text stringlengths 1 349 | subset stringclasses 2
values | audio audioduration (s) 1 28.3 | duration float64 1 28.3 |
|---|---|---|---|
ⴷⴰⵔⵉ ⵢⴰⵏ ⵓⵎⵓⴽⵔⵉⵙ | subset_1 | 1.386667 | |
ⵎⴰⴷ ⴰⴽ ⵉⵙⵎ ? | subset_1 | 1.450667 | |
ⵉⵙ ⴰⵔ ⴽⴰ ⵙⵎⵓⵇⵇⵓⵍⵖ. | subset_1 | 1.664 | |
ⵙⵔⵙ ⵉⴼⴰⵙⵙⵏ ⵏⵏⵎ ⴼ ⵓⵖⵔⴰⴱ. | subset_1 | 1.621333 | |
ⵉⵄⵇⴱ ⴷ ⵙⴰⵎⵉ ⵖⵔ ⵓⵅⵅⴰⵎ. | subset_1 | 1.984 | |
ⴰⴷ ⵏⵏ ⵓⵔ ⵜⵎⴰⵟⵍⵎ ⴼ ⵜⵉⵏⵎⵍ. | subset_1 | 2.069333 | |
ⵀⴰ ⵏⵏ ⴽⵢⵢ ⴽⴰ ⵙ ⵏⵙⵙⵓⵎⴷ, ⴷ ⴽⵢⵢ ⴽⴰ ⴰⴷ ⵏⵎⵎⵜⵔ | subset_1 | 3.328 | |
ⵎⴰⵏ ⵜⵉⵎⵖⵔⵉⵜ ⵖ ⵜⵍⵍⵉⵜ? | subset_1 | 1.92 | |
ⵉⵙ ⴰⵔ ⵀⵍⵍⵉ ⵜⵜⴼⵉⵔⵔⵉⵢⵏ ⵡⵓⵙⵙⴰⵏ. | subset_1 | 2.197333 | |
ⵎⴰⵢⵎⵎⵉ ⵜⴹⵚⵚⴰⴷ? | subset_1 | 1.28 | |
ⴰⵔⴰ ⵙ ⵜⵎⴰⵣⵉⵖⵜ | subset_1 | 1.322667 | |
ⵓ, ⴱⵓ, ⴳⵓ , ⴷⵓ, ⴹⵓ. | subset_1 | 5.290667 | |
ⵢⴰⵏ | subset_1 | 1.536 | |
ⴰⵙⵙ ⵏ ⵜⵍⴰⵍⵉⵜ ⵉⵖⵓⴷⴰⵏ! | subset_1 | 1.536 | |
ⵎⴰⵎⵛ ⵖⴰ ⴰⵔⵉⵖ ⵜⴰⵙⴽⵍⴰ ⴰ ⵜⵉⵍⵉ ⵙ ⵜⴱⵓⵍⴳⴰⵔⵉⵜ? | subset_1 | 2.56 | |
ⴽⵔⴰⵢⴳⴰⵜ ⴰⵙⵙ ⴰⵔ ⵉⵜⵜⵛⵓⵛⵓⴼ. | subset_1 | 1.557333 | |
ⵜⵎⵍⴰ ⵍⴰⵢⵍⴰ ⵉⵜⵍⵉ ⵉ ⵙⴰⵎⵉ. | subset_1 | 2.090667 | |
ⴷⵉⵏⵏⴰ ⴳ ⵏⵏ ⵜⵍⵍⴰⵎⵜ ⴰⴷ ⴷ ⵜⴰⵙⵎⵜ ⵖⵔ ⴷⴰ. | subset_1 | 2.069333 | |
ⵛⵛⵉⵖ ⵢⴰⵏ ⵓⴱⴰⵡ ⵉⴹⵏⵏⴰⵟ ⴰⴽⴰⴷ ⵉⵎⵎⵉⵎ. | subset_1 | 2.432 | |
ⴱⴱ | subset_1 | 1.301333 | |
ⵉⵔⵡⴰⵙ ⵉⵙ ⴷ ⵏⴽⴽ ⴰⴷ ⵜ ⵉⴳⴰⵏ. | subset_1 | 1.941333 | |
ⴱⴱⴻ | subset_1 | 1.066667 | |
ⵙⵔⵙⵏ ⴰⴽⴽⵯ ⵉⵎⴰⴽⵔⵏ ⴰⵢⵍⵍⵉ ⴷⴰⵔⵙⵏ ⵉⵍⵍⴰⵏ ⵔⵡⵍⵏ ⵙ ⴽⵔⴰⵢⴳⴰⵜ ⵜⴰⵙⴳⴰ. | subset_1 | 3.328 | |
ⵎⴰⵢⵎⵎⵉ ⵓⵔ ⴰⵙ ⵜⵏⵏⵉⵜ ⵖⵉⴽ ⴰⵏⵏ? | subset_1 | 2.389333 | |
ⴰⵎⵛⵛⵉⵡⵕ. | subset_1 | 1.152 | |
ⴼⴻ, ⴽⴻ, ⵀⴻ, ⵃⴻ | subset_1 | 3.648 | |
ⵉⴼⵖⴰⴽⴰⵍⵏ, ⵉⵎⵙⴰⵢⵔⴰⵔⵏ, ⴰⴼⵖⴰⴽⴰⵍ, ⵉⴼⵖⴰⴽⴰⵍⵏ, ⴰⵎⵙⴰⵢⵔⴰⵔ, ⴰⵙⴰⵢⵔⴰⵔ, ⴰⵙⴰⵢⵔⵓⵔ | subset_1 | 6.912 | |
ⵓⵔ ⵙⵙⵉⵏⵖ ⵎⴰ ⵉⴳⴰ ⴰⵙⴰⴳⵎ ⵏⵏⵙ. | subset_1 | 2.154667 | |
ⴰⴷ ⴰⴽⴽⵯ ⵓⵔ ⵜⵣⵔⵉⴱⵎⵜ ⵙ ⵜⴽⵏⴰⵔⵉⵜ. | subset_1 | 2.218667 | |
ⵜⴰⵄⵕⴰⴱⵜ ⴷ ⵜⴼⵕⴰⵏⵙⵉⵙⵜ | subset_1 | 1.92 | |
ⵢⴰⵖⵖ | subset_1 | 1.045333 | |
ⵉⵎⴰⵍⵍⴰⵢⵏ ⴰⴷ ⴰⴽⴽⵯ ⴳⴰⵏ. | subset_1 | 1.664 | |
ⵙⵍⵍⴰⵖ ⵛⴰ ⵉⵜⵜⵓⵙⵓ. | subset_1 | 1.386667 | |
ⵎⴰⵢⵎⵎⵉ ⵓⵔ ⴰⵙ ⵜⵏⵏⵉⴷ ⵖⵉⴽⴰⵏⵏ? | subset_1 | 2.432 | |
ⵜⵓⴳⵜ ⴰⴷ ⴷ ⴼⵍⵍⴰⵖ ⵉⵙⵇⵙⴰⵏ. | subset_1 | 1.770667 | |
ⵜⴹⵕ ⴷ ⵜⴳⵎⵎⵉ. | subset_1 | 1.578667 | |
ⴰⵙⵉⵏⴰⴳ ⴰⵏⴰⴼⵍⵍⴰ ⵏ ⵜⵏⴱⴹⴰⵢⵜ. | subset_1 | 2.474667 | |
ⵎⴰⴼ ⴱⴰⵀⵔⴰ ⵜⵇⵇⵉⵎⵎ ⴷⴳ ⴱⵓⵚⵟⵏ? | subset_1 | 2.261333 | |
ⴰⵔ ⴼⵜⵜⵓⵏ ⵡⵓⵙⵙⴰⵏ ⵙ ⵜⴰⵣⵣⵍⴰ. | subset_1 | 2.261333 | |
ⴹⵓ | subset_1 | 1.194667 | |
ⵉⵥⵥⴰ ⵙⴰⵎⵉ ⵍⴰⵢⵍⴰ ⵖ ⵜⴳⵎⵎⵉ. | subset_1 | 1.92 | |
ⴼⴽⴰⵜ ⵉⵢⵉ ⴰⵎⵓⵔ ⵉⵏⵓ. | subset_1 | 1.493333 | |
ⴽⴽⵉⵖ ⵜⵜ ⵉⵏⵏ ⵓⵔ ⵙⵙⵉⵏⵖ ⵎⴰ ⵉⴳⴰⵏ ⵜⵉⵣⵉ ⵏ ⵓⵙⵓⵏⴼⵓ | subset_1 | 2.837333 | |
ⵡⴰⴷ ⴷ ⴰⵎⴷⴷⴰⴽⵯⵍ ⵉⵏⵓ | subset_1 | 1.514667 | |
ⴳⴳⴰⵡⵔⵖ ⴳ ⵜⴳⵎⵎⵉ ⴰⵛⴽⵓ ⵢⵓⵜ ⵉⵢⵉ ⵡⴰⴹⵓ. | subset_1 | 2.432 | |
ⵜⵡⵜⵎⵜ ⵜ? | subset_1 | 1.130667 | |
ⵎⴰⴷ ⴰⵖ ⵢⴰⵖⵏ | subset_1 | 1.152 | |
ⵇⵇⴰⴷ ⵉⵜⵜⵡⴰⵣⵎⵎⴰⵎ ⴳ ⵢⴰⵜ ⵜⵙⴰⵡⵔⵜ ⵏ ⵜⵥⵓⵕⵉ. | subset_1 | 2.581333 | |
ⵢⴰⵡⵡ | subset_1 | 1.493333 | |
ⵛⴽⴽ ⴷ ⴰⵏⴱⵔⴰⵣ ⵏⵏⵖ. | subset_1 | 1.429333 | |
ⵉⵙⵙⵏ ⴰⴷ ⵉⵙⴰⵡⴰⵍ. | subset_1 | 1.493333 | |
ⵎⴰⵅⵅ ⵍⵍⵉⵖ ⴱⴰⵀⵔⴰ ⵜⵖⴰⵎⴰⵎ ⴳ ⴱⵓⵚⵟⵏ? | subset_1 | 2.432 | |
ⵜⴽⴽⴰ ⵜⵜ ⵉⵏⵏ ⵍⴰⵢⵍⴰ ⴰⵔ ⵜⵍⵙⵙⴰ ⵢⴰⵜ ⵜⴽⵔⴱⵜ ⵉⵙⴳⴳⴰⵏⵏ. | subset_1 | 2.773333 | |
ⵏⴽⴽ ⴳⵉⵖ ⵉⵎⵉⵖⵉⵙ ⴳ ⵜⵎⴰⵙⵙⴰⵏⵉⵏ. | subset_1 | 2.304 | |
ⵓⵔ ⴷⴰⵔⵉ ⵉⴽⵔⵉⵙⵏ ⴷ ⵜⴰⴷ. | subset_1 | 2.112 | |
ⴰⵔ ⴱⴷⴷⴰ ⴷⵉⴷⵏⵖ ⵜⵜⵉⵍⵉⵏ. | subset_1 | 1.557333 | |
ⵉⵔⴳⴳⵉⴳ ⵙⵓⵍ ⵓⵏⵣⵡⵉ ⴳ ⵍⵉⵏⴳⵍⵉⵣ ⴼ ⵙⴽⵓⵜⵍⴰⵏⴷⴰ. | subset_1 | 2.730667 | |
ⵢⴰⵠ | subset_1 | 1.109333 | |
ⵢⴰⴹⴹ | subset_1 | 1.344 | |
ⵀⴰ ⵜ ⵓⵔ ⵖⵉⴷ ⵜⵔⵖⵉ ⵎⴽⵍⵍⵉ ⵢⴰⴷ ⵍⵍⵉ ⵜⴰⵎⵖ. | subset_1 | 2.325333 | |
ⵎⵎⵓⵜⵏ ⵢⴰⵏ ⵙ ⵢⴰⵏ. | subset_1 | 1.472 | |
ⴰⵡⴷ ⵢⴰⵏ ⵓⵔ ⴰⵔ ⵉⴹⵚⵚⴰ. | subset_1 | 1.749333 | |
ⵏⴽⴽⵏⵉ ⵓⵔ ⵜ ⵏⵙⵙⵉⵏ. | subset_1 | 1.749333 | |
ⵜⴰⵎⴰⵣⵉⵖⵜ ⵜⵍⵍⴰ ⴳ ⵎⵓⵔⴰⴽⵓⵛ, ⴷⵣⴰⵢⵔ, ⵍⵉⴱⵢⴰ ⴷ ⵙⵉⵡⴰ. | subset_1 | 3.818667 | |
ⵎⴰⵏⵉⴽ ⴰ ⵙ ⵜⵙⵙⵏⴷ? | subset_1 | 1.408 | |
ⴰⴽⴰⵍ ⴷ ⵉⵥⵕⴰⵏ ⴰⴷ ⴷ ⵙⵓⵍ ⵢⴰⴳⵓⵔⵏ ⴳ ⵜⴰⴷⴷⴰⵔⵜ ⵏⵏⵖ ⵍⵍⵉⵖ ⵜⵜ ⵊⴷⵔⵏ ⴰⵢⵜ ⵓⵙⵓⵏ. | subset_1 | 4.352 | |
ⴰⵔⴳⴰⵣ ⵏⵏⵙ ⴷ ⴰⵣⵡⴰⵡⵉ. | subset_1 | 1.664 | |
ⴰⵊⵊⴰⵎⵜ ⵉⵢⵉ ⴰⴷ ⴼⴼⵖⵖ! | subset_1 | 2.005333 | |
ⵉⵙⵍⵍⴰ ⵙⴰⵎⵉ ⵉ ⵓⵙⵓⵙⵔ ⵉⵙⵍⵍ ⵉ ⵓⵎⵙⵍⵉ ⵏ ⵢⴰⵏ ⵓⵔⴳⴰⵣ ⴳ ⴹⴰⵕⴰⵜ. | subset_1 | 3.413333 | |
ⵎⴰⴷ ⵉⴳⴰ ⵜⴰⵖⴰⵡⵙⴰ ⵍⵍⵉ ⴰⴽⴽⵯ ⵉⴼⵓⵍⴽⵉⵏ ⵜⵙⴽⵔⴷ ⵜⵜ ⴳ ⵜⵓⴷⵔⵜ ⵏⵏⴽ? | subset_1 | 3.797333 | |
ⵢⴰⵇ | subset_1 | 1.493333 | |
ⵎⵓⵏⵖ ⴷ ⵉⵙⵜⵎⴰ ⵙ ⴷⴰⵔ ⵢⴰⵜ ⵜⵏⴰⵔⴰⴳⵜ ⵏⵏⵖ. | subset_1 | 2.474667 | |
ⴰⴷ ⵏ ⵓⵔ ⵜⵎⴰⵟⵍⵎ ⴼ ⵜⵉⵏⵎⵍ. | subset_1 | 1.642667 | |
ⵖⵔⵏⵖ ⴰⵟⵟⴰⵚ ⵏ ⵉⵏⴷⵓⴷⵉⵢⵏ ⴳ ⵊⵊⴰⴱⴱⵓⵏ. | subset_1 | 2.432 | |
ⵎⵏⵛⴽ ⴰⴷ ⵉⴳⴰ ⵡⴰⵜⵉⴳ ⵏ ⵜⵀⵉⵔⵉⵜ ⴰⴽⴽⵯ ⵉⵖⵯⵍⴰⵏ. | subset_1 | 2.645333 | |
ⵎⴰⵏⵉ ⵣⴰ ⵙⵓⵍ ⵏⵔⴰ. | subset_1 | 1.301333 | |
ⵀⴰⵜ ⵏⵏⵉⵖ ⵙ ⵓⵖⵉⵍⵓⴼ. | subset_1 | 1.621333 | |
ⵜⴰⵎⴰⵡⴰⵙⵜ ⵏ ⵜⵣⵔⴼⵜ. | subset_1 | 1.642667 | |
ⵔⵉⵖ ⵓⴽⴰⵏ ⴰⴷ ⴰⵎ ⵄⴰⵕⴷⵖ ⵙ ⵢⵉⵡⵉⵣ. | subset_1 | 2.496 | |
ⴰⵏⵙⵙⵉⵅⴼ ⵏ ⵜⵏⴱⴰⴹⵜ. | subset_1 | 1.301333 | |
ⴰⵔ ⵜⵜ ⵜⴻⵜⵜⵉⵏⵉⴷ ⴽⵔⴰⵢⴳⴰⵜ ⴰⵙⵙ | subset_1 | 2.069333 | |
ⵃⴰⵇⵇⴰⵏ ⵉⵙ ⴷ ⴷⵉⴽ ⵎⵙⴰⵛⴽⴰⵏ ⴽⵔⴰ ⵏ ⵉⵡⴷⴰⵏ. | subset_1 | 2.624 | |
ⵏⴽⴽ ⴰⵔ ⵙⴰⵡⴰⵍⵖ ⵙ ⵜⵎⴰⵣⵉⵖⵜ | subset_1 | 2.043356 | |
ⵙⵓⵍ ⵓⴽⴰⵏ ⵏⴷⴷⵔ. | subset_1 | 1.664 | |
ⴰⵔ ⵉⵊⴷⴷⵔ ⵓⴽⵛⵛⵓⴹ. | subset_1 | 1.706667 | |
ⴷ ⴰⵙⵍⵎⴰⴷ ⵏ ⵜⵓⴱⵉⵔⵜ. | subset_1 | 1.92 | |
ⵎⴰⴷ ⴰⵡⴰ ⴳⵉⵙ ⵜⵔⵉⴷ? | subset_1 | 1.408 | |
ⵓⵍⵉ ⴰⵙ ⵉⴷⴰⵎⵎⵏ ⵉ ⵄⵣⵣⵉ. | subset_1 | 2.026667 | |
ⵉⴹⴳⴰⵎ ⴰⴷ ⵏⵖⵉⵖ ⵢⴰⵜ ⵜⴰⴽⴽⴰⵍⵜ ⴳ ⵜⴳⵎⵎⵉ ⵏⵏⵖ. | subset_1 | 2.901333 | |
ⵍⴱⴷⴰ ⵜⵜⵉⵍⵉⵏ ⴰⴽⵉⴷⵏⵖ. | subset_1 | 1.621333 | |
ⵀⴰⵜⵉ ⴰⵔ ⵜⵜⵡⴰⵔⴳⴰⴷ? | subset_1 | 1.642667 | |
ⴼⴼⴰ | subset_1 | 1.194667 | |
ⵎⵍ ⵉⵢⵉ ⵎⴰⴷ ⴷ ⵜⴻⵜⵜⵎⵓⵏⴷ ⴰⴷ ⴰⴽ ⵎⵍⵖ ⵎⴰⴷ ⵜⴳⵉⴷ. | subset_1 | 2.176 | |
ⴳⴳⴻ | subset_1 | 1.152 | |
ⵓⵔ ⵉⴳⵉ ⵖⴰⵙ ⴽⵢⵢ ⵡⴰⵢⵏⵏⵉ ⵀⴰⵜ ⴰⵡⴷ ⵏⴽⴽ ⵉⵔⴰ ⵉⵢⵉ ⵏⵏ ⵡⴰⴷⴷⴰⴷ. | subset_1 | 2.901333 | |
ⵉⵙ ⵉⵍⵍⴰ ⵓⴱⵓⴱⴱⴰⵥ ⴳ ⵜⵉⵥⴳⵉ ⴰ? | subset_1 | 2.304 | |
ⵢⵓⵖⴰⵍ ⴷ ⵙⴰⵎⵉ ⵙ ⵜⴰⴷⴷⴰⵔⵜ. | subset_1 | 2.176 | |
ⵖⴰⵢⵏ ⴰ ⴼ ⴷ ⵜⵓⵛⴽⵉⴷ ⵙ ⵖⵉⵏ? | subset_1 | 1.749333 | |
ⵓⵙⵔⵖ ⴽⵔⴰ ⵏ ⵓⵖⴰⵏⵉⴱ. | subset_1 | 1.429333 | |
ⵜ, ⵜⴰ, ⵜⵓ, ⵜⵉ, ⵜⴻ | subset_1 | 4.053333 |
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 1,801 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).subset: The dataset has two different subsets recorded using different microphones. Whilesubset_1has mono audio,subset_2is stereo.
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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