Datasets:
metadata
language:
- pl
license: cc-by-nc-nd-4.0
task_categories:
- automatic-speech-recognition
pretty_name: Polish TEDx ASR Eval
size_categories: n<1K
tags:
- polish
- speech
- tedx
- asr-evaluation
- spontaneous-speech
- public-speaking
configs:
- config_name: default
data_files:
- split: train
path: corpus.csv
dataset_info:
features:
- name: file
dtype: string
- name: start
dtype: float32
- name: end
dtype: float32
- name: text
dtype: string
- name: text_norm
dtype: string
- name: duration
dtype: float32
- name: annotator
dtype: string
- name: verified_by
dtype: string
- name: source_url
dtype: string
- name: source_license
dtype: string
splits:
- name: train
num_examples: 344
Polish-TEDx-ASR-Eval
A dataset for evaluating automatic speech recognition (ASR) systems for Polish in the domain of TEDx public talks.
Contains audio segments from Polish TEDx talks available on YouTube (CC BY-NC-ND 4.0) and synthetic speech generated with KugelAudio (MIT), with manually created and cross-verified transcriptions. Created as part of the course "Workshops on Evaluation of Speech Recognition Systems" (ZWESUI, AMU 2026) by Group 1.
Statistics
| Feature | Value |
|---|---|
| Total segments | 344 |
| - YouTube segments | 294 |
| - TTS segments (KugelAudio) | 50 |
| Total audio duration | 2601 s (~43 min) |
| - YouTube | ~39 min |
| - TTS | ~4 min |
| Minimum segment length | 3.1 s |
| Maximum segment length | 18.3 s |
| Average segment length | 7.6 s |
| Median segment length | 6.9 s |
| Number of source recordings (YouTube) | 16 |
| Number of annotators | 5 |
| Sample rate | 16 kHz mono |
| Format | WAV |
| Speech type | spontaneous, planned public presentations + synthetic |
| Acoustics | conference/theatre hall, reverberation, audience reactions |
Sources
YouTube (CC BY-NC-ND 4.0)
All YouTube recordings come from Polish TEDx talks.
Synthetic TTS (KugelAudio - MIT)
50 segments generated with KugelAudio by annotator s485936. Files: s485936_tts_sample_000.wav – s485936_tts_sample_049.wav.
Speaker Distribution
Speaker list (YouTube recordings)
| Speaker | Audio file | Gender | Age group | Acoustics | Topic | Segments |
|---|---|---|---|---|---|---|
| Radosław Kotarski | audio_grupa1_0t-sG8FhC4E.wav | M | 30-39 | reverberant_hall | education | 27 |
| Adam Wajrak | audio_s441306_2-_tJd9FFK0.wav | M | 40-49 | quiet_hall | ecology | 12 |
| Mariusz Czarnecki | audio_s441306_4XMKrqabpds.wav | M | 40-49 | reverberant_hall | business | 32 |
| Jan A.P. Kaczmarek | audio_s441306_DOIllGDNKw4.wav | M | 50+ | reverberant_hall | culture | 10 |
| Adam Haertle | audio_s485704_cuRVUT3bmek.wav | M | 40-49 | quiet_hall | cybersecurity | 17 |
| Piotr Zielonka | audio_s485704_owA_Z3navgg.wav | M | 50+ | reverberant_hall | psychology | 17 |
| Joanna Flis | audio_s485704_qaOljbJ-kSU.wav | F | 30-39 | reverberant_hall | psychology | 17 |
| Maciej Makselon | audio_s485936_MYH2qGScEVk.wav | M | 30-39 | reverberant_hall | linguistics | 17 |
| Magdalena Komsta | audio_s485936_eANk3-vRpCM.wav | F | 30-39 | reverberant_hall | medicine | 17 |
| Janina Bąk | audio_s485936_f6W_8V7wFJA.wav | F | 30-39 | reverberant_hall | psychology | 17 |
| Mateusz Kurleto | audio_s512752_7_OWTJtUK5k.wav | M | 30-39 | reverberant_hall | technology | 20 |
| Artur Małek | audio_s512752_SIcY83qBTsQ.wav | M | 30-39 | quiet_hall | psychology | 20 |
| Bogusław Pranszke | audio_s512752_pNzlSnMjIjE.wav | M | 50+ | quiet_hall | science | 20 |
| Róża Hajkuś | audio_s512757_RF8eJ2iN2Rk.wav | F | 30-39 | reverberant_hall | education | 17 |
| Kamil Kozieł | audio_s512757_sHY7TdnWFuQ.wav | M | 30-39 | reverberant_hall | education | 17 |
| Piotr Konieczny | audio_s512757_wDGAkp1sHM4.wav | M | 30-39 | reverberant_hall | cybersecurity | 17 |
Gender distribution
| Gender | Speakers | Segments | % |
|---|---|---|---|
| M | 12 | 226 | 76.9% |
| F | 4 | 68 | 23.1% |
Age distribution
| Age group | Speakers | Segments | % |
|---|---|---|---|
| 30-39 | 10 | 186 | 63.3% |
| 40-49 | 3 | 61 | 20.7% |
| 50+ | 3 | 47 | 16.0% |
Acoustic conditions
| Conditions | Speakers | Segments | % |
|---|---|---|---|
| reverberant_hall | 12 | 225 | 76.5% |
| quiet_hall | 4 | 69 | 23.5% |
Topic distribution
| Topic | Segments | % |
|---|---|---|
| psychology | 71 | 24.1% |
| education | 61 | 20.7% |
| cybersecurity | 34 | 11.6% |
| business | 32 | 10.9% |
| technology | 20 | 6.8% |
| science | 20 | 6.8% |
| linguistics | 17 | 5.8% |
| medicine | 17 | 5.8% |
| ecology | 12 | 4.1% |
| culture | 10 | 3.4% |
Data Structure
The file corpus.csv contains the following columns:
| Column | Type | Description |
|---|---|---|
file |
string | WAV filename |
start |
float | Segment start time [s] |
end |
float | Segment end time [s] |
text |
string | Raw transcription (original spelling) |
text_norm |
string | Normalised transcription (ready for WER computation) |
duration |
float | Segment duration [s] |
annotator |
string | Username of the transcription author |
verified_by |
string | Username of the verifier |
source_url |
string | Source recording URL |
source_license |
string | Source recording licence |
Example Entries
{
"file": "audio_grupa1_0t-sG8FhC4E.wav",
"start": 0.436,
"end": 5.405,
"text": "w ogóle być w stanie proponować jakiekolwiek alternatywne systemowe rozwiązania",
"text_norm": "w ogóle być w stanie proponować jakiekolwiek alternatywne systemowe rozwiązania",
"duration": 4.97,
"annotator": "s485704",
"verified_by": "s512752",
"source_url": "https://www.youtube.com/watch?v=0t-sG8FhC4E",
"source_license": "CC BY-NC-ND 4.0 (TEDx Talks)"
}
{
"file": "audio_s512757_wDGAkp1sHM4.wav",
"start": 834.307,
"end": 839.642,
"text": "wiele osób się obawia i zakleja kamerę czy jest na sali ktoś kto zakleja kamerę na na swoim laptopie ręka w górę",
"text_norm": "wiele osób się obawia i zakleja kamerę czy jest na sali ktoś kto zakleja kamerę na na swoim laptopie ręka w górę",
"duration": 5.335,
"annotator": "s512757",
"verified_by": "",
"source_url": "https://www.youtube.com/watch?v=wDGAkp1sHM4",
"source_license": "CC BY-NC-ND 4.0 (TEDx Talks)"
}
{
"file": "s485936_tts_sample_000.wav",
"start": 0.0,
"end": 3.867,
"text": "Zostawiam was dzisiaj z tym jednym pytaniem: kim będziecie, kiedy zgaśnie to sceniczne światło?",
"text_norm": "zostawiam was dzisiaj z tym jednym pytaniem kim będziecie kiedy zgaśnie to sceniczne światło",
"duration": 3.867,
"annotator": "s485936",
"verified_by": "",
"source_url": "",
"source_license": "MIT (KugelAudio)"
}
Annotators
| Annotator | YouTube segments | TTS segments | Total |
|---|---|---|---|
| s441306 | 59 | 0 | 59 |
| s485704 | 56 | 0 | 56 |
| s485936 | 57 | 50 | 107 |
| s512752 | 66 | 0 | 66 |
| s512757 | 56 | 0 | 56 |
Transcription Methodology
- Who transcribed: 5 student annotators working independently on assigned source recordings
- Guidelines: Team normalisation protocol agreed before transcription began; rules cover lowercase conversion, punctuation removal, hesitation removal, loanword normalisation, and ordinal numeral expansion
- Verification: Cross-verification - each segment reviewed by a second annotator (field
verified_by) - Inter-annotator agreement: Not formally measured.
- Tools: Audacity (label tracks exported as
.txt), ffmpeg (audio conversion to 16 kHz mono WAV), KugelAudio (TTS synthesis)
Normalisation Protocol
Transcriptions in text_norm were processed by normalize() from normalize.py. Rules applied in order:
- R7 - ordinal numerals expanded (
1.→pierwszy) - R1 - lowercase conversion
- R6 - loanword normalisation (
email→e-mail,wifi→wi-fi) - R3 - hesitation removal (
ee,yy,hm,mm) - R2 - punctuation and symbol removal
- Whitespace collapse
Licence
YouTube source recordings: CC BY-NC-ND 4.0 (TEDx Talks / YouTube).
KugelAudio TTS audio: MIT (KugelAudio).
Transcriptions and metadata: CC BY-NC-ND 4.0.