SilencioNetwork/complete-voiceai-speech-dataset

Silencio Voice AI Sample Dataset

Speaker-attributed spontaneous speech. 363 labelled contributors across 111 self-reported origin varieties, recorded on their own devices in their own environments. Every clip carries origin region / variety, mother tongue, gender, device, OS, recording environment.

Hours	16.05
Clips	1,305
Speakers	363
Origin varieties	111
Languages	21
Configs	44
Speaker metadata	origin region / variety, mother tongue, gender, device, OS, recording environment
Audio	48 kHz stereo WAV, as captured
Transcripts	ASR-generated, 64% coverage, not human-reviewed — human-validated transcription available on demand
Licence	cc-by-nc-4.0

Transcripts are ASR-generated and cover 64% of clips. They have not been human-reviewed.

Human-validated transcription is available on demand for this data. Word-level forced alignment included, as shipped in cebuano-speech and tagalog-filipino-speech. Available for this sample, for a larger subset of the same language, or for commissioned collection — silencio.network/contact

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 86% 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.

What this is for

Transcripts here are unreviewed ASR output — usable as pseudo-labels for semi-supervised work, but not a human-validated ASR training set. What it does carry is speaker-level labels on real-world audio, which supports a range of work needing no transcripts at all:

• Accent and dialect classification. 111 self-reported origin varieties across 363 speakers, labelled per clip. No transcripts needed.
• Spoken language identification. 21 languages in one release, recorded through the same app under the same conditions, so language is not confounded with recording setup.
• Speaker identification and verification. 363 labelled speakers. Speaker-disjoint splits can be constructed directly from speaker_id.
• Age and gender estimation. Self-reported birth year and gender on every speaker.
• Robustness and fairness auditing. Device, operating system and recording environment are recorded per clip, so error rates 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.

If you need transcripts, they are available on demand over this data — see below.

Load it

from datasets import load_dataset

ds = load_dataset("SilencioNetwork/complete-voiceai-speech-dataset", "amharic_ethiopia", split="train")
print(ds[0])

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

Configs: amharic_ethiopia, english_algeria, english_australia, english_belarus, english_china, english_egypt, english_french_speaking, english_haiti, english_ireland, english_jamaica, english_kenya, english_mandarin, english_medical, english_nigeria, english_pakistan, english_poland, english_russia, english_south_africa, english_uganda, english_ukraine, english_united_kingdom, english_united_states, french_canada, french_global, german_germany, global_medical, gujarati_india, hausa_nigeria, igbo_nigeria, italian_italy, japanese_japan, kannada_india, malayalam_india, mandarin_chinese_china, marathi_india, odia_india, portuguese_brazil, russian_russia, spanish_mexico, spanish_mx_mexico, telugu_india, turkish_turkey, vietnamese_vietnam, yoruba_nigeria

Speaker and recording metadata

Speaker origin / variety	Speakers	%
Nigeria - Nigerian Standard	17	9.3%
United States - Californian	16	8.8%
Canada (Québec) - Montréalais	14	7.7%
Germany - Hochdeutsch (Standard)	13	7.1%
Mainland China - Beijing	12	6.6%
unknown	11	6.0%
France - Lyonnais	10	5.5%
Nigeria - Ibadan	9	4.9%
Nigeria - Lagos Yoruba	9	4.9%
Nigeria - Pidgin-influenced	8	4.4%
Nigeria - Kano	8	4.4%
United States - General American	8	4.4%
Ethiopia - Addis Ababa	7	3.8%
France - Parisian	6	3.3%
France - Normand	6	3.3%
United States - Midwestern	6	3.3%
Mexico - Chilango (CDMX)	6	3.3%
United States - New York City	6	3.3%
Nigeria - Katsina	5	2.7%
Mainland China - Shandong	5	2.7%

Gender	Speakers	%
male	313	86.2%
female	47	12.9%
prefer_not_to_say	2	0.6%
non_binary	1	0.3%

Ethnicity (self-reported)	Speakers	%
White	134	36.9%
Black or African American	102	28.1%
Asian	70	19.3%
Other	18	5.0%
Hispanic or Latino	18	5.0%
Middle Eastern	10	2.8%
Mixed Race	7	1.9%
Native American	2	0.6%
Prefer not to say	2	0.6%

Device	Clips	%
Mobile	946	72.5%
Desktop	359	27.5%

Environment	Clips	%
home	1,031	79.0%
office	120	9.2%
outdoor	115	8.8%
public_transport	12	0.9%
cafe	9	0.7%
other	8	0.6%
library	5	0.4%
restaurant	3	0.2%
gym	1	0.1%
shopping_mall	1	0.1%

Ethnicity is self-reported by the contributor at enrolment, using a fixed category list; it is not inferred from the audio and it is not a label of the speech. It is included because accent and speaker-attribute fairness work needs it, and it is collected under the same consent as the rest of the metadata.

Human-validated transcription — available on demand

This release is not human-transcribed. Silencio provides human transcription with word-level forced alignment on demand, over this sample or over a larger subset of the same language, and as part of commissioned collection.

These releases show exactly what that deliverable looks like — human transcript text, machine forced alignment, per-token start and end times:

• SilencioNetwork/cebuano-speech — Cebuano (Bisaya) Spontaneous Speech
• SilencioNetwork/tagalog-filipino-speech — Tagalog / Filipino Spontaneous Speech

To request transcription over this data or any other Silencio subset: silencio.network/contact

Limitations

• Sample scale. 1,305 clips, 363 speakers, 16.05 hours. This is a demonstration sample, not a training corpus.
• Partial, unreviewed transcripts. 64% coverage, ASR-generated.
• Gender imbalance. 313 of 363 speakers are male. Do not use this release for gender-comparative work. Balanced cohorts are available through the collection programme.
• No acoustic annotation. Recording environment, background-noise class and SNR are not annotated in this release. Available for commissioned collection.
• Audio is 48 kHz stereo WAV as captured. Resample and downmix before batching.
• No baseline. No reference WER is published with this release.

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.

License

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

Attribution string: Silencio Network, Silencio Voice AI Sample Dataset, 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.

Commercial licensing: silencio.network/contact

Citation

@misc{silencio_complete_voiceai_speech_dataset_2026,
  title  = {Silencio Voice AI Sample Dataset},
  author = {Silencio Network},
  year   = {2026},
  url    = {https://huggingface.co/datasets/SilencioNetwork/complete-voiceai-speech-dataset}
}

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