SilencioNetwork/yoruba-speech

Yoruba Spontaneous Speech — Silencio

Spontaneous Yoruba from 106 distinct speakers — one clip each. Seven self-reported accent varieties across Lagos, Ibadan, Ijebu and Nigerian Standard. Audio and speaker metadata only: no transcripts, by design — human transcription is available on demand.

Hours	2.9
Clips	106
Speakers	106
Countries	1
Speaker origin regions	7
L1 speakers of the recorded language	102 of 106 (102 clips)
Audio	48 kHz stereo WAV
Mean clip length	98.4 s
Transcripts	Human-validated transcription available on demand
Licence	cc-by-nc-4.0

This release ships audio and speaker metadata only — it does not include transcripts. That is deliberate, not an omission: what it carries is speaker-level labelling on real-world spontaneous audio, which supports accent and dialect classification, speaker identification, age and gender estimation, robustness auditing and self-supervised pretraining — none of which need a transcript. Human-validated transcription with word-level alignment is available on demand over this sample or a larger subset of the same language.

Contributors answer an open prompt in their own words, on their own devices, in their own environments. Every clip is spontaneous speech, not read from a script.

Load it

from datasets import load_dataset

ds = load_dataset("SilencioNetwork/yoruba-speech", split="train")
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	106	100.0%

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

Speaker origin	Speakers	%
Nigeria - Lagos Yoruba	49	46.2%
Nigeria - Ibadan	37	34.9%
Nigeria - Nigerian Standard	13	12.3%
Nigeria - Lagos	2	1.9%
Nigeria - Ijebu	2	1.9%
Nigeria - Pidgin-influenced	2	1.9%
United States - General American	1	0.9%

Demographics

Gender	Speakers	%
male	78	73.6%
female	28	26.4%

Age band	Speakers	%
25-34	59	55.7%
35-44	21	19.8%
18-24	18	17.0%
45-59	6	5.7%
60+	2	1.9%

Recording conditions

Device	Clips	%
Mobile	94	88.7%
Desktop	12	11.3%

Splits

Single split, test, 106 rows. No train/dev/test partition is provided: at this scale a partition would leave each part too small to be meaningful. Speaker identifiers are stable, so a speaker-disjoint split can be constructed at load time.

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	106 distinct
language	Language of the recording	constant: Yoruba
transcript	Empty in this release — this sample ships audio and speaker metadata only	Human-validated transcription available on demand
transcript_type	Provenance of the transcript	constant: none — see transcript above
gender	Self-reported	female, male
country	Speaker's country	constant: Nigeria
mother_tongue	Speaker's self-reported first language	English, Nigerian Pidgin, 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	Android, Linux, Windows, macOS
device	Recording device class	Desktop, Mobile
duration	Seconds	105 distinct
script_type	Elicitation style	constant: free_speech
age_band	Self-reported age, banded	18-24, 25-34, 35-44, 45-59, 60+
native_speaker	True where mother_tongue matches the recorded language	2 distinct
proficiency	Speaker's self-declared proficiency in the recorded language	basic, 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	2.90 h	106	Spontaneous, dialect-labelled, one clip per speaker	CC BY-NC 4.0

This does not compete on volume and does not try to. NaijaVoices is three orders of magnitude larger. What it adds is a different axis: 106 independent speakers of spontaneous Yoruba, each contributing exactly one clip, with accent variety, declared language profile, age band, gender, device and recording environment attached. Speaker count is the field's systematic blind spot — of the resources above, only NaijaVoices states one, and only in aggregate across three languages.

Also relevant: facebook/mms-1b-all ships a yor adapter. Whisper has no Yoruba.

What this is for

This release has no transcripts, and it is not tagged as an ASR dataset. What it carries is speaker-level labelling on real-world spontaneous audio, which supports a range of work that needs no transcript at all:

• Accent and dialect classification. 7 self-reported origin varieties across 106 speakers, labelled per clip.
• Speaker identification and verification. 106 labelled speakers, one clip each, so any split you construct is speaker-disjoint by construction and there is no speaker leakage to control for.
• Age and gender estimation. Self-reported birth year and gender on every speaker.
• Robustness and fairness auditing. Device, OS and recording environment per clip, so performance can be broken down by capture condition as well as by speaker attribute.
• Self-supervised pretraining. Unlabelled real-world speech is the input these methods want; the absence of transcripts is not a limitation here.
• Not an ASR benchmark. No reference text ships with this release.

Human-validated transcription with word-level forced alignment is available on demand over this sample, over a larger subset of the same language, or as commissioned collection. The format is shipped in Kenyan Swahili, Cebuano and Tagalog.

Speaker profile

Every clip is a different contributor — one clip per speaker, no exceptions. That is the design choice this release is built around: for estimating how a model behaves across a population of speakers, the binding constraint on precision is the number of independent speakers, not the number of hours.

Accent variety is self-reported at enrolment and is not the same thing as country of origin; where a speaker reports a variety that does not match their origin, it is left as recorded rather than corrected. native_speaker and proficiency are derived from each contributor's full declared language profile rather than from a single primary-language field.

Limitations

• No transcripts. Audio and speaker metadata only. This is not an ASR benchmark. Human transcription with word-level alignment is available on demand.

• Sample scale. 106 clips, 106 speakers, 2.90 hours. An evaluation and probing set, not a training corpus. Silencio holds 5,971 hours of Yoruba off the shelf.

• One clip per speaker. Excellent for speaker diversity, but no within-speaker variation and no speaker-adaptive use is possible.

• Gender imbalance. 78 of 106 speakers are male. Balanced cohorts are available through the collection programme.

• Lagos- and Ibadan-weighted. Those two varieties are 86 of 106 speakers; northern and diaspora Yoruba are barely represented.

• Accent labels are self-reported and not independently assessed.

• No acoustic annotation. Background-noise class and SNR are not annotated. Available for commissioned collection.

• Mixed audio format. Source audio is shipped untouched at its captured sample rate and channel count — see the spec table. Resample and downmix before batching.

• Pseudonymous speakers. speaker_id values are pseudonyms, coherent within this dataset, deliberately not linkable to speakers in other Silencio releases.

• Repeated transcript strings. 106 clips fall into 1 groups where the same transcript string appears for more than one speaker. 0 of 106 transcripts are unique. Noted so that per-clip statistics are computed with this in mind.

Provenance and consent

Every recording is contributed by an opted-in participant through the Silencio app, 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.

License

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

Attribution string: Silencio Network, Yoruba Spontaneous Speech, 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: info@silencio.network

Citation

@misc{silencio_yoruba_2026,
  title  = {Yoruba Spontaneous Speech — Silencio},
  author = {Silencio Network},
  year   = {2026},
  url    = {https://huggingface.co/datasets/SilencioNetwork/yoruba-speech}
}

Yoruba off the shelf — 5,971 hours

This release is a 2.9-hour sample. The off-the-shelf Yoruba inventory behind it is 5,971 hours from 16,831 contributors — already recorded, with metadata, licensable today. Direct count over the recording archive as of 9 August 2026.

Hours recorded	5,971
Recordings	638,325
Contributors	16,831

Alongside 3,580 hours of Hausa and 2,723 hours of Igbo from the same contributor network, and 7,872 hours of Nigerian-origin English — see Nigerian English.

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

Silencio corpus and collection network

Two distinct figures, because they answer different questions.

Recorded and available off the shelf — audio already collected, with metadata, licensable today:

Hours recorded	127,793
Recordings	9,392,870
Contributors who recorded	222,145
Languages	156
Countries and territories of origin	216

Contributor network available for commissioned collection — registered, consented contributors who can be activated for a specific brief. These are not active contributors to the corpus above; they are the pool it is drawn from and extended through:

Registered contributors	2,000,000+
Countries	180+
Languages reachable	250+

For volume licensing, human-validated transcription over a larger subset, or commissioned collection: info@silencio.network