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351 lines
14 KiB
351 lines
14 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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# namedtupe need global for pickle.
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TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
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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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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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raw_wav=True, # use raw_wav or kaldi feature
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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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dither=1.0, # feature dither
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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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random_seed=0,
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keep_transcription_text=False,
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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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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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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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dither=config.data.dither,
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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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random_seed=config.data.random_seed,
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keep_transcription_text=config.data.keep_transcription_text)
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return dataset
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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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stride_ms=10.0,
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window_ms=20.0,
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n_fft=None,
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max_freq=None,
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target_sample_rate=16000,
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specgram_type='linear',
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feat_dim=None,
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delta_delta=False,
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dither=1.0,
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use_dB_normalization=True,
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target_dB=-20,
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random_seed=0,
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keep_transcription_text=False):
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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._stride_ms = stride_ms
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self._target_sample_rate = target_sample_rate
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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._augmentation_pipeline = AugmentationPipeline(
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augmentation_config=augmentation_config, random_seed=random_seed)
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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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self._rng = np.random.RandomState(random_seed)
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self._keep_transcription_text = keep_transcription_text
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# for caching tar files info
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self._local_data = TarLocalData(tar2info={}, tar2object={})
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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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@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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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 _instance_reader_creator(self, manifest):
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"""
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Instance reader creator. Create a callable function to produce
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instances of data.
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Instance: a tuple of ndarray of audio spectrogram and a list of
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token indices for transcript.
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"""
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def reader():
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for instance in manifest:
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inst = self.process_utterance(instance["feat"],
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instance["text"])
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yield inst
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return reader
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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 self.process_utterance(instance["feat"], instance["text"])
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