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# Copyright (c) 2022 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.
"""
Credits
This code is modified from https://github.com/GitYCC/g2pW
"""
from typing import Dict
from typing import List
from typing import Tuple
import numpy as np
from .utils import tokenize_and_map
ANCHOR_CHAR = '▁'
def prepare_onnx_input(tokenizer,
labels: List[str],
char2phonemes: Dict[str, List[int]],
chars: List[str],
texts: List[str],
query_ids: List[int],
use_mask: bool=False,
window_size: int=None,
max_len: int=512) -> Dict[str, np.array]:
if window_size is not None:
truncated_texts, truncated_query_ids = _truncate_texts(
window_size=window_size, texts=texts, query_ids=query_ids)
input_ids = []
token_type_ids = []
attention_masks = []
phoneme_masks = []
char_ids = []
position_ids = []
for idx in range(len(texts)):
text = (truncated_texts if window_size else texts)[idx].lower()
query_id = (truncated_query_ids if window_size else query_ids)[idx]
try:
tokens, text2token, token2text = tokenize_and_map(
tokenizer=tokenizer, text=text)
except Exception:
print(f'warning: text "{text}" is invalid')
return {}
text, query_id, tokens, text2token, token2text = _truncate(
max_len=max_len,
text=text,
query_id=query_id,
tokens=tokens,
text2token=text2token,
token2text=token2text)
processed_tokens = ['[CLS]'] + tokens + ['[SEP]']
input_id = list(
np.array(tokenizer.convert_tokens_to_ids(processed_tokens)))
token_type_id = list(np.zeros((len(processed_tokens), ), dtype=int))
attention_mask = list(np.ones((len(processed_tokens), ), dtype=int))
query_char = text[query_id]
phoneme_mask = [1 if i in char2phonemes[query_char] else 0 for i in range(len(labels))] \
if use_mask else [1] * len(labels)
char_id = chars.index(query_char)
position_id = text2token[
query_id] + 1 # [CLS] token locate at first place
input_ids.append(input_id)
token_type_ids.append(token_type_id)
attention_masks.append(attention_mask)
phoneme_masks.append(phoneme_mask)
char_ids.append(char_id)
position_ids.append(position_id)
outputs = {
'input_ids': np.array(input_ids).astype(np.int64),
'token_type_ids': np.array(token_type_ids).astype(np.int64),
'attention_masks': np.array(attention_masks).astype(np.int64),
'phoneme_masks': np.array(phoneme_masks).astype(np.float32),
'char_ids': np.array(char_ids).astype(np.int64),
'position_ids': np.array(position_ids).astype(np.int64),
}
return outputs
def _truncate_texts(window_size: int, texts: List[str],
query_ids: List[int]) -> Tuple[List[str], List[int]]:
truncated_texts = []
truncated_query_ids = []
for text, query_id in zip(texts, query_ids):
start = max(0, query_id - window_size // 2)
end = min(len(text), query_id + window_size // 2)
truncated_text = text[start:end]
truncated_texts.append(truncated_text)
truncated_query_id = query_id - start
truncated_query_ids.append(truncated_query_id)
return truncated_texts, truncated_query_ids
def _truncate(max_len: int,
text: str,
query_id: int,
tokens: List[str],
text2token: List[int],
token2text: List[Tuple[int]]):
truncate_len = max_len - 2
if len(tokens) <= truncate_len:
return (text, query_id, tokens, text2token, token2text)
token_position = text2token[query_id]
token_start = token_position - truncate_len // 2
token_end = token_start + truncate_len
font_exceed_dist = -token_start
back_exceed_dist = token_end - len(tokens)
if font_exceed_dist > 0:
token_start += font_exceed_dist
token_end += font_exceed_dist
elif back_exceed_dist > 0:
token_start -= back_exceed_dist
token_end -= back_exceed_dist
start = token2text[token_start][0]
end = token2text[token_end - 1][1]
return (text[start:end], query_id - start, tokens[token_start:token_end], [
i - token_start if i is not None else None
for i in text2token[start:end]
], [(s - start, e - start) for s, e in token2text[token_start:token_end]])
def get_phoneme_labels(polyphonic_chars: List[List[str]]
) -> Tuple[List[str], Dict[str, List[int]]]:
labels = sorted(list(set([phoneme for char, phoneme in polyphonic_chars])))
char2phonemes = {}
for char, phoneme in polyphonic_chars:
if char not in char2phonemes:
char2phonemes[char] = []
char2phonemes[char].append(labels.index(phoneme))
return labels, char2phonemes
def get_char_phoneme_labels(polyphonic_chars: List[List[str]]
) -> Tuple[List[str], Dict[str, List[int]]]:
labels = sorted(
list(set([f'{char} {phoneme}' for char, phoneme in polyphonic_chars])))
char2phonemes = {}
for char, phoneme in polyphonic_chars:
if char not in char2phonemes:
char2phonemes[char] = []
char2phonemes[char].append(labels.index(f'{char} {phoneme}'))
return labels, char2phonemes
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