Datasets:
Tags:
speech-modeling
License:
| 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-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Data Fields](#data-fiels) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Statistics](#statistics) | |
| - [Document Types](#document-types) | |
| - [Languages](#languages) | |
| - [Publish Periode](#publish-periode) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation 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](mailto:per.solberg@nb.no) | |
| The Norwegian Parliament Speech Corpus (NPSC) is a corpus for training a Norwegian ASR (Automatic Speech Recognition) models. | |
| ## How to Use | |
| ```python | |
| from datasets import load_dataset | |
| data = load_dataset("NbAiLab/NPSC", streaming=True) | |
| ``` | |
| ## Download Data | |
| If you do not want to use the HuggingFace Dataset-library for training, or if you want to do additional pre-processing, it is also possible to download the files locally. | |
| ```bash | |
| # Clone the training set | |
| git clone https://huggingface.co/datasets/NbAiLab/NPSC | |
| # Create one large training file of all shards without unpacking | |
| cat NPSC/data/train*.gz > onefile.json.gz | |
| ``` | |
| <details> | |
| <summary>List of all the files.</summary> | |
| * [eval](https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/eval.json.gz) | |
| * [test](https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/test.json.gz) | |
| * [train](https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/train.json.gz) | |
| </details> | |
| ### Dataset Summary | |
| The NPSC dataset contains json lines with language training data. Here is an example json line: | |
| ```json | |
| { | |
| "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. | |
| 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. | |
| ### Discussion of Biases | |
| Please refer to our paper. | |
| ### Dataset Curators | |
| [Freddy Wetjen](mailto:Freddy.wetjen@nb.no) and [Andre Kaasen](mailto:andre.kasen@nb.no) | |
| ### Licensing Information | |
| Licensed for use outside the National Library of Norway. | |
| ## License | |
| CC-ZERO(https://creativecommons.org/publicdomain/zero/1.0/) | |
| ### 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: | |
| ``` | |
| @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.", | |
| } | |
| ``` | |