Datasets:

NbAiLab
/

NPSC

@@ -44,10 +44,10 @@ The corpus is in total sound recordings from 40 entire days of meetings. This am
 _HOMEPAGE = "https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/"
-# Example: https://huggingface.co/datasets/NB/NPSC/resolve/main/data/train/20170110_48K_mp3.tar.gz
-_DATA_URL = "https://huggingface.co/datasets/NB/NPSC/resolve/main/data/{split}/{shard}_{config}.tar.gz"
-# Example: https://huggingface.co/datasets/NB/NPSC/resolve/main/data/test/20170207.json
-_METADATA_URL = "https://huggingface.co/datasets/NB/NPSC/resolve/main/data/{split}/{shard}.json"
 _SHARDS = {
     "validation": ["20170209", "20180109", "20180201", "20180307", "20180611"],

 _HOMEPAGE = "https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/"
+# Example: https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/train/20170110_48K_mp3.tar.gz
+_DATA_URL = "https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/{split}/{shard}_{config}.tar.gz"
+# Example: https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/test/20170207.json
+_METADATA_URL = "https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/{split}/{shard}.json"
 _SHARDS = {
     "validation": ["20170209", "20180109", "20180201", "20180307", "20180611"],

README.md ADDED Viewed

	@@ -0,0 +1,180 @@

+---
+YAML tags:
+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("nb/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/nb/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.",
+}
+```

data/eval/20170209.json ADDED Viewed