def process_utterance(self, audio_file, transcript):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Filepath or file object of audio file.
:type audio_file: basestring | file
:param transcript: Transcription text.
:type transcript: basestring
:return: Tuple of audio feature tensor and data of transcription part,
where transcription part could be token ids or text.
:rtype: tuple of (2darray, list)
"""
try:
is_str = isinstance(audio_file, basestring)
except:
is_str = isinstance(audio_file, str)
if is_str and audio_file.startswith('tar:'):
speech_segment = SpeechSegment.from_file(
self._subfile_from_tar(audio_file), transcript)
else:
speech_segment = SpeechSegment.from_file(audio_file, transcript)
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, transcript_part = self._speech_featurizer.featurize(
speech_segment, self._keep_transcription_text)
specgram = self._normalizer.apply(specgram)
return specgram, transcript_part
def get_audio_mfcc_features(txt_files,
wav_files,
n_input,
n_context,
word_num_map,
txt_labels=None,
specgram_type='mfcc',
mean_std_filepath='data/aishell/mean_std.npz'):
""" Get MFCC/linear specgram features. The dim of MFCC is 39, contains 13 mfcc + 13 delta1 + 13 delta2.
Linear specgram contains 161 features in different frequency section.
:param txt_files:
:param wav_files:
:param n_input:
:param n_context:
:param word_num_map:
:param txt_labels:
:return:
"""
audio_features = []
audio_features_len = []
text_vector = []
text_vector_len = []
if txt_files != None:
txt_labels = txt_files
get_feature = AudioFeaturizer(specgram_type)
normalizer = FeatureNormalizer(mean_std_filepath)
for txt_obj, wav_file in zip(txt_labels, wav_files):
# Turn inputs into features
if specgram_type == 'mfcc':
audio_data = audiofile_to_input_vector(
wav_file, n_input, n_context) # get mfcc feature ( ???, 741 )
elif specgram_type == 'linear':
speech_segment = SpeechSegment.from_file(wav_file, "")
specgram = get_feature.featurize(speech_segment)
audio_data = normalizer.apply(specgram)
audio_data = np.transpose(
audio_data) # get linear specgram feature, (?, 161)
audio_data = audio_data.astype('float32')
audio_features.append(audio_data)
audio_features_len.append(np.int32(len(audio_data)))
target = []
if txt_files != None: # txt_obj是文件
target = trans_text_ch_to_vector(txt_obj, word_num_map)
else:
target = trans_text_ch_to_vector(None, word_num_map,
txt_obj) # txt_obj是labels
text_vector.append(target)
text_vector_len.append(len(target))
audio_features = np.asarray(audio_features)
audio_features_len = np.asarray(audio_features_len)
text_vector = np.asarray(text_vector)
text_vector_len = np.asarray(text_vector_len)
return audio_features, audio_features_len, text_vector, text_vector_len
def audiofile_to_input_vector(audio_filename, n_input, n_context):
""" Compute MFCC features with n_context
:param audio_filename:
:param n_input:
:param n_context:
:return:
"""
fs, audio = wav.read(audio_filename)
# get mfcc features with dim 39
get_feature = AudioFeaturizer("mfcc")
speech_segment = SpeechSegment.from_file(audio_filename, "")
orig_inputs = get_feature.featurize(speech_segment) # (39, ?)
orig_inputs = np.transpose(orig_inputs) # trans to time major (?, 39)
train_inputs = np.zeros((orig_inputs.shape[0], n_input + 2 * n_input * n_context)) #(***/2, 195)
empty_mfcc = np.zeros((n_input))
# Prepare input data, consist of three parts,
# output is (past hyparam.n_context * 39 + current + future hyparam.n_context * 39)
time_slices = range(train_inputs.shape[0])
context_past_min = time_slices[0] + n_context
context_future_max = time_slices[-1] - n_context
for time_slice in time_slices:
# padding with 0 for the first of 9，mfcc features
need_empty_past = max(0, (context_past_min - time_slice))
empty_source_past = list(empty_mfcc for empty_slots in range(need_empty_past))
data_source_past = orig_inputs[ max(0, time_slice - n_context):time_slice]
# padding with 0 for the last of 9，mfcc features
need_empty_future = max(0, (time_slice - context_future_max))
empty_source_future = list(empty_mfcc for empty_slots in range(need_empty_future))
data_source_future = orig_inputs[time_slice + 1:time_slice + n_context + 1]
if need_empty_past:
past = np.concatenate((empty_source_past, data_source_past))
else:
past = data_source_past
if need_empty_future:
future = np.concatenate((data_source_future, empty_source_future))
else:
future = data_source_future
past = np.reshape(past, n_context * 39)
now = orig_inputs[time_slice]
future = np.reshape(future, n_context * n_input)
train_inputs[time_slice] = np.concatenate((past, now, future))
# Tran data to Norm distribution, minus mean value then over the varr
train_inputs = (train_inputs - np.mean(train_inputs)) / np.std(train_inputs)
# shape of train_inputs: (shape(orig_inputs)/2, n_context * 2 * 39 + 39)
return train_inputs
def process_utterance(self, filename, transcript):
"""Load, augment, featurize and normalize for speech data.
:param filename: Audio filepath
:type filename: basestring | file
:param transcript: Transcription text.
:type transcript: basestring
:return: Tuple of audio feature tensor and list of token ids for
transcription.
:rtype: tuple of (2darray, list)
"""
if filename.startswith('tar:'):
speech_segment = SpeechSegment.from_file(
self._subfile_from_tar(filename), transcript)
else:
speech_segment = SpeechSegment.from_file(filename, transcript)
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, text_ids = self._speech_featurizer.featurize(speech_segment)
specgram = self._normalizer.apply(specgram)
return specgram, text_ids
def process_utterance(self, audio_file):
"""对语音数据加载、预处理
:param audio_file: 音频文件的文件路径或文件对象
:type audio_file: str | file
:return: 预处理的音频数据
:rtype: 2darray
"""
speech_segment = SpeechSegment.from_file(audio_file, "")
specgram, _ = self._speech_featurizer.featurize(speech_segment, False)
specgram = self._normalizer.apply(specgram)
return specgram
def process_utterance(self, audio_file, transcript):
"""对语音数据加载、扩充、特征化和归一化
:param audio_file: 音频文件的文件路径或文件对象
:type audio_file: str | file
:param transcript: 音频对应的文本
:type transcript: str
:return: 经过归一化等预处理的音频数据，音频文件对应文本的ID
:rtype: tuple of (2darray, list)
"""
speech_segment = SpeechSegment.from_file(audio_file, transcript)
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, transcript_part = self._speech_featurizer.featurize(
speech_segment, self._keep_transcription_text)
specgram = self._normalizer.apply(specgram)
return specgram, transcript_part
def process_utterance_from_bytes(self, bytes, transcript,
**soundfile_options):
"""Load, augment, featurize and normalize for speech data.
:param audio_file: Bytes read from the file.
:type audio_file: byte string.
:param transcript: Transcription text.
:type transcript: basestring
:param soundfile_options: Options for opening with soundfile library.
:type soundfile_options: **kwargs
:return: Tuple of audio feature tensor and data of transcription part,
where transcription part could be token ids or text.
:rtype: tuple of (2darray, list)
"""
speech_segment = SpeechSegment.from_bytes(bytes, transcript,
**soundfile_options)
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, transcript_part = self._speech_featurizer.featurize(
speech_segment, self._keep_transcription_text)
specgram = self._normalizer.apply(specgram)
return specgram, transcript_part
