Datasets:
Tasks:
Automatic Speech Recognition
Formats:
parquet
Languages:
Slovak
Size:
100K - 1M
ArXiv:
License:
File size: 4,999 Bytes
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dataset_info:
features:
- name: id
dtype: int64
- name: start_time
dtype: float32
- name: end_time
dtype: float32
- name: session_num
dtype: int64
- name: snapshot
dtype: date32
- name: segment_number
dtype: int64
- name: text
dtype: string
- name: duration
dtype: float32
- name: audio
dtype: audio
splits:
- name: train
num_examples: 402966
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- automatic-speech-recognition
language:
- sk
tags:
- speech
- asr
- slovak
- parliament
- legal
- politics
- whisper
pretty_name: SloPalSpeech
size_categories:
- 1M<n<10M
license: cc-by-4.0
---
# SloPalSpeech
This dataset contains **aligned and segmented Slovak speech–text pairs** sourced from official plenary session recordings of the **Slovak National Council** (Národná rada Slovenskej republiky).
It was prepared by collecting raw audio from [MediaPortál NR SR](https://tv.nrsr.sk) and matching it with official full-text transcripts from the [Joint Czech and Slovak Digital Parliamentary Library](https://www.nrsr.sk/dl/).
A custom alignment and filtering pipeline segmented the recordings into **short clips (≤30 seconds)** with their corresponding transcriptions, producing a **2,806-hour ASR-ready dataset**.
## 📌 Dataset Summary
- **Total duration**: 2,806 hours
- **Number of segments**: 402,966
- **Average segment length**: ~25.1 seconds
- **Domain**: formal Slovak parliamentary speech
- **Use cases**: ASR model fine-tuning, speech research
## 📁 Data Structure
Each dataset entry has the following fields:
```json
{
"id": 101036,
"start_time": 2949.985,
"end_time": 2974.976,
"session_num": 19,
"snapshot": "2024-09-13",
"segment_number": 108,
"text": "všetkých členov výboru, ktorí neprišli na zasadnutie výboru, lebo sa rozhodli, že ho budú ignorovať, ako to urobili viackrát poslanci koalície, keď chceli znefunkčniť výbor? Potom asi tých by sme mali odvolať pri takomto postupe, lebo ja som riadne zasadnutie urobila, no ale koaliční poslanci sa rozhodli na niektorých nebyť, lebo sa im nechcelo. A potom tie ostatné dva body, no to je smiešne! Ďalší, tretí bod, že verejne spochybňujem a tým sa propagujem. No, prosím",
"duration": 24.99,
"audio": {
"path": "19_2024-09-13_108.mp3",
"array": [
1.32518352e-07, -2.97130391e-08, -1.27864055e-07, ...,
-1.25169004e-06, 1.16862184e-05, 4.28082467e-06
],
"sampling_rate": 16000
}
}
```
### 🔍 Field Descriptions
- **`id`**: Unique integer identifier for the segment.
- **`start_time`**: Start timestamp (in seconds) within the original session recording.
- **`end_time`**: End timestamp (in seconds) within the original session recording.
- **`session_num`**: Parliamentary session number in the election term.
- **`snapshot`**: Date of the session.
- **`segment_number`**: Sequential index of the segment in the session.
- **`text`**: Slovak transcript (transcriber notes removed).
- **`duration`**: Segment duration in seconds.
- **`audio`**: Audio object containing local file path, waveform array, and sampling rate.
## ⚙️ Processing & Alignment
1. **Collection**
- Audio scraped to `.mp3` format.
- Transcripts downloaded in `.docx` format.
2. **Parsing**
- Extracted speaker-segmented utterances.
- Removed transcriber notes.
3. **Alignment**
- Matched words from the ground-truth transcript with a pseudo-labeled transcript generated from the audio.
- **Assigned timestamps** to the matched words using the pseudo-labeled transcript, which had been force-aligned.
4. **Segmentation**
- Created segments ≤30 seconds based on the matched words.
5. **Filtering**
- Re-transcribed each segment.
- Discarded any with WER > 40%.
- Final dataset: 2,806 hours; 402,966 segments.
## 📊 Statistics
| Metric | Value |
|----------------|--------------:|
| Total hours | 2,806 |
| Total segments | 402,966 |
| Avg. duration | 25.1 sec |
| Sample rate | 16 kHz |
## 🚀 Example usage
```python
from datasets import load_dataset
ds = load_dataset("erikbozik/SloPalSpeech", split="train")
sample = ds[0]
print(sample["text"])
print(sample["duration"], "seconds")
sample["audio"]["array"] # numpy waveform
```
## 📝 Citation & Paper
For more details, please see our paper on [arXiv](https://arxiv.org/abs/2509.19270). If you use this dataset in your work, please cite it as:
```bibtex
@misc{božík2025slopalspeech2800hourslovakspeech,
title={SloPalSpeech: A 2,800-Hour Slovak Speech Corpus from Parliamentary Data},
author={Erik Božík and Marek Šuppa},
year={2025},
eprint={2509.19270},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.19270},
}
``` |