Source code for paddlespeech.t2s.frontend.mix_frontend
# 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 re
from typing import Dict
from typing import List
import numpy as np
import paddle
from paddlespeech.t2s.frontend.en_frontend import English as EnFrontend
from paddlespeech.t2s.frontend.ssml.xml_processor import MixTextProcessor
from paddlespeech.t2s.frontend.zh_frontend import Frontend as ZhFrontend
[docs]class MixFrontend():
def __init__(self,
g2p_model="pypinyin",
phone_vocab_path=None,
tone_vocab_path=None):
self.zh_frontend = ZhFrontend(
phone_vocab_path=phone_vocab_path, tone_vocab_path=tone_vocab_path)
self.en_frontend = EnFrontend(phone_vocab_path=phone_vocab_path)
self.sp_id = self.zh_frontend.vocab_phones["sp"]
self.sp_id_numpy = np.array([self.sp_id])
self.sp_id_tensor = paddle.to_tensor([self.sp_id])
[docs] def is_chinese(self, char):
if char >= '\u4e00' and char <= '\u9fa5':
return True
else:
return False
[docs] def is_alphabet(self, char):
if (char >= '\u0041' and char <= '\u005a') or (char >= '\u0061' and
char <= '\u007a'):
return True
else:
return False
[docs] def is_other(self, char):
if not (self.is_chinese(char) or self.is_alphabet(char)):
return True
else:
return False
[docs] def split_by_lang(self, text: str) -> List[str]:
# sentence --> [ch_part, en_part, ch_part, ...]
segments = []
types = []
# Determine the type of each character. type: chinese, alphabet, other.
for ch in text:
if self.is_chinese(ch):
types.append("zh")
elif self.is_alphabet(ch):
types.append("en")
else:
types.append("other")
assert len(types) == len(text)
flag = 0
temp_seg = ""
temp_lang = ""
for i in range(len(text)):
# find the first char of the seg
if flag == 0:
temp_seg += text[i]
temp_lang = types[i]
flag = 1
else:
if temp_lang == "other":
# text start is not lang.
temp_seg += text[i]
if types[i] != temp_lang:
temp_lang = types[i]
else:
if types[i] == temp_lang or types[i] == "other":
# merge same lang or other
temp_seg += text[i]
else:
# change lang
segments.append((temp_seg, temp_lang))
temp_seg = text[i]
temp_lang = types[i] # new lang
segments.append((temp_seg, temp_lang))
return segments
[docs] def get_input_ids(self,
sentence: str,
merge_sentences: bool=False,
get_tone_ids: bool=False,
add_sp: bool=True,
to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
# XML Document Object Model (DOM)
doms = MixTextProcessor.get_dom_split(sentence)
lang_splits = []
for dom in doms:
if dom.lower().startswith("<say-as pinyin="):
# `<say-as pinyin=` for zh lang
lang_splits.append((dom, "zh"))
else:
# process zh, en and zh/en
lang_splits.extend(self.split_by_lang(dom))
# merge adjacent zh segment
segments = []
currentSeg = ["", ""]
for seg in lang_splits:
if seg[1] == "en" or seg[1] == "other":
if currentSeg[0] == '':
# first see
segments.append(seg)
else:
# zh
currentSeg[0] = "<speak>" + currentSeg[0] + "</speak>"
segments.append(tuple(currentSeg))
# en
segments.append(seg)
# reset
currentSeg = ["", ""]
else:
# zh
if currentSeg[0] == '':
# first see
currentSeg[0] = seg[0]
currentSeg[1] = seg[1]
else:
# merge zh
currentSeg[0] = currentSeg[0] + seg[0]
if currentSeg[0] != '':
# last zh
currentSeg[0] = "<speak>" + currentSeg[0] + "</speak>"
segments.append(tuple(currentSeg))
phones_list = []
result = {}
# 008 我们要去云南 team building, 非常非常 happy.
# seg ('我们要去云南 ', 'zh')
# seg ('team building, ', 'en')
# seg ('非常非常 ', 'zh')
# seg ('happy.', 'en')
# [('<speak>我们要去云南 </speak>', 'zh'), ('team building, ', 'en'), ('<speak>非常非常 </speak>', 'zh'), ('happy.', 'en')]
for seg in segments:
content = seg[0]
lang = seg[1]
if not content:
continue
if lang == "en":
input_ids = self.en_frontend.get_input_ids(
content, merge_sentences=False, to_tensor=to_tensor)
else:
if content.strip() != "" and \
re.match(r".*?<speak>.*?</speak>.*", content, re.DOTALL):
# process ssml
input_ids = self.zh_frontend.get_input_ids_ssml(
content,
merge_sentences=False,
get_tone_ids=get_tone_ids,
to_tensor=to_tensor)
else:
# process plain text
input_ids = self.zh_frontend.get_input_ids(
content,
merge_sentences=False,
get_tone_ids=get_tone_ids,
to_tensor=to_tensor)
if add_sp:
# add sp between zh and en
if to_tensor:
input_ids["phone_ids"][-1] = paddle.concat(
[input_ids["phone_ids"][-1], self.sp_id_tensor])
else:
input_ids["phone_ids"][-1] = np.concatenate(
(input_ids["phone_ids"][-1], self.sp_id_numpy))
phones_list.extend(input_ids["phone_ids"])
if merge_sentences:
merge_list = paddle.concat(phones_list)
# rm the last 'sp' to avoid the noise at the end
# cause in the training data, no 'sp' in the end
if (to_tensor and merge_list[-1] == self.sp_id_tensor) or (
not to_tensor and merge_list[-1] == self.sp_id_numpy):
merge_list = merge_list[:-1]
phones_list = []
phones_list.append(merge_list)
result["phone_ids"] = phones_list
return result