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from transformers import Wav2Vec2CTCTokenizer
class Wav2Vec2WordpieceTokenizer(Wav2Vec2CTCTokenizer):
def __init__(
self,
vocab_file,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
word_delimiter_token="|",
do_lower_case=False,
**kwargs
):
super().__init__(
vocab_file=vocab_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
do_lower_case=do_lower_case,
word_delimiter_token=word_delimiter_token,
**kwargs,
)
self._create_trie(self.all_special_tokens_extended)
def _tokenize(self, text, **kwargs):
"""
Converts a string in a sequence of tokens (string), using the tokenizer.
"""
special_cases = set(['gia', 'qui', 'quy', 'que', 'qua'])
output_tokens = []
for token_idx, token in enumerate(text.split()):
if token in special_cases:
sub_tokens = [token[:2], token[2:]]
else:
end = len(token)
sub_tokens = []
while end > 0:
start = 0
cur_substr = None
while start < end:
substr = token[start:end]
if substr in self.encoder:
cur_substr = substr
break
start += 1
if cur_substr is None:
sub_tokens.insert(0, self.unk_token)
end = start - 1
else:
sub_tokens.insert(0, cur_substr)
end = start
if token_idx > 0:
output_tokens.append(self.word_delimiter_token)
output_tokens.extend(sub_tokens)
return output_tokens
def decode_ids(
self,
token_ids,
skip_special_tokens = False,
clean_up_tokenization_spaces = True,
group_tokens: bool = True,
spaces_between_special_tokens: bool = False,
) -> str:
# For compatible with speechbrain interfaces
return self.decode(
token_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
group_tokens=group_tokens,
spaces_between_special_tokens=spaces_between_special_tokens
) |