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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
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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:

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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