def __init__(self,
vocab_filepath,
stride_ms=10.0,
window_ms=20.0,
target_sample_rate=16000,
use_dB_normalization=True,
target_dB=-20):
self._audio_featurizer = AudioFeaturizer(
stride_ms=stride_ms,
window_ms=window_ms,
target_sample_rate=target_sample_rate,
use_dB_normalization=use_dB_normalization,
target_dB=target_dB)
self._text_featurizer = TextFeaturizer(vocab_filepath)
def __init__(self,
vocab_filepath,
specgram_type='linear',
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
target_sample_rate=16000,
use_dB_normalization=True,
target_dB=-20):
self._audio_featurizer = AudioFeaturizer(specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq,
target_sample_rate=target_sample_rate,
use_dB_normalization=use_dB_normalization,
target_dB=target_dB)
self._text_featurizer = TextFeaturizer(vocab_filepath)
class NormalizerDataset(Dataset):
def __init__(self, sampled_manifest):
super(NormalizerDataset, self).__init__()
self.audio_featurizer = AudioFeaturizer()
self.sampled_manifest = sampled_manifest
def __getitem__(self, idx):
instance = self.sampled_manifest[idx]
# 获取音频特征
audio = AudioSegment.from_file(instance["audio_filepath"])
feature = self.audio_featurizer.featurize(audio)
return feature, 0
def __len__(self):
return len(self.sampled_manifest)
def main():
print_arguments(args)
augmentation_pipeline = AugmentationPipeline('{}')
audio_featurizer = AudioFeaturizer(specgram_type=args.specgram_type)
def augment_and_featurize(audio_segment):
augmentation_pipeline.transform_audio(audio_segment)
return audio_featurizer.featurize(audio_segment)
normalizer = FeatureNormalizer(
mean_std_filepath=None,
manifest_path=args.manifest_path,
featurize_func=augment_and_featurize,
num_samples=args.num_samples)
normalizer.write_to_file(args.output_path)
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,
vocab_filepath,
specgram_type='linear',
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
target_sample_rate=16000,
use_dB_normalization=True,
target_dB=-20):
self._audio_featurizer = AudioFeaturizer(
specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq,
target_sample_rate=target_sample_rate,
use_dB_normalization=use_dB_normalization,
target_dB=target_dB)
self._text_featurizer = TextFeaturizer(vocab_filepath)
def featurize(self, speech_segment, keep_transcription_text):
"""提取语音片段的特征
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.
:param audio_segment: Speech segment to extract features from.
:type audio_segment: SpeechSegment
:return: A tuple of 1) spectrogram audio feature in 2darray, 2) list of
char-level token indices.
:rtype: tuple
"""
audio_feature = self._audio_featurizer.featurize(speech_segment)
if keep_transcription_text:
return audio_feature, speech_segment.transcript
text_ids = self._text_featurizer.featurize(speech_segment.transcript)
return audio_feature, text_ids
@property
def vocab_size(self):
"""返回词汇表大小
:return: Vocabulary size.
:rtype: int
"""
return self._text_featurizer.vocab_size
@property
def vocab_list(self):
"""返回词汇表的list
:return: Vocabulary in list.
:rtype: list
"""
return self._text_featurizer.vocab_list
def __init__(self, sampled_manifest):
super(NormalizerDataset, self).__init__()
self.audio_featurizer = AudioFeaturizer()
self.sampled_manifest = sampled_manifest
