Datasets:
audio_path
audioduration (s) 0.77
20.7
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Ɔbebue n'ani ɔkyena |
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Wɔ ńkwankyɛn |
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Ne yere |
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Adwoa Aboagye |
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To nnwom |
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Apoobɔ nnyɛ |
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Ɔmee ne Ɛfeɛ |
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Ne nea Ɔsɛe de yɛɛ buburoo wei |
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Dan mu |
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Tuu no |
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Kɔɔ dua bi akyi |
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Ko buu no |
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Ɛsoo aba pii |
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Awi no rehwehwɛ ahu |
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Maa wɔn ho yɛɛ huam |
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Pia no bio |
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Ɛsoe pɛ |
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Na ɔkɔe kowui |
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Na biribiara dwoi |
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Neho ayɛ fi paa |
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A ohuu wɔ nkran |
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Mepaa’kyɛw (spoken. Written: Mepawokyew) |
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ɛte sɛn |
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ɛyɛ |
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Wo pɛsɛ wo yɛ deɛn ɛnɛ |
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Mee yɛ credit transfer 10 cedis |
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Me pɛsɛ me tɔ credit 10 cedis |
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Network bɛn |
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Mtn |
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Number bɛn na wo pɛse wo tɔ gu su |
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Aane |
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Yoo medaase |
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Woo tɔ akɔ number bɛn so |
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Mepaa’kyɛw Aane (spoken) |
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Mepaa’kyɛw me pɛsɛ me tɔ mtn credit |
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Mepaa’kyɛw me number no yɛ 059 |
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Medaase |
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Me number yɛ 024 |
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Daabi daabi me pɛ sɛ me tɔ |
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Aane |
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Mepaa’kyɛw ma sesa m’adwene |
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Medaase Nyame nhyira wo |
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Hello good morning. Mepaa’kyew mɛ tɔ credit (Maakye mepaa’kyew mɛ tɔ credit) |
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Me pɛse me tɔ credit na me de gu mtn number so |
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Mehu, mayɛ ewie |
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Medaase mensa aka |
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Mepawokyɛw aane |
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Mepawokyɛw me nso medaase |
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Me credit aka sɛn |
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Akwadaa no to dwom |
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Fa to hɔ |
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Ma me bi |
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Saa ɔkɔdeɛ no |
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Mefri Ghana |
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Akwadaa wei |
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Hwan akonnwa |
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Woanyini kyɛn me |
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Deɛ edi kan |
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Meda wo ase |
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Mame bebree |
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Yɛ wɔ |
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Emu biara |
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Nsuo |
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ɛberɛ nni hɔ/ mmerɛ nni hɔ |
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Nnipa yɛ bad (informal) / Nnipa nnyɛ (formal) |
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Wɔn ti |
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Hwɛ soro |
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Bra ha |
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Me ho yɛ pa ara |
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Me ho yɛ paa |
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Efiri sɛ |
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Esian sɛ |
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Akwaaba |
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Sua adeɛ |
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Gye di |
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Tu amirika |
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Wo se sɛn |
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Wo ewie |
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Kosɛ, manhyɛ da |
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Kafra |
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Aane |
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Daabi |
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Wofiri he |
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Wote borɔfo |
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Woka borɔfo |
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Twerɛ ma me |
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Me nsa aka |
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Mayera |
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Wobɛtumi akyerɛ me hɔ |
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ɛkɔm de me |
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Merehwehwɛ Yaw |
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Boa me |
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Me hia wo mmoa |
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Nante yie |
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ɔkɔtɔ nnwo anomaa |
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Woforo dua pa a na yɛ pia wo |
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Nokware di etuo |
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Dadeɛ bi twa dadeɛ bi mu |
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Ebi didi ebi akyi |
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Tikorɔ nnkɔ agyina |
Twi Multispeaker Audio Transcribed Dataset
Overview
The Twi Multispeaker Audio Transcribed dataset is a collection of speech recordings and their transcriptions in Asante Twi, a widely spoken dialect of the Akan language in Ghana. The dataset is designed for training and evaluating automatic speech recognition (ASR) models and other natural language processing (NLP) applications.
Dataset Details
- Source: The dataset is derived from the Financial Inclusion Speech Dataset, which focuses on financial conversations in Asante Twi.
- Format: The dataset consists of audio recordings (.wav) and corresponding transcriptions (.txt or .csv).
- Speakers: Multiple speakers contribute to the dataset, making it useful for speaker-independent ASR models.
- Domain: Primarily focused on financial and general conversations.
Splits
The dataset is divided as follows:
- Train Set: 90% of the data
- Test Set: 10% of the data
Use Cases
This dataset is useful for:
- Training and evaluating ASR models for Asante Twi.
- Developing Twi language models and NLP applications.
- Linguistic analysis of Asante Twi speech.
Usage
To use this dataset in your Hugging Face project, you can load it as follows:
from datasets import load_dataset
dataset = load_dataset("michsethowusu/twi_multispeaker_audio_transcribed")
License
Refer to the original dataset repository for licensing details: Financial Inclusion Speech Dataset.
Acknowledgments
This dataset is based on the work by Ashesi-Org. Special thanks to contributors who helped in data collection and annotation. I am only making it more accessible for machine learning.
Citation
If you use this dataset in your research or project, please cite it appropriately:
@misc{financialinclusion2022,
author = {Asamoah Owusu, D., Korsah, A., Quartey, B., Nwolley Jnr., S., Sampah, D., Adjepon-Yamoah, D., Omane Boateng, L.},
title = {Financial Inclusion Speech Dataset},
year = {2022},
publisher = {Ashesi University and Nokwary Technologies},
url = {https://github.com/Ashesi-Org/Financial-Inclusion-Speech-Dataset}
}
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