AfriSwitch: In-the-Wild African Code-Switched Speech Benchmark

Dataset Description

AfriSwitch is a 20.40-hour, human-transcribed benchmark of in-the-wild, conversational code-switched speech spanning five African languages: Swahili, Kinyarwanda, Zulu, Amharic, and Hausa, each switching with English.

• Languages: Swahili, Kinyarwanda, Zulu, Amharic, Hausa (each code-switched with English)
• License: CC BY NC SA 4.0
• Total duration: 20.40 hours
• Total utterances: 2,468
• Total code-switch events: 17,292

Dataset Statistics

Language	Hours	Utterances	Avg. Switch Points (S*)	Total S*	CMI
Swahili	3.41	412	6.72	2,769	28.47
Kinyarwanda	3.84	464	9.81	4,554	27.70
Zulu	5.57	670	5.80	3,887	22.16
Amharic	5.40	649	7.31	4,746	15.11
Hausa	2.18	273	4.89	1,336	7.03
Total	20.40	2,468	7.01	17,292	20.73

Switch points (S*) count alternation points where a token's language tag differs from the preceding token's, following Gambäck and Das (2016).

Code-Mixing Index (CMI) follows Das and Gambäck (2014): a CMI of 0 indicates a fully monolingual utterance, while higher values reflect a more balanced mix of languages within an utterance. Across the five languages, mixing intensity varies considerably: Hausa is closest to monolingual (lowest CMI and S*), while Swahili and Kinyarwanda show the densest, most balanced mixing with English.

Dataset Structure

Each example in the dataset is expected to include:

• audio_path: the speech segment (VAD-segmented, concatenated up to 30 seconds per utterance to increase the likelihood of capturing a code-switch)
• transcription: verbatim human transcription
• language: the primary/matrix language of the utterance (Swahili, Kinyarwanda, Zulu, Amharic, or Hausa)

(Adjust the field list above to match the exact schema of your uploaded files/splits before publishing.)

Dataset Creation

Source Data

Audio was sourced from publicly available YouTube videos and podcasts under permissive licenses. Bilingual annotators on African crowdsourcing platforms selected source material specifically for the presence of code-switching.

The released dataset contains transcriptions and processed audio segments only, with no links back to original source videos or podcasts, preventing direct tracing to original content creators. No annotator demographic information is included in the released benchmark.

Licensing Information

This dataset is released under the Attribution-NonCommercial-ShareAlike 4.0 (CC BY NC SA 4.0 ) license.