# 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 download_and_decompress from ..utils.env import DATA_HOME from ..utils.log import logger from .dataset import feat_funcs __all__ = ['LIBRISPEECH'] class LIBRISPEECH(Dataset): """ LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. Reference: LIBRISPEECH: AN ASR CORPUS BASED ON PUBLIC DOMAIN AUDIO BOOKS http://www.danielpovey.com/files/2015_icassp_librispeech.pdf https://arxiv.org/abs/1709.05522 """ source_url = 'http://www.openslr.org/resources/12/' archieves = [ { 'url': source_url + 'train-clean-100.tar.gz', 'md5': '2a93770f6d5c6c964bc36631d331a522', }, { 'url': source_url + 'train-clean-360.tar.gz', 'md5': 'c0e676e450a7ff2f54aeade5171606fa', }, { 'url': source_url + 'train-other-500.tar.gz', 'md5': 'd1a0fd59409feb2c614ce4d30c387708', }, { 'url': source_url + 'dev-clean.tar.gz', 'md5': '42e2234ba48799c1f50f24a7926300a1', }, { 'url': source_url + 'dev-other.tar.gz', 'md5': 'c8d0bcc9cca99d4f8b62fcc847357931', }, { 'url': source_url + 'test-clean.tar.gz', 'md5': '32fa31d27d2e1cad72775fee3f4849a9', }, { 'url': source_url + 'test-other.tar.gz', 'md5': 'fb5a50374b501bb3bac4815ee91d3135', }, ] speaker_meta = os.path.join('LibriSpeech', 'SPEAKERS.TXT') utt_info = collections.namedtuple('META_INFO', ( 'file_path', 'utt_id', 'text', 'spk_id', 'spk_gender')) audio_path = 'LibriSpeech' manifest_path = os.path.join('LibriSpeech', 'manifest') subset = [ 'train-clean-100', 'train-clean-360', 'train-clean-500', 'dev-clean', 'dev-other', 'test-clean', 'test-other' ] def __init__(self, subset: str='train-clean-100', 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(LIBRISPEECH, self).__init__() def _get_speaker_info(self) -> Dict[str, str]: ret = {} with open(os.path.join(DATA_HOME, self.speaker_meta), 'r') as rf: for line in rf.readlines(): if ';' in line: # Skip dataset abstract continue spk_id, gender = map(str.strip, line.split('|')[:2]) # spk_id, gender ret.update({spk_id: gender}) return ret def _get_text_info(self, trans_file) -> Dict[str, str]: ret = {} with open(trans_file, 'r') as rf: for line in rf.readlines(): utt_id, text = map(str.strip, line.split(' ', 1)) # utt_id, text ret.update({utt_id: text}) 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.speaker_meta)): download_and_decompress(self.archieves, DATA_HOME, len(self.archieves)) # Speaker info speaker_info = self._get_speaker_info() # Text info text_info = {} for root, _, files in os.walk( os.path.join(DATA_HOME, self.audio_path, self.subset)): for file in files: if file.endswith('.trans.txt'): text_info.update( self._get_text_info(os.path.join(root, file))) data = [] for root, _, files in os.walk( os.path.join(DATA_HOME, self.audio_path, self.subset)): for file in files: if file.endswith('.flac'): utt_id = os.path.splitext(file)[0] spk_id = utt_id.split('-')[0] if utt_id not in text_info \ or spk_id not in speaker_info : # Skip samples with incomplete data continue file_path = os.path.join(root, file) text = text_info[utt_id] spk_gender = speaker_info[spk_id] data.append( self.utt_info(file_path, utt_id, text, spk_id, spk_gender)) 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'], 'spk': record['spk_id'], 'gender': record['spk_gender'], }, 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)