returns
|
|
----------
|
|
List[int]
|
|
List of a character id sequence.
|
|
"""
|
|
ids = [
|
|
self.vocab.lookup(item) for item in sentence
|
|
if item in self.vocab.stoi
|
|
]
|
|
return ids
|
|
|
|
def reverse(self, ids):
|
|
""" Convert a character id sequence into text.
|
|
Parameters
|
|
-----------
|
|
ids: List[int]
|
|
List of a character id sequence.
|
|
Returns
|
|
----------
|
|
str
|
|
The input text sequence.
|
|
"""
|
|
return [self.vocab.reverse(i) for i in ids]
|
|
|
|
def __call__(self, sentence):
|
|
""" Normalize the input text sequence and convert it into character id sequence.
|
|
Parameters
|
|
-----------
|
|
sentence: str
|
|
The input text sequence.
|
|
Returns
|
|
----------
|
|
List[int]
|
|
List of a character id sequence.
|
|
"""
|
|
return self.numericalize(self.phoneticize(sentence))
|
|
|
|
@property
|
|
def vocab_size(self):
|
|
""" Vocab size.
|
|
"""
|
|
return len(self.vocab)
|
|
|
|
|
|
class Chinese(Phonetics):
|
|
"""Normalize Chinese text sequence and convert it into ids.
|
|
"""
|
|
|
|
def __init__(self):
|
|
# self.opencc_backend = OpenCC('t2s.json')
|
|
self.backend = G2pM()
|
|
self.phonemes = self._get_all_syllables()
|
|
self.punctuations = get_punctuations("cn")
|
|
self.vocab = Vocab(self.phonemes + self.punctuations)
|
|
|
|
def _get_all_syllables(self):
|
|
all_syllables = set([
|
|
syllable for k, v in self.backend.cedict.items() for syllable in v
|
|
])
|
|
return list(all_syllables)
|
|
|
|
def phoneticize(self, sentence):
|
|
""" Normalize the input text sequence and convert it into pronunciation sequence.
|
|
Parameters
|
|
-----------
|
|
sentence: str
|
|
The input text sequence.
|
|
Returns
|
|
----------
|
|
List[str]
|
|
The list of pronunciation sequence.
|
|
"""
|
|
# simplified = self.opencc_backend.convert(sentence)
|
|
simplified = sentence
|
|
phonemes = self.backend(simplified)
|
|
start = self.vocab.start_symbol
|
|
end = self.vocab.end_symbol
|
|
phonemes = ([] if start is None else [start]) \
|
|
+ phonemes \
|
|
+ ([] if end is None else [end])
|
|
return self._filter_symbols(phonemes)
|
|
|
|
def _filter_symbols(self, phonemes):
|
|
cleaned_phonemes = []
|
|
for item in phonemes:
|
|
if item in self.vocab.stoi:
|
|
cleaned_phonemes.append(item)
|
|
else:
|
|
for char in item:
|
|
if char in self.vocab.stoi:
|
|
cleaned_phonemes.append(char)
|
|
return cleaned_phonemes
|
|
|
|
def numericalize(self, phonemes):
|
|
""" Convert pronunciation sequence into pronunciation id sequence.
|
|
Parameters
|
|
-----------
|
|
phonemes: List[str]
|
|
The list of pronunciation sequence.
|
|
Returns
|
|
----------
|
|
List[int]
|
|
The list of pronunciation id sequence.
|
|
"""
|
|
ids = [self.vocab.lookup(item) for item in phonemes]
|
|
return ids
|
|
|
|
def __call__(self, sentence):
|
|
""" Convert the input text sequence into pronunciation id sequence.
|
|
Parameters
|
|
-----------
|
|
sentence: str
|
|
The input text sequence.
|
|
Returns
|
|
----------
|
|
List[str]
|
|
The list of pronunciation id sequence.
|
|
"""
|
|
return self.numericalize(self.phoneticize(sentence))
|
|
|
|
@property
|
|
def vocab_size(self):
|
|
""" Vocab size.
|
|
"""
|
|
return len(self.vocab)
|
|
|
|
def reverse(self, ids):
|
|
""" Reverse the list of pronunciation id sequence to a list of pronunciation sequence.
|
|
Parameters
|
|
-----------
|
|
ids: List[int]
|
|
The list of pronunciation id sequence.
|
|
Returns
|
|
----------
|
|
List[str]
|
|
The list of pronunciation sequence.
|
|
"""
|
|
return [self.vocab.reverse(i) for i in ids]
|