Datasets:
annotations_creators:
- no-annotation
language_creators:
- found
languages:
- nb,no,nn
licenses:
- CC-ZERO
multilinguality:
- monolingual
pretty_name: NPSC
size_categories:
- 2G<n<1B
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- speech-modeling
Dataset Card for NbAiLab/NPSC
Table of Contents
- Dataset Description
- Dataset Summary
- Data Fields
- Dataset Creation
- Statistics
- Document Types
- Languages
- Publish Periode
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
Dataset Description
- Homepage: https://www.nb.no/sprakbanken/
- Repository: https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/
- Paper: https://www.nb.no/sprakbanken/
- Point of Contact: Per Erik Solberg
The Norwegian Parliament Speech Corpus (NPSC) is a corpus for training a Norwegian ASR (Automatic Speech Recognition) models. !!!NEDS TO BE UPDATED!!! We need a real description here. About one paragraph summing up that is on the main web page, and telling how this dataset is the same but different - ie it is in a streaming format...
How to Use
# Loads the 16K Bokmål corpus in streaming mode
from datasets import load_dataset
data = load_dataset("NbAiLab/NPSC", config="16K_mp3_bokmaal", streaming=True)
Dataset Summary
The NPSC dataset contains json lines with language training data. Here is an example json line:
{
"sentence_id": 49853,
"sentence_order": 0,
"speaker_id": 32,
"speaker_name": "Olemic Thommessen",
"sentence_text": "Stortingets møte er lovlig satt",
"sentence_language_code": "nb-NO",
"text": "Stortingets møte er lovlig satt",
"start_time": 320246, "end_time": 323590,
"normsentence_text": "Stortingets møte er lovlig satt",
"transsentence_text": "Stortingets møte er lovleg sett",
"translated": 1,
"audio": {"path": "audio/20170110-095504_320246_323590.wav",
"array": [.......]
}
}
Data Fields
| id: | String with id to source of line and a unique identifier |
|---|---|
| sentence_order | String with order of sentence |
| speaker id | Integer id of speaker |
| speaker_name | String name of speaker |
| sentence_text | String sentence text |
| sentence_language_code | String sentence text |
| text | String sentence text |
| start_time | int start time |
| end_time | int end time |
| normsentence_text | String normalised sentence text |
| transsentence_text | String translated sentence text |
| translated | int text translated |
| audio | audio audio record with 'path',(mp3) 'array','sampling_rate' (48000) |
Dataset Creation
We are providing a train and a validation split. The standard size of the validation is a single 1GB file, while train is sharded in 1GB chunks. All files are gzipped. There is also a test split available for the dataset but this is hidden. Please contact Per Erik Solberg for access to the test set.
Build date: 22012022
Initial Data Collection and Curation
The procedure for the dataset creation is described in detail in our paper.
Statistics
| Feature | Value |
|---|---|
| Duration, pauses included | 140,3 hours |
| Duration, pauses not included | 125,7 hours |
| Word count | 1,2 million |
| Sentence count | 64.531 |
| Language distribution | Nynorsk: 12,8% |
| Bokmål: 87,2%% | |
| Gender distribution | Female: 38,3% |
| Male: 61.7% |
Considerations for Using the Data
This corpus contains speech data and is allowed to be used outside the National Library of Norway for speech recognition technology purposes.
Dataset Curators
Freddy Wetjen and Andre Kaasen !!!NEDS TO BE UPDATED!!! Underline the origin of the data and the project. Add Javier de la Rosa and Per Egil Kummervold to the people having worked on this!!!
Licensing Information
Licensed for use outside the National Library of Norway.
License
The dataset is released under the CC-ZERO-license. The curation of the data
Citation Information
We are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus:
**!!!NEDS TO BE UPDATED!!!**
@inproceedings{kummervold-etal-2021-operationalizing,
title = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
author = {Kummervold, Per E and
De la Rosa, Javier and
Wetjen, Freddy and
Brygfjeld, Svein Arne",
booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
year = "2021",
address = "Reykjavik, Iceland (Online)",
publisher = {Link{"o}ping University Electronic Press, Sweden},
url = "https://aclanthology.org/2021.nodalida-main.3",
pages = "20--29",
abstract = "In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
in several token and sequence classification tasks for both Norwegian Bokm{aa}l and Norwegian Nynorsk. Our model also improves the mBERT performance for other
languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore,
we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.",
}