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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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from typing import Dict
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from typing import List
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import numpy as np
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import paddle
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from paddlespeech.t2s.frontend import English
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from paddlespeech.t2s.frontend.ssml.xml_processor import MixTextProcessor
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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class MixFrontend():
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def __init__(self,
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g2p_model="pypinyin",
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phone_vocab_path=None,
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tone_vocab_path=None):
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self.zh_frontend = Frontend(
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phone_vocab_path=phone_vocab_path, tone_vocab_path=tone_vocab_path)
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self.en_frontend = English(phone_vocab_path=phone_vocab_path)
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self.sp_id = self.zh_frontend.vocab_phones["sp"]
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self.sp_id_numpy = np.array([self.sp_id])
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self.sp_id_tensor = paddle.to_tensor([self.sp_id])
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def is_chinese(self, char):
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if char >= '\u4e00' and char <= '\u9fa5':
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return True
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else:
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return False
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def is_alphabet(self, char):
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if (char >= '\u0041' and char <= '\u005a') or (char >= '\u0061' and
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char <= '\u007a'):
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return True
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else:
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return False
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def is_other(self, char):
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if not (self.is_chinese(char) or self.is_alphabet(char)):
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return True
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else:
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return False
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def get_segment(self, text: str) -> List[str]:
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# sentence --> [ch_part, en_part, ch_part, ...]
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segments = []
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types = []
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flag = 0
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temp_seg = ""
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temp_lang = ""
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# Determine the type of each character. type: blank, chinese, alphabet, number, unk and point.
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for ch in text:
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if self.is_chinese(ch):
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types.append("zh")
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elif self.is_alphabet(ch):
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types.append("en")
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else:
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types.append("other")
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assert len(types) == len(text)
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for i in range(len(types)):
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# find the first char of the seg
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if flag == 0:
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temp_seg += text[i]
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temp_lang = types[i]
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flag = 1
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else:
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if temp_lang == "other":
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if types[i] == temp_lang:
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temp_seg += text[i]
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else:
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temp_seg += text[i]
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temp_lang = types[i]
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else:
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if types[i] == temp_lang:
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temp_seg += text[i]
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elif types[i] == "other":
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temp_seg += text[i]
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else:
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segments.append((temp_seg, temp_lang))
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temp_seg = text[i]
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temp_lang = types[i]
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flag = 1
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segments.append((temp_seg, temp_lang))
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return segments
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def get_input_ids(self,
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sentence: str,
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merge_sentences: bool=False,
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get_tone_ids: bool=False,
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add_sp: bool=True,
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to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
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''' 1. 添加SSML支持，先列出 文字 和 <say-as>标签内容，
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然后添加到tmpSegments数组里
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'''
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d_inputs = MixTextProcessor.get_dom_split(sentence)
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tmpSegments = []
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for instr in d_inputs:
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''' 暂时只支持 say-as '''
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if instr.lower().startswith("<say-as"):
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tmpSegments.append((instr, "zh"))
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else:
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tmpSegments.extend(self.get_segment(instr))
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''' 2. 把zh的merge到一起，避免合成结果中间停顿
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'''
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segments = []
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currentSeg = ["", ""]
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for seg in tmpSegments:
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if seg[1] == "en" or seg[1] == "other":
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if currentSeg[0] == '':
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segments.append(seg)
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else:
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currentSeg[0] = "<speak>" + currentSeg[0] + "</speak>"
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segments.append(tuple(currentSeg))
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segments.append(seg)
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currentSeg = ["", ""]
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else:
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if currentSeg[0] == '':
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currentSeg[0] = seg[0]
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currentSeg[1] = seg[1]
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else:
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currentSeg[0] = currentSeg[0] + seg[0]
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if currentSeg[0] != '':
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currentSeg[0] = "<speak>" + currentSeg[0] + "</speak>"
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segments.append(tuple(currentSeg))
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phones_list = []
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result = {}
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for seg in segments:
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content = seg[0]
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lang = seg[1]
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if content != '':
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if lang == "en":
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input_ids = self.en_frontend.get_input_ids(
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content, merge_sentences=False, to_tensor=to_tensor)
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else:
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''' 3. 把带speak tag的中文和普通文字分开处理
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'''
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if content.strip() != "" and \
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re.match(r".*?<speak>.*?</speak>.*", content, re.DOTALL):
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input_ids = self.zh_frontend.get_input_ids_ssml(
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content,
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merge_sentences=False,
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get_tone_ids=get_tone_ids,
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to_tensor=to_tensor)
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else:
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input_ids = self.zh_frontend.get_input_ids(
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content,
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merge_sentences=False,
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get_tone_ids=get_tone_ids,
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to_tensor=to_tensor)
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if add_sp:
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if to_tensor:
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input_ids["phone_ids"][-1] = paddle.concat(
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[input_ids["phone_ids"][-1], self.sp_id_tensor])
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else:
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input_ids["phone_ids"][-1] = np.concatenate(
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(input_ids["phone_ids"][-1], self.sp_id_numpy))
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for phones in input_ids["phone_ids"]:
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phones_list.append(phones)
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if merge_sentences:
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merge_list = paddle.concat(phones_list)
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# rm the last 'sp' to avoid the noise at the end
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# cause in the training data, no 'sp' in the end
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if (to_tensor and merge_list[-1] == self.sp_id_tensor) or (
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not to_tensor and merge_list[-1] == self.sp_id_numpy):
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merge_list = merge_list[:-1]
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phones_list = []
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phones_list.append(merge_list)
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result["phone_ids"] = phones_list
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return result
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