SilencioNetwork/yoruba-speech-transcribed

Yoruba Spontaneous Speech, Transcribed — Silencio

Spontaneous Yoruba with human-validated, fully tone-marked transcription and word-level alignment. 49 clips from 30 distinct speakers, 29 of them from Nigeria, across Ibadan, Lagos, Oyo and Nigerian Standard varieties. Transcripts keep the tonal diacritics and under-dots, and keep the Yoruba–English code-switching as it was spoken.

Hours	0.53
Clips	49
Speakers	30
Countries	2
Speaker origin regions	7
Native Yoruba speakers (declared)	28 of 30 (46 clips)
Audio	48 kHz stereo WAV
Mean clip length	38.8 s
Transcripts	human_validated: 49
Aligned words	4,694
Licence	cc-by-nc-4.0

All 49 clips carry a human-validated transcription.

Recordings are unscripted answers to open prompts, captured on contributors' own devices in their own environments. This is spontaneous speech, not read sentences.

This is a sample, not the catalogue. It is drawn to show format, audio quality and metadata, and its speaker mix does not represent what Silencio holds. The off-the-shelf catalogue behind it is far more diverse across regions, age groups, accents and recording conditions. Men are 77% of the speakers in this sample. Across the catalogue the gender split is roughly 40% female to 60% male, with men in the majority in most languages. Licensed subsets can be drawn to a specified gender, age or regional balance.

Load it

from datasets import load_dataset

ds = load_dataset("SilencioNetwork/yoruba-speech-transcribed", split="test")
print(ds[0]["transcript"], ds[0]["dialect"], ds[0]["country"])

# datasets v4 returns a torchcodec AudioDecoder:
s = ds[0]["audio"].get_all_samples()
audio, sr = s.data, s.sample_rate

Requires pip install "datasets>=4.0" and FFmpeg ≥ 4.

Speaker and recording metadata

By country

Country	Speakers	%
Nigeria	29	96.7%
United States	1	3.3%

Speaker origin / self-reported variety: this is the speaker's own background, not a dialect classification of the recorded language

Speaker origin	Speakers	%
Nigeria - Ibadan	14	46.7%
Nigeria - Lagos Yoruba	9	30.0%
Nigeria - Nigerian Standard	3	10.0%
Nigerian Yoruba - Lagos Yoruba	1	3.3%
Nigerian Yoruba - Oyo (Standard)	1	3.3%
Saudi Arabia - Riyadh	1	3.3%
United Kingdom - Scouse (Liverpool)	1	3.3%

Demographics

Gender	Speakers	%
male	23	76.7%
female	7	23.3%

Age band	Speakers	%
25-34	14	46.7%
18-24	9	30.0%
35-44	7	23.3%

Recording conditions

Device	Clips	%
Mobile	46	93.9%
Desktop	3	6.1%

Splits

Single split, test, 49 rows. No train/dev/test partition is provided: at this scale a partition would leave each part too small to be meaningful.

Speakers contribute more than one clip. 16 speakers have one clip, 10 have two, 3 have three and 1 has four. Any split you build must be speaker-disjoint, which means grouping on speaker_id rather than splitting by row, or the same voice will appear on both sides.

Fields

Column	Description	Values in this release
audio	Audio payload. Stored at source rate; see the spec table for the exact distribution	48 kHz stereo WAV
speaker_id	Pseudonymous speaker identifier. Coherent within this dataset; deliberately not linkable to other Silencio releases	30 distinct
language	Language of the recording	constant: Yoruba
transcript	Human-validated transcription of the recording, tone-marked	49 distinct
transcript_type	Provenance of the transcript	constant: human_validated
gender	Self-reported	female, male
country	Speaker's country	Nigeria, United States
mother_tongue	Speaker's self-reported first language	Arabic, English, Yoruba
dialect	Self-reported speaker origin / regional variety. This is the speaker's own background, NOT a dialect classification of the recorded language	7 distinct
os	Operating system of the recording device	Linux, Windows, macOS
device	Recording device class	Desktop, Mobile
duration	Seconds	49 distinct
script_type	Elicitation style	constant: free_speech
words	Word-level forced alignment: text, normalised text, start and end in seconds	4,694 entries across 49 clips
n_words	Number of aligned tokens in this clip	40 distinct
age_band	Self-reported age, banded	18-24, 25-34, 35-44
native_speaker	True where the speaker declares Yoruba as mother tongue or at native level in their language profile	2 distinct
aligner	Method used to produce the word timings	constant: forced-alignment
transcript_model	How the transcript text was produced	constant: human
proficiency	Speaker's self-declared proficiency in Yoruba	fluent, native

Related Yoruba speech resources

Yoruba has roughly 45 million speakers and is comparatively well served on the Hub, but almost entirely by read speech, and almost none of it states a speaker count.

Resource	Scale	Speakers	Type	Licence
naijavoices/naijavoices-dataset	1,800 h (yo+ha+ig)	5,000+ combined	Read, curated prompts; gated	CC BY-NC-SA 4.0
Professor/yoruba-speech-data	~1,039 h	not stated	Read, pooled from several corpora	unclear
google/WaxalNLP (yor_tts)	part of Waxal	not stated	Read	CC BY-SA 4.0
This dataset	0.53 h	30	Spontaneous, human-validated tone-marked transcription, word-level alignment	CC BY-NC 4.0

Use this one for evaluation, not training. It is tiny by hours. What it offers is reference text for spontaneous Yoruba: the hesitations, repairs, restarts and English borrowings that read-speech corpora leave out. Read-speech benchmarks overstate how well a model handles Yoruba as people actually speak it.

Also relevant: facebook/mms-1b-all ships a yor adapter. Whisper lists Yoruba among its languages but was trained on very little Yoruba audio.

Also from Silencio. Kenyan Swahili, Cebuano and Tagalog / Filipino are published under the same protocol.

Transcription and alignment

Two provenances, kept separate because they carry different confidence.

Text: human-validated. Every transcript was produced and checked by a native-speaker human reviewer against the audio. Recorded per clip in transcript_model.

Orthography. Transcripts use standard Yoruba orthography with tone marks (acute, grave) and under-dotted ẹ, ọ, ṣ. Around three-quarters of words carry a diacritic. English words used in speech ("WiFi", "password", "email", "connect") are written as spoken, not translated, so code-switching is visible in the reference. Text is Unicode; normalise to NFC before computing WER, because the same character can be encoded as precomposed or combining.

Timings: machine. Word-level start and end times come from forced alignment, recorded per clip in aligner. The words column holds one entry per token with text, normalized_text, start and end in seconds:

ds = load_dataset("SilencioNetwork/yoruba-speech-transcribed", split="test")
for w in ds[0]["words"][:5]:
    print(f"{w['start']:6.2f}-{w['end']:6.2f}  {w['text']}")

Speaker proficiency and dialect

Proficiency comes from each contributor's own declared language profile rather than a single primary-language field. Four speakers give a mother tongue other than Yoruba (three English, one Arabic) but declare native Yoruba; they are counted as native speakers. Two speakers declare Yoruba at fluent rather than native level and are marked native_speaker = False. Filter on native_speaker, or on proficiency and dialect for finer control.

Dialect is self-reported at enrolment and is left as recorded rather than corrected. Two labels (Saudi Arabia - Riyadh, United Kingdom - Scouse (Liverpool)) describe a speaker's own background rather than the Yoruba spoken in the clip. Nigeria - Lagos Yoruba and Nigerian Yoruba - Lagos Yoruba come from two versions of the enrolment form and can be merged.

Recording conditions

Contributors record on their own devices, wherever they are. Self-reported setting for the 49 clips in this release, recorded at capture:

Setting	Clips	%
Home	43	87.8%
Outdoor	4	8.2%
Office	2	4.1%

The setting is recorded at capture but is not shipped as a column, so the schema stays identical to the Swahili, Cebuano and Tagalog releases.

Audio quality screen. Every clip was checked for level, clipping, silence and signal-to-noise before release. All 49 passed and none were substituted. The median SNR estimate is 33 dB and the lowest is 24 dB. Audio is shipped at source quality and has not been resampled or denoised.

What this is useful for

• Spontaneous-speech ASR evaluation for Yoruba, with human-validated reference text that keeps the tone marks, in a language whose public data is overwhelmingly read speech.
• Tone and diacritic restoration. Fully tone-marked reference text lined up with audio.
• Yoruba–English code-switching. Borrowings are kept in the reference as spoken.
• Forced-alignment and VAD work. Per-token timings across the whole release.

Limitations

• Small. 0.53 hours from 30 speakers is a sample for evaluation and for checking the format, not a training set.
• Not one clip per speaker. See Splits: split by speaker_id.
• Gender and age skew. 23 of 30 speakers are male, and no speaker is over 44.
• One clip has a silent right channel. The content is intact on the left channel; downmix to mono, or read the left channel only, before scoring.

Provenance and consent

Every recording is contributed by an opted-in participant through the Silencio platform, under a consent record covering AI/ML training use. Contributors can request deletion, and deletion propagates to downstream releases. Full provenance documentation is available to licensees.

Speaker and recording identifiers in this release are pseudonymised afresh, so they do not match Silencio's internal IDs or those in any other Silencio release.

License

cc-by-nc-4.0: free for research and non-commercial use with attribution.

Attribution string: Silencio Network, Yoruba Spontaneous Speech, Transcribed, 2026. CC BY-NC 4.0.

Non-commercial covers research, evaluation and publication. Benchmarking a commercial product model against this data is a commercial use and needs a licence. Ask; it is usually granted for evaluation. Model weights trained on this sample inherit the non-commercial restriction. Contributors may withdraw consent; withdrawal propagates to subsequent releases but places no retroactive obligation on an existing licensee.

Commercial licensing, including terms for models trained on this data: silencio.network/contact

Citation

@misc{silencio_yoruba_transcribed_2026,
  title  = {Yoruba Spontaneous Speech, Transcribed — Silencio},
  author = {Silencio Network},
  year   = {2026},
  url    = {https://huggingface.co/datasets/SilencioNetwork/yoruba-speech-transcribed}
}

Yoruba off the shelf

This release is a 0.53-hour sample. The Yoruba catalogue behind it sits in Silencio's Tier 1 production band, 10,000 hours and above. Silencio publishes catalogue depth in bands rather than exact hour counts. Bands reflect the September 2026 off-the-shelf catalogue and move as collection continues.

Hausa and Igbo sit in the Tier 2 band, 5,000 to 10,000 hours each, and Nigeria is the largest single origin in the English catalogue.

Available by speech style, accent variety, demographic profile and recording condition. Human-validated transcription with word-level alignment, as shipped in this sample, is available over any subset.

Silencio: the catalogue behind this sample

This listing is a small fraction of what Silencio provides. Silencio specialises in underrepresented languages, accents and niche domains, delivered at scale. It draws on the largest global community of speech contributors and holds the largest off-the-shelf catalogue of niche-language speech available.

Recorded and available off the shelf. Audio already collected, with metadata, licensable today. Catalogue position as of September 2026:

Hours, off the shelf	around 500,000
Countries of contributor origin	around 180
Catalogue refresh	Weekly at record level, quarterly at catalogue level

Contributor community available on demand. Registered, consented contributors who can be activated for a specific brief:

Contributors available on demand	around 2,500,000
Countries	180+
Languages that can be collected	around 350

Anything not off the shelf can be sourced through the community. A language, an accent or dialect region, a demographic, a recording condition, a speech style or a specialist domain can be collected to a client's brief. Deep coverage across Africa, South-East Asia, South Asia and the Middle East.

Human-validated transcription is available for every language, scoped to each client's needs: script and orthography conventions, normalisation rules, word- or segment-level alignment, speaker labelling and turnaround. Every delivered clip is checked by a native-speaker reviewer. Published samples with human-validated transcripts: Kenyan Swahili, Cebuano, Tagalog / Filipino, Yoruba, Hausa and Amharic.

Proprietary and first-party. Every recording is collected directly by Silencio from consenting contributors. Nothing is scraped, and these recordings are not available in any other dataset on the internet.

Ethical sourcing, with provenance records. Every recording carries a consent record covering AI/ML training use, and contributor-level provenance documentation is available to licensees, including for EU AI Act training-data summaries. Contributors can withdraw consent, and withdrawal propagates to subsequent releases.

More at silencio.network.

For volume licensing, bespoke transcription or commissioned collection: silencio.network/contact