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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from deepspeech.frontend.utility import IGNORE_ID
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from deepspeech.io.utility import pad_sequence
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from deepspeech.utils.log import Log
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from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
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from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
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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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__all__ = ["SpeechCollator"]
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logger = Log(__name__).getlog()
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# namedtupe need global for pickle.
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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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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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if isinstance(config.data.augmentation_config, (str, bytes)):
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if config.data.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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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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assert isinstance(aug_file, io.StringIO)
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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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self._normalizer = FeatureNormalizer(
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config.data.mean_std_filepath) if config.data.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._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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def _parse_tar(self, file):
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"""Parse a tar file to get a tarfile object
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and a map containing tarinfoes
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"""
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result = {}
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f = tarfile.open(file)
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for tarinfo in f.getmembers():
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result[tarinfo.name] = tarinfo
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return f, result
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def _subfile_from_tar(self, file):
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"""Get subfile object from tar.
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It will return a subfile object from tar file
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and cached tar file info for next reading request.
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"""
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tarpath, filename = file.split(':', 1)[1].split('#', 1)
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if 'tar2info' not in self._local_data.__dict__:
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self._local_data.tar2info = {}
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if 'tar2object' not in self._local_data.__dict__:
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self._local_data.tar2object = {}
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if tarpath not in self._local_data.tar2info:
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object, infoes = self._parse_tar(tarpath)
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self._local_data.tar2info[tarpath] = infoes
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self._local_data.tar2object[tarpath] = object
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return self._local_data.tar2object[tarpath].extractfile(
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self._local_data.tar2info[tarpath][filename])
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def process_utterance(self, audio_file, transcript):
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"""Load, augment, featurize and normalize for speech data.
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:param audio_file: Filepath or file object of audio file.
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:type audio_file: str | file
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:param transcript: Transcription text.
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:type transcript: str
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:return: Tuple of audio feature tensor and data of transcription part,
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where transcription part could be token ids or text.
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:rtype: tuple of (2darray, list)
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"""
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start_time = time.time()
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if isinstance(audio_file, str) and audio_file.startswith('tar:'):
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speech_segment = SpeechSegment.from_file(
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self._subfile_from_tar(audio_file), transcript)
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else:
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speech_segment = SpeechSegment.from_file(audio_file, transcript)
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load_wav_time = time.time() - start_time
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#logger.debug(f"load wav time: {load_wav_time}")
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# audio augment
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start_time = time.time()
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self._augmentation_pipeline.transform_audio(speech_segment)
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audio_aug_time = time.time() - start_time
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#logger.debug(f"audio augmentation time: {audio_aug_time}")
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start_time = time.time()
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specgram, transcript_part = self._speech_featurizer.featurize(
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speech_segment, self._keep_transcription_text)
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if self._normalizer:
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specgram = self._normalizer.apply(specgram)
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feature_time = time.time() - start_time
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#logger.debug(f"audio & test feature time: {feature_time}")
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# specgram augment
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start_time = time.time()
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specgram = self._augmentation_pipeline.transform_feature(specgram)
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feature_aug_time = time.time() - start_time
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#logger.debug(f"audio feature augmentation time: {feature_aug_time}")
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return specgram, transcript_part
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def __call__(self, batch):
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"""batch examples
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Args:
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batch ([List]): batch is (audio, text)
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audio (np.ndarray) shape (D, T)
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text (List[int] or str): shape (U,)
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Returns:
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tuple(audio, text, audio_lens, text_lens): batched data.
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audio : (B, Tmax, D)
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audio_lens: (B)
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text : (B, Umax)
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text_lens: (B)
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"""
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audios = []
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audio_lens = []
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texts = []
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text_lens = []
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utts = []
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for utt, audio, text in batch:
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audio, text = self.process_utterance(audio, text)
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#utt
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utts.append(utt)
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# audio
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audios.append(audio.T) # [T, D]
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audio_lens.append(audio.shape[1])
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# text
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# for training, text is token ids
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# else text is string, convert to unicode ord
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tokens = []
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if self._keep_transcription_text:
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assert isinstance(text, str), (type(text), text)
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tokens = [ord(t) for t in text]
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else:
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tokens = text # token ids
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tokens = tokens if isinstance(tokens, np.ndarray) else np.array(
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tokens, dtype=np.int64)
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texts.append(tokens)
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text_lens.append(tokens.shape[0])
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padded_audios = pad_sequence(
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audios, padding_value=0.0).astype(np.float32) #[B, T, D]
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audio_lens = np.array(audio_lens).astype(np.int64)
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padded_texts = pad_sequence(
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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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