SpeechTaxi / README.md
Lennart Keller
release
77dfcf5
metadata
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

# 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

@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}, 
}