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Add SLR83 to OpenSLR (#3125)
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* Add SLR83 to OpenSLR

* Fix en-GB and en-IE locale identifiers for openslr/SLR83

Co-authored-by: Jonathan Zimmerman <jonazi01@noa.nintendo.com>
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tyrius02 and Jonathan Zimmerman authored Oct 22, 2021
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16 changes: 16 additions & 0 deletions datasets/openslr/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -60,6 +60,9 @@ languages:
- kn
SLR80:
- my
SLR83:
- en-GB
- en-IE
SLR86:
- yo
licenses:
Expand Down Expand Up @@ -494,6 +497,19 @@ /~https://github.com/google/language-resources#license for license information.

Copyright 2018, 2019 Google, Inc.

#### SLR83: Crowdsourced high-quality UK and Ireland English Dialect speech data set
This data set contains transcribed high-quality audio of English sentences recorded by volunteers speaking different dialects of the language.
The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.csv contains a line id, an anonymized FileID and the transcription of audio in the file.

The data set has been manually quality checked, but there might still be errors.

The recordings from the Welsh English speakers were collected in collaboration with Cardiff University.

The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License.
See [LICENSE](https://www.openslr.org/resources/83/LICENSE) file and /~https://github.com/google/language-resources#license for license information.

Copyright 2018, 2019 Google, Inc.

#### SLR86: Crowdsourced high-quality multi-speaker speech data set
This data set contains transcribed high-quality audio of sentences recorded by volunteers. The data set
consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and
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2 changes: 1 addition & 1 deletion datasets/openslr/dataset_infos.json

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47 changes: 47 additions & 0 deletions datasets/openslr/openslr.py
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Expand Up @@ -112,6 +112,20 @@
ISBN = {979-10-95546-34-4},
}
SLR83
@inproceedings{demirsahin-etal-2020-open,
title = {{Open-source Multi-speaker Corpora of the English Accents in the British Isles}},
author = {Demirsahin, Isin and Kjartansson, Oddur and Gutkin, Alexander and Rivera, Clara},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
month = may,
year = {2020},
pages = {6532--6541},
address = {Marseille, France},
publisher = {European Language Resources Association (ELRA)},
url = {https://www.aclweb.org/anthology/2020.lrec-1.804},
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
Expand Down Expand Up @@ -479,6 +493,39 @@
"IndexFiles": ["line_index.tsv"],
"DataDirs": [""],
},
"SLR83": {
"Language": "English",
"LongName": "Crowdsourced high-quality UK and Ireland English Dialect speech data set",
"Category": "Speech",
"Summary": "Data set which contains male and female recordings of English from various dialects of the UK and Ireland",
"Files": [
"irish_english_male.zip",
"midlands_english_female.zip",
"midlands_english_male.zip",
"northern_english_female.zip",
"northern_english_male.zip",
"scottish_english_female.zip",
"scottish_english_male.zip",
"southern_english_female.zip",
"southern_english_male.zip",
"welsh_english_female.zip",
"welsh_english_male.zip",
],
"IndexFiles": [
"line_index.csv",
"line_index.csv",
"line_index.csv",
"line_index.csv",
"line_index.csv",
"line_index.csv",
"line_index.csv",
"line_index.csv",
"line_index.csv",
"line_index.csv",
"line_index.csv",
],
"DataDirs": ["", "", "", "", "", "", "", "", "", "", ""],
},
"SLR86": {
"Language": "Yoruba",
"LongName": "Crowdsourced high-quality Yoruba speech data set",
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Show benchmarks

PyArrow==3.0.0

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.010459 / 0.011353 (-0.000894) 0.004911 / 0.011008 (-0.006098) 0.035222 / 0.038508 (-0.003286) 0.037921 / 0.023109 (0.014812) 0.360942 / 0.275898 (0.085044) 0.459928 / 0.323480 (0.136448) 0.010435 / 0.007986 (0.002450) 0.006606 / 0.004328 (0.002278) 0.010589 / 0.004250 (0.006338) 0.040132 / 0.037052 (0.003079) 0.377868 / 0.258489 (0.119379) 0.391266 / 0.293841 (0.097425) 0.036415 / 0.128546 (-0.092131) 0.012783 / 0.075646 (-0.062863) 0.313220 / 0.419271 (-0.106051) 0.057295 / 0.043533 (0.013762) 0.377373 / 0.255139 (0.122234) 0.385187 / 0.283200 (0.101987) 0.107592 / 0.141683 (-0.034090) 2.097331 / 1.452155 (0.645176) 2.088902 / 1.492716 (0.596186)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.223574 / 0.018006 (0.205568) 0.568083 / 0.000490 (0.567593) 0.009368 / 0.000200 (0.009168) 0.000231 / 0.000054 (0.000177)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.047368 / 0.037411 (0.009957) 0.026315 / 0.014526 (0.011789) 0.029409 / 0.176557 (-0.147148) 0.135114 / 0.737135 (-0.602022) 0.030724 / 0.296338 (-0.265614)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.599837 / 0.215209 (0.384628) 6.008125 / 2.077655 (3.930470) 2.225876 / 1.504120 (0.721756) 1.812282 / 1.541195 (0.271088) 1.915777 / 1.468490 (0.447287) 0.624514 / 4.584777 (-3.960263) 6.581308 / 3.745712 (2.835596) 1.429502 / 5.269862 (-3.840360) 1.349714 / 4.565676 (-3.215962) 0.063459 / 0.424275 (-0.360816) 0.006049 / 0.007607 (-0.001558) 0.700609 / 0.226044 (0.474565) 7.316799 / 2.268929 (5.047870) 2.920138 / 55.444624 (-52.524486) 2.249907 / 6.876477 (-4.626570) 2.177277 / 2.142072 (0.035205) 0.763408 / 4.805227 (-4.041819) 0.144415 / 6.500664 (-6.356249) 0.053904 / 0.075469 (-0.021565)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 1.994814 / 1.841788 (0.153027) 15.169304 / 8.074308 (7.094995) 44.184980 / 10.191392 (33.993588) 0.982142 / 0.680424 (0.301718) 0.651893 / 0.534201 (0.117692) 0.291283 / 0.579283 (-0.288000) 0.750450 / 0.434364 (0.316086) 0.255784 / 0.540337 (-0.284554) 0.271125 / 1.386936 (-1.115811)
PyArrow==latest
Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric read_batch_formatted_as_numpy after write_array2d read_batch_formatted_as_numpy after write_flattened_sequence read_batch_formatted_as_numpy after write_nested_sequence read_batch_unformated after write_array2d read_batch_unformated after write_flattened_sequence read_batch_unformated after write_nested_sequence read_col_formatted_as_numpy after write_array2d read_col_formatted_as_numpy after write_flattened_sequence read_col_formatted_as_numpy after write_nested_sequence read_col_unformated after write_array2d read_col_unformated after write_flattened_sequence read_col_unformated after write_nested_sequence read_formatted_as_numpy after write_array2d read_formatted_as_numpy after write_flattened_sequence read_formatted_as_numpy after write_nested_sequence read_unformated after write_array2d read_unformated after write_flattened_sequence read_unformated after write_nested_sequence write_array2d write_flattened_sequence write_nested_sequence
new / old (diff) 0.010308 / 0.011353 (-0.001044) 0.004755 / 0.011008 (-0.006254) 0.038660 / 0.038508 (0.000152) 0.037397 / 0.023109 (0.014287) 0.363177 / 0.275898 (0.087279) 0.370844 / 0.323480 (0.047365) 0.008625 / 0.007986 (0.000639) 0.004071 / 0.004328 (-0.000258) 0.010348 / 0.004250 (0.006098) 0.044408 / 0.037052 (0.007355) 0.355040 / 0.258489 (0.096550) 0.394172 / 0.293841 (0.100331) 0.035788 / 0.128546 (-0.092759) 0.013579 / 0.075646 (-0.062068) 0.293243 / 0.419271 (-0.126028) 0.057992 / 0.043533 (0.014460) 0.367541 / 0.255139 (0.112402) 0.366237 / 0.283200 (0.083037) 0.091141 / 0.141683 (-0.050542) 1.944716 / 1.452155 (0.492561) 1.936943 / 1.492716 (0.444227)

Benchmark: benchmark_getitem_100B.json

metric get_batch_of_1024_random_rows get_batch_of_1024_rows get_first_row get_last_row
new / old (diff) 0.242175 / 0.018006 (0.224169) 0.557129 / 0.000490 (0.556639) 0.014757 / 0.000200 (0.014557) 0.000754 / 0.000054 (0.000700)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.044576 / 0.037411 (0.007165) 0.026670 / 0.014526 (0.012144) 0.030061 / 0.176557 (-0.146495) 0.159772 / 0.737135 (-0.577364) 0.032492 / 0.296338 (-0.263846)

Benchmark: benchmark_iterating.json

metric read 5000 read 50000 read_batch 50000 10 read_batch 50000 100 read_batch 50000 1000 read_formatted numpy 5000 read_formatted pandas 5000 read_formatted tensorflow 5000 read_formatted torch 5000 read_formatted_batch numpy 5000 10 read_formatted_batch numpy 5000 1000 shuffled read 5000 shuffled read 50000 shuffled read_batch 50000 10 shuffled read_batch 50000 100 shuffled read_batch 50000 1000 shuffled read_formatted numpy 5000 shuffled read_formatted_batch numpy 5000 10 shuffled read_formatted_batch numpy 5000 1000
new / old (diff) 0.598219 / 0.215209 (0.383010) 6.053066 / 2.077655 (3.975411) 2.237954 / 1.504120 (0.733834) 1.907826 / 1.541195 (0.366632) 1.954446 / 1.468490 (0.485955) 0.629695 / 4.584777 (-3.955082) 6.636461 / 3.745712 (2.890749) 1.543779 / 5.269862 (-3.726082) 1.343863 / 4.565676 (-3.221814) 0.063339 / 0.424275 (-0.360936) 0.005487 / 0.007607 (-0.002120) 0.786014 / 0.226044 (0.559969) 7.794069 / 2.268929 (5.525140) 3.153929 / 55.444624 (-52.290696) 2.492001 / 6.876477 (-4.384476) 2.481114 / 2.142072 (0.339042) 0.806975 / 4.805227 (-3.998252) 0.170912 / 6.500664 (-6.329752) 0.064423 / 0.075469 (-0.011046)

Benchmark: benchmark_map_filter.json

metric filter map fast-tokenizer batched map identity map identity batched map no-op batched map no-op batched numpy map no-op batched pandas map no-op batched pytorch map no-op batched tensorflow
new / old (diff) 1.833509 / 1.841788 (-0.008278) 14.505067 / 8.074308 (6.430759) 45.752139 / 10.191392 (35.560747) 0.924047 / 0.680424 (0.243623) 0.641405 / 0.534201 (0.107204) 0.268820 / 0.579283 (-0.310463) 0.655120 / 0.434364 (0.220757) 0.210760 / 0.540337 (-0.329577) 0.224565 / 1.386936 (-1.162371)

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