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license: cc0-1.0
language:
- 'no'
pretty_name: Destil Stortinget
size_categories:
- 10K<n<100K
---
# Dataset Card for `NbAiLab/nb_distil_speech_noconcat_stortinget`
## Dataset Summary
`NbAiLab/nb_distil_speech_noconcat_stortinget` is a curated subset of the [Stortinget Speech Corpus (SSC)](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-91/), a large-scale Norwegian parliamentary speech dataset. This subset focuses on non-concatenated speech segments and includes automatic transcriptions generated using OpenAI's Whisper model. It is designed to facilitate the development and evaluation of Automatic Speech Recognition (ASR) systems, particularly in Norwegian.
## Dataset Structure
### Data Fields
Each entry in the dataset comprises the following fields:
- `id`: Unique identifier for the audio segment.
- `group_id`: Identifier grouping related segments.
- `source`: Origin of the audio, e.g., "stortinget".
- `audio`: Path to the audio file.
- `audio_duration`: Duration of the audio segment in seconds.
- `previous_text`: Text preceding the current segment in the original transcript.
- `text`: Official transcript of the audio segment.
- `text_en`: English translation of the transcript (if available).
- `text_language`: Language code of the transcript (e.g., "no").
- `whisper_transcript`: Transcript generated by the Whisper model.
- `whisper_wer`: Word Error Rate (WER) of the Whisper transcription.
- `wav2vec_wer`: WER of the wav2vec transcription.
- `verbosity_level`: Indicator of the verbosity level of the speech.
- `file`: Filename of the audio segment.
- `channels`: Number of audio channels.
- `frequency`: Sampling frequency of the audio.
- `language`: Language code of the audio (e.g., "no").
- `task`: Task type, e.g., "transcribe".
### Data Splits
The dataset is divided into the following splits:
- `train`: Approximately 195,000 segments.
- `validation`: Approximately 1,540 segments.
- `validation_clean_stortinget_no`: 697 segments.
- `validation_stortinget_no`: Approximately 1,540 segments.
*Note: The dataset does not include a test split.*
## Dataset Creation
### Source Data
#### Original Corpus
The dataset is derived from the [Stortinget Speech Corpus (SSC)](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-91/), which comprises over 5,000 hours of Norwegian parliamentary speech. The SSC includes:
- **Segments**: 724,783
- **Total Duration**: 5,190 hours
- **Unique Speakers**: 729
Each segment is up to 30 seconds long and is accompanied by transcriptions in Norwegian Bokmål and Nynorsk.
#### Reference
The creation of the original SSC is detailed in:
> Solberg, P. E., Beauguitte, P., Kummervold, P. E., & Wetjen, F. (2023, May). A Large Norwegian Dataset for Weak Supervision ASR. In *Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)* (pp. 48-52). [https://aclanthology.org/2023.resourceful-1.7/](https://aclanthology.org/2023.resourceful-1.7/)
### Processing
This subset was created by:
- Selecting non-concatenated speech segments from the SSC.
- Generating automatic transcriptions using NB-Whisper Large.
- Including both the official and Whisper-generated transcriptions to facilitate comparative studies and filtering.
Users can leverage the provided WER metrics (`whisper_wer` and `wav2vec_wer`) to filter and select high-quality transcriptions for their specific use cases.
## Intended Uses
This dataset is intended for:
- Training and evaluating ASR and TTS systems in Norwegian.
- Research on weakly supervised learning for speech recognition.
- Comparative studies between human and machine-generated transcriptions.
## Licensing Information
The dataset is released under the [Creative Commons Zero (CC0) license](https://creativecommons.org/publicdomain/zero/1.0/), allowing unrestricted use, distribution, and reproduction in any medium.
## Citation
If you use this dataset, please cite the following work:
> Solberg, P. E., Beauguitte, P., Kummervold, P. E., & Wetjen, F. (2023, May). A Large Norwegian Dataset for Weak Supervision ASR. In *Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)* (pp. 48-52). [https://aclanthology.org/2023.resourceful-1.7/](https://aclanthology.org/2023.resourceful-1.7/)
## Acknowledgements
The dataset was curated by Per E Kummervold and Freddy Wetjen and released by the [Nasjonalbiblioteket AI Lab (NbAiLab)](https://huggingface.co/NbAiLab), building upon the resources provided by Språkbanken at the National Library of Norway. |