Kennethdot/Ghana_English-Twi_Code-switching_Speech

Dataset Card for KasaSpeech

Dataset Summary

KasaSpeech is a large-scale English–Twi code-switching speech dataset developed to advance research in speech technologies for English and Twi. The dataset comprises 54,855 transcribed speech recordings collected from speakers across Ghana and is designed to capture natural code-switching between English and Twi across a diverse range of everyday topics and communication scenarios

With over 95 hours of manually transcribed speech, KasaSpeech establishes a new benchmark and gold-standard corpus for English–Twi code-switching speech recognition and text-to-speech research. It is designed to support the development, evaluation, and comparison of ASR systems, speech representation models, and multilingual speech technologies for English–Twi.

Supported Tasks

KasaSpeech is suitable for:

• Automatic Speech Recognition (ASR) for Code-switching speech
• Text-To-Speech (TTS)
• Multilingual speech modeling
• Speech representation learning
• Speech foundation model fine-tuning and evaluation
• African language speech technology research

Dataset Structure

Data Splits

Split	Samples	Duration
Train	50,965	83.94 hours
Validation	2,159	6.80 hours
Test	1,731	4.84 hours
Total	54,855	95.58 hours

Data Fields

Each example contains the following fields:

Field	Type	Description
speaker_id	string	Anonymous speaker identifier
age_range	string	Speaker age group
gender	string	Speaker gender
prompt_set	string	Prompt category used during recording
transcript	string	Human-annotated English–Twi code-switched transcript
duration	float32	Audio duration in seconds
split	string	Dataset split (train, validation, or test)
audio	Audio	Speech recording
file_name	string	Original audio filename
error	string	Optional annotation or recording error label

Example

from datasets import load_dataset, Audio

dataset = load_dataset(
    "Kennethdot/Ghana_English-Twi_Code_switching_ASR",
    split="train"
)

dataset = dataset.cast_column(
    "audio",
    Audio(sampling_rate=16000)
)

sample = dataset[0]

print(sample["transcript"])

Example transcript:

Me phone no a-crack-i, henfa na mɛtumi a-fix-i screen no?

Dataset Creation

Collection Process

Speech recordings were voluntarily contributed by participants using a custom data collection platform. Speakers were presented with prompts designed to encourage natural English–Twi code-switching while covering a broad range of everyday topics and communication scenarios.

Annotation Process

All recordings were manually transcribed following standardized annotation guidelines developed for English–Twi code-switched speech. Multiple quality assurance steps were performed to improve transcription consistency and remove corrupted or invalid recordings.

Speaker Information

The dataset includes recordings from speakers spanning multiple age groups and genders. Speaker identities have been anonymized using unique identifiers.

Dataset Characteristics

• Total recordings: 54,855
• Total duration: 95.58 hours
• Languages: English, Twi, and English–Twi code-switching
• Sampling rate: 48 kHz (can be resampled to 16 kHz for model training)
• Recording style: Prompted, natural code-switched speech
• Transcriptions: Human-annotated

Limitations

• Demographic representation may not be perfectly balanced across speaker groups.
• Recording conditions vary across devices and environments.
• The dataset primarily reflects Ghanaian English–Twi code-switching and may not generalize to all Akan dialects or other multilingual contexts.
• Although carefully curated, minor transcription inconsistencies may remain.

Citation

If you use KasaSpeech in your work, please cite:

@dataset{kasaspeech2026,
  title={KasaSpeech: A Large-Scale English--Twi Code-Switching Speech Dataset},
  author={Dotse, Kenneth},
  year={2026},
  url={https://huggingface.co/datasets/Kennethdot/Ghana_English-Twi_Code_switching_ASR}
}

Contact

For questions, bug reports, or collaboration opportunities, please open a discussion on the Hugging Face dataset page. Contributions, feedback, and research collaborations are welcome.