Datasets:
Tasks:
Automatic Speech Recognition
Multilinguality:
multilingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
Tags:
License:
| # coding=utf-8 | |
| # Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # 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. | |
| """ OpenSLR Dataset""" | |
| from __future__ import absolute_import, division, print_function | |
| import os | |
| import re | |
| from pathlib import Path | |
| import datasets | |
| from datasets.tasks import AutomaticSpeechRecognition | |
| _DATA_URL = "https://openslr.org/resources/{}" | |
| _CITATION = """\ | |
| SLR32: | |
| @inproceedings{van-niekerk-etal-2017, | |
| title = {{Rapid development of TTS corpora for four South African languages}}, | |
| author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson | |
| and Martin Jansche and Linne Ha}, | |
| booktitle = {Proc. Interspeech 2017}, | |
| pages = {2178--2182}, | |
| address = {Stockholm, Sweden}, | |
| month = aug, | |
| year = {2017}, | |
| URL = {http://dx.doi.org/10.21437/Interspeech.2017-1139} | |
| } | |
| SLR35, SLR36, SLR52, SLR53, SLR54: | |
| @inproceedings{kjartansson-etal-sltu2018, | |
| title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}}, | |
| author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha}, | |
| booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, | |
| year = {2018}, | |
| address = {Gurugram, India}, | |
| month = aug, | |
| pages = {52--55}, | |
| URL = {https://dx.doi.org/10.21437/SLTU.2018-11}, | |
| } | |
| SLR41, SLR42, SLR43, SLR44: | |
| @inproceedings{kjartansson-etal-tts-sltu2018, | |
| title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, | |
| Khmer, Nepali, Sinhala, and Sundanese}}, | |
| author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu | |
| De Silva and Supheakmungkol Sarin}, | |
| booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, | |
| year = {2018}, | |
| address = {Gurugram, India}, | |
| month = aug, | |
| pages = {66--70}, | |
| URL = {https://dx.doi.org/10.21437/SLTU.2018-14} | |
| } | |
| SLR63, SLR64, SLR65, SLR66, SLR78, SLR79: | |
| @inproceedings{he-etal-2020-open, | |
| title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and | |
| Telugu Speech Synthesis Systems}}, | |
| author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, | |
| Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot}, | |
| booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, | |
| month = may, | |
| year = {2020}, | |
| address = {Marseille, France}, | |
| publisher = {European Language Resources Association (ELRA)}, | |
| pages = {6494--6503}, | |
| url = {https://www.aclweb.org/anthology/2020.lrec-1.800}, | |
| ISBN = "{979-10-95546-34-4}, | |
| } | |
| SLR69, SLR76, SLR77: | |
| @inproceedings{kjartansson-etal-2020-open, | |
| title = {{Open-Source High Quality Speech Datasets for Basque, Catalan and Galician}}, | |
| author = {Kjartansson, Oddur and Gutkin, Alexander and Butryna, Alena and Demirsahin, Isin and Rivera, Clara}, | |
| booktitle = {Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages | |
| (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)}, | |
| year = {2020}, | |
| pages = {21--27}, | |
| month = may, | |
| address = {Marseille, France}, | |
| publisher = {European Language Resources association (ELRA)}, | |
| url = {https://www.aclweb.org/anthology/2020.sltu-1.3}, | |
| ISBN = {979-10-95546-35-1}, | |
| } | |
| SLR71, SLR71, SLR72, SLR73, SLR74, SLR75: | |
| @inproceedings{guevara-rukoz-etal-2020-crowdsourcing, | |
| title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}}, | |
| author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, | |
| Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur}, | |
| booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, | |
| year = {2020}, | |
| month = may, | |
| address = {Marseille, France}, | |
| publisher = {European Language Resources Association (ELRA)}, | |
| url = {https://www.aclweb.org/anthology/2020.lrec-1.801}, | |
| pages = {6504--6513}, | |
| ISBN = {979-10-95546-34-4}, | |
| } | |
| SLR80 | |
| @inproceedings{oo-etal-2020-burmese, | |
| title = {{Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application | |
| to Text-to-Speech}}, | |
| author = {Oo, Yin May and Wattanavekin, Theeraphol and Li, Chenfang and De Silva, Pasindu and Sarin, | |
| Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Kjartansson, Oddur and Gutkin, Alexander}, | |
| booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, | |
| month = may, | |
| year = {2020}, | |
| pages = "6328--6339", | |
| address = {Marseille, France}, | |
| publisher = {European Language Resources Association (ELRA)}, | |
| url = {https://www.aclweb.org/anthology/2020.lrec-1.777}, | |
| ISBN = {979-10-95546-34-4}, | |
| } | |
| SLR86 | |
| @inproceedings{gutkin-et-al-yoruba2020, | |
| title = {{Developing an Open-Source Corpus of Yoruba Speech}}, | |
| author = {Alexander Gutkin and Işın Demirşahin and Oddur Kjartansson and Clara Rivera and Kọ́lá Túbọ̀sún}, | |
| booktitle = {Proceedings of Interspeech 2020}, | |
| pages = {404--408}, | |
| month = {October}, | |
| year = {2020}, | |
| address = {Shanghai, China}, | |
| publisher = {International Speech and Communication Association (ISCA)}, | |
| doi = {10.21437/Interspeech.2020-1096}, | |
| url = {https://dx.doi.org/10.21437/Interspeech.2020-1096}, | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, | |
| and software related to speech recognition. We intend to be a convenient place for anyone to put resources that | |
| they have created, so that they can be downloaded publicly. | |
| """ | |
| _HOMEPAGE = "https://openslr.org/" | |
| _LICENSE = "" | |
| _RESOURCES = { | |
| "SLR32": { | |
| "Language": "South African", | |
| "LongName": "High quality TTS data for four South African languages (af, st, tn, xh)", | |
| "Category": "Speech", | |
| "Summary": "Multi-speaker TTS data for four South African languages, Afrikaans, Sesotho, " | |
| "Setswana and isiXhosa.", | |
| "Files": ["af_za.tar.gz", "st_za.tar.gz", "tn_za.tar.gz", "xh_za.tar.gz"], | |
| "IndexFiles": [ | |
| "https://s3.amazonaws.com/datasets.huggingface.co/openslr/SLR32/af_za/line_index.tsv", | |
| "https://s3.amazonaws.com/datasets.huggingface.co/openslr/SLR32/st_za/line_index.tsv", | |
| "https://s3.amazonaws.com/datasets.huggingface.co/openslr/SLR32/tn_za/line_index.tsv", | |
| "https://s3.amazonaws.com/datasets.huggingface.co/openslr/SLR32/xh_za/line_index.tsv", | |
| ], | |
| "DataDirs": ["af_za/za/afr/wavs", "st_za/za/sso/wavs", "tn_za/za/tsn/wavs", "xh_za/za/xho/wavs"], | |
| }, | |
| "SLR35": { | |
| "Language": "Javanese", | |
| "LongName": "Large Javanese ASR training data set", | |
| "Category": "Speech", | |
| "Summary": "Javanese ASR training data set containing ~185K utterances", | |
| "Files": [ | |
| "asr_javanese_0.zip", | |
| "asr_javanese_1.zip", | |
| "asr_javanese_2.zip", | |
| "asr_javanese_3.zip", | |
| "asr_javanese_4.zip", | |
| "asr_javanese_5.zip", | |
| "asr_javanese_6.zip", | |
| "asr_javanese_7.zip", | |
| "asr_javanese_8.zip", | |
| "asr_javanese_9.zip", | |
| "asr_javanese_a.zip", | |
| "asr_javanese_b.zip", | |
| "asr_javanese_c.zip", | |
| "asr_javanese_d.zip", | |
| "asr_javanese_e.zip", | |
| "asr_javanese_f.zip", | |
| ], | |
| "IndexFiles": ["asr_javanese/utt_spk_text.tsv"] * 16, | |
| "DataDirs": ["asr_javanese/data"] * 16, | |
| }, | |
| "SLR36": { | |
| "Language": "Sundanese", | |
| "LongName": "Large Sundanese ASR training data set", | |
| "Category": "Speech", | |
| "Summary": "Sundanese ASR training data set containing ~220K utterances", | |
| "Files": [ | |
| "asr_sundanese_0.zip", | |
| "asr_sundanese_1.zip", | |
| "asr_sundanese_2.zip", | |
| "asr_sundanese_3.zip", | |
| "asr_sundanese_4.zip", | |
| "asr_sundanese_5.zip", | |
| "asr_sundanese_6.zip", | |
| "asr_sundanese_7.zip", | |
| "asr_sundanese_8.zip", | |
| "asr_sundanese_9.zip", | |
| "asr_sundanese_a.zip", | |
| "asr_sundanese_b.zip", | |
| "asr_sundanese_c.zip", | |
| "asr_sundanese_d.zip", | |
| "asr_sundanese_e.zip", | |
| "asr_sundanese_f.zip", | |
| ], | |
| "IndexFiles": ["asr_sundanese/utt_spk_text.tsv"] * 16, | |
| "DataDirs": ["asr_sundanese/data"] * 16, | |
| }, | |
| "SLR41": { | |
| "Language": "Javanese", | |
| "LongName": "High quality TTS data for Javanese", | |
| "Category": "Speech", | |
| "Summary": "Multi-speaker TTS data for Javanese (jv-ID)", | |
| "Files": ["jv_id_female.zip", "jv_id_male.zip"], | |
| "IndexFiles": ["jv_id_female/line_index.tsv", "jv_id_male/line_index.tsv"], | |
| "DataDirs": ["jv_id_female/wavs", "jv_id_male/wavs"], | |
| }, | |
| "SLR42": { | |
| "Language": "Khmer", | |
| "LongName": "High quality TTS data for Khmer", | |
| "Category": "Speech", | |
| "Summary": "Multi-speaker TTS data for Khmer (km-KH)", | |
| "Files": ["km_kh_male.zip"], | |
| "IndexFiles": ["km_kh_male/line_index.tsv"], | |
| "DataDirs": ["km_kh_male/wavs"], | |
| }, | |
| "SLR43": { | |
| "Language": "Nepali", | |
| "LongName": "High quality TTS data for Nepali", | |
| "Category": "Speech", | |
| "Summary": "Multi-speaker TTS data for Nepali (ne-NP)", | |
| "Files": ["ne_np_female.zip"], | |
| "IndexFiles": ["ne_np_female/line_index.tsv"], | |
| "DataDirs": ["ne_np_female/wavs"], | |
| }, | |
| "SLR44": { | |
| "Language": "Sundanese", | |
| "LongName": "High quality TTS data for Sundanese", | |
| "Category": "Speech", | |
| "Summary": "Multi-speaker TTS data for Javanese Sundanese (su-ID)", | |
| "Files": ["su_id_female.zip", "su_id_male.zip"], | |
| "IndexFiles": ["su_id_female/line_index.tsv", "su_id_male/line_index.tsv"], | |
| "DataDirs": ["su_id_female/wavs", "su_id_male/wavs"], | |
| }, | |
| "SLR52": { | |
| "Language": "Sinhala", | |
| "LongName": "Large Sinhala ASR training data set", | |
| "Category": "Speech", | |
| "Summary": "Sinhala ASR training data set containing ~185K utterances", | |
| "Files": [ | |
| "asr_sinhala_0.zip", | |
| "asr_sinhala_1.zip", | |
| "asr_sinhala_2.zip", | |
| "asr_sinhala_3.zip", | |
| "asr_sinhala_4.zip", | |
| "asr_sinhala_5.zip", | |
| "asr_sinhala_6.zip", | |
| "asr_sinhala_7.zip", | |
| "asr_sinhala_8.zip", | |
| "asr_sinhala_9.zip", | |
| "asr_sinhala_a.zip", | |
| "asr_sinhala_b.zip", | |
| "asr_sinhala_c.zip", | |
| "asr_sinhala_d.zip", | |
| "asr_sinhala_e.zip", | |
| "asr_sinhala_f.zip", | |
| ], | |
| "IndexFiles": ["asr_sinhala/utt_spk_text.tsv"] * 16, | |
| "DataDirs": ["asr_sinhala/data"] * 16, | |
| }, | |
| "SLR53": { | |
| "Language": "Bengali", | |
| "LongName": "Large Bengali ASR training data set", | |
| "Category": "Speech", | |
| "Summary": "Bengali ASR training data set containing ~196K utterances", | |
| "Files": [ | |
| "asr_bengali_0.zip", | |
| "asr_bengali_1.zip", | |
| "asr_bengali_2.zip", | |
| "asr_bengali_3.zip", | |
| "asr_bengali_4.zip", | |
| "asr_bengali_5.zip", | |
| "asr_bengali_6.zip", | |
| "asr_bengali_7.zip", | |
| "asr_bengali_8.zip", | |
| "asr_bengali_9.zip", | |
| "asr_bengali_a.zip", | |
| "asr_bengali_b.zip", | |
| "asr_bengali_c.zip", | |
| "asr_bengali_d.zip", | |
| "asr_bengali_e.zip", | |
| "asr_bengali_f.zip", | |
| ], | |
| "IndexFiles": ["asr_bengali/utt_spk_text.tsv"] * 16, | |
| "DataDirs": ["asr_bengali/data"] * 16, | |
| }, | |
| "SLR54": { | |
| "Language": "Nepali", | |
| "LongName": "Large Nepali ASR training data set", | |
| "Category": "Speech", | |
| "Summary": "Nepali ASR training data set containing ~157K utterances", | |
| "Files": [ | |
| "asr_nepali_0.zip", | |
| "asr_nepali_1.zip", | |
| "asr_nepali_2.zip", | |
| "asr_nepali_3.zip", | |
| "asr_nepali_4.zip", | |
| "asr_nepali_5.zip", | |
| "asr_nepali_6.zip", | |
| "asr_nepali_7.zip", | |
| "asr_nepali_8.zip", | |
| "asr_nepali_9.zip", | |
| "asr_nepali_a.zip", | |
| "asr_nepali_b.zip", | |
| "asr_nepali_c.zip", | |
| "asr_nepali_d.zip", | |
| "asr_nepali_e.zip", | |
| "asr_nepali_f.zip", | |
| ], | |
| "IndexFiles": ["asr_nepali/utt_spk_text.tsv"] * 16, | |
| "DataDirs": ["asr_nepali/data"] * 16, | |
| }, | |
| "SLR63": { | |
| "Language": "Malayalam", | |
| "LongName": "Crowdsourced high-quality Malayalam multi-speaker speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of native speakers of Malayalam", | |
| "Files": ["ml_in_female.zip", "ml_in_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR64": { | |
| "Language": "Marathi", | |
| "LongName": "Crowdsourced high-quality Marathi multi-speaker speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of native speakers of Marathi", | |
| "Files": ["mr_in_female.zip"], | |
| "IndexFiles": ["line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR65": { | |
| "Language": "Tamil", | |
| "LongName": "Crowdsourced high-quality Tamil multi-speaker speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of native speakers of Tamil", | |
| "Files": ["ta_in_female.zip", "ta_in_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR66": { | |
| "Language": "Telugu", | |
| "LongName": "Crowdsourced high-quality Telugu multi-speaker speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of native speakers of Telugu", | |
| "Files": ["te_in_female.zip", "te_in_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR69": { | |
| "Language": "Catalan", | |
| "LongName": "Crowdsourced high-quality Catalan speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Catalan", | |
| "Files": ["ca_es_female.zip", "ca_es_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR70": { | |
| "Language": "Nigerian English", | |
| "LongName": "Crowdsourced high-quality Nigerian English speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Nigerian English", | |
| "Files": ["en_ng_female.zip", "en_ng_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR71": { | |
| "Language": "Chilean Spanish", | |
| "LongName": "Crowdsourced high-quality Chilean Spanish speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Chilean Spanish", | |
| "Files": ["es_cl_female.zip", "es_cl_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR72": { | |
| "Language": "Columbian Spanish", | |
| "LongName": "Crowdsourced high-quality Columbian Spanish speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Columbian Spanish", | |
| "Files": ["es_co_female.zip", "es_co_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR73": { | |
| "Language": "Peruvian Spanish", | |
| "LongName": "Crowdsourced high-quality Peruvian Spanish speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Peruvian Spanish", | |
| "Files": ["es_pe_female.zip", "es_pe_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR74": { | |
| "Language": "Puerto Rico Spanish", | |
| "LongName": "Crowdsourced high-quality Puerto Rico Spanish speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Puerto Rico Spanish", | |
| "Files": ["es_pr_female.zip"], | |
| "IndexFiles": ["line_index.tsv"], | |
| "DataDirs": [""], | |
| }, | |
| "SLR75": { | |
| "Language": "Venezuelan Spanish", | |
| "LongName": "Crowdsourced high-quality Venezuelan Spanish speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Venezuelan Spanish", | |
| "Files": ["es_ve_female.zip", "es_ve_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR76": { | |
| "Language": "Basque", | |
| "LongName": "Crowdsourced high-quality Basque speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Basque", | |
| "Files": ["eu_es_female.zip", "eu_es_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR77": { | |
| "Language": "Galician", | |
| "LongName": "Crowdsourced high-quality Galician speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Galician", | |
| "Files": ["gl_es_female.zip", "gl_es_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR78": { | |
| "Language": "Gujarati", | |
| "LongName": "Crowdsourced high-quality Gujarati multi-speaker speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of native speakers of Gujarati", | |
| "Files": ["gu_in_female.zip", "gu_in_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR79": { | |
| "Language": "Kannada", | |
| "LongName": "Crowdsourced high-quality Kannada multi-speaker speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of native speakers of Kannada", | |
| "Files": ["kn_in_female.zip", "kn_in_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| "SLR80": { | |
| "Language": "Burmese", | |
| "LongName": "Crowdsourced high-quality Burmese speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Burmese", | |
| "Files": ["my_mm_female.zip"], | |
| "IndexFiles": ["line_index.tsv"], | |
| "DataDirs": [""], | |
| }, | |
| "SLR86": { | |
| "Language": "Yoruba", | |
| "LongName": "Crowdsourced high-quality Yoruba speech data set", | |
| "Category": "Speech", | |
| "Summary": "Data set which contains recordings of Yoruba", | |
| "Files": ["yo_ng_female.zip", "yo_ng_male.zip"], | |
| "IndexFiles": ["line_index.tsv", "line_index.tsv"], | |
| "DataDirs": ["", ""], | |
| }, | |
| } | |
| class OpenSlrConfig(datasets.BuilderConfig): | |
| """BuilderConfig for OpenSlr.""" | |
| def __init__(self, name, **kwargs): | |
| """ | |
| Args: | |
| data_dir: `string`, the path to the folder containing the files in the | |
| downloaded .tar | |
| citation: `string`, citation for the data set | |
| url: `string`, url for information about the data set | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| self.language = kwargs.pop("language", None) | |
| self.long_name = kwargs.pop("long_name", None) | |
| self.category = kwargs.pop("category", None) | |
| self.summary = kwargs.pop("summary", None) | |
| self.files = kwargs.pop("files", None) | |
| self.index_files = kwargs.pop("index_files", None) | |
| self.data_dirs = kwargs.pop("data_dirs", None) | |
| description = ( | |
| f"Open Speech and Language Resources dataset in {self.language}. Name: {self.name}, " | |
| f"Summary: {self.summary}." | |
| ) | |
| super(OpenSlrConfig, self).__init__(name=name, description=description, **kwargs) | |
| class OpenSlr(datasets.GeneratorBasedBuilder): | |
| DEFAULT_WRITER_BATCH_SIZE = 32 | |
| BUILDER_CONFIGS = [ | |
| OpenSlrConfig( | |
| name=resource_id, | |
| language=_RESOURCES[resource_id]["Language"], | |
| long_name=_RESOURCES[resource_id]["LongName"], | |
| category=_RESOURCES[resource_id]["Category"], | |
| summary=_RESOURCES[resource_id]["Summary"], | |
| files=_RESOURCES[resource_id]["Files"], | |
| index_files=_RESOURCES[resource_id]["IndexFiles"], | |
| data_dirs=_RESOURCES[resource_id]["DataDirs"], | |
| ) | |
| for resource_id in _RESOURCES.keys() | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "path": datasets.Value("string"), | |
| "audio": datasets.Audio(sampling_rate=48_000), | |
| "sentence": datasets.Value("string"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="sentence")], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| resource_number = self.config.name.replace("SLR", "") | |
| urls = [f"{_DATA_URL.format(resource_number)}/{file}" for file in self.config.files] | |
| if urls[0].endswith(".zip"): | |
| dl_paths = dl_manager.download_and_extract(urls) | |
| path_to_indexs = [os.path.join(path, f"{self.config.index_files[i]}") for i, path in enumerate(dl_paths)] | |
| path_to_datas = [os.path.join(path, f"{self.config.data_dirs[i]}") for i, path in enumerate(dl_paths)] | |
| archives = None | |
| else: | |
| archives = dl_manager.download(urls) | |
| path_to_indexs = dl_manager.download(self.config.index_files) | |
| path_to_datas = self.config.data_dirs | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "path_to_indexs": path_to_indexs, | |
| "path_to_datas": path_to_datas, | |
| "archive_files": [dl_manager.iter_archive(archive) for archive in archives] if archives else None, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, path_to_indexs, path_to_datas, archive_files): | |
| """Yields examples.""" | |
| counter = -1 | |
| if self.config.name in ["SLR35", "SLR36", "SLR52", "SLR53", "SLR54"]: | |
| sentence_index = {} | |
| for i, path_to_index in enumerate(path_to_indexs): | |
| with open(path_to_index, encoding="utf-8") as f: | |
| lines = f.readlines() | |
| for id_, line in enumerate(lines): | |
| field_values = re.split(r"\t\t?", line.strip()) | |
| filename, user_id, sentence = field_values | |
| sentence_index[filename] = sentence | |
| for path_to_data in sorted(Path(path_to_datas[i]).rglob("*.flac")): | |
| filename = path_to_data.stem | |
| if path_to_data.stem not in sentence_index: | |
| continue | |
| path = str(path_to_data.resolve()) | |
| sentence = sentence_index[filename] | |
| counter += 1 | |
| yield counter, {"path": path, "audio": path, "sentence": sentence} | |
| elif self.config.name in ["SLR32"]: # use archives | |
| for path_to_index, path_to_data, files in zip(path_to_indexs, path_to_datas, archive_files): | |
| sentences = {} | |
| with open(path_to_index, encoding="utf-8") as f: | |
| for line in f: | |
| # Following regexs are needed to normalise the lines, since the datasets | |
| # are not always consistent and have bugs: | |
| line = re.sub(r"\t[^\t]*\t", "\t", line.strip()) | |
| field_values = re.split(r"\t\t?", line) | |
| if len(field_values) != 2: | |
| continue | |
| filename, sentence = field_values | |
| # set absolute path for audio file | |
| path = f"{path_to_data}/{filename}.wav" | |
| sentences[path] = sentence | |
| for path, f in files: | |
| if path.startswith(path_to_data): | |
| counter += 1 | |
| audio = {"path": path, "bytes": f.read()} | |
| yield counter, {"path": path, "audio": audio, "sentence": sentences[path]} | |
| else: | |
| for i, path_to_index in enumerate(path_to_indexs): | |
| with open(path_to_index, encoding="utf-8") as f: | |
| lines = f.readlines() | |
| for id_, line in enumerate(lines): | |
| # Following regexs are needed to normalise the lines, since the datasets | |
| # are not always consistent and have bugs: | |
| line = re.sub(r"\t[^\t]*\t", "\t", line.strip()) | |
| field_values = re.split(r"\t\t?", line) | |
| if len(field_values) != 2: | |
| continue | |
| filename, sentence = field_values | |
| # set absolute path for audio file | |
| path = os.path.join(path_to_datas[i], f"{filename}.wav") | |
| counter += 1 | |
| yield counter, {"path": path, "audio": path, "sentence": sentence} | |