audio audioduration (s) 1 28.3 | text stringlengths 1 349 | duration float64 1 28.3 |
|---|---|---|
ⵉⵙⵍⵍⴰ ⵙⴰⵎⵉ ⵉ ⵓⵙⵓⵙⵔ ⵉⵙⵍⵍ ⵉ ⵓⵎⵙⵍⵉ ⵏ ⵢⴰⵏ ⵓⵔⴳⴰⵣ ⴳ ⴹⴰⵕⴰⵜ. | 3.413 | |
ⴰⵣⵓⵍ ⵎⴰⵙⵙⴰ ⵜⵉⵍⵉⵍⴰ. | 1.643 | |
ⵓⵔ ⵔⵉⵖ ⴰⴷ ⵍⵎⴷⵖ ⵜⵉⵏⴳⵍⵉⵣⵉⵜ. | 1.899 | |
ⵉⵙ ⵥⴹⴰⵕⵖ ⴰⴷ ⴰⴼⵖ ⴽⵔⴰ ⵏ ⵓⴽⵔⵡⴰⵙ ⵏ ⵢⵉⵙⵎⴷ ⵓⵎⵍⵉⵍ? | 2.837 | |
ⵉⵕⵥⴰ ⵉⵖⵙⴰⵏ ⵏ ⵓⴹⴰⵕ ⵏⵏⵙ | 2.091 | |
ⵜⴰⴼⵔⵎⵍⵉⵜ ⴰⴷ ⵜⴳⴰ ⵏⵜⵜⴰⵜ ⴰⵔ ⵖⵉⴷ ⵜⵙⵡⵓⵔⵉⵢ. | 2.325 | |
ⴰⴼⵓⵍⴽⵉ ⵉⴳⴰ ⵎⴰ ⵉⵙⴽⴰⵔ ⵢⴰⵏ ⵉⵜⵜⵓ ⵜ. | 2.005 | |
ⵉⴽⵚⵓⴹ ⴰⵎ ⵓⵢⴰⵥⵉⴹ. | 1.429 | |
ⵏ ⵓⴱⵔⵙⵉⵔⴰ | 1.28 | |
ⵓⴱⵔⵙⵉⵔⴰ | 1.28 | |
ⴰⵔ ⴱⴷⴷⴰ ⵙⵇⵇⴱⴰⵍⵏ ⵉⵎⵓⵙⵍⵎ ⴽⵉⵖ ⴰⵔ ⵜⵜⵥⴰⵍⵍⴰⵏ. | 2.709 | |
ⵔⵉⵖ ⵏⵉⵜ ⴰⴷ ⴷⵉⵙ ⴰⵏⵏⴰⵢⵖ ⴰⵙⴰⵔⵓ ⴰⴷ. | 1.536 | |
ⵉⵏⵏⴰ ⵙⴰⵎⵉ ⵎⴰⵙ ⵢⴰⴷ ⵢⵓⴹⵏ ⴳ ⵢⴰⵏ ⵓⵙⴳⵏⴰⴼ ⴳ ⵍⵇⴰⵀⵉⵔⴰ ⵍⵍⵉⵖ ⴽⴰ ⴷⴰⵔⵙ ⵚⴹⵉⵚ ⴷ ⵎⵔⴰⵡ ⵓⵙⴳⴳⴰⵙ. | 5.589 | |
ⵎⴰⵏ ⵜⵉⵎⵖⵔⵉⵜ ⴳ ⵜⵍⵍⵉⴷ? | 1.6 | |
ⴰⴷ ⵓⵔ ⵜⵜⴱⴱⵊⵎ ⵉⴳⵍⴳⵉⵣⵏ ⴳ ⵓⵖⴰⵔⴰⵙ. | 2.219 | |
ⵉⴳ ⴽ ⵢⵓⵡⵜ ⵡⴰⴹⵓ, ⵢⵓⴼ ⴰⴽ ⴰⴷ ⵜⴳⵏⴷ ⴱⴰⵛ ⴰⴷ ⵜⵙⵙⵓⵏⴼⵓⴷ. | 3.541 | |
ⵓⵔ ⴰⵡⴷⴷⵉ ⵙⵙⵉⵏⵖ. | 1.365 | |
ⵓⵔ ⴰⴽⴽⵯ ⵣⵡⴰⵔⵖ ⵙ ⵍⵇⵇⵚⵕ. | 1.621 | |
ⵉⵙ ⴷ ⴰⴷ ⴰⵎ ⵜⵏⵜ ⵉⴷ ⴰⵡⵉⵖ? | 1.643 | |
ⵉ | 1.109 | |
ⵢⴰⵔⵔ | 1.621 | |
ⴰⴼⵟⴻⵟⵟⵓ | 1.237 | |
ⵎⴰⴷ ⵏⵏ ⵣⴰ ⵖⵉⵏⵏ ⵓⴼⵉⵖ ⴷ ⵜⵣⵣⵡⴰ ⴽⵙⴰⵏⵜ. | 2.24 | |
ⴰⵡⵉ ⵜⵜ ⴳ ⵜⵎⵓⵎⵉ ⵏⵏⴽ. | 1.451 | |
ⴰⴹⵓ ⴷ ⴷⵖⵢⴰ. | 1.131 | |
ⵎⴰⴷ ⵉⵙⴷⵓⵇⵇⵔⵏ? | 1.472 | |
ⴷ ⵍⵎⵍⵉⵃ ⵎⴰ ⵕⴰⵃⵖ ⵖⵔ ⵓⵛⵓⵛⵛⴼ. | 2.219 | |
ⴱⵔⵔⴽⴰⵜ ⴰⵡ | 1.387 | |
ⵉⵙ ⴰⵡⴰ ⵜⵙⵙⵏⴷ ⵎⵏⵛⴽ ⵏ ⵉⴱⵔⵣⴷⵓⵖⵏ ⴰⴷ ⵜⵚⴽⴽⴰ ⵎⴰⵔⵉⴽⴰⵏ ⴳ ⴱⵓⵙⵜⵏ? | 3.605 | |
ⵉⵙ ⵜⵓⵊⴰⴷⴷ ⵖⵉⵍⴰⴷ? | 1.28 | |
ⵅ ⴰⵙⵙ ⴰⴷ. | 1.408 | |
ⵏⵎⵛⴰⵛⴽⴰ ⴷ ⵅⴼ ⵓⵙⴰⴽⵓⴷ ⵏ ⵉⴳⵉⵡⵔ ⵏⵏⴰ ⴷ ⵢⵓⵙⴰⵏ. | 2.923 | |
ⵎⵎⵓⵍⵥⵖ ⴳ ⵓⴹⴰⵕ, ⵉⴱⵣⴳ ⵉⵢⵉ ⵜⴰⵎⴷⴷⵉⵜ ⴳⴳⴰⵎⵎⵉⵖ ⴰⴷ ⵉⵙⵙ ⴼⴼⵖⵖ. | 4.373 | |
ⴰⴳⵍⵍⵉⴷ ⵏ ⵡⴰⵙⵙ ⵏ ⵓⴼⵔⴰ, ⴰⵙⵙ ⵏ ⵓⵙⵙⵃⵙⵓ, ⴽⵔⴰⵢⴳⴰⵜ ⵢⴰⵏ ⴷ ⵎⴰⴷ ⵉⵙⴽⵔ | 4.651 | |
ⵢⴰⵏ ⵓⵏⴰⵎⴽ ⴰⴷ ⵉⵍⵍⴰⵏ ⴷⴰⵔ ⵓⴱⵔⵉⴷ ⴷ ⵓⵖⴰⵔⴰⵙ | 2.581 | |
ⵛⴰⵏ ⵜⵉⴽⴽⴰⵍ ⵍⴰ ⵉⵜⵜⴰⵔⵉ ⵜⵓⵣⵉⵏⵜ ⵉ ⵢⵉⵎⵎⴰⵙ. | 2.667 | |
ⵖⵓⵔⵉ ⴰⵅⵅⴰⵎ. | 1.195 | |
ⵍⵍⵉⴳ ⵉⵙⵙⵏ ⴼⴰⴹⵉⵍ ⴷⴰⵏⵢⴰ ⵉⵖⵔ ⵏⵉⵜ ⵉ ⵉⴱⵓⵍⵉⵙⵏ. | 2.987 | |
ⵎⴰ ⴰⴽ ⵉⵙⵎ ? | 1.237 | |
ⴱⴱⴰ | 1.152 | |
ⵉⵙ ⴰⵖ ⵏⵉⵜ ⵉⵣⵣⵉⵡⵣ ⵓⵎⵓⴽⵔⵉⵙ ⴰ. | 1.963 | |
ⵉⴽⴽⴰ ⵜⵜ ⵉⵏⵏ ⵙⴰⵎⵉ ⵉⵔⴰ ⴰⴷ ⴰⴽⴽⵯ ⵢⴰⵔⵎ ⴽⵔⴰⵢⴳⴰⵜ. | 2.581 | |
ⵡⵉⵏⵏⴰ ⵉⵔⵎⵙⵏ, ⴰⵍⵍⵏ ⵉⴼⴰⵙⵙⵏ ⵏⵏⵙⵏ. | 2.347 | |
ⴽⴰⴷⴰ ⵡⴰⴽⴰⴷⴰ ⵏ ⵉⴱⵓⴼⵓⵛⴽⵏ ⴰⴷ ⵉⵍⵍⴰⵏ ⴳ ⵢⵉⵍⵍ ⵏ ⵓⴳⴰⴷⵉⵔ ⵍⴰ ⵛⵜⵜⴰⵏ ⵉⵙⵍⵎⴰⵏ ⴽⵉⵖ ⵜⵏ ⵉⴷ ⴳⵯⵎⵔⵏ ⵉⵏⴳⵯⵎⴰⵔⵏ. | 5.525 | |
ⵉⵙⵙⵓⵙⵙ ⵙⴰⵎⵉ ⵜⴰⴷⴷⴰⴳⵜ ⴰⵏⵏ. | 2.197 | |
ⵙⵍⵍⴰⵖ ⵉ ⴽⵔⴰ ⴰⵔ ⵉⵜⵜⵓⵙⵓ. | 1.771 | |
ⵜⴰⴽⵜⵜⴰⵢ ⵏ ⵜⵍⴰⵍⵉⵜ ⵉⵖⵓⴷⴰⵏ | 1.728 | |
ⵎⴰⵏⵏⴰⴷ ⴷ ⴰⵎⵥⵥⵢⴰⵏ. | 1.408 | |
ⵜⴰⵎⵖⴰⵔⵜ ⵏⵏⴽ ⴷ ⵜⴰⵎⴰⵣⵉⵖⵜ? | 2.155 | |
ⵉⵙ ⵖⵉⵢⵖ ⴰⴷ ⴷ ⵖⵓⵔⴽ ⴽⴽⵖ ⴰⵙⴽⴽⴰ? | 2.069 | |
ⵜⴰⴹⵚⴰ ⴼⵍⵍⴰⵏⵖ ⵜⴰⵎⴷⴰ ⴰ ⴰⵢⵜⵎⴰ | 1.643 | |
ⵉⵖⴰⵍ ⵢⴰⴷⵍⵍⵉ ⵙⴰⵎⵉ ⵉⵙ ⵜⵥⴹⴰⵕ ⵍⴰⵢⵍⴰ ⴰⴷ ⴰⵙ ⵜⴰⵡⵙ. | 3.157 | |
ⴰⵢⵍⴰⵍ ⴰⴷ ⵓⵔ ⵉⵣⵎⵉⵔ ⴰⴷ ⵉⵜⵜⴰⵢⵍⴰⵍ. | 2.581 | |
ⵉⵖⵜⵙ ⵙⴰⵎⵉ ⴰⴷ ⵉⴳ ⴰⴳⵔⵉ ⵉ ⵜⵓⴷⵔⵜ ⵏⵏⵙ. | 2.453 | |
ⵇⴰⴷ ⵏⵎⵙⴰⵡⴰⵍ ⴳ ⵓⵢⵏⵏⴰⵖ ⴰⵙⴽⴽⴰ. | 2.645 | |
ⵉⵙ ⵜⵓⴼⵉⴷ ⴰⴷ ⵉⵢⵉ ⵜⴰⵡⵙⴷ ⴰⴷ ⴰⴼⵖ ⵉⵍⵍⵉ | 2.56 | |
ⵎⴰⵏⵉⴽ ⵙ ⵔⴰⴷ ⵏⵉⵏⵉ ⵜⵉⵜⵔⵉⵜ ⵙ ⵜⴷⴰⵏⵉⵜ? | 2.261 | |
ⴰⵡⵉ ⵜⵜ ⴳ ⵉⵅⴼ ⵏⵏⴽ. | 1.6 | |
ⵣⵣⴰⵡⴰⵇ ⴰⵢⴷ ⵉⵍⵍⴰⵏ | 1.472 | |
ⵢⵓⴼ ⴷⴰⵔⵉ ⵉⵖ ⵉⵢⵉ ⵉⵏⵖⴰ ⵍⴰⵥ ⵓⵍⴰ ⴷ ⵉⵖ ⵓⴽⵔⵖ. | 2.517 | |
ⵎⴰⵙⵉⵏ ⵉⴳⵍⵍⵉⵏ ⵎⵎⵓⵍⵍⵉⵏ ⵜ ⵢⴰⵏ ⵎⵏⵏⴰⵡ ⵉⵎⴰⵍⴰⵙⵙ ⴰⴷ ⵉⵣⵔⵉⵏ ⴽⴽⵙⵏ ⴰⵙ ⴰⴽⴽⵯ ⴰⵢⵍⵍⵉ ⵉⵟⵟⴰⴼ. | 4.117 | |
ⴰⵖⴰⵔⴰⵙ ⵏ ⵖⵡⵉⵍⵍⵉ ⵜⵙⵏⵏⵓⴼⴰⵜ, ⵓⵔ ⴷ ⴰⵢⵜ ⵜⵉⵢⵓⵔⵉ, ⵓⵍⴰ ⵉⵎⵓⴹⴹⴰⵕ | 4.139 | |
ⵔⴰⴷ ⵙⵖⵍⵖ ⵜⵉⵔⵖⵉ ⵏⵏⴽ ⵖⵉⵍⴰⴷ. | 1.877 | |
ⵉⵎⵓⴷⴰⵔ ⴰⴽⴽⵯ ⵎⴳⴰⴷⴷⴰⵏ, ⵎⴰⵛⵛ ⴽⵔⴰ ⴳ ⵉⵜⵙⵏ ⵎⴳⴰⴷⴷⴰⵏ ⵓⴳⴳⴰⵔ ⵏ ⵡⵉⵢⵢⴰⴹ. ⵊⵓⵕⵊ ⵓⵕⵡⵉⵍ | 5.653 | |
ⵉⵔⵡⴰⵙ ⵉⵙ ⴷⵉⴽ ⵎⵙⴰⵙⴰⵏ ⴽⵔⴰ ⵏ ⵎⵉⴷⴷⵏ. | 2.283 | |
ⴱⴱⵉⵏ ⵉⴼⴰⴷⴷⵏ ⵉⵏⵓ | 1.451 | |
ⴷⴳⵙ ⵉⴳⵍⵍⵉⵏ ⵢⴰⵏ ⵓⴱⵔⵔⴰⵢ ⴱⴰⵀⵔⴰ ⵉⵅⵯⵛⵏⵏ. | 2.432 | |
ⵇⴰⴷ ⵢⵉⴷⵡⵏ ⵇⵚⵚⵕⵖ ⴰⵙⴽⴽⴰ. | 1.963 | |
ⵓⵔ ⴷⴰⵔⵉ ⵉⴽⵔⵉⵙⵏ ⴷ ⵜⴰⴷ. | 2.155 | |
ⵜⵍⴰⵎⵜ ⵜⵓⴳⵜ ⵏ ⵉⵎⴷⴷⵓⴽⴽⴰⵍ. | 1.771 | |
ⴰⵇⵇ ⵜⵢⵓⵙⴰ ⴷ. | 1.707 | |
ⵡⵉⴽⵉⵒⵉⴷⵢⴰ ⵜⴳⴰ ⵜⵉⵏ ⵡⵉⴽⵉⵎⵉⴷⵢⴰ | 2.133 | |
ⵎⴰⴼ ⵉⵔⴰ ⵜⵓⵎ ⴰⴷ ⴷ ⴰⵛⴽⵖ ⵙ ⵖⵉⴷ? | 1.835 | |
ⵢⴰⵜ ⵜⵉⵣⵉ ⵉⴷⵔⵓⵙⵏ ⴽⴰ ⴰⴷ ⵉⵢⵉ ⴷ ⵢⴰⴳⵓⵔⵏ. | 2.603 | |
ⵓⵔ ⵢⴰⵜⵜⵓⵢ ⵓⴷⵔⴰⵔ ⵏ ⴼⵓⵊⵉ ⵣⵓⵏ ⴷ ⵡⵉⵏ ⵉⴱⵔⵙⵜ. | 2.453 | |
ⵓⵔ ⵏⵏ ⵉⵜⴰⵎ ⵙⴰⵎⵉ ⴷ ⵍⴰⵢⵍⴰ ⵜⵉⵔⴱⵉⵜ. | 2.688 | |
ⵙⵓⵍ ⵓⴽⴰⵏ ⵉⵍⵍⴰ ⵓⵙⴽⴰⵙⵉ ⴳ ⵜⵎⴰⵡⴰⵙⵜ ⵏ ⴱⵕⵕⴰ ⵅⴼ ⵜⵙⵔⵜⵉⵜ ⵏ ⵓⴷⴳⴳⵉ ⴷ ⵡⴰⵎⵎⴰⵣ ⵏ ⵉⴱⵕⵕⴰⵏⵉⵢⵏ ⴳ ⵡⴰⵎⵓⵏ ⴰⵊⴰⴱⵓⵏⵉ. | 5.973 | |
ⵓⵔ ⵙⵙⵉⵏⵖ ⵎⴰ ⵉⴳⴰ ⴰⵙⴰⴳⵎ ⵏⵏⵙ. | 2.155 | |
ⵔⴰⴷ ⴰⴽ ⵉⵙⵊⵊⵉ ⵓⵙⴰⴼⴰⵔ ⴰⴷ ⴰⴳⵥⵥⴰⵥ ⵏⵏⴽ. | 3.051 | |
ⵏⴽⴽ ⴳⵉⵖ ⵉⵎⵉⵖⵉⵙ ⵖ ⵜⵎⴰⵙⵙⴰⵏⵉⵏ. | 2.176 | |
ⴰⵇⵇⴰ ⴰⵏⵙⵎⴰⴷⴷⵓ ⵢⵉⵡⴹ ⴷ! | 1.899 | |
ⴰⵔ ⵜⵜⵍⵎⴰⴷⵏ ⵉⴷⵣⴰⵢⵔⵉⵢⵏ ⵜⴰⵎⴰⵣⵉⵖⵜ ⴳ ⵓⵙⵉⵏⴰⵏ. | 2.795 | |
ⴷⴰ ⵓⴽⴰⵏ ⵙⵓⵍ ⵉⵙⵡⵓⵔⵉⵢ ⵙⴰⵎⵉ ⴳ ⵜⵙⵉⵔⵎⵜ ⴰⵏⵏ. | 2.731 | |
ⵎⴰⵟⵍⵖ ⵏⵏ ⴼ ⵜⵉⵔⵎⵜ. | 1.707 | |
ⵉⵏⴳⵉ ⵡⴰⵏⵅⴰⵔ ⵏⵏⴽ. | 1.6 | |
ⵉⵅⵯⵍⴰ ⵓⵍⵇⵯⵏⵉⵏ ⴰⴷ. | 1.685 | |
ⴷⴷⵓⵢⴰⵜ ⵙ ⵜⴰⴷⴷⴰⵔⵜ ⵖⵉⴽⴽⴰ. | 1.515 | |
ⵜⵎⵙⴰⵔ ⵙⵓⵍ ⵖⵉⴽ ⵍⵍⵉ ⵏⵏ ⵓⵔ ⵏⵜⴰⵎ. | 2.283 | |
ⵉⵙⵏⴼⴰⵣⵣⴰⵇⵇ ⴰⴽⴽⵯ ⴳⴰⵔ ⴰⵖⴰⵔⴰⵙ ⴰⴷ ⵉⵀⵔⴽⵓⵙⵏ ⵉⵏⵓ ⵉⵎⴰⵢⵏⵓⵜⵏ. | 3.883 | |
ⴰⴷ ⵜ ⵜⴻⵜⵜⵉⵏⵉⴷ | 1.493 | |
ⵓⵔ ⵙⵙⵉⵏⵖ ⵎⴰⵏⵉ ⴷ ⵜⴽⴽⴰ. | 1.813 | |
ⵏⵎⵙⴰⴳⴰⵔ ⴷ ⵓⵙⵍⵎⴰⴷ ⵏⵏⵖ. | 1.493 | |
ⵎⴰⵜⵜⴰ ⵜⵉⵎⵖⵔⵉⵜ ⵖ ⵜⵍⵍⵉⵜ ⴽⵢⵢ? | 2.048 | |
ⵎⴰ ⵜⵏⵏⵉⴷ? | 1.472 | |
ⵓⵔ ⵉⵢⵉ ⵜⴱⴰⵢⵏ ⵜⵅⵛⵏ ⵜⵎⵖⴰⵔⵜ ⵏⵙ. | 1.899 | |
ⵡⴰⴷ ⴷ ⴰⵎⴷⴷⴰⴽⴽⵍ ⵉⵏⵓ | 1.728 | |
ⵎⵉⵏ ⵜⵙⵙⵏⵡⴰⵜ? | 1.707 | |
ⵉⵍⵍⴰ ⴼ ⴽⵔⴰⵢⴳⴰⵜ ⵢⴰⵜ ⴰⴷ ⵜⵎⵓⵣⵣⵍ ⴰⵎⵓⵔ ⵏⵏⵙ. | 2.496 | |
ⵓⵔ ⵏⵏ ⴰⴽⴽⵯ ⵢⴰⴷⵍⵍⵉ ⵉⵜⴰⵎ ⵜⵓⵎ ⵎⴰⵙ ⵜⵥⴹⴰⵕ ⵎⴰⵔⵉ ⴰⴷ ⵜⵙⴽⵔ ⵎⴰⵢⴰⵏⵏ. | 3.627 | |
ⵉⵙ ⴰⵔ ⵀⵍⵍⵉ ⵙⵎⵓⵇⵇⵓⵍⵖ. | 1.92 |
Dataset Card for Tamazight Open Speech Dataset
This dataset provides a parsed, formatted, and ready-to-use Amazigh Voice Dataset. It contains voice recordings and corresponding text transcripts in Standard Moroccan Amazigh (ⵜⴰⵎⴰⵣⵉⵖⵜ ⵜⴰⵏⴰⵡⴰⵢⵜ ⵜⴰⵎⵓⵔⴰⴽⵓⵛⵜ) intended for training Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models.
This specific repository is published by a collaborator. You may visit the raw dataset repository which has additional dataset that hasn't been uploaded here yet: Amazigh-Speech-Dataset.
Dataset Details
Dataset Sources
- Parsed Repository: https://huggingface.co/datasets/Tamazight-NLP/TOSD
- Additional Data: https://huggingface.co/datasets/abdelhaqueidali/Amazigh-Speech-Dataset
Uses
Direct Use
- Training or fine-tuning Speech-to-Text (STT / ASR) models.
- Training or fine-tuning Text-to-Speech (TTS) models.
- Linguistic research regarding Amazigh phonetics and speech.
Out-of-Scope Use
This data should not be used to generate malicious voice clones or deepfakes intended for impersonation, fraud, or harassment.
Dataset Structure
Unlike the raw dataset which uses separate .wav and .txt files in .zip archives, this dataset has been parsed into a structured format for immediate use with the Hugging Face datasets library.
It contains 1,567 examples with the following fields:
audio: The audio data feature, containing the decoded audio array and sampling rate.text: The string transcript of the audio in the Tifinagh script.duration: The length of the audio clip in seconds (float64).
Dataset Creation
Curation Rationale
Amazigh is a low-resource language in AI. This dataset was created to contribute high-quality, openly licensed voice data to help the open-source community build better voice technologies for the Amazigh-speaking community.
Source Data
Data Collection and Processing
The audio was recorded by a fluent speaker reading from pre-selected Standard Moroccan Amazigh texts. This specific repository hosts the processed and structured version of those original recordings.
Who are the source data producers?
The audio was originally recorded by Abdelhaque Id Ali, a speaker of Southern Moroccan Amazigh.
Personal and Sensitive Information
The dataset contains the voice recordings of the creator. No other personally identifiable information (PII) is included in the audio or text.
Bias, Risks, and Limitations
This dataset represents the voice, accent, and pronunciation of a single speaker using Standard Moroccan Amazigh. It may not fully capture the phonetic diversity of other regional Amazigh varieties. Models trained solely on this data may struggle with accents or dialects not represented here.
Dataset Card Authors
- Abdelhaque Id Ali
- Mohamed Aymane Farhi
- Tamazight-NLP
Dataset Card Contact
- Email: Abdelhaque379@gmail.com
- LinkedIn: Abdelhaque Id Ali
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