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667 lines
26 KiB
667 lines
26 KiB
3 years ago
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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 io
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from collections import namedtuple
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from typing import Optional
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from typing import Tuple
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import kaldiio
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import numpy as np
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from yacs.config import CfgNode
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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.featurizer.text_featurizer import TextFeaturizer
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from deepspeech.frontend.normalizer import FeatureNormalizer
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from deepspeech.frontend.speech import SpeechSegment
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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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__all__ = ["SpeechCollator", "KaldiPrePorocessedCollator"]
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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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@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=False))
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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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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.collator
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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.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.collator.augmentation_config,
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mode='r',
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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.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.collator.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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return speech_collator
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def __init__(
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self,
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aug_file,
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mean_std_filepath,
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vocab_filepath,
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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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"""SpeechCollator Collator
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Args:
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unit_type(str): token unit type, e.g. char, word, spm
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vocab_filepath (str): vocab file path.
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mean_std_filepath (str): mean and std file path, which suffix is *.npy
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spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
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augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
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stride_ms (float, optional): stride size in ms. Defaults to 10.0.
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window_ms (float, optional): window size in ms. Defaults to 20.0.
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n_fft (int, optional): fft points for rfft. Defaults to None.
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max_freq (int, optional): max cut freq. Defaults to None.
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target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000.
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specgram_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
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feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None.
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delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False.
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use_dB_normalization (bool, optional): do dB normalization. Defaults to True.
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target_dB (int, optional): target dB. Defaults to -20.
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random_seed (int, optional): for random generator. Defaults to 0.
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keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
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if ``keep_transcription_text`` is False, text is token ids else is raw string.
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Do augmentations
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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 batch.
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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(), random_seed=random_seed)
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self._normalizer = FeatureNormalizer(
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mean_std_filepath) if mean_std_filepath else None
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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=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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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, translation):
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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 translation: translation text.
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:type translation: str
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:return: Tuple of audio feature tensor and data of translation part,
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where translation part could be token ids or text.
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:rtype: tuple of (2darray, list)
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"""
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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), translation)
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else:
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speech_segment = SpeechSegment.from_file(audio_file, translation)
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# audio augment
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self._augmentation_pipeline.transform_audio(speech_segment)
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specgram, translation_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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# specgram augment
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specgram = self._augmentation_pipeline.transform_feature(specgram)
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specgram = specgram.transpose([1, 0])
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return specgram, translation_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, D]
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audio_lens.append(audio.shape[0])
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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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@property
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def manifest(self):
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return self._manifest
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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._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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class TripletSpeechCollator(SpeechCollator):
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def process_utterance(self, audio_file, translation, 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 translation: translation text.
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:type translation: str
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:return: Tuple of audio feature tensor and data of translation part,
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where translation part could be token ids or text.
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:rtype: tuple of (2darray, list)
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"""
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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), translation)
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else:
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speech_segment = SpeechSegment.from_file(audio_file, translation)
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# audio augment
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self._augmentation_pipeline.transform_audio(speech_segment)
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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)
|
||
|
|
specgram = specgram.transpose([1, 0])
|
||
|
|
return specgram, translation_part, transcript_part
|
||
|
|
|
||
|
|
def __call__(self, batch):
|
||
|
|
"""batch examples
|
||
|
|
|
||
|
|
Args:
|
||
|
|
batch ([List]): batch is (audio, text)
|
||
|
|
audio (np.ndarray) shape (D, T)
|
||
|
|
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)
|
||
|
|
specgram = specgram.transpose([1, 0])
|
||
|
|
assert specgram.shape[
|
||
|
|
0] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
|
||
|
|
self._feat_dim, specgram.shape[0])
|
||
|
|
|
||
|
|
# specgram augment
|
||
|
|
specgram = self._augmentation_pipeline.transform_feature(specgram)
|
||
|
|
|
||
|
|
specgram = specgram.transpose([1, 0])
|
||
|
|
if self._keep_transcription_text:
|
||
|
|
return specgram, translation
|
||
|
|
else:
|
||
|
|
text_ids = self._text_featurizer.featurize(translation)
|
||
|
|
return specgram, text_ids
|
||
|
|
|
||
|
|
@property
|
||
|
|
def manifest(self):
|
||
|
|
return self._manifest
|
||
|
|
|
||
|
|
@property
|
||
|
|
def vocab_size(self):
|
||
|
|
return self._text_featurizer.vocab_size
|
||
|
|
|
||
|
|
@property
|
||
|
|
def vocab_list(self):
|
||
|
|
return self._text_featurizer.vocab_list
|
||
|
|
|
||
|
|
@property
|
||
|
|
def vocab_dict(self):
|
||
|
|
return self._text_featurizer.vocab_dict
|
||
|
|
|
||
|
|
@property
|
||
|
|
def text_feature(self):
|
||
|
|
return self._text_featurizer
|
||
|
|
|
||
|
|
@property
|
||
|
|
def feature_size(self):
|
||
|
|
return self._feat_dim
|
||
|
|
|
||
|
|
@property
|
||
|
|
def stride_ms(self):
|
||
|
|
return self._stride_ms
|
||
|
|
|
||
|
|
|
||
|
|
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)
|
||
|
|
specgram = specgram.transpose([1, 0])
|
||
|
|
assert specgram.shape[
|
||
|
|
0] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
|
||
|
|
self._feat_dim, specgram.shape[0])
|
||
|
|
|
||
|
|
# specgram augment
|
||
|
|
specgram = self._augmentation_pipeline.transform_feature(specgram)
|
||
|
|
|
||
|
|
specgram = specgram.transpose([1, 0])
|
||
|
|
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 (D, T)
|
||
|
|
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)
|