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PaddleSpeech/deepspeech/frontend/featurizer/speech_featurizer.py

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# Copyright (c) 2021 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.
"""Contains the speech featurizer class."""
from deepspeech.frontend.featurizer.audio_featurizer import AudioFeaturizer
from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
class SpeechFeaturizer(object):
"""Speech featurizer, for extracting features from both audio and transcript
contents of SpeechSegment.
Currently, for audio parts, it supports feature types of linear
spectrogram and mfcc; for transcript parts, 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: str
:param specgram_type: Specgram feature type. Options: 'linear', 'mfcc'.
:type specgram_type: str
:param stride_ms: Striding size (in milliseconds) for generating frames.
:type stride_ms: float
:param window_ms: Window size (in milliseconds) for generating frames.
:type window_ms: float
:param max_freq: When specgram_type is 'linear', only FFT bins
corresponding to frequencies between [0, max_freq] are
returned; when specgram_type is 'mfcc', max_freq is the
highest band edge of mel filters.
:types max_freq: None|float
:param target_sample_rate: Speech are resampled (if upsampling or
downsampling is allowed) to this before
extracting spectrogram features.
:type target_sample_rate: float
:param use_dB_normalization: Whether to normalize the audio to a certain
decibels before extracting the features.
:type use_dB_normalization: bool
:param target_dB: Target audio decibels for normalization.
:type target_dB: float
"""
def __init__(self,
unit_type,
vocab_filepath,
spm_model_prefix=None,
specgram_type='linear',
feat_dim=None,
delta_delta=False,
stride_ms=10.0,
window_ms=20.0,
n_fft=None,
max_freq=None,
target_sample_rate=16000,
use_dB_normalization=True,
target_dB=-20,
dither=1.0):
self._audio_featurizer = AudioFeaturizer(
specgram_type=specgram_type,
feat_dim=feat_dim,
delta_delta=delta_delta,
stride_ms=stride_ms,
window_ms=window_ms,
n_fft=n_fft,
max_freq=max_freq,
target_sample_rate=target_sample_rate,
use_dB_normalization=use_dB_normalization,
target_dB=target_dB,
dither=dither)
self._text_featurizer = TextFeaturizer(unit_type, vocab_filepath,
spm_model_prefix)
def featurize(self, speech_segment, keep_transcription_text):
"""Extract features for speech segment.
1. For audio parts, extract the audio features.
2. For transcript parts, keep the original text or convert text string
to a list of token indices in char-level.
Args:
speech_segment (SpeechSegment): Speech segment to extract features from.
keep_transcription_text (bool): True, keep transcript text, False, token ids
Returns:
tuple: 1) spectrogram audio feature in 2darray, 2) list oftoken indices.
"""
spec_feature = self._audio_featurizer.featurize(speech_segment)
if keep_transcription_text:
return spec_feature, speech_segment.transcript
if speech_segment.has_token:
text_ids = speech_segment.token_ids
else:
text_ids = self._text_featurizer.featurize(
speech_segment.transcript)
return spec_feature, text_ids
@property
def vocab_size(self):
"""Return the vocabulary size.
Returns:
int: Vocabulary size.
"""
return self._text_featurizer.vocab_size
@property
def vocab_list(self):
"""Return the vocabulary in list.
Returns:
List[str]:
"""
return self._text_featurizer.vocab_list
@property
def vocab_dict(self):
"""Return the vocabulary in dict.
Returns:
Dict[str, int]:
"""
return self._text_featurizer.vocab_dict
@property
def feature_size(self):
"""Return the audio feature size.
Returns:
int: audio feature size.
"""
return self._audio_featurizer.feature_size
@property
def stride_ms(self):
"""time length in `ms` unit per frame
Returns:
float: time(ms)/frame
"""
return self._audio_featurizer.stride_ms
@property
def text_feature(self):
"""Return the text feature object.
Returns:
TextFeaturizer: object.
"""
return self._text_featurizer