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PaddleSpeech/data_utils/featurizer/text_featurizer.py

69 lines
2.1 KiB

"""Contains the text featurizer class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import codecs
class TextFeaturizer(object):
"""Text featurizer, for processing or extracting features from text.
Currently, it only supports char-level tokenizing and conversion into
a list of token indices. Note that the token indexing order follows the
given vocabulary file.
:param vocab_filepath: Filepath to load vocabulary for token indices
conversion.
:type specgram_type: basestring
"""
def __init__(self, vocab_filepath):
self._vocab_dict, self._vocab_list = self._load_vocabulary_from_file(
vocab_filepath)
def featurize(self, text):
"""Convert text string to a list of token indices in char-level.Note
that the token indexing order follows the given vocabulary file.
:param text: Text to process.
:type text: basestring
:return: List of char-level token indices.
:rtype: list
"""
tokens = self._char_tokenize(text)
return [self._vocab_dict[token] for token in tokens]
@property
def vocab_size(self):
"""Return the vocabulary size.
:return: Vocabulary size.
:rtype: int
"""
return len(self._vocab_list)
@property
def vocab_list(self):
"""Return the vocabulary in list.
:return: Vocabulary in list.
:rtype: list
"""
return self._vocab_list
def _char_tokenize(self, text):
"""Character tokenizer."""
return list(text.strip())
def _load_vocabulary_from_file(self, vocab_filepath):
"""Load vocabulary from file."""
vocab_lines = []
with codecs.open(vocab_filepath, 'r', 'utf-8') as file:
vocab_lines.extend(file.readlines())
vocab_list = [line[:-1] for line in vocab_lines]
vocab_dict = dict(
[(token, id) for (id, token) in enumerate(vocab_list)])
return vocab_dict, vocab_list