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95 lines
3.8 KiB
Python
95 lines
3.8 KiB
Python
# Copyright (c) 2022, NVIDIA CORPORATION. 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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from typing import Optional
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from nemo.collections.asr.data.feature_to_text import FeatureToBPEDataset, FeatureToCharDataset
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from nemo.utils import logging
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def get_char_dataset(config: dict, augmentor: Optional['FeatureAugmentor'] = None) -> FeatureToCharDataset:
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"""
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Instantiates a Character Encoding based FeatureToCharDataset.
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Args:
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config: Config of the FeatureToCharDataset.
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augmentor: Optional AudioAugmentor object for augmentations on audio data.
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Returns:
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An instance of FeatureToCharDataset.
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"""
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if 'labels' not in config:
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logging.warning(f"dataset does not have explicitly defined labels")
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dataset = FeatureToCharDataset(
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manifest_filepath=config['manifest_filepath'],
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labels=config.get('labels', None),
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normalize=config.get('normalize', 'post_norm'),
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normalize_type=config.get('normalize_type', 'per_feature'),
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use_rttm=config.get('use_rttm', False),
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rttm_mode=config.get('rttm_mode', 'mask'),
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feat_min_len=config.get('feat_min_len', 4),
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feat_mask_val=config.get('feat_mask_val', None),
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frame_unit_time_secs=config.get('frame_unit_time_secs', 0.01),
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sample_rate=config.get('sample_rate', 16000),
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augmentor=augmentor,
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max_duration=config.get('max_duration', None),
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min_duration=config.get('min_duration', None),
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max_utts=config.get('max_utts', 0),
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blank_index=config.get('blank_index', -1),
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unk_index=config.get('unk_index', -1),
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trim=config.get('trim_silence', False),
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parser=config.get('parser', 'en'),
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return_sample_id=config.get('return_sample_id', False),
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channel_selector=config.get('channel_selector', None),
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)
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return dataset
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def get_bpe_dataset(
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config: dict, tokenizer: 'TokenizerSpec', augmentor: Optional['FeatureAugmentor'] = None
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) -> FeatureToBPEDataset:
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"""
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Instantiates a Byte Pair Encoding / Word Piece Encoding based FeatureoToBPEDataset.
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Args:
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config: Config of the FeatureToBPEDataset.
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tokenizer: An instance of a TokenizerSpec object.
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augmentor: Optional FeatureAugmentor object for augmentations on audio features.
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Returns:
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An instance of FeatureToBPEDataset.
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"""
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dataset = FeatureToBPEDataset(
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manifest_filepath=config['manifest_filepath'],
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tokenizer=tokenizer,
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normalize=config.get('normalize', 'post_norm'),
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normalize_type=config.get('normalize_type', 'per_feature'),
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use_rttm=config.get('use_rttm', False),
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rttm_mode=config.get('rttm_mode', 'mask'),
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feat_min_len=config.get('feat_min_len', 4),
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feat_mask_val=config.get('feat_mask_val', None),
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frame_unit_time_secs=config.get('frame_unit_time_secs', 0.01),
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sample_rate=config.get('sample_rate', 16000),
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augmentor=augmentor,
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max_duration=config.get('max_duration', None),
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min_duration=config.get('min_duration', None),
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max_utts=config.get('max_utts', 0),
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trim=config.get('trim_silence', False),
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use_start_end_token=config.get('use_start_end_token', True),
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return_sample_id=config.get('return_sample_id', False),
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channel_selector=config.get('channel_selector', None),
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)
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return dataset
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