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Update Cantonese and Mandarin to combine tone with vowel
Browse files- Cantonese/train.csv +2 -2
- Cantonese/valid.csv +2 -2
- Mandarin/train.csv +2 -2
- Mandarin/valid.csv +2 -2
- README.md +3 -3
Cantonese/train.csv
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Cantonese/valid.csv
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Mandarin/train.csv
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Mandarin/valid.csv
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README.md
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@@ -221,7 +221,7 @@ This dataset contains utterance downloaded from CHILDES which have been pre-proc
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| `is_child`| Whether the utterance was spoken by a child or not. Note that this is set to `False` for all utterances in this dataset, but the processing script has the ability to preserve child utterances.|
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| `processed_gloss`| The pre-processed orthographic utterance. This includes lowercasing, fixing English spelling and adding punctuation marks. This is based on the [AOChildes](https://github.com/UIUCLearningLanguageLab/AOCHILDES) preprocessing.|
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| `phonemized_utterance`| A phonemic transcription of the utterance, space-separated with word boundaries marked with the `WORD_BOUNDARY` token.|
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| `language_code`| Language code used for producing the phonemic transcriptions. May not match the `language` column provided by CHILDES (e.g. Eng-NA and Eng-UK tend to be transcribed with
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| `character_split_utterance`| A space separated transcription of the utterance, produced simply by splitting the processed gloss by character. This is intended to have a very similar format to `phonemized_utterance` for studies comparing phonetic to orthographic transcriptions. |
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The last two columns are designed for training character-based (phoneme-based) language models using a simple tokenizer that splits around whitespace. The `processed_gloss` column is suitable for word-based (or subword-based) language models with standard tokenizers.
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@@ -236,15 +236,15 @@ Each subset of the data is split into a training split containing most of the ut
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| English (UK) | Taken from 14 corpora in Eng-NA collection of CHILDES and phonemized using language code `en-gb`. | 588 | 1,246,828 | 5,166,197 | 15,727,550
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| German | Taken from 10 corpora in German collection of CHILDES and phonemized using language code `ge`. | 627 | 850,888 | 3,893,168 | 14,058,836
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| Indonesian | Taken from 1 corpus in EastAsian/Indonesian collection of CHILDES and phonemized using language code `id`. | 389 | 534,469 | 1,587,526 | 6,367,721
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| Mandarin | Taken from 15 corpora in Chinese/Mandarin collection of CHILDES and phonemized using a [pinyin to IPA convertor](https://github.com/stefantaubert/pinyin-to-ipa/tree/master). | 15 | 883 | 326,759 | 1,511,851 | 6,106,770
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| French | Taken from 11 corpora in French collection of CHILDES and phonemized using language code `fr-fr`. | 722 | 432,133 | 1,995,063 | 5,510,523
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| Spanish | Taken from 18 corpora in Spanish collection of CHILDES and phonemized using language code `es`. | 562 | 286,462 | 1,266,366 | 4,511,261
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| Japanese | Taken from 9 corpora in Japanese collection of CHILDES and phonemized using segments with language `japanese`. | 320 | 412,079 | 1,113,194 | 4,346,638
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| Dutch | Taken from 5 corpora in DutchAfricaans/Dutch collection of CHILDES and phonemized using language code `nl`. | 86 | 297,497 | 1,246,006 | 4,034,742
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| Estonian | Taken from 9 corpora in Other/Estonian collection of CHILDES and phonemized using language code `et`. | 118 | 103,343 | 544,680 | 2,347,066
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| Cantonese | Taken from 2 corpora in Chinese/Cantonese collection of CHILDES and phonemized by converting from jyutping to IPA using the [pingyam database](https://github.com/kfcd/pingyam/tree/master). | 80 | 136,727 | 591,314 | 2,118,731
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| Swedish | Taken from 3 corpora in Scandinavian/Swedish collection of CHILDES and phonemized using language code `sv`. | 32 | 85,299 | 396,800 | 1,241,459
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| Portuguese (Portugal) | Taken from 3 corpora in Romance/Portuguese collection of CHILDES and phonemized using language code `pt`. | 33 | 81,444 | 368,032 | 1,175,413
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| Korean | Taken from 3 corpora in EastAsian/Korean collection of CHILDES and phonemized using language code `ko`. | 95 | 66,576 | 201,078 | 1,076,296
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| Italian | Taken from 5 corpora in Romance/Italian collection of CHILDES and phonemized using language code `it`. | 92 | 57,542 | 264,479 | 996,295
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| Catalan | Taken from 5 corpora in Romance/Catalan collection of CHILDES and phonemized using language code `ca`. | 159 | 56,588 | 248,999 | 839,462
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| `is_child`| Whether the utterance was spoken by a child or not. Note that this is set to `False` for all utterances in this dataset, but the processing script has the ability to preserve child utterances.|
|
222 |
| `processed_gloss`| The pre-processed orthographic utterance. This includes lowercasing, fixing English spelling and adding punctuation marks. This is based on the [AOChildes](https://github.com/UIUCLearningLanguageLab/AOCHILDES) preprocessing.|
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| `phonemized_utterance`| A phonemic transcription of the utterance, space-separated with word boundaries marked with the `WORD_BOUNDARY` token.|
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+
| `language_code`| Language code used for producing the phonemic transcriptions. May not match the `language` column provided by CHILDES (e.g. Eng-NA and Eng-UK tend to be transcribed with en-us and en-gb). |
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| `character_split_utterance`| A space separated transcription of the utterance, produced simply by splitting the processed gloss by character. This is intended to have a very similar format to `phonemized_utterance` for studies comparing phonetic to orthographic transcriptions. |
|
226 |
|
227 |
The last two columns are designed for training character-based (phoneme-based) language models using a simple tokenizer that splits around whitespace. The `processed_gloss` column is suitable for word-based (or subword-based) language models with standard tokenizers.
|
|
|
236 |
| English (UK) | Taken from 14 corpora in Eng-NA collection of CHILDES and phonemized using language code `en-gb`. | 588 | 1,246,828 | 5,166,197 | 15,727,550
|
237 |
| German | Taken from 10 corpora in German collection of CHILDES and phonemized using language code `ge`. | 627 | 850,888 | 3,893,168 | 14,058,836
|
238 |
| Indonesian | Taken from 1 corpus in EastAsian/Indonesian collection of CHILDES and phonemized using language code `id`. | 389 | 534,469 | 1,587,526 | 6,367,721
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| French | Taken from 11 corpora in French collection of CHILDES and phonemized using language code `fr-fr`. | 722 | 432,133 | 1,995,063 | 5,510,523
|
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| Spanish | Taken from 18 corpora in Spanish collection of CHILDES and phonemized using language code `es`. | 562 | 286,462 | 1,266,366 | 4,511,261
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+
| Mandarin | Taken from 15 corpora in Chinese/Mandarin collection of CHILDES and phonemized using a [pinyin to IPA convertor](https://github.com/stefantaubert/pinyin-to-ipa/tree/master). | 883 | 324,071 | 1,506,475 | 4,397,547
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| Japanese | Taken from 9 corpora in Japanese collection of CHILDES and phonemized using segments with language `japanese`. | 320 | 412,079 | 1,113,194 | 4,346,638
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| Dutch | Taken from 5 corpora in DutchAfricaans/Dutch collection of CHILDES and phonemized using language code `nl`. | 86 | 297,497 | 1,246,006 | 4,034,742
|
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| Estonian | Taken from 9 corpora in Other/Estonian collection of CHILDES and phonemized using language code `et`. | 118 | 103,343 | 544,680 | 2,347,066
|
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245 |
| Swedish | Taken from 3 corpora in Scandinavian/Swedish collection of CHILDES and phonemized using language code `sv`. | 32 | 85,299 | 396,800 | 1,241,459
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| Portuguese (Portugal) | Taken from 3 corpora in Romance/Portuguese collection of CHILDES and phonemized using language code `pt`. | 33 | 81,444 | 368,032 | 1,175,413
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+
| Cantonese | Taken from 2 corpora in Chinese/Cantonese collection of CHILDES and phonemized by converting from jyutping to IPA using the [pingyam database](https://github.com/kfcd/pingyam/tree/master). | 80 | 136,727 | 591,314 | 1,450,183
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| Korean | Taken from 3 corpora in EastAsian/Korean collection of CHILDES and phonemized using language code `ko`. | 95 | 66,576 | 201,078 | 1,076,296
|
249 |
| Italian | Taken from 5 corpora in Romance/Italian collection of CHILDES and phonemized using language code `it`. | 92 | 57,542 | 264,479 | 996,295
|
250 |
| Catalan | Taken from 5 corpora in Romance/Catalan collection of CHILDES and phonemized using language code `ca`. | 159 | 56,588 | 248,999 | 839,462
|