---
language:
- asm
- bgc
- bht
- ckb
- eng
- ewe
- fra
- guj
- ibo
- kan
- lin
- luo
- mal
- mar
- nag
- nde
- nlx
- pan
- peg
- rus
- tam
- tel
- twi
- ukr
- urd
- vie
- yor
task_categories:
- text-classification
- audio-classification
size_categories:
- 10K<n<100K
---
# SpeechTaxi

## Usage

```python
# pip install datasets pandas soundfile
from datasets import load_dataset
dataset = load_dataset(
    "LennartKeller/SpeechTaxi",
    name="ukr",
    split="train",
    trust_remote_code=True
)
```

## Overview

|    | Language                 | alpha3     |   train |   test |   dev |   total |
|---:|:-------------------------|:-----------|--------:|-------:|------:|--------:|
|  0 | Vietnamese               | vie        |     856 |    111 |   106 |    1073 |
|  1 | French                   | fra        |     851 |    108 |   106 |    1065 |
|  2 | Russian                  | rus        |     822 |    107 |   102 |    1031 |
|  3 | Ukrainian                | ukr        |     751 |     97 |    89 |     937 |
|  4 | Kannada                  | kan        |     740 |    100 |    89 |     929 |
|  5 | Gujarati                 | guj        |     740 |    100 |    89 |     929 |
|  6 | Yoruba                   | yor        |     739 |    100 |    88 |     927 |
|  7 | Punjabi                  | pan        |     739 |    100 |    88 |     927 |
|  8 | Naga Pidgin              | nag        |     739 |    100 |    89 |     928 |
|  9 | Luo (Kenya and Tanzania) | luo        |     738 |    100 |    88 |     926 |
| 10 | Tamil                    | tam        |     733 |    100 |    89 |     922 |
| 11 | Marathi                  | mar        |     733 |     99 |    87 |     919 |
| 12 | Assamese                 | asm        |     732 |     98 |    88 |     918 |
| 13 | Haryanvi                 | bgc        |     729 |    100 |    87 |     916 |
| 14 | Bhattiyali               | bht        |     726 |     98 |    88 |     912 |
| 15 | Malayalam                | mal        |     724 |    100 |    89 |     913 |
| 16 | Ewe                      | ewe        |     724 |     98 |    86 |     908 |
| 17 | Central Kurdish          | ckb        |     723 |     93 |    82 |     898 |
| 18 | Telugu                   | tel        |     722 |     96 |    85 |     903 |
| 19 | Igbo                     | ibo        |     720 |     96 |    87 |     903 |
| 20 | Pengo                    | peg        |     707 |     94 |    86 |     887 |
| 21 | Ndebele                  | nde        |     699 |     88 |    85 |     872 |
| 22 | Asante Twi               | tw-asante  |     693 |     92 |    88 |     873 |
| 23 | Akuapem Twi              | tw-akuapem |     692 |     91 |    84 |     867 |
| 24 | Urdu                     | urd        |     674 |     95 |    80 |     849 |
| 25 | Nahali                   | nlx        |     672 |     92 |    85 |     849 |
| 26 | English                  | eng        |     569 |     81 |    74 |     724 |
| 27 | Lingala                  | lin        |     560 |     75 |    61 |     696 |


## Citation

```bibtex
@misc{keller2024speechtaximultilingualsemanticspeech,
      title={SpeechTaxi: On Multilingual Semantic Speech Classification}, 
      author={Lennart Keller and Goran Glavaš},
      year={2024},
      eprint={2409.06372},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.06372}, 
}
```