# 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. import io from collections import namedtuple from typing import Optional import kaldiio import numpy as np from yacs.config import CfgNode from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer from deepspeech.frontend.normalizer import FeatureNormalizer from deepspeech.frontend.speech import SpeechSegment from deepspeech.frontend.utility import IGNORE_ID from deepspeech.io.utility import pad_sequence from deepspeech.utils.log import Log __all__ = ["SpeechCollator", "KaldiPrePorocessedCollator"] logger = Log(__name__).getlog() # namedtupe need global for pickle. TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object']) class SpeechCollator(): @classmethod def params(cls, config: Optional[CfgNode]=None) -> CfgNode: default = CfgNode( dict( augmentation_config="", random_seed=0, mean_std_filepath="", unit_type="char", vocab_filepath="", spm_model_prefix="", specgram_type='linear', # 'linear', 'mfcc', 'fbank' feat_dim=0, # 'mfcc', 'fbank' delta_delta=False, # 'mfcc', 'fbank' stride_ms=10.0, # ms window_ms=20.0, # ms n_fft=None, # fft points max_freq=None, # None for samplerate/2 target_sample_rate=16000, # target sample rate use_dB_normalization=True, target_dB=-20, dither=1.0, # feature dither keep_transcription_text=False)) if config is not None: config.merge_from_other_cfg(default) return default @classmethod def from_config(cls, config): """Build a SpeechCollator object from a config. Args: config (yacs.config.CfgNode): configs object. Returns: SpeechCollator: collator object. """ assert 'augmentation_config' in config.collator assert 'keep_transcription_text' in config.collator assert 'mean_std_filepath' in config.collator assert 'vocab_filepath' in config.collator assert 'specgram_type' in config.collator assert 'n_fft' in config.collator assert config.collator if isinstance(config.collator.augmentation_config, (str, bytes)): if config.collator.augmentation_config: aug_file = io.open( config.collator.augmentation_config, mode='r', encoding='utf8') else: aug_file = io.StringIO(initial_value='{}', newline='') else: aug_file = config.collator.augmentation_config assert isinstance(aug_file, io.StringIO) speech_collator = cls( aug_file=aug_file, random_seed=0, mean_std_filepath=config.collator.mean_std_filepath, unit_type=config.collator.unit_type, vocab_filepath=config.collator.vocab_filepath, spm_model_prefix=config.collator.spm_model_prefix, specgram_type=config.collator.specgram_type, feat_dim=config.collator.feat_dim, delta_delta=config.collator.delta_delta, stride_ms=config.collator.stride_ms, window_ms=config.collator.window_ms, n_fft=config.collator.n_fft, max_freq=config.collator.max_freq, target_sample_rate=config.collator.target_sample_rate, use_dB_normalization=config.collator.use_dB_normalization, target_dB=config.collator.target_dB, dither=config.collator.dither, keep_transcription_text=config.collator.keep_transcription_text) return speech_collator def __init__( self, aug_file, mean_std_filepath, vocab_filepath, spm_model_prefix, random_seed=0, unit_type="char", specgram_type='linear', # 'linear', 'mfcc', 'fbank' feat_dim=0, # 'mfcc', 'fbank' delta_delta=False, # 'mfcc', 'fbank' stride_ms=10.0, # ms window_ms=20.0, # ms n_fft=None, # fft points max_freq=None, # None for samplerate/2 target_sample_rate=16000, # target sample rate use_dB_normalization=True, target_dB=-20, dither=1.0, keep_transcription_text=True): """SpeechCollator Collator Args: unit_type(str): token unit type, e.g. char, word, spm vocab_filepath (str): vocab file path. mean_std_filepath (str): mean and std file path, which suffix is *.npy spm_model_prefix (str): spm model prefix, need if `unit_type` is spm. augmentation_config (str, optional): augmentation json str. Defaults to '{}'. stride_ms (float, optional): stride size in ms. Defaults to 10.0. window_ms (float, optional): window size in ms. Defaults to 20.0. n_fft (int, optional): fft points for rfft. Defaults to None. max_freq (int, optional): max cut freq. Defaults to None. target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000. specgram_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'. feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None. delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False. use_dB_normalization (bool, optional): do dB normalization. Defaults to True. target_dB (int, optional): target dB. Defaults to -20. random_seed (int, optional): for random generator. Defaults to 0. keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False. if ``keep_transcription_text`` is False, text is token ids else is raw string. Do augmentations Padding audio features with zeros to make them have the same shape (or a user-defined shape) within one batch. """ self._keep_transcription_text = keep_transcription_text self._local_data = TarLocalData(tar2info={}, tar2object={}) self._augmentation_pipeline = AugmentationPipeline( augmentation_config=aug_file.read(), random_seed=random_seed) self._normalizer = FeatureNormalizer( mean_std_filepath) if mean_std_filepath else None self._stride_ms = stride_ms self._target_sample_rate = target_sample_rate self._speech_featurizer = SpeechFeaturizer( unit_type=unit_type, vocab_filepath=vocab_filepath, spm_model_prefix=spm_model_prefix, specgram_type=specgram_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) def _parse_tar(self, file): """Parse a tar file to get a tarfile object and a map containing tarinfoes """ result = {} f = tarfile.open(file) for tarinfo in f.getmembers(): result[tarinfo.name] = tarinfo return f, result def _subfile_from_tar(self, file): """Get subfile object from tar. It will return a subfile object from tar file and cached tar file info for next reading request. """ tarpath, filename = file.split(':', 1)[1].split('#', 1) if 'tar2info' not in self._local_data.__dict__: self._local_data.tar2info = {} if 'tar2object' not in self._local_data.__dict__: self._local_data.tar2object = {} if tarpath not in self._local_data.tar2info: object, infoes = self._parse_tar(tarpath) self._local_data.tar2info[tarpath] = infoes self._local_data.tar2object[tarpath] = object return self._local_data.tar2object[tarpath].extractfile( self._local_data.tar2info[tarpath][filename]) @property def manifest(self): return self._manifest @property def vocab_size(self): return self._speech_featurizer.vocab_size @property def vocab_list(self): return self._speech_featurizer.vocab_list @property def vocab_dict(self): return self._speech_featurizer.vocab_dict @property def text_feature(self): return self._speech_featurizer.text_feature @property def feature_size(self): return self._speech_featurizer.feature_size @property def stride_ms(self): return self._speech_featurizer.stride_ms def process_utterance(self, audio_file, translation): """Load, augment, featurize and normalize for speech data. :param audio_file: Filepath or file object of audio file. :type audio_file: str | file :param translation: translation text. :type translation: str :return: Tuple of audio feature tensor and data of translation part, where translation part could be token ids or text. :rtype: tuple of (2darray, list) """ if isinstance(audio_file, str) and audio_file.startswith('tar:'): speech_segment = SpeechSegment.from_file( self._subfile_from_tar(audio_file), translation) else: speech_segment = SpeechSegment.from_file(audio_file, translation) # audio augment self._augmentation_pipeline.transform_audio(speech_segment) specgram, translation_part = self._speech_featurizer.featurize( speech_segment, self._keep_transcription_text) if self._normalizer: specgram = self._normalizer.apply(specgram) # specgram augment specgram = self._augmentation_pipeline.transform_feature(specgram) return specgram, translation_part def __call__(self, batch): """batch examples Args: batch ([List]): batch is (audio, text) audio (np.ndarray) shape (T, D) text (List[int] or str): shape (U,) Returns: tuple(audio, text, audio_lens, text_lens): batched data. audio : (B, Tmax, D) audio_lens: (B) text : (B, Umax) text_lens: (B) """ audios = [] audio_lens = [] texts = [] text_lens = [] utts = [] for utt, audio, text in batch: audio, text = self.process_utterance(audio, text) #utt utts.append(utt) # audio audios.append(audio) # [T, D] audio_lens.append(audio.shape[0]) # text # for training, text is token ids # else text is string, convert to unicode ord tokens = [] if self._keep_transcription_text: assert isinstance(text, str), (type(text), text) tokens = [ord(t) for t in text] else: tokens = text # token ids tokens = tokens if isinstance(tokens, np.ndarray) else np.array( tokens, dtype=np.int64) texts.append(tokens) text_lens.append(tokens.shape[0]) padded_audios = pad_sequence( audios, padding_value=0.0).astype(np.float32) #[B, T, D] audio_lens = np.array(audio_lens).astype(np.int64) padded_texts = pad_sequence( texts, padding_value=IGNORE_ID).astype(np.int64) text_lens = np.array(text_lens).astype(np.int64) return utts, padded_audios, audio_lens, padded_texts, text_lens class TripletSpeechCollator(SpeechCollator): def process_utterance(self, audio_file, translation, transcript): """Load, augment, featurize and normalize for speech data. :param audio_file: Filepath or file object of audio file. :type audio_file: str | file :param translation: translation text. :type translation: str :return: Tuple of audio feature tensor and data of translation part, where translation part could be token ids or text. :rtype: tuple of (2darray, list) """ if isinstance(audio_file, str) and audio_file.startswith('tar:'): speech_segment = SpeechSegment.from_file( self._subfile_from_tar(audio_file), translation) else: speech_segment = SpeechSegment.from_file(audio_file, translation) # audio augment self._augmentation_pipeline.transform_audio(speech_segment) specgram, translation_part = self._speech_featurizer.featurize( speech_segment, self._keep_transcription_text) transcript_part = self._speech_featurizer._text_featurizer.featurize( transcript) if self._normalizer: specgram = self._normalizer.apply(specgram) # specgram augment specgram = self._augmentation_pipeline.transform_feature(specgram) return specgram, translation_part, transcript_part def __call__(self, batch): """batch examples Args: batch ([List]): batch is (audio, text) audio (np.ndarray) shape (T, D) text (List[int] or str): shape (U,) Returns: tuple(audio, text, audio_lens, text_lens): batched data. audio : (B, Tmax, D) audio_lens: (B) text : (B, Umax) text_lens: (B) """ audios = [] audio_lens = [] translation_text = [] translation_text_lens = [] transcription_text = [] transcription_text_lens = [] utts = [] for utt, audio, translation, transcription in batch: audio, translation, transcription = self.process_utterance( audio, translation, transcription) #utt utts.append(utt) # audio audios.append(audio) # [T, D] audio_lens.append(audio.shape[0]) # text # for training, text is token ids # else text is string, convert to unicode ord tokens = [[], []] for idx, text in enumerate([translation, transcription]): if self._keep_transcription_text: assert isinstance(text, str), (type(text), text) tokens[idx] = [ord(t) for t in text] else: tokens[idx] = text # token ids tokens[idx] = tokens[idx] if isinstance( tokens[idx], np.ndarray) else np.array( tokens[idx], dtype=np.int64) translation_text.append(tokens[0]) translation_text_lens.append(tokens[0].shape[0]) transcription_text.append(tokens[1]) transcription_text_lens.append(tokens[1].shape[0]) padded_audios = pad_sequence( audios, padding_value=0.0).astype(np.float32) #[B, T, D] audio_lens = np.array(audio_lens).astype(np.int64) padded_translation = pad_sequence( translation_text, padding_value=IGNORE_ID).astype(np.int64) translation_lens = np.array(translation_text_lens).astype(np.int64) padded_transcription = pad_sequence( transcription_text, padding_value=IGNORE_ID).astype(np.int64) transcription_lens = np.array(transcription_text_lens).astype(np.int64) return utts, padded_audios, audio_lens, ( padded_translation, padded_transcription), (translation_lens, transcription_lens) class KaldiPrePorocessedCollator(SpeechCollator): @classmethod def params(cls, config: Optional[CfgNode]=None) -> CfgNode: default = CfgNode( dict( augmentation_config="", random_seed=0, unit_type="char", vocab_filepath="", spm_model_prefix="", feat_dim=0, stride_ms=10.0, keep_transcription_text=False)) if config is not None: config.merge_from_other_cfg(default) return default @classmethod def from_config(cls, config): """Build a SpeechCollator object from a config. Args: config (yacs.config.CfgNode): configs object. Returns: SpeechCollator: collator object. """ assert 'augmentation_config' in config.collator assert 'keep_transcription_text' in config.collator assert 'vocab_filepath' in config.collator assert config.collator if isinstance(config.collator.augmentation_config, (str, bytes)): if config.collator.augmentation_config: aug_file = io.open( config.collator.augmentation_config, mode='r', encoding='utf8') else: aug_file = io.StringIO(initial_value='{}', newline='') else: aug_file = config.collator.augmentation_config assert isinstance(aug_file, io.StringIO) speech_collator = cls( aug_file=aug_file, random_seed=0, unit_type=config.collator.unit_type, vocab_filepath=config.collator.vocab_filepath, spm_model_prefix=config.collator.spm_model_prefix, feat_dim=config.collator.feat_dim, stride_ms=config.collator.stride_ms, keep_transcription_text=config.collator.keep_transcription_text) return speech_collator def __init__(self, aug_file, vocab_filepath, spm_model_prefix, random_seed=0, unit_type="char", feat_dim=0, stride_ms=10.0, keep_transcription_text=True): """SpeechCollator Collator Args: unit_type(str): token unit type, e.g. char, word, spm vocab_filepath (str): vocab file path. spm_model_prefix (str): spm model prefix, need if `unit_type` is spm. augmentation_config (str, optional): augmentation json str. Defaults to '{}'. random_seed (int, optional): for random generator. Defaults to 0. keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False. if ``keep_transcription_text`` is False, text is token ids else is raw string. Do augmentations Padding audio features with zeros to make them have the same shape (or a user-defined shape) within one batch. """ self._keep_transcription_text = keep_transcription_text self._feat_dim = feat_dim self._stride_ms = stride_ms self._local_data = TarLocalData(tar2info={}, tar2object={}) self._augmentation_pipeline = AugmentationPipeline( augmentation_config=aug_file.read(), random_seed=random_seed) self._text_featurizer = TextFeaturizer(unit_type, vocab_filepath, spm_model_prefix) def process_utterance(self, audio_file, translation): """Load, augment, featurize and normalize for speech data. :param audio_file: Filepath or file object of kaldi processed feature. :type audio_file: str | file :param translation: Translation text. :type translation: str :return: Tuple of audio feature tensor and data of translation part, where translation part could be token ids or text. :rtype: tuple of (2darray, list) """ specgram = kaldiio.load_mat(audio_file) assert specgram.shape[ 1] == self._feat_dim, 'expect feat dim {}, but got {}'.format( self._feat_dim, specgram.shape[1]) # specgram augment specgram = self._augmentation_pipeline.transform_feature(specgram) if self._keep_transcription_text: return specgram, translation else: text_ids = self._text_featurizer.featurize(translation) return specgram, text_ids class TripletKaldiPrePorocessedCollator(KaldiPrePorocessedCollator): def process_utterance(self, audio_file, translation, transcript): """Load, augment, featurize and normalize for speech data. :param audio_file: Filepath or file object of kali processed feature. :type audio_file: str | file :param translation: Translation text. :type translation: str :param transcript: Transcription text. :type transcript: str :return: Tuple of audio feature tensor and data of translation and transcription parts, where translation and transcription parts could be token ids or text. :rtype: tuple of (2darray, (list, list)) """ specgram = kaldiio.load_mat(audio_file) assert specgram.shape[ 1] == self._feat_dim, 'expect feat dim {}, but got {}'.format( self._feat_dim, specgram.shape[1]) # specgram augment specgram = self._augmentation_pipeline.transform_feature(specgram) if self._keep_transcription_text: return specgram, translation, transcript else: translation_text_ids = self._text_featurizer.featurize(translation) transcript_text_ids = self._text_featurizer.featurize(transcript) return specgram, translation_text_ids, transcript_text_ids def __call__(self, batch): """batch examples Args: batch ([List]): batch is (audio, text) audio (np.ndarray) shape (T, D) translation (List[int] or str): shape (U,) transcription (List[int] or str): shape (V,) Returns: tuple(audio, text, audio_lens, text_lens): batched data. audio : (B, Tmax, D) audio_lens: (B) translation_text : (B, Umax) translation_text_lens: (B) transcription_text : (B, Vmax) transcription_text_lens: (B) """ audios = [] audio_lens = [] translation_text = [] translation_text_lens = [] transcription_text = [] transcription_text_lens = [] utts = [] for utt, audio, translation, transcription in batch: audio, translation, transcription = self.process_utterance( audio, translation, transcription) #utt utts.append(utt) # audio audios.append(audio) # [T, D] audio_lens.append(audio.shape[0]) # text # for training, text is token ids # else text is string, convert to unicode ord tokens = [[], []] for idx, text in enumerate([translation, transcription]): if self._keep_transcription_text: assert isinstance(text, str), (type(text), text) tokens[idx] = [ord(t) for t in text] else: tokens[idx] = text # token ids tokens[idx] = tokens[idx] if isinstance( tokens[idx], np.ndarray) else np.array( tokens[idx], dtype=np.int64) translation_text.append(tokens[0]) translation_text_lens.append(tokens[0].shape[0]) transcription_text.append(tokens[1]) transcription_text_lens.append(tokens[1].shape[0]) padded_audios = pad_sequence( audios, padding_value=0.0).astype(np.float32) #[B, T, D] audio_lens = np.array(audio_lens).astype(np.int64) padded_translation = pad_sequence( translation_text, padding_value=IGNORE_ID).astype(np.int64) translation_lens = np.array(translation_text_lens).astype(np.int64) padded_transcription = pad_sequence( transcription_text, padding_value=IGNORE_ID).astype(np.int64) transcription_lens = np.array(transcription_text_lens).astype(np.int64) return utts, padded_audios, audio_lens, ( padded_translation, padded_transcription), (translation_lens, transcription_lens)