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226 lines
8.5 KiB
226 lines
8.5 KiB
# 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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import tarfile
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import time
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from collections import namedtuple
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from typing import Optional
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import numpy as np
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from paddle.io import Dataset
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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.normalizer import FeatureNormalizer
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from deepspeech.frontend.speech import SpeechSegment
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from deepspeech.frontend.utility import read_manifest
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from deepspeech.utils.log import Log
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__all__ = [
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"ManifestDataset",
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]
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logger = Log(__name__).getlog()
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class ManifestDataset(Dataset):
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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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train_manifest="",
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dev_manifest="",
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test_manifest="",
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manifest="",
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unit_type="char",
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vocab_filepath="",
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spm_model_prefix="",
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mean_std_filepath="",
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augmentation_config="",
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max_input_len=27.0,
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min_input_len=0.0,
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max_output_len=float('inf'),
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min_output_len=0.0,
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max_output_input_ratio=float('inf'),
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min_output_input_ratio=0.0,
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batch_size=32, # batch size
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num_workers=0, # data loader workers
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sortagrad=False, # sorted in first epoch when True
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shuffle_method="batch_shuffle", # 'batch_shuffle', 'instance_shuffle'
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))
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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 ManifestDataset 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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ManifestDataset: dataet object.
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"""
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assert 'manifest' in config.data
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assert config.data.manifest
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assert 'keep_transcription_text' in config.data
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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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dataset = cls(
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manifest_path=config.data.manifest,
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unit_type=config.data.unit_type,
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vocab_filepath=config.data.vocab_filepath,
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mean_std_filepath=config.data.mean_std_filepath,
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spm_model_prefix=config.data.spm_model_prefix,
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augmentation_config=aug_file.read(),
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max_input_len=config.data.max_input_len,
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min_input_len=config.data.min_input_len,
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max_output_len=config.data.max_output_len,
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min_output_len=config.data.min_output_len,
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max_output_input_ratio=config.data.max_output_input_ratio,
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min_output_input_ratio=config.data.min_output_input_ratio,
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)
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return dataset
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def _read_vocab(self, vocab_filepath):
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"""Load vocabulary from file."""
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vocab_lines = []
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with open(vocab_filepath, 'r', encoding='utf-8') as file:
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vocab_lines.extend(file.readlines())
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vocab_list = [line[:-1] for line in vocab_lines]
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return vocab_list
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def __init__(self,
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manifest_path,
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unit_type,
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vocab_filepath,
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mean_std_filepath,
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spm_model_prefix=None,
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augmentation_config='{}',
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max_input_len=float('inf'),
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min_input_len=0.0,
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max_output_len=float('inf'),
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min_output_len=0.0,
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max_output_input_ratio=float('inf'),
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min_output_input_ratio=0.0):
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"""Manifest Dataset
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Args:
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manifest_path (str): manifest josn file path
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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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max_input_len ([type], optional): maximum output seq length, in seconds for raw wav, in frame numbers for feature data. Defaults to float('inf').
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min_input_len (float, optional): minimum input seq length, in seconds for raw wav, in frame numbers for feature data. Defaults to 0.0.
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max_output_len (float, optional): maximum input seq length, in modeling units. Defaults to 500.0.
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min_output_len (float, optional): minimum input seq length, in modeling units. Defaults to 0.0.
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max_output_input_ratio (float, optional): maximum output seq length/output seq length ratio. Defaults to 10.0.
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min_output_input_ratio (float, optional): minimum output seq length/output seq length ratio. Defaults to 0.05.
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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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"""
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super().__init__()
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# self._rng = np.random.RandomState(random_seed)
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# read manifest
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self._manifest = read_manifest(
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manifest_path=manifest_path,
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max_input_len=max_input_len,
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min_input_len=min_input_len,
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max_output_len=max_output_len,
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min_output_len=min_output_len,
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max_output_input_ratio=max_output_input_ratio,
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min_output_input_ratio=min_output_input_ratio)
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self._manifest.sort(key=lambda x: x["feat_shape"][0])
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self._vocab_list = self._read_vocab(vocab_filepath)
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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 the vocabulary size.
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Returns:
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int: Vocabulary size.
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"""
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return len(self._vocab_list)
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@property
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def vocab_list(self):
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"""Return the vocabulary in list.
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Returns:
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List[str]:
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"""
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return self._vocab_list
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@property
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def vocab_dict(self):
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"""Return the vocabulary in dict.
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Returns:
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Dict[str, int]:
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"""
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vocab_dict = dict(
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[(token, idx) for (idx, token) in enumerate(self._vocab_list)])
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return vocab_dict
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@property
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def feature_size(self):
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"""Return the audio feature size.
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Returns:
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int: audio feature size.
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"""
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return self._manifest[0]["feat_shape"][-1]
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def __len__(self):
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return len(self._manifest)
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def __getitem__(self, idx):
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instance = self._manifest[idx]
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return instance["utt"], instance["feat"], instance["text"]
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