Datasets:
audio audioduration (s) 109 180 | gender stringclasses 1
value | ethnicity stringclasses 2
values | birth_place stringclasses 2
values | mother_tongue stringclasses 1
value | dialect stringclasses 2
values | languages_data stringclasses 2
values | os stringclasses 1
value | device stringclasses 1
value | browser stringclasses 1
value | duration float64 109 177 | emotions stringclasses 3
values | language stringclasses 1
value | location stringclasses 2
values | noise_sources stringclasses 4
values | script_id stringclasses 8
values | type_of_script stringclasses 1
value | script stringclasses 8
values | transcript stringclasses 1
value | speaker_id stringclasses 2
values | age_band stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
female | Black or African American | South Africa | English | South Africa - Johannesburg | [{"level": "native", "language": "English"}, {"level": "basic", "language": "Afrikaans"}, {"level": "basic", "language": "Zulu"}, {"level": "basic", "language": "German"}, {"level": "basic", "language": "Tswana"}, {"level": "basic", "language": "Southern Sotho"}] | Linux | Mobile | Chrome | 173 | {relaxed} | English | home | {silence} | cebeb991-b98f-4821-9cce-95484fd3c670 | medical | # How do doctors use the pain scale and how should patients describe their pain?
*💡 Kickstart ideas*
1. You could explain the 0-10 scale and what different numbers mean
2. Maybe help patients think about how to rate their pain
3. Feel free to mention other ways to describe pain like sharp or dull
4. You might talk a... | unknown | MED_007 | 25-34 | |
female | Black or African American | South Africa | English | South Africa - Johannesburg | [{"level": "native", "language": "English"}, {"level": "basic", "language": "Afrikaans"}, {"level": "basic", "language": "Zulu"}, {"level": "basic", "language": "German"}, {"level": "basic", "language": "Tswana"}, {"level": "basic", "language": "Southern Sotho"}] | Linux | Mobile | Chrome | 146 | {relaxed} | English | home | {silence} | 7a6b6104-652f-47a5-8b9c-a9af3fb458bf | medical | # How should a patient use an asthma inhaler correctly?
*💡 Kickstart ideas*
1. You could describe the proper technique step by step
2. Maybe explain the difference between reliever and preventer inhalers
3. Feel free to talk about when to use each type
4. You might mention common mistakes people make
5. It could hel... | unknown | MED_007 | 25-34 | |
female | White | United States | English | United States - Midwestern | [{"level": "native", "language": "English"}] | Linux | Mobile | Chrome | 136 | {focused} | English | home | {silence,appliances} | 653ac38a-b541-4991-8042-9612c5d3d7e8 | medical | # How should a minor wound be cleaned and cared for at home?
*💡 Kickstart ideas*
1. You might start with washing hands before touching the wound
2. Maybe describe how to clean it properly with water
3. Feel free to talk about when to apply antiseptic
4. You could mention how to cover it and when to change dressings
... | unknown | MED_004 | 35-44 | |
female | White | United States | English | United States - Midwestern | [{"level": "native", "language": "English"}] | Linux | Mobile | Chrome | 177 | {focused} | English | home | {silence,appliances} | efbd8ba3-b637-4ca1-8b5f-48c165cd5812 | medical | # What are antihistamines used for and how do they work?
*💡 Kickstart ideas*
1. You could explain what histamine does and why we block it
2. Maybe mention common allergies they help with
3. Feel free to talk about drowsy versus non-drowsy types
4. You might bring up how quickly they start working
5. It could help to... | unknown | MED_004 | 35-44 | |
female | White | United States | English | United States - Midwestern | [{"level": "native", "language": "English"}] | Linux | Mobile | Chrome | 169 | {anxious} | English | home | {silence,appliances} | a2b181dd-ccbf-4c16-bd72-75d38cac33ea | medical | # Describe what happens during an ultrasound scan. What should a patient expect?
*💡 Kickstart ideas*
1. You might start with how the equipment works using sound waves
2. Maybe describe the gel and why it's applied
3. Feel free to mention common reasons for having an ultrasound
4. You could talk about how long it typ... | unknown | MED_004 | 35-44 | |
female | White | United States | English | United States - Midwestern | [{"level": "native", "language": "English"}] | Linux | Mobile | Chrome | 133 | {focused} | English | other | {appliances} | 5214b593-1cb2-4920-8f6e-370b5af03bdf | medical | # Describe what happens during a CT scan. How is it different from an X-ray?
*💡 Kickstart ideas*
1. You might explain how the scanner takes multiple images
2. Maybe describe lying on the table as it moves through the machine
3. Feel free to mention if contrast dye might be used
4. You could talk about how long the s... | unknown | MED_004 | 35-44 | |
female | White | United States | English | United States - Midwestern | [{"level": "native", "language": "English"}] | Linux | Mobile | Chrome | 173 | {focused} | English | other | {"Car door closing",appliances} | ef824955-d830-4c21-853b-1fde221089bc | medical | # Explain the basic steps of CPR. What should someone do in an emergency?
*💡 Kickstart ideas*
1. You might start with checking if the person is responsive
2. Maybe describe the proper hand position for chest compressions
3. Feel free to talk about the rhythm and depth of compressions
4. You could mention when to giv... | unknown | MED_004 | 35-44 | |
female | White | United States | English | United States - Midwestern | [{"level": "native", "language": "English"}] | Linux | Mobile | Chrome | 109 | {focused} | English | other | {appliances} | a9d18ec9-c532-41d2-8e2d-f071c9233743 | medical | # What is ibuprofen used for, and what are the common side effects patients should know about?
*💡 Kickstart ideas*
1. You might start with the main reasons people take it
2. Maybe mention a few common conditions it helps with
3. Feel free to touch on how to take it safely
4. You could bring up any side effects worth... | unknown | MED_004 | 35-44 |
Medical Speech Dataset
A protocol sample. 11 contributors, recorded on their own devices in their own environments. Every clip carries origin region / variety, mother tongue, gender, device, OS, recording environment. Small by design — see What this is for below before downloading.
| Hours | 0.69 |
| Clips | 33 |
| Speakers | 11 |
| Origin varieties | 11 |
| Languages | 2 |
| Configs | 2 |
| Speaker metadata | origin region / variety, mother tongue, gender, device, OS, recording environment |
| Audio | 48 kHz stereo WAV, as captured |
| Transcripts | none in this release — 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.
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 — info@silencio.network
What this is for
This release carries no transcripts, so it is not a supervised ASR set and is no longer tagged as one. It is a small protocol sample. Plainly, what it is and is not good for:
- Not suitable for speaker-level or accent-level work. 11 contributors is too few to support conclusions about either, and we would rather say so than have you discover it after downloading.
- Recording-condition preview. Real audio from the Silencio app, captured on contributors' own devices in their own environments, with device, OS and environment logged. Use it to judge acoustic quality before commissioning at scale.
- Schema and pipeline validation. Same field layout as the larger Silencio releases, so a loader written against this sample works against a full delivery.
- Qualitative review of clinical-domain speech. Listen to what the collection protocol actually produces.
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/medical-speech-dataset", "english_medical", split="train")
print(ds[0])
Requires pip install "datasets>=4.0" and FFmpeg ≥ 4.
Configs: english_medical, global_medical
Speaker and recording metadata
| Speaker origin / variety | Speakers | % |
|---|---|---|
| Argentina - Porteño (Buenos Aires) | 1 | 9.1% |
| Ethiopia - Addis Ababa | 1 | 9.1% |
| India - South Indian English | 1 | 9.1% |
| Nigeria - Kano | 1 | 9.1% |
| Philippines - Cavite | 1 | 9.1% |
| South Africa - Johannesburg | 1 | 9.1% |
| Spain - Castellano (Madrid) | 1 | 9.1% |
| United Kingdom - RP (Received Pronunciation) | 1 | 9.1% |
| United States - Midwestern | 1 | 9.1% |
| Venezuela - Caraqueño | 1 | 9.1% |
| unknown | 1 | 9.1% |
| Gender | Speakers | % |
|---|---|---|
| male | 6 | 54.5% |
| female | 5 | 45.5% |
| Ethnicity (self-reported) | Speakers | % |
|---|---|---|
| Black or African American | 4 | 36.4% |
| White | 4 | 36.4% |
| Hispanic or Latino | 2 | 18.2% |
| Asian | 1 | 9.1% |
| Device | Clips | % |
|---|---|---|
| Desktop | 19 | 57.6% |
| Mobile | 14 | 42.4% |
| Environment | Clips | % |
|---|---|---|
| home | 29 | 87.9% |
| other | 3 | 9.1% |
| outdoor | 1 | 3.0% |
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 SpeechSilencioNetwork/tagalog-filipino-speech— Tagalog / Filipino Spontaneous Speech
To request transcription over this data or any other Silencio subset: info@silencio.network
Limitations
- Sample scale. 33 clips, 11 speakers, 0.69 hours. This is a demonstration sample, not a training corpus.
- No transcripts. Audio and speaker metadata only. See above.
- Few speakers. 11 contributors, so speaker-level conclusions are not supportable.
- 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 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, Medical Speech 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: info@silencio.network
Citation
@misc{silencio_medical_speech_dataset_2026,
title = {Medical Speech Dataset},
author = {Silencio Network},
year = {2026},
url = {https://huggingface.co/datasets/SilencioNetwork/medical-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:
| 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 transcription over an existing subset, or commissioned collection in a language not listed: info@silencio.network
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