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--- |
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license: creativeml-openrail-m |
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dataset_info: |
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features: |
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- name: audio |
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dtype: audio |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': A canoon |
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'1': A cinj |
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'2': A keen |
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'3': A lanq |
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'4': A ñaaƴ |
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'5': A ñamaak |
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'6': Alaa |
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'7': Bacaac |
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'8': Benn |
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'9': Bálamuk |
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'10': Búbaar |
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'11': Caggal |
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'12': Ceme |
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'13': Ci ginnaaw |
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'14': Ci kanam |
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'15': Ci kow |
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'16': Ci suuf |
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'17': Càmmoñ |
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'18': Darnde |
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'19': Dow |
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'20': Doxal |
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'21': Déedet |
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'22': Eey |
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'23': Esuwa |
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'24': Eyen |
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'25': Eé |
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'26': Fatiya |
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'27': Fukk |
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'28': Funoom |
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'29': Futok |
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'30': Futok di sibaakiir |
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'31': Futok di sigaba |
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'32': Futok di sífeejir |
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'33': Futok di yákon |
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'34': Fácul |
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'35': Garab |
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'36': Goo |
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'37': Hani |
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'38': Jaay |
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'39': Jeegom |
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'40': Jeenay |
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'41': Jeetati |
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'42': Jeeɗiɗi |
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'43': Jik |
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'44': Jiku |
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'45': Joy |
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'46': Juni |
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'47': Junne |
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'48': Juroom |
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'49': Juroom-benn |
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'50': Juroom-ñaar |
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'51': Juroom-ñeent |
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'52': Juroom-ñett |
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'53': Jënd |
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'54': Kakamben |
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'55': Kamay |
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'56': Kanoomen |
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'57': Kákambul |
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'58': Kárir |
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'59': Lal |
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'60': Lees |
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'61': Leng |
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'62': Leɗki |
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'63': Li |
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'64': Mbaamir |
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'65': Mbalndi |
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'66': Nano |
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'67': Naxik |
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'68': Nay |
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'69': Ndaxar |
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'70': Ndeyjoor |
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'71': Ndiga |
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'72': Ndiiƭ |
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'73': Njong |
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'74': O ɓox |
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'75': Picc |
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'76': Rawaandu |
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'77': Sappo |
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'78': Sibaakiir |
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'79': Sigaba |
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'80': Solndu |
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'81': Soodde |
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'82': Sífeejir |
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'83': Tadik |
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'84': Tati |
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'85': Taxawal |
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'86': Teemedere |
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'87': Teemeed |
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'88': Tentaam |
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'89': Tik |
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'90': Took |
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'91': Tus |
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'92': Téemeer |
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'93': Ub /Tëj |
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'94': Ub/Tëj |
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'95': Ubbi /Tijji |
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'96': Udditde |
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'97': Uddude |
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'98': Ujaw |
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'99': Ujunere |
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'100': Ujuum |
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'101': Uñen |
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'102': Waafulet |
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'103': Waaw |
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'104': Weg |
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'105': Wet |
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'106': Wúli |
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'107': Xa-aa |
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'108': Xaj |
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'109': Xarɓaxay |
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'110': Yahdu |
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'111': Yeeso |
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'112': Yeeyde |
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'113': Yákon |
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'114': Ñaamo |
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'115': Ñaar |
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'116': Ñeent |
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'117': Ñett |
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'118': Ɗiɗi |
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'119': Ƥetaa-fo-leng |
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'120': Ƥetaa-naxak |
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'121': Ƥetaa-tadak |
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'122': Ƥetaa-ƭaq |
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'123': Ƥetik |
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- name: translation |
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dtype: string |
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- name: locale_id |
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dtype: int64 |
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- name: transcript |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 567773923.639 |
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num_examples: 26387 |
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download_size: 546144081 |
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dataset_size: 567773923.639 |
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--- |
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### Dataset Summary |
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Keyword spotting refers to the task of learning to detect spoken keywords. It interfaces all modern voice-based virtual assistants on the market: Amazon’s Alexa, Apple’s Siri, and the Google Home device. Contrarily to speech recognition models, keyword spotting doesn’t run on the cloud, but directly on the device. |
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The motivation of this paper is to extend the Speech commands dataset (Warden 2018) with African languages. In particular, we are going to focus on 4 Senegalese languages: Wolof, Pulaar, Serer, Diola. |
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The choice of these languages is guided, on the one hand, by their status as languages considered to be the languages of the first generation, that is to say, the first codified languages (endowed with a writing system and considered by the state of Senegal as national languages) with decree n ° 68-871 of July 24, 1968. On the other hand, they represent the languages that are most spoken in Senegal. |
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### Languages |
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The ID of the languages are the following: |
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- Wolof: `7` |
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- Pulaar: `5` |
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- Serer: `6` |
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- Diola: `3` |
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## Dataset Structure |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("galsenai/waxal_dataset") |
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DatasetDict({ |
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train: Dataset({ |
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features: ['audio', 'label', 'translation', 'locale_id'], |
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num_rows: 26387 |
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}) |
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}) |
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``` |
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### Data Fields |
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- `audio`: Audio file in MP3 format |
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- `label`: label of the audio file |
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- `translation` : Translation of the keyword in french |
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- `locale_id`: ID of the language |
