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PaddleSpeech/audio/paddleaudio/datasets/aishell.py

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6.2 KiB

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import codecs
import collections
import json
import os
from typing import Dict
from paddle.io import Dataset
from tqdm import tqdm
from ..backends import load as load_audio
from ..utils.download import decompress
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from ..utils.log import logger
from .dataset import feat_funcs
__all__ = ['AISHELL1']
class AISHELL1(Dataset):
"""
This Open Source Mandarin Speech Corpus, AISHELL-ASR0009-OS1, is 178 hours long.
It is a part of AISHELL-ASR0009, of which utterance contains 11 domains, including
smart home, autonomous driving, and industrial production. The whole recording was
put in quiet indoor environment, using 3 different devices at the same time: high
fidelity microphone (44.1kHz, 16-bit,); Android-system mobile phone (16kHz, 16-bit),
iOS-system mobile phone (16kHz, 16-bit). Audios in high fidelity were re-sampled
to 16kHz to build AISHELL- ASR0009-OS1. 400 speakers from different accent areas
in China were invited to participate in the recording. The manual transcription
accuracy rate is above 95%, through professional speech annotation and strict
quality inspection. The corpus is divided into training, development and testing
sets.
Reference:
AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
https://arxiv.org/abs/1709.05522
"""
archieves = [
{
'url': 'http://www.openslr.org/resources/33/data_aishell.tgz',
'md5': '2f494334227864a8a8fec932999db9d8',
},
]
text_meta = os.path.join('data_aishell', 'transcript',
'aishell_transcript_v0.8.txt')
utt_info = collections.namedtuple('META_INFO',
('file_path', 'utt_id', 'text'))
audio_path = os.path.join('data_aishell', 'wav')
manifest_path = os.path.join('data_aishell', 'manifest')
subset = ['train', 'dev', 'test']
def __init__(self, subset: str='train', feat_type: str='raw', **kwargs):
assert subset in self.subset, 'Dataset subset must be one in {}, but got {}'.format(
self.subset, subset)
self.subset = subset
self.feat_type = feat_type
self.feat_config = kwargs
self._data = self._get_data()
super(AISHELL1, self).__init__()
def _get_text_info(self) -> Dict[str, str]:
ret = {}
with open(os.path.join(DATA_HOME, self.text_meta), 'r') as rf:
for line in rf.readlines()[1:]:
utt_id, text = map(str.strip, line.split(' ',
1)) # utt_id, text
ret.update({utt_id: ''.join(text.split())})
return ret
def _get_data(self):
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.text_meta)):
download_and_decompress(self.archieves, DATA_HOME)
# Extract *wav from *.tar.gz.
for root, _, files in os.walk(
os.path.join(DATA_HOME, self.audio_path)):
for file in files:
if file.endswith('.tar.gz'):
decompress(os.path.join(root, file))
os.remove(os.path.join(root, file))
text_info = self._get_text_info()
data = []
for root, _, files in os.walk(
os.path.join(DATA_HOME, self.audio_path, self.subset)):
for file in files:
if file.endswith('.wav'):
utt_id = os.path.splitext(file)[0]
if utt_id not in text_info: # There are some utt_id that without label
continue
text = text_info[utt_id]
file_path = os.path.join(root, file)
data.append(self.utt_info(file_path, utt_id, text))
return data
def _convert_to_record(self, idx: int):
sample = self._data[idx]
record = {}
# To show all fields in a namedtuple: `type(sample)._fields`
for field in type(sample)._fields:
record[field] = getattr(sample, field)
waveform, sr = load_audio(
sample[0]) # The first element of sample is file path
feat_func = feat_funcs[self.feat_type]
feat = feat_func(
waveform, sample_rate=sr,
**self.feat_config) if feat_func else waveform
record.update({'feat': feat, 'duration': len(waveform) / sr})
return record
def create_manifest(self, prefix='manifest'):
if not os.path.isdir(os.path.join(DATA_HOME, self.manifest_path)):
os.makedirs(os.path.join(DATA_HOME, self.manifest_path))
manifest_file = os.path.join(DATA_HOME, self.manifest_path,
f'{prefix}.{self.subset}')
with codecs.open(manifest_file, 'w', 'utf-8') as f:
for idx in tqdm(range(len(self))):
record = self._convert_to_record(idx)
record_line = json.dumps(
{
'utt': record['utt_id'],
'feat': record['file_path'],
'feat_shape': (record['duration'], ),
'text': record['text']
},
ensure_ascii=False)
f.write(record_line + '\n')
logger.info(f'Manifest file {manifest_file} created.')
def __getitem__(self, idx):
record = self._convert_to_record(idx)
return tuple(record.values())
def __len__(self):
return len(self._data)