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@ -22,6 +22,8 @@ from deepspeech.frontend.normalizer import FeatureNormalizer
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from deepspeech.frontend.speech import SpeechSegment
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import io
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import time
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from yacs.config import CfgNode
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from typing import Optional
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from collections import namedtuple
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@ -33,51 +35,134 @@ logger = Log(__name__).getlog()
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TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
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class SpeechCollator():
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def __init__(self, config, keep_transcription_text=True):
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"""
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Padding audio features with zeros to make them have the same shape (or
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a user-defined shape) within one bach.
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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default = CfgNode(
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dict(
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augmentation_config="",
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random_seed=0,
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mean_std_filepath="",
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unit_type="char",
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vocab_filepath="",
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spm_model_prefix="",
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specgram_type='linear', # 'linear', 'mfcc', 'fbank'
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feat_dim=0, # 'mfcc', 'fbank'
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delta_delta=False, # 'mfcc', 'fbank'
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stride_ms=10.0, # ms
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window_ms=20.0, # ms
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n_fft=None, # fft points
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max_freq=None, # None for samplerate/2
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target_sample_rate=16000, # target sample rate
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use_dB_normalization=True,
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target_dB=-20,
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dither=1.0, # feature dither
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keep_transcription_text=True
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))
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if ``keep_transcription_text`` is False, text is token ids else is raw string.
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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@classmethod
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def from_config(cls, config):
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"""Build a SpeechCollator object from a config.
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Args:
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config (yacs.config.CfgNode): configs object.
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Returns:
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SpeechCollator: collator object.
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"""
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self._keep_transcription_text = keep_transcription_text
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assert 'augmentation_config' in config.collator
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assert 'keep_transcription_text' in config.collator
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assert 'mean_std_filepath' in config.collator
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assert 'vocab_filepath' in config.data
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assert 'specgram_type' in config.collator
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assert 'n_fft' in config.collator
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assert config.collator
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if isinstance(config.data.augmentation_config, (str, bytes)):
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if config.data.augmentation_config:
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if isinstance(config.collator.augmentation_config, (str, bytes)):
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if config.collator.augmentation_config:
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aug_file = io.open(
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config.data.augmentation_config, mode='r', encoding='utf8')
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config.collator.augmentation_config, mode='r', encoding='utf8')
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else:
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aug_file = io.StringIO(initial_value='{}', newline='')
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else:
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aug_file = config.data.augmentation_config
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aug_file = config.collator.augmentation_config
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assert isinstance(aug_file, io.StringIO)
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speech_collator = cls(
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aug_file=aug_file,
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random_seed=0,
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mean_std_filepath=config.collator.mean_std_filepath,
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unit_type=config.collator.unit_type,
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vocab_filepath=config.data.vocab_filepath,
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spm_model_prefix=config.collator.spm_model_prefix,
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specgram_type=config.collator.specgram_type,
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feat_dim=config.collator.feat_dim,
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delta_delta=config.collator.delta_delta,
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stride_ms=config.collator.stride_ms,
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window_ms=config.collator.window_ms,
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n_fft=config.collator.n_fft,
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max_freq=config.collator.max_freq,
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target_sample_rate=config.collator.target_sample_rate,
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use_dB_normalization=config.collator.use_dB_normalization,
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target_dB=config.collator.target_dB,
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dither=config.collator.dither,
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keep_transcription_text=config.collator.keep_transcription_text
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)
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return speech_collator
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def __init__(self, aug_file, mean_std_filepath,
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vocab_filepath, spm_model_prefix,
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random_seed=0,
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unit_type="char",
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specgram_type='linear', # 'linear', 'mfcc', 'fbank'
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feat_dim=0, # 'mfcc', 'fbank'
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delta_delta=False, # 'mfcc', 'fbank'
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stride_ms=10.0, # ms
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window_ms=20.0, # ms
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n_fft=None, # fft points
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max_freq=None, # None for samplerate/2
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target_sample_rate=16000, # target sample rate
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use_dB_normalization=True,
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target_dB=-20,
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dither=1.0,
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keep_transcription_text=True):
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"""
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Padding audio features with zeros to make them have the same shape (or
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a user-defined shape) within one bach.
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if ``keep_transcription_text`` is False, text is token ids else is raw string.
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"""
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self._keep_transcription_text = keep_transcription_text
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self._local_data = TarLocalData(tar2info={}, tar2object={})
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self._augmentation_pipeline = AugmentationPipeline(
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augmentation_config=aug_file.read(),
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random_seed=config.data.random_seed)
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random_seed=random_seed)
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self._normalizer = FeatureNormalizer(
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config.data.mean_std_filepath) if config.data.mean_std_filepath else None
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mean_std_filepath) if mean_std_filepath else None
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self._stride_ms = config.data.stride_ms
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self._target_sample_rate = config.data.target_sample_rate
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self._stride_ms = stride_ms
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self._target_sample_rate = target_sample_rate
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self._speech_featurizer = SpeechFeaturizer(
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unit_type=config.data.unit_type,
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vocab_filepath=config.data.vocab_filepath,
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spm_model_prefix=config.data.spm_model_prefix,
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specgram_type=config.data.specgram_type,
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feat_dim=config.data.feat_dim,
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delta_delta=config.data.delta_delta,
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stride_ms=config.data.stride_ms,
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window_ms=config.data.window_ms,
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n_fft=config.data.n_fft,
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max_freq=config.data.max_freq,
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target_sample_rate=config.data.target_sample_rate,
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use_dB_normalization=config.data.use_dB_normalization,
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target_dB=config.data.target_dB,
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dither=config.data.dither)
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unit_type=unit_type,
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vocab_filepath=vocab_filepath,
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spm_model_prefix=spm_model_prefix,
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specgram_type=specgram_type,
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feat_dim=feat_dim,
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delta_delta=delta_delta,
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stride_ms=stride_ms,
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window_ms=window_ms,
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n_fft=n_fft,
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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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dither=dither)
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def _parse_tar(self, file):
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"""Parse a tar file to get a tarfile object
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@ -196,3 +281,28 @@ class SpeechCollator():
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texts, padding_value=IGNORE_ID).astype(np.int64)
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text_lens = np.array(text_lens).astype(np.int64)
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return utts, padded_audios, audio_lens, padded_texts, text_lens
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@property
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def vocab_size(self):
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return self._speech_featurizer.vocab_size
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@property
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def vocab_list(self):
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return self._speech_featurizer.vocab_list
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@property
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def vocab_dict(self):
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return self._speech_featurizer.vocab_dict
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@property
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def text_feature(self):
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return self._text_featurizer
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self._speech_featurizer.text_feature
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@property
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def feature_size(self):
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return self._speech_featurizer.feature_size
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@property
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def stride_ms(self):
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return self._speech_featurizer.stride_ms
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