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155 lines
6.2 KiB
155 lines
6.2 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 codecs
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import collections
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import json
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import os
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from typing import Dict
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from paddle.io import Dataset
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from tqdm import tqdm
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from ..backends import load as load_audio
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from ..utils.download import decompress
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from ..utils.download import download_and_decompress
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from ..utils.env import DATA_HOME
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from ..utils.log import logger
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from .dataset import feat_funcs
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__all__ = ['AISHELL1']
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class AISHELL1(Dataset):
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"""
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This Open Source Mandarin Speech Corpus, AISHELL-ASR0009-OS1, is 178 hours long.
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It is a part of AISHELL-ASR0009, of which utterance contains 11 domains, including
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smart home, autonomous driving, and industrial production. The whole recording was
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put in quiet indoor environment, using 3 different devices at the same time: high
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fidelity microphone (44.1kHz, 16-bit,); Android-system mobile phone (16kHz, 16-bit),
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iOS-system mobile phone (16kHz, 16-bit). Audios in high fidelity were re-sampled
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to 16kHz to build AISHELL- ASR0009-OS1. 400 speakers from different accent areas
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in China were invited to participate in the recording. The manual transcription
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accuracy rate is above 95%, through professional speech annotation and strict
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quality inspection. The corpus is divided into training, development and testing
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sets.
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Reference:
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AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
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https://arxiv.org/abs/1709.05522
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"""
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archieves = [
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{
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'url': 'http://www.openslr.org/resources/33/data_aishell.tgz',
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'md5': '2f494334227864a8a8fec932999db9d8',
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},
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]
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text_meta = os.path.join('data_aishell', 'transcript',
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'aishell_transcript_v0.8.txt')
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utt_info = collections.namedtuple('META_INFO',
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('file_path', 'utt_id', 'text'))
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audio_path = os.path.join('data_aishell', 'wav')
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manifest_path = os.path.join('data_aishell', 'manifest')
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subset = ['train', 'dev', 'test']
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def __init__(self, subset: str='train', feat_type: str='raw', **kwargs):
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assert subset in self.subset, 'Dataset subset must be one in {}, but got {}'.format(
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self.subset, subset)
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self.subset = subset
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self.feat_type = feat_type
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self.feat_config = kwargs
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self._data = self._get_data()
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super(AISHELL1, self).__init__()
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def _get_text_info(self) -> Dict[str, str]:
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ret = {}
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with open(os.path.join(DATA_HOME, self.text_meta), 'r') as rf:
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for line in rf.readlines()[1:]:
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utt_id, text = map(str.strip, line.split(' ',
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1)) # utt_id, text
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ret.update({utt_id: ''.join(text.split())})
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return ret
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def _get_data(self):
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if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
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not os.path.isfile(os.path.join(DATA_HOME, self.text_meta)):
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download_and_decompress(self.archieves, DATA_HOME)
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# Extract *wav from *.tar.gz.
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for root, _, files in os.walk(
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os.path.join(DATA_HOME, self.audio_path)):
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for file in files:
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if file.endswith('.tar.gz'):
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decompress(os.path.join(root, file))
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os.remove(os.path.join(root, file))
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text_info = self._get_text_info()
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data = []
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for root, _, files in os.walk(
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os.path.join(DATA_HOME, self.audio_path, self.subset)):
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for file in files:
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if file.endswith('.wav'):
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utt_id = os.path.splitext(file)[0]
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if utt_id not in text_info: # There are some utt_id that without label
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continue
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text = text_info[utt_id]
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file_path = os.path.join(root, file)
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data.append(self.utt_info(file_path, utt_id, text))
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return data
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def _convert_to_record(self, idx: int):
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sample = self._data[idx]
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record = {}
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# To show all fields in a namedtuple: `type(sample)._fields`
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for field in type(sample)._fields:
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record[field] = getattr(sample, field)
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waveform, sr = load_audio(
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sample[0]) # The first element of sample is file path
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feat_func = feat_funcs[self.feat_type]
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feat = feat_func(
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waveform, sample_rate=sr,
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**self.feat_config) if feat_func else waveform
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record.update({'feat': feat, 'duration': len(waveform) / sr})
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return record
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def create_manifest(self, prefix='manifest'):
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if not os.path.isdir(os.path.join(DATA_HOME, self.manifest_path)):
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os.makedirs(os.path.join(DATA_HOME, self.manifest_path))
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manifest_file = os.path.join(DATA_HOME, self.manifest_path,
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f'{prefix}.{self.subset}')
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with codecs.open(manifest_file, 'w', 'utf-8') as f:
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for idx in tqdm(range(len(self))):
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record = self._convert_to_record(idx)
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record_line = json.dumps(
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{
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'utt': record['utt_id'],
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'feat': record['file_path'],
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'feat_shape': (record['duration'], ),
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'text': record['text']
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},
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ensure_ascii=False)
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f.write(record_line + '\n')
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logger.info(f'Manifest file {manifest_file} created.')
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def __getitem__(self, idx):
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record = self._convert_to_record(idx)
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return tuple(record.values())
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def __len__(self):
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return len(self._data)
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