first submit

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*json* filter=lfs diff=lfs merge=lfs -text
+*wav* filter=lfs diff=lfs merge=lfs -text
+*mp3* filter=lfs diff=lfs merge=lfs -text

NPSC.py

@@ -0,0 +1,96 @@
+"""NPSC dataset."""
+import gzip
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters."""
+_CITATION = """
+TO BE DONE
+"""
+_URL = "https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/"
+_DATA_URL = "https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/{split_suffix}-shard-{index:04d}-of-{n_shards:04d}.json.gz"
+_N_SHARDS_PER_SPLIT = {
+        "train": 1, "dev": 1, "test": 1
+}
+
+
+class NPSCConfig(datasets.BuilderConfig):
+    """BuilderConfig for NbNn."""
+
+    def __init__(self, *args, **kwargs):
+        """BuilderConfig for NbNn.
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(
+            *args,
+            name="NPSC",
+            **kwargs,
+        )
+
+
+class NPSC(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [NPSCConfig()]
+    BUILDER_CONFIG_CLASS = NPSCConfig
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence_order":  datasets.Value("int32"),
+                    "speaker_id" : datasets.Value("int32"),
+                    "speaker_name": datasets.Value("string"),
+                    "sentence_text": datasets.Value("string"),
+                    "sentence_language_code": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "start_time": datasets.Value("int32"),
+                    "end_time": datasets.Value("int32"),
+                    "normsentence_text": datasets.Value("string"),
+                    "transsentence_text": datasets.Value("string"),
+                    "translated": datasets.Value("int32"),
+                    "audio": datasets.features.Audio(sampling_rate=48000),
+
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_urls = {}
+        for split in ["train", "dev", "test"]:
+            data_urls[split] = [
+                _DATA_URL.format(
+                    language=self.config.name,
+                    split_suffix=split,
+                    index=index,
+                    n_shards=_N_SHARDS_PER_SPLIT[split],
+                )
+                for index in range(1, _N_SHARDS_PER_SPLIT[split] + 1)
+            ]
+        train_downloaded_files = dl_manager.download(data_urls["train"])
+        dev_downloaded_files = dl_manager.download(data_urls["dev"])
+        test_downloaded_files = dl_manager.download(data_urls["test"])
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
+            ),
+
+        ]
+
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_ = 0
+        for filepath in filepaths:
+            logger.info("generating examples from = %s", filepath)
+            with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                for line in f:
+                    if line:
+                        example = json.loads(line)
+                        yield id_, example
+                        id_ += 1

README.md

@@ -0,0 +1,161 @@
+---
+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:[email protected])
+
+The Norwegian Parliament Speech Corpus (NPSC) is a corpus for training a Norwegian ASR (Automatic Speech Recognition) models. The corpus is created by Språkbanken at the National Library in Norway. 
+
+NPSC is based on sound recording from meeting in the Norwegian Parliament. These talks are orthographically transcribed to either Norwegian Bokmål or Norwegian Nynorsk. There are a significant amount of metadata related to the speakers, as well as minutes from the actual meetings. 
+
+The corpus is in total sound recordings from 40 entire days of meetings. This amounts to 140 hours of speech, 65,000 sentences or 1.2 million words. 
+
+This corpus is an adaption of the original corpus made for efficiant ASR training. For simplicity and portability, a few of the original datasets features, like the token transcription, is ommitted. You can find the complete dataset at [the Resource Catalogue at Språkbanken](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/).
+
+## How to Use (This needs to be edited of course)
+```python
+from datasets import load_dataset
+data = load_dataset("nb/NPSC", streaming=True)
+```
+
+## 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
+Currently there are two versions included in this repo.
+
+### Version A
+This verison has a short list of the metadata and includes the audio (48k mp3) encoded as a float32 array in the dataset itself.
+
+The current dataloader script is associated with this version.
+
+|**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) |
+
+
+### Version B
+This verison does not contain the audio encoded in the dataset. Instead it has the audio files placed in sub-directories. There are currently both samples in clips_48k_wav and clips_16k_mp3. Only the base filename is referred in the dataset. Please not that there are both sentence-based audio clips as well at meeting-based audio clips. The dataset contains referrals to both, the latter referral has start and stop time as well.
+
+
+### Dataset Creation
+We are providing a **train**, **dev** and **test** split. These are the same as in the orginal corpus.
+
+Build date: 20012022
+
+#### Initial Data Collection and Curation
+The procedure for the dataset creation is described in detail in the 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
+[Per Erik Solberg](mailto:[email protected])
+
+[Freddy Wetjen](mailto:[email protected]), [Andre Kaasen](mailto:[email protected]) and [Per Egil Kummervold](mailto: [email protected]) has contributed to porting it to Hugging Face.
+
+### 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:
+```
+ANDRE: TO BE DONE
+
+```

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