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Datasets:
michsethowusu
/
vagla-speech-text-parallel

Tasks:
Automatic Speech Recognition
Text-to-Speech
Modalities:
Audio
Text
Formats:
parquet
Sub-tasks:
keyword-spotting
Languages:
Vagla
Size:
10K - 100K
Tags:
speech
vagla
ghana
african-languages
low-resource
parallel-corpus
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
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Immanuɛl!”. Hɛssi hina,
AIZAAYA 8. Kɔraawiizi n kpa Asiiria koorizeŋ hum u huma wia. Denii Kɔraawiizi faa kaa ŋɔ mìŋ́ Aizaaya be,
bá di wia la hin niŋ sɛbi la daansɛɛ.”. Too,
u kpogli hia lul biibaal. Kɔraawiizi ŋɔ n be,
Maha Salal Has Baz. N daŋ yir Kɔraawiizi kizeehiŋ Yuria ni Zɛkaria biibaal Jɛbɛrɛkia la n ɛɛ wibii hina,
denii n ni n haaŋ chɔɔ,
“Dau u sɔŋ Maha Salal Has Baz. Mɔɔ ka a bie zum u maa ni u mɛɛ yiraa,
Asiiria koorizeŋ daŋ wa fɔɔ Damaskus ni bolzeŋ Samaria,
“Kpa hɔŋ sɛbi sɔŋ naa dau u ni. Waabuu,
ba ra see ba koori ni Kɔraawiizi la banna hum la nua. Ba daŋ ɔɔ bir binni hɛri di na biinichɔg werge ni bɔbiri limpiriŋ n du de,
di kuugi. bá kpa sidɛɛzi. lauri baa.”. N hiŋra,
Kɔraawiizi. du yaa benii. Ií ra ɔm Kɔraawiizi. Kɔraawiizi ŋɔ n benii ni u banonii,
a waa dɛkkɛ nɛra n na pɛɛzi Kɔraawiizi la banna humaa? Ɛrɛɛ wiarɛ ii gaa pɛɛzi lalaa nyinee wiai? Ií gilɛɛ ra nuu gunla banna ŋɔ,
ba baana danna fuuh werge maa,
ɔnsii werge ɔɔ lau baa. Ka ba daŋ gbuuri baa ga du a bɔbiri hiŋ biini.
ií lɔ laal wile,
dikuubɛɛwiai,
ka gil ii biine chɔgi! Hɛhjaŋa hina,
di ɛɛ u biinɔŋŋii la,
guŋ lɛi u ɛɛ ka lul baa. Newea fɔŋŋa biini lɛi ba wer birgi u biizi. Kɔraawiizi fɔŋŋa biinii ba wer birgi u biizi. Wiŋɔɔlii la n faa du de saŋa maa la,
u pɛɛzi baa,
Yeesu ra ba u be. Jɔɔn kaa nau unna ba,
“Bɛɛ ii ra nyiŋŋei?”. Denii ba ŋɔ “Rabai,
n na ŋɔ ii benii lugɔ baal naa ɛɛ Kɔraawiizi Biibaallɛ.”. Yeesu lugli hiŋra wia. Hɔɔŋ wa pizɛɛ,
di ŋɔ u be,
Kɔraawiizi mɛlikazi ga de ra lii di ra tuu Mìŋ́ Newee Biibaal be,
“Miŋ ŋɔ min nai fig daa zɛɛ la wiarɛ i yardiŋ gɛɛ? I daŋ haa na wizenaa aŋhina,
“Akai.”. “Amɔ,
di bagli u wia,
ka bir tinni ra kaali.”.
di du u be guŋŋ wiizi wa chol. Baala la n faa nuu Jɔɔn wiŋɔzi di hiŋ Yeesu la,
u sɔnnɛ Filip,
“Akai. Min lɛi Kristo la.”. Ba ŋɔ,
yá wer ga bagli tɛɛ banla n hum yaa la. Ɛrɛɛ i ge ra yir i hinnɛi?”. U ŋɔ,
Yeesu ra nyiŋŋi u kaali Galilii hɛri. U ga na baal bɛl,
“Jus hina nɛnanii bɛllɛ ra ba. Waa beela maa dɔɔwɛ.”. Natanɛl ŋɔ,
u ŋɔ,
di wa togni u nyuu lugɔ komma. N faa wɔɔ zummɛ,
“Min lɛi.”. Ba ŋɔ,
haa a dɔgtɛɛ gba ɛɛwɛ,
ka ɛrɛɛ i ra sɔ nɛra Kɔraawiizi nii kennei?”. U ŋɔ,
a hinnɛ keŋ. N himmaa gba faa wɔɔ zummɛ. Ka u wiarɛ n ba di waa sɔ nɛra Kɔraawiizi nii,
JƆƆN 1. Wiŋɔɔlii la n dɔɔ miibɔɔ wia. Lugli la,
wa birgi neweere di ba wa du yaa be. Yaa na u girme,
ka u ɛɛ nabɔma n ni werge maa,
“Yaa na nɛr la n ɛɛ Mɛsia labuu.” Andru n yiruu Kristo la,
“Ií na Kɔraawiizi Pebii la!”. U hiŋra la nuu guŋ,
ka uŋ wa ba ba be,
ba waa wette ba zumuu. U ba u himmaa hɛrii. U himmaa nɛra vɛɛu ta. Ka banla waa maa n sɛɛ u wia di yarduu nɛnnɛ la,
yaa wer zumuu. Jɔɔn Kɔraawiizi Niisɔɔri amaniye lizaa wia. Denii Jus hina baŋhina n du Jɛrusalɛm hum ba Kizeehina baŋhina ni Levi dee ge baŋhina,
i ɛɛ Kɔraawiizi Biibaallɛ. Izrɛl hina koorii hiŋ.”. Yeesu ŋɔ,
mɔɔ ka Filip ba i be di wa yirii la,
Jɔɔn ni u hiŋra banɛɛ kaa chi de. Yeesu ge ga ba kɛra di waa kaali. Jɔɔn nau di ŋɔ u hiŋra be,
amɔ,
di ŋɔ u be,
“Lee i lii zuminnɛi?”. Yeesu ŋɔ,
“Hiŋ faa du fig daa zɛɛ,
Ízrɛl hina wer zumuu.”. U kaa ŋɔ,
di wer zum lugɔ Kɔraawiizi biikpaŋkpannɛ. U wia maa wessoo werge. U kaa dɔɔ wibii ge werge. Jɔɔn faa di u daansɛɛrɛ,
“Ba wa na.”. Ba kpa la. Baŋ gaa hel Yeesu be,
waabuu bagli yaa u wia,
á kaali annii. Ií gil n ŋɔ wibii maa tɛɛ ia. I daŋ ŋmɛnɛ na chiŋ n pillɛɛ,
‘Yaa Dɔɔri ra bau. Ií ra ɛɛ wia á ra deŋli ni a ŋmɛnizi.’ ”. Denii banla n ɛɛ Farisizi la pɛɛzuu,
di ra ŋɔ,
haa kaa ɛɛ Ɛlija,
ka Kɔraawiizi la n humiŋ la feli ŋɔ lugɔ di n wa na u Dumnanii n tuuwe di wa togni nɛr maa ni,
“Kɔraawiizi dɔgtɛɛ Aizaaya faa ŋɔ n wiarɛ u hɔŋ biini,
“N Nabɔma,
“Ba faa ra yiriibuu Jɔɔn bie Saimɔn. Filaŋkeŋ yaa daŋ jaa ra yirii Sifas.”. Yeesu n yir Filip ni Natanɛl wia. Hɔɔŋ wa pizɛɛ,
ba wɔɔ zummɛ. Waabuu faa ta baa,
waabuu nɛr la n niŋ kpa u Dumnanii di du nɛra ni la. Too. N na guŋ. Awia,
Bɛtani la n du Jɔɔdan muu keŋkere,
di du de mɔɔ ka ba haa wa luliŋ la,
bɔr la Jɔɔn n faa ra sɔ nɛra Kɔraawiizi nii labuu ba du. Kɔraawiizi pebii wia. Hɔɔŋ wa pizɛɛ,
lee i dia duɛi?”. U ŋɔ ba be,
di izi ga hiŋ Yeesu di ra la. Yeesu kɛɛ binni di na banna hiŋuu,
hinuu Kɔraawiizi dɔgtɛɛ laa?”. U ŋɔ,
Ba daŋ nuu nɛbɛl n na sii hɛribɔduma biini,
di du de mɔɔ ka ba haa wa luliŋ la,
a hinnɛ keŋ.” U faa dɔɔ Kɔraawiizi hiinaare werge,
Yeesu binni Natanɛl ka ŋɔ,
di ŋɔ u wia bɔkpega ni,
“Aa! Boljaga naa! Maa sɛɛwɛ nɛnanii maa n niŋ lii a Nazarɛf.”. Filip ŋɔ,
“Di haa Kristo ɛɛwɛ,
“Ka anɛɛ hinnɛi? Bagli yaa i wia,
Kristo la Kɔraawiizi n faa ŋɔ lugɔ u daŋ ŋmɛnɛ ba la wiarɛ u ŋɔ. U kpogluu la ú ga zum Yeesu. Yeesu n wa na Saimɔn la,
“Yaa na nɛr la Moosis ni dɔgtɛɛzi n faa sɛbi u wia Kɔraawiizi Hɔŋ biini labuu. Waabuu Nazarɛf hinuu Joosɛf biibaal Yeesu.”. Natanɛl ŋɔ,
“Ií na Kɔraawiizi Pebii. Waabuu niŋ waa lizi durnya wichɔgaa di ra ha la. Miŋ faa ŋɔ baal bɛl wia lugɔ mìŋ́ feluu ba,
bɛl sɔnnɛ faa Andru. U kpɛɛma ge sɔnnɛ faa Saimɔn Pitta. U la ga nyiŋŋi u kpɛɛma la di ŋɔ u be,
di ŋɔ nɛra la n du de la be,
ii siwee waa u ni chaanɛɛ. Mìŋ́ ballɛ mɔɔ ka u ge ba. Ka waabuu ɛɛ nabɔma n ni werge maa. Maa dɛg min niŋ lizi u gawa gba tɛu.”. Ba maa n na ŋɔ wia naa,
“Ií ba wa na.”. Ba la ga na u duuribɔr a chɔɔzi ni,
“Ka anɛɛ hinnɛi? Hinuu Ɛlijaa?”. U ŋɔ,
“Izi ra hiŋiŋ.”. A Filip ni Andru ni Pitta maa bolle faa Bɛtseida. Filip ge ga na baal bɛl banna yir Natanɛl,
ka waabuu ɛɛ nabɔma n ni werge maa,
n naibuu de.”. Natanɛl ŋɔ,
“Niirɛ miŋwaa kpa ra sɔ baa. Ka baal bɛl du ii be,
Wiŋɔɔlii bɛllɛ faa du de. A Wiŋɔɔlii faa du Kɔraawiizi benii,
“N na Kɔraawiizi Dumnanii n lii chiŋ tuu,
“Nɛr la miŋ faa ŋɔ u wia lugɔ mìŋ́ feluu ba,
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Vagla Speech-Text Parallel Dataset

Dataset Description

This dataset contains 48605 parallel speech-text pairs for Vagla, 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: Vagla - vag
  • Task: Speech Recognition, Text-to-Speech
  • Size: 48605 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 Vagla speech to text
  • Text-to-Speech (TTS): Use parallel data for TTS model development
  • Keyword Spotting: Identify specific Vagla words in audio
  • Phonetic Analysis: Study Vagla 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 48605 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 Vagla, supporting:

  • Language technology development for underrepresented languages
  • Educational resources for Vagla 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).

Acknowledgments

  • Audio processing and alignment performed using MMS-300M-1130 Forced Aligner
  • The original audio is produced by The Ghana Institute of Linguistics, Literacy and Bible Translation in partnership with Davar Partners

Citation Information

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

@dataset{vagla_words_parallel_2025,
  title={Vagla Words Speech-Text Parallel Dataset},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/[your-username]/vagla-speech-text-parallel}}
}

Contact

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

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