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99 lines
3.9 KiB
99 lines
3.9 KiB
"""Contains the speech featurizer class."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from data_utils.featurizer.audio_featurizer import AudioFeaturizer
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from data_utils.featurizer.text_featurizer import TextFeaturizer
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class SpeechFeaturizer(object):
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"""Speech featurizer, for extracting features from both audio and transcript
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contents of SpeechSegment.
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Currently, for audio parts, it supports feature types of linear
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spectrogram and mfcc; for transcript parts, it only supports char-level
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tokenizing and conversion into a list of token indices. Note that the
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token indexing order follows the given vocabulary file.
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:param vocab_filepath: Filepath to load vocabulary for token indices
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conversion.
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:type specgram_type: basestring
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:param specgram_type: Specgram feature type. Options: 'linear', 'mfcc'.
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:type specgram_type: str
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:param stride_ms: Striding size (in milliseconds) for generating frames.
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:type stride_ms: float
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:param window_ms: Window size (in milliseconds) for generating frames.
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:type window_ms: float
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:param max_freq: When specgram_type is 'linear', only FFT bins
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corresponding to frequencies between [0, max_freq] are
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returned; when specgram_type is 'mfcc', max_freq is the
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highest band edge of mel filters.
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:types max_freq: None|float
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:param target_sample_rate: Speech are resampled (if upsampling or
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downsampling is allowed) to this before
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extracting spectrogram features.
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:type target_sample_rate: float
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:param use_dB_normalization: Whether to normalize the audio to a certain
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decibels before extracting the features.
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:type use_dB_normalization: bool
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:param target_dB: Target audio decibels for normalization.
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:type target_dB: float
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"""
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def __init__(self,
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vocab_filepath,
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specgram_type='linear',
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stride_ms=10.0,
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window_ms=20.0,
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max_freq=None,
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target_sample_rate=16000,
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use_dB_normalization=True,
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target_dB=-20):
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self._audio_featurizer = AudioFeaturizer(
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specgram_type=specgram_type,
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stride_ms=stride_ms,
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window_ms=window_ms,
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max_freq=max_freq,
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target_sample_rate=target_sample_rate,
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use_dB_normalization=use_dB_normalization,
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target_dB=target_dB)
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self._text_featurizer = TextFeaturizer(vocab_filepath)
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def featurize(self, speech_segment, keep_transcription_text):
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"""Extract features for speech segment.
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1. For audio parts, extract the audio features.
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2. For transcript parts, keep the original text or convert text string
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to a list of token indices in char-level.
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:param audio_segment: Speech segment to extract features from.
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:type audio_segment: SpeechSegment
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:return: A tuple of 1) spectrogram audio feature in 2darray, 2) list of
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char-level token indices.
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:rtype: tuple
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"""
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audio_feature = self._audio_featurizer.featurize(speech_segment)
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if keep_transcription_text:
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return audio_feature, speech_segment.transcript
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text_ids = self._text_featurizer.featurize(speech_segment.transcript)
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return audio_feature, text_ids
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@property
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def vocab_size(self):
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"""Return the vocabulary size.
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:return: Vocabulary size.
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:rtype: int
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"""
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return self._text_featurizer.vocab_size
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@property
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def vocab_list(self):
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"""Return the vocabulary in list.
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:return: Vocabulary in list.
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:rtype: list
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"""
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return self._text_featurizer.vocab_list
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