michaelodafe/pidgin-asr-combined

Pidgin ASR Combined

A unified Nigerian Pidgin English speech-to-text dataset that combines publicly available Pidgin ASR sources into a single train / validation / test setup with a consistent schema. Built for fine-tuning Whisper-family models on Nigerian Pidgin (Naija, pcm).

~8.6 hours, 4,278 clips, 10 source speakers, 16 kHz mono WAV.

Used to train michaelodafe/whisper-pidgin-v1 (21.37% WER on the test split, beating the published Wav2Vec2-XLSR-53 baseline by 8.2 pp).

Sources

Source	Clips	Hours	License	Notes
asr-nigerian-pidgin/nigerian-pidgin-1.0	4,277	~8.6 h	CC-BY-4.0	10 native speakers (5 M / 5 F, ages 20–28), studio-quality
Rexe/nigerian-pidgin-speech	73	~0.05 h	unspecified	Single YouTube song; routed to test only

The Rexe set is too small to add training signal, so it's routed entirely to the test pool to slightly broaden the eval distribution beyond the studio recordings.

Splits

Split	Clips	Hours	Mean dur	Min/Max dur
train	2,708	5.41	7.2 s	0.5 / 40.5 s
validation	677	1.37	7.3 s	0.6 / 38.2 s
test	893	1.78	7.2 s	1.4 / 44.7 s
total	4,278	~8.56

Splits preserved from the upstream asr-nigerian-pidgin/nigerian-pidgin-1.0 release. Speaker IDs may be shared across splits in the source dataset; for stricter speaker-disjoint evaluation, consult the upstream publication.

Note: a small number of clips exceed Whisper's 30-second input window. Filter those out (duration <= 30.0) when fine-tuning Whisper.

Schema

Column	Type	Description
audio	Audio(sampling_rate=16000)	Audio array, 16 kHz mono
text	string	Transcription, lowercased, punctuation-light
source	string	Origin dataset identifier
duration	float	Clip duration in seconds
speaker_id	string	Speaker identifier (may be empty for Rexe rows)

How to load

from datasets import load_dataset

ds = load_dataset("michaelodafe/pidgin-asr-combined")
print(ds)
# DatasetDict({
#   train: Dataset({features: ['audio','text','source','duration','speaker_id'], num_rows: 2708}),
#   validation: ...,
#   test: ...
# })

example = ds["train"][0]
audio = example["audio"]["array"]   # 16kHz float32 numpy array
text  = example["text"]              # "salt di group also tok say too much salt no good"

Content notes

The data is read-style news Pidgin — articles from BBC News Pidgin and similar sources, read aloud in a studio setting. Lexicon is rich in:

• Pidgin function words: dey, wey, na, di, pikin, pipo, tori, sabi, becos, neva, wetin, oga.
• Nigerian proper nouns: politicians, states (Lagos, Anambra, Delta, Kogi, etc.), political parties (APC, PDP), organizations (NEMA, JAMB, BRT).
• Naturally code-switched English (proper nouns, loanwords, formal registers).

What's not present:

• Casual / conversational Pidgin
• Heavy code-switching with Yoruba / Igbo / Hausa
• Older speakers (training data is ages 20–28)
• Noisy real-world acoustic conditions (street, crowd, vehicle, etc.)

Build pipeline / reproducibility

The combination, normalization, and dedupe pipeline is open source:

• Code: michaelodafe/Naija-Pidgin-Whisper · scripts/01_fetch_data.py

To rebuild from sources:

git clone https://github.com/michaelodafe/Naija-Pidgin-Whisper.git
cd Naija-Pidgin-Whisper
pip install -r requirements.txt
HF_HUB_DISABLE_XET=1 python scripts/01_fetch_data.py

License and attribution

This combined dataset is released under CC-BY-4.0, inheriting the primary source license.

If you use this dataset, attribution to the upstream sources is required:

• The bulk of the data (4,277 clips) comes from asr-nigerian-pidgin/nigerian-pidgin-1.0 by the Nigerian Pidgin ASR project team. Please cite their work in any publication.
• A small subset (73 clips, test-only) comes from Rexe/nigerian-pidgin-speech.

Citation

@misc{odafe2026pidginasrcombined,
  title  = {Pidgin ASR Combined: a unified Nigerian Pidgin speech corpus},
  author = {Odafe, Michael},
  year   = {2026},
  url    = {https://huggingface.co/datasets/michaelodafe/pidgin-asr-combined},
  note   = {Combines asr-nigerian-pidgin/nigerian-pidgin-1.0 (CC-BY-4.0) and Rexe/nigerian-pidgin-speech}
}

And please also cite the primary upstream source:

@misc{nigerianpidginasr2025,
  title  = {Nigerian Pidgin ASR Dataset v1.0},
  author = {asr-nigerian-pidgin project team},
  year   = {2025},
  url    = {https://huggingface.co/datasets/asr-nigerian-pidgin/nigerian-pidgin-1.0}
}

Related

• 🤖 Model trained on this dataset: michaelodafe/whisper-pidgin-v1
• 💻 Source code and full design notes: https://github.com/michaelodafe/Naija-Pidgin-Whisper
• 🎤 Live demo: HF Space