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Esali kwo déem maa ye Nagayi.
Rewu kwo déem maa ye Serugi.
Abam nam yé gare á boŋe á pa á tete we,
Judah kwo déem maa ye Josɛf.
” O ma leiri-ba o we,
Adi kwo déem maa ye Kosam.
” O ma leiri-ba o we,
Lusania de maa ye Abilena yuu tu.
Nagayi kwo déem maa ye Maata.
Kayinam kwo déem maa ye Arapazade.
Natan kwo déem maa ye David.
Resa kwo déem maa ye Zerubabel.
Sɛfi kwo déem maa ye Adam.
Arawu kwo déem maa ye Pɛlega.
” O ma leiri-ba o we,
Eliakim kwo déem maa ye Melea.
Kayinam kwo déem maa ye Enosi.
Ba maa we,
Zekaria bu Jɔn bere Wɛ kwora Matiyu :
Obeda kwo déem maa ye Bowaza.
Melaki kwo déem maa ye Janayi.
” Nɔɔna déem maa jege teena Wɛ tei ne lanyerane,
Eliazera kwo déem maa ye Jorim.
- -Ko déem na kɛ ye Tiberia ye logo kom Pa-faro wom ko maŋe de bena fuga-yanu to mo Wɛ kwora déem tu Zekaria bu Jɔn tei.
Kwora laam ma ŋɔɔne Wɛ telaao ne we:
Matatia kwo déem maa ye Amosi.
Hezeron kwo déem maa ye Pɛreza.
Yezu na jwoŋi sɛɛm konto to,
Faŋa faŋa to o déem tage o we:
Joanan kwo déem maa ye Resa.
Aminadabi kwo déem maa ye Adimina.
se Wɛ laam wó yage ba wɛleera o ma chɛ-ba.
Josɛf kwo déem maa ye Matatia.
Mahalaleli kwo déem maa ye Kayinam.
Izaak kwo déem maa ye Abaraham.
Elimadam kwo déem maa ye Ara.
Sɛm kwo déem maa ye Noah.
Enosi kwo déem maa ye Sɛfi.
Ebera kwo déem maa ye Sela.
Melea kwo déem maa ye Menna.
Noah kwo déem maa ye Lameke.
Matata kwo déem maa ye Natan.
Jorim kwo déem maa ye Matati.
Arani kwo déem maa ye Hezeron.
nɔn-kɔgɔ kom ma bwei‑o ba we,
Menna kwo déem maa ye Matata.
Pɛlega kwo déem maa ye Ebera.
ba jege se ba leiri ba wo Wɛ ŋwaane se ba daare ba jwoŋi sɛɛm,
Matatia kwo déem maa ye Semeini.
Herodi maa ye Galili yuu tu,
“Dé nam wó ke bɛ mo?
David kwo déem maa ye Jesse.
Bowaza kwo déem maa ye Salimon.
Maake :
Konto to,
Josua kwo déem maa ye Eliazera.
” Ba maama na boŋe konto to,
ye o zembaaro Filipi de maa ye Yituria de Terakonia teene dem yuu tu.
naa á gane-ba á jwoŋi.
O ma zaŋe o tole Jɔdan buga ni teene dem maama,
Ba ma bwei‑o ba we,
o déem wo kagoa yuu ne mo.
Enoki kwo déem maa ye Jaredi.
Ko daare Anas de Kayafa déem maa ye Wɛ diga kam kaanem yuu tiina.
A ta wó che balo na toŋe lwarem dwi maama to se ba yé zo Baŋa Wɛ tete teo kom wone.
ye a ma a kwaga a ya lwarem kikia maama.
Amo tete sɔŋɔ kom ne a wó taa toŋe a tɔge chega kam.
Wolo maama ná yɔɔre o tɔge chwoŋa,
Amo wó leeni a tei nmo Baŋa Wɛ yere dem.
Zezeŋa maama wone amo wó chɔge balo na toŋe puseŋa tega kam ne to a yage.
Ko nam ná ye wo-yɔɔro dwi maama,
Amo wó leeni a seini n swono kom de n na bore chega bora tei to.
Nmo nam bá‑n ba n woli-ne lela na?
a bá ke-te a boboŋa ne.
Ko ta ná ye balo na bere ba tete kamunim to,
Pɛ wom na pwooli se o ke kolo to David déem pane leiŋa kanto.
Vwa-fɔra de nam bá taa twɛ-ne.
Wolo maama ná bae o dwoŋi bebara,
ye a ba me de balo na ke-ya to.
A ta wó pwɛ a tete de balo boboŋa na ywori to,
kotu wó taa toŋe o pa-ne.
A maa chuli tusim kikia,
Amo bá sɛ se kampin-nyena taa zoore a sɔŋɔ kom ne.
A ta wó taa cho a tete mo se a yé tusi chwoŋa kam wone.
Amo yiga nam wó taa wo balo na toŋe chega to wone mo.
A lage a pa-ba chwoŋa mo se ba taa zoore amo tei ne.
amo bá pa-ba pwola se ba toŋe.
amo wó chɔge kotu a yage.
-“Nam ta de Isarele tiina bam we:
se kotu yé chɔge jei selo á na zoore ye amo wo á tetare ne to.
Kaanem tu wom wó pa se kaane wom zege da,
naa o daŋe talwoŋ-ywoŋa de baŋa ne.
Wolo na tusi to wó ŋwe-ko o pa kaanem tu wom mo.
se á pa wolo maama na jege digiru Wɛ chullu tem seeni to nwoŋi o vo dáa,
á wó ke konto mo.
se kaanem tu wom kwei-ka o ke kaanem se o le konto tu tusim dem Wɛ yiga ne o yage.
nenɛɛne o ná ywori o baro kwaga ne,
naa o ná wo toŋe vwa de o baro wom.
“Ta n bere Isarele tiina bam we:
se n puga kam de foose.
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Kasem Speech-Text Parallel Dataset

Dataset Description

This dataset contains 75990 parallel speech-text pairs for Kasem, a language spoken primarily in Ghana. The dataset consists of audio recordings paired with their corresponding text transcriptions, making it suitable for automatic speech recognition (ASR) and text-to-speech (TTS) tasks.

Dataset Summary

  • Language: Kasem - xsm
  • Task: Speech Recognition, Text-to-Speech
  • Size: 75990 audio files > 1KB (small/corrupted files filtered out)
  • Format: WAV audio files with corresponding text files
  • Modalities: Audio + Text

Supported Tasks

  • Automatic Speech Recognition (ASR): Train models to convert Kasem speech to text
  • Text-to-Speech (TTS): Use parallel data for TTS model development
  • Keyword Spotting: Identify specific Kasem words in audio
  • Phonetic Analysis: Study Kasem pronunciation patterns

Dataset Structure

Data Fields

  • audio: Audio file in WAV format
  • text: Corresponding text transcription from paired text file

Data Splits

The dataset contains a single training split with 75990 filtered audio-text pairs.

Dataset Creation

Source Data

The audio data has been sourced ethically from consenting contributors. To protect the privacy of the original authors and speakers, specific source information cannot be shared publicly.

Data Processing

  1. Audio files and corresponding text files were collected from organized folder structure
  2. Text content was read from separate .txt files with matching filenames
  3. Files smaller than 1KB were filtered out to ensure audio quality
  4. Empty text files were excluded from the dataset
  5. Audio was processed using the MMS-300M-1130 Forced Aligner tool for alignment and quality assurance

Annotations

Text annotations are stored in separate text files with matching filenames to the audio files, representing the spoken content in each audio recording.

Considerations for Using the Data

Social Impact of Dataset

This dataset contributes to the preservation and digital representation of Kasem, supporting:

  • Language technology development for underrepresented languages
  • Educational resources for Kasem language learning
  • Cultural preservation through digital archives

Discussion of Biases

  • The dataset may reflect the pronunciation patterns and dialects of specific regions or speakers
  • Audio quality and recording conditions may vary across samples
  • The vocabulary is limited to the words present in the collected samples

Other Known Limitations

  • Limited vocabulary scope (word-level rather than sentence-level)
  • Potential audio quality variations
  • Regional dialect representation may be uneven

Additional Information

Licensing Information

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Citation Information

If you use this dataset in your research, please cite:

@dataset{kasem_words_parallel_2025,
  title={Kasem Words Speech-Text Parallel Dataset},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/[your-username]/kasem-speech-text-parallel}}
}

Acknowledgments

  • Audio processing and alignment performed using MMS-300M-1130 Forced Aligner
  • Thanks to all contributors who provided audio samples while maintaining privacy protection

Contact

For questions or concerns about this dataset, please open an issue in the dataset repository.

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