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PaddleSpeech/deepspeech/frontend/featurizer/speech_featurizer.py

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3.8 KiB

# 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():
"""Speech and Text feature extraction.
"""
def __init__(self,
unit_type,
vocab_filepath,
spm_model_prefix=None,
spectrum_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,
maskctc=False):
self.stride_ms = stride_ms
self.window_ms = window_ms
self.audio_feature = AudioFeaturizer(
spectrum_type=spectrum_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.feature_size = self.audio_feature.feature_size
self.text_feature = TextFeaturizer(
unit_type=unit_type,
vocab_filepath=vocab_filepath,
spm_model_prefix=spm_model_prefix,
maskctc=maskctc)
self.vocab_size = self.text_feature.vocab_size
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_feature.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_feature.featurize(speech_segment.transcript)
return spec_feature, text_ids
def text_featurize(self, text, 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:
text (str): text.
keep_transcription_text (bool): True, keep transcript text, False, token ids
Returns:
(str|List[int]): text, or list of token indices.
"""
if keep_transcription_text:
return text
text_ids = self.text_feature.featurize(text)
return text_ids