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 — hello@silencio.network

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: hello@silencio.network

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: hello@silencio.network

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 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. Catalogue position as of September 2026:

Hours, off the shelf	around 375,000
Languages	around 150
Countries of contributor origin	around 180
Active contributors	around 300,000
Sustained production	around 1,000 hours per day
Catalogue refresh	Weekly at record level, quarterly at catalogue level

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

Contributors available on demand	around 2,500,000
Countries	180+
Languages reachable	250+

Deep coverage across Africa, South-East Asia, South Asia and the Middle East. Silencio publishes catalogue depth in bands rather than exact hour counts.

Human-validated transcription is live for Cebuano, Tagalog / Filipino, Hiligaynon, Swahili, Amharic, Yoruba, Hausa, Wolof, and 10 Arabic dialects (Emirati, Saudi, Kuwaiti, Qatari, Moroccan, Tunisian, Syrian, Egyptian, Jordanian, Lebanese). Every clip in delivery is validated by a native-speaker human reviewer. Published samples that carry human-validated transcripts currently cover Cebuano, Tagalog / Filipino and Swahili; samples for the remaining languages are in preparation.

More at silencio.network.

For volume licensing, human transcription over an existing subset, or commissioned collection in a language not listed: hello@silencio.network