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114 lines
4.2 KiB
114 lines
4.2 KiB
# 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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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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import ToJyutping
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from paddlespeech.t2s.frontend.zh_normalization.text_normlization import TextNormalizer
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INITIALS = [
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'aa', 'aai', 'aak', 'aap', 'aat', 'aau', 'ai', 'au', 'ap', 'at', 'ak', 'a',
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'p', 'b', 'e', 'ts', 't', 'dz', 'd', 'kw', 'k', 'gw', 'g', 'f', 'h', 'l',
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'm', 'ng', 'n', 's', 'y', 'w', 'c', 'z', 'j', 'ong', 'on', 'ou', 'oi', 'ok',
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'o', 'uk', 'ung'
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]
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INITIALS += ['sp', 'spl', 'spn', 'sil']
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def get_lines(cantons: List[str]):
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phones = []
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for canton in cantons:
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for consonant in INITIALS:
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if canton.startswith(consonant):
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if canton.startswith("nga"):
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c, v = canton[:len(consonant)], canton[len(consonant):]
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phones = phones + [canton[2:]]
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else:
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c, v = canton[:len(consonant)], canton[len(consonant):]
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phones = phones + [c, v]
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break
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return phones
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class CantonFrontend():
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def __init__(self, phone_vocab_path: str):
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self.text_normalizer = TextNormalizer()
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self.punc = "：，；。？！“”‘’':,;.?!"
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self.vocab_phones = {}
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if phone_vocab_path:
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with open(phone_vocab_path, 'rt', encoding='utf-8') as f:
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phn_id = [line.strip().split() for line in f.readlines()]
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for phn, id in phn_id:
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self.vocab_phones[phn] = int(id)
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# if merge_sentences, merge all sentences into one phone sequence
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def _g2p(self, sentences: List[str],
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merge_sentences: bool=True) -> List[List[str]]:
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phones_list = []
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for sentence in sentences:
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phones_str = ToJyutping.get_jyutping_text(sentence)
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phones_split = get_lines(phones_str.split(' '))
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phones_list.append(phones_split)
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return phones_list
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def _p2id(self, phonemes: List[str]) -> np.ndarray:
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# replace unk phone with sp
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phonemes = [
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phn if phn in self.vocab_phones else "sp" for phn in phonemes
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]
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phone_ids = [self.vocab_phones[item] for item in phonemes]
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return np.array(phone_ids, np.int64)
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def get_phonemes(self,
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sentence: str,
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merge_sentences: bool=True,
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print_info: bool=False) -> List[List[str]]:
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sentences = self.text_normalizer.normalize(sentence)
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phonemes = self._g2p(sentences, merge_sentences=merge_sentences)
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if print_info:
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print("----------------------------")
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print("text norm results:")
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print(sentences)
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print("----------------------------")
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print("g2p results:")
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print(phonemes)
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print("----------------------------")
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return phonemes
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def get_input_ids(self,
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sentence: str,
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merge_sentences: bool=True,
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print_info: bool=False,
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to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
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phonemes = self.get_phonemes(
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sentence, merge_sentences=merge_sentences, print_info=print_info)
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result = {}
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temp_phone_ids = []
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for phones in phonemes:
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if phones:
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phone_ids = self._p2id(phones)
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# if use paddle.to_tensor() in onnxruntime, the first time will be too low
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if to_tensor:
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phone_ids = paddle.to_tensor(phone_ids)
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temp_phone_ids.append(phone_ids)
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if temp_phone_ids:
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result["phone_ids"] = temp_phone_ids
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return result
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