Datasets:

timniel
/

Pidgin_ASR_Dataset_Combined

audio audio	text string
	How you dey now?
	Hope sey you sleep well?
	Ah my sister, I sleep well o!
	Una get light for una area last night?
	We no get o.
	Ah wetin happen now?
	Toh na NEPA o!
	Aha dese people, I hear sey de dey share di light.
	My sister, di tin tire me o!
	De just dey share am, but de go dey bring big bill.
	In fact, dat bill na anoder tin again.
	Na so my sister.
	How much de dey, how much, how much de dey give una for una bill?
	Ah my sister, na seven thousand o!
	Seven thousand for di light wey we no dey see?
	Ah dese people, eh de no go kill person o!
	Toh how you dey manage now?
	My sister, I dey manage am.
	Eyah God go help us.
	E get one soup like dat wey Mama Maro bin teach me.
	Di soup make sense.
	I hear sey na Ibo soup.
	I know sey you go like am.
	Teach me too now.
	Shebi you know banga soup?
	I know banga soup.
	Ehn na di soup.
	So it's like I go come, me, and you go, go do am.
	Di soup go make sense.
	You go like am well, well.
	Abi meh I give you.
	You know how de take dey cook di soup?
	Ah my sister, my junior sister bin teach me how de take dey cook am o.
	You fit tell me small?
	Eh e say you go parboil di banga.
	After you parboil am, you go come pound am.
	When you pound am, you filter am.
	Den you put eh your ingredient inside.
	Na so my teacher, my sister take teach me.
	Na starch, or na eba?
	I dey like starch well, well.
	Starch hm, na starch I dey like.
	Ehn na dat one I dey like pass.
	Dis morning, wetin you eat before you come here?
	Ah my sister o, mtschew toh I eat eba o!
	Hehe why you no drink tea?
	Hm my sister, I no dey too like tea.
	Na swallow I like pass.
	Okay wetin you eat before you sleep for di night?
	Ah well my sister, I just take small rice.
	Hope sey di rice sweet sha?
	So, any, any news?
	You hear any news for area?
	Concerning dis our NEPA issue, I bin hear sey de say de wan come cut light tomorrow.
	My sister, na so I hear am o.
	De say de wan come cut light tomorrow.
	Make dese people no come o because we don tire for dem!
	Me masef, I no even get money to pay.
	Hey dis one na wahala o.
	Dis one wey everywhere dry, no money.
	I just pray make dem no come.
	Make dem no come!
	Because if de come, dat means na all di whole area dem go cut deir light o.
	Because people no, no money.
	And we no dey see light.
	Hm meh NEPA no try us o!
	I no sure sey dem go even come sef.
	Madam, I wan make me, and you go market next market day.
	Gwagwalada Market, abi Kwali?
	Which day be Kwali market?
	Ah we go try go o.
	Wetin you wan go buy now?
	I wan go buy shop tins.
	Mtschew e get some kind tins wey I need for my shop.
	I wan make me, and you go.
	Because you no go see provision dere o.
	Eh na true o.
	I fit see shampoo, and attach ba?
	My sister, you no go see o!
	Na food product dey dere.
	If we go toh, we would just buy tins wey we go take cook some kind soup.
	When I wan go, I go come call you.
	I go house dey wait you.
	Me too, mey I go hustle for money so dat if we go, I go buy enough tins.
	I go call you.
	Dat your number wey give me last, eh last week, shebi na di number you still dey use?
	Na im I still dey use.
	X sometime, I dey try di number.
	Di number no dey go o.
	Ah dis network again!
	I go flash you with my number before we leave here.
	I dey expect am.
	Which church you dey go now?
	My sister, na CAC.
	You don comot for Pathfinders?
	Hm mtschew I don comot jor.
	Dey go CAC now.
	Eyah how di church now?
	Ah we thank God.
	Hope sey de dey preach well?

End of preview. Expand in Data Studio

Naija-ASR-Corpus v1.0 (NAC-v1.0)

A Foundational Automatic Speech Recognition Corpus for Nigerian Pidgin (Naija, PCM)

📌 Dataset Summary

Naija-ASR-Corpus (NAC-v1.0) is a speech dataset derived from the Universal Dependencies Naija Spoken Corpus (UD_Naija-NSC).

The NAC Team processed the original long-form recordings by:

Segmenting the audio into sentence-level clips.
Transcribing/Aligning the text to create paired audio-text data suitable for ASR training.

This dataset is designed to support the development of Automatic Speech Recognition (ASR) systems for Nigerian Pidgin (Naija, ISO 639-3: PCM).

🎯 Use Cases

Automatic speech recognition (ASR)
Speech-to-text research
Linguistic analysis of segmented Naija speech

🌍 Language

Language: Nigerian Pidgin (Naija)
ISO 639-3 Code: PCM

🧩 Dataset Composition

Component	Description
Total Utterances	5,883 processed speech clips
Audio Format	`.wav` files, mono, 16kHz
Transcripts	UTF-8 text

💻 Code & Reproduction

The scripts used to download, slice, process, and upload this dataset are open source available on GitHub:

Data Preparation (Slicing & Filtering):
- https://github.com/Timniel/naija-pidgin-asr-prep
Hugging Face Uploader Pipeline:
- https://github.com/Timniel/naija-asr-hf-uploader

🛠️ Data Source & Attribution

Source Material: This dataset is entirely derived from the Universal Dependencies Naija Spoken Corpus (UD_Naija-NSC).

modifications by NAC Team: The original continuous recordings were split into shorter clips (pidgin_0001.wav to pidgin_5883.wav) and matched with specific transcripts to facilitate machine learning tasks.

Required Attribution:

This dataset contains content derived from UD_Naija-NSC (Universal Dependencies Naija Spoken Corpus), licensed under CC-BY-SA 4.0.

Original Authors: UD_Naija-NSC contributors.

Original License: https://creativecommons.org/licenses/by-sa/4.0/

📜 Licensing

Since this dataset is a derivative work of UD_Naija-NSC (CC-BY-SA 4.0), this dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

You are free to:

Share — copy and redistribute the material in any medium or format.
Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Under the following terms:

Attribution — You must give appropriate credit to UD_Naija-NSC and the NAC Team.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.