nvidia-nemo--speech
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69 行
2.7 KiB
Python
69 行
2.7 KiB
Python
# Copyright (c) 2020, 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 Any, Optional
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from nemo.collections.asr.data import feature_to_label
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def get_feature_seq_speakerlabel_dataset(
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feature_loader, config: dict
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) -> feature_to_label.FeatureToSeqSpeakerLabelDataset:
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"""
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Instantiates a FeatureSeqSpeakerLabelDataset.
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Args:
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config: Config of the FeatureToSeqSpeakerLabelDataset.
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Returns:
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An instance of FeatureToSeqSpeakerLabelDataset.
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"""
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dataset = feature_to_label.FeatureToSeqSpeakerLabelDataset(
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manifest_filepath=config['manifest_filepath'],
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labels=config['labels'],
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feature_loader=feature_loader,
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)
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return dataset
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def get_feature_label_dataset(config: dict, augmentor: Optional[Any] = None) -> feature_to_label.FeatureToLabelDataset:
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dataset = feature_to_label.FeatureToLabelDataset(
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manifest_filepath=config['manifest_filepath'],
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labels=config['labels'],
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augmentor=augmentor,
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window_length_in_sec=config.get("window_length_in_sec", 0.63),
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shift_length_in_sec=config.get("shift_length_in_sec", 0.08),
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is_regression_task=config.get("is_regression_task", False),
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cal_labels_occurrence=config.get("cal_labels_occurrence", False),
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zero_spec_db_val=config.get("zero_spec_db_val", -16.635),
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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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)
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return dataset
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def get_feature_multi_label_dataset(
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config: dict, augmentor: Optional[Any] = None
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) -> feature_to_label.FeatureToMultiLabelDataset:
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dataset = feature_to_label.FeatureToMultiLabelDataset(
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manifest_filepath=config['manifest_filepath'],
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labels=config['labels'],
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augmentor=augmentor,
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delimiter=config.get('delimiter', None),
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is_regression_task=config.get("is_regression_task", False),
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cal_labels_occurrence=config.get("cal_labels_occurrence", False),
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zero_spec_db_val=config.get("zero_spec_db_val", -16.635),
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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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)
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return dataset
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