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Datasets:
NbAiLab
/
NPSC_test

Tasks:
Automatic Speech Recognition
Audio Classification
Modalities:
Audio
Formats:
soundfolder
Languages:
Norwegian Bokmål
Norwegian
Norwegian Nynorsk
Size:
< 1K
Tags:
speech-modeling
Libraries:
Datasets
License:
Dataset card Viewer Files Community
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  2. NPSC.py +96 -0
  3. README.md +161 -0
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  46. test.json +3 -0
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.gitattributes CHANGED
@@ -25,3 +25,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
25
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ *json* filter=lfs diff=lfs merge=lfs -text
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+ *wav* filter=lfs diff=lfs merge=lfs -text
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+ *mp3* filter=lfs diff=lfs merge=lfs -text
NPSC.py ADDED
@@ -0,0 +1,96 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """NPSC dataset."""
2
+ import gzip
3
+ import json
4
+ import datasets
5
+
6
+ logger = datasets.logging.get_logger(__name__)
7
+ _DESCRIPTION = """\\nNorwegian Colossal Corpus v2. Short sequences of maximum 100k characters."""
8
+ _CITATION = """
9
+ TO BE DONE
10
+ """
11
+ _URL = "https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/"
12
+ _DATA_URL = "https://huggingface.co/datasets/NbAiLab/NPSC/resolve/main/data/{split_suffix}-shard-{index:04d}-of-{n_shards:04d}.json.gz"
13
+ _N_SHARDS_PER_SPLIT = {
14
+ "train": 1, "dev": 1, "test": 1
15
+ }
16
+
17
+
18
+ class NPSCConfig(datasets.BuilderConfig):
19
+ """BuilderConfig for NbNn."""
20
+
21
+ def __init__(self, *args, **kwargs):
22
+ """BuilderConfig for NbNn.
23
+ Args:
24
+ **kwargs: keyword arguments forwarded to super.
25
+ """
26
+ super().__init__(
27
+ *args,
28
+ name="NPSC",
29
+ **kwargs,
30
+ )
31
+
32
+
33
+ class NPSC(datasets.GeneratorBasedBuilder):
34
+ BUILDER_CONFIGS = [NPSCConfig()]
35
+ BUILDER_CONFIG_CLASS = NPSCConfig
36
+
37
+ def _info(self):
38
+ return datasets.DatasetInfo(
39
+ description=_DESCRIPTION,
40
+ features=datasets.Features(
41
+ {
42
+ "sentence_order": datasets.Value("int32"),
43
+ "speaker_id" : datasets.Value("int32"),
44
+ "speaker_name": datasets.Value("string"),
45
+ "sentence_text": datasets.Value("string"),
46
+ "sentence_language_code": datasets.Value("string"),
47
+ "text": datasets.Value("string"),
48
+ "start_time": datasets.Value("int32"),
49
+ "end_time": datasets.Value("int32"),
50
+ "normsentence_text": datasets.Value("string"),
51
+ "transsentence_text": datasets.Value("string"),
52
+ "translated": datasets.Value("int32"),
53
+ "audio": datasets.features.Audio(sampling_rate=48000),
54
+
55
+ }
56
+ ),
57
+ supervised_keys=None,
58
+ homepage=_URL,
59
+ citation=_CITATION,
60
+ )
61
+
62
+ def _split_generators(self, dl_manager):
63
+ data_urls = {}
64
+ for split in ["train", "dev", "test"]:
65
+ data_urls[split] = [
66
+ _DATA_URL.format(
67
+ language=self.config.name,
68
+ split_suffix=split,
69
+ index=index,
70
+ n_shards=_N_SHARDS_PER_SPLIT[split],
71
+ )
72
+ for index in range(1, _N_SHARDS_PER_SPLIT[split] + 1)
73
+ ]
74
+ train_downloaded_files = dl_manager.download(data_urls["train"])
75
+ dev_downloaded_files = dl_manager.download(data_urls["dev"])
76
+ test_downloaded_files = dl_manager.download(data_urls["test"])
77
+
78
+ return [
79
+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
80
+ datasets.SplitGenerator(
81
+ name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
82
+ ),
83
+
84
+ ]
85
+
86
+ def _generate_examples(self, filepaths):
87
+ """This function returns the examples in the raw (text) form by iterating on all the files."""
88
+ id_ = 0
89
+ for filepath in filepaths:
90
+ logger.info("generating examples from = %s", filepath)
91
+ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
92
+ for line in f:
93
+ if line:
94
+ example = json.loads(line)
95
+ yield id_, example
96
+ id_ += 1
README.md ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ YAML tags:
3
+
4
+ annotations_creators:
5
+ - no-annotation
6
+ language_creators:
7
+ - found
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+ languages:
9
+ - nb,no,nn
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+ licenses:
11
+ - CC-ZERO
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+ multilinguality:
13
+ - monolingual
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+ pretty_name: NPSC
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+ size_categories:
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+ - 2G<n<1B
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+ source_datasets:
18
+ - original
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+ task_categories:
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+ - sequence-modeling
21
+ task_ids:
22
+ - speech-modeling
23
+
24
+ ---
25
+ # Dataset Card for NBAiLab/NPSC
26
+
27
+
28
+ ## Table of Contents
29
+ - [Dataset Description](#dataset-description)
30
+ - [Dataset Summary](#dataset-summary)
31
+ - [Data Fields](#data-fiels)
32
+ - [Dataset Creation](#dataset-creation)
33
+ - [Statistics](#statistics)
34
+ - [Document Types](#document-types)
35
+ - [Languages](#languages)
36
+ - [Publish Periode](#publish-periode)
37
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
38
+ - [Social Impact of Dataset](#social-impact-of-dataset)
39
+ - [Discussion of Biases](#discussion-of-biases)
40
+ - [Other Known Limitations](#other-known-limitations)
41
+ - [Additional Information](#additional-information)
42
+ - [Dataset Curators](#dataset-curators)
43
+ - [Licensing Information](#licensing-information)
44
+ - [Citation Information](#citation-information)
45
+
46
+ ## Dataset Description
47
+ - **Homepage:** https://www.nb.no/sprakbanken/
48
+ - **Repository:** https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/
49
+ - **Paper:** https://www.nb.no/sprakbanken/
50
+ - **Point of Contact:** [Per Erik Solberg](mailto:per.solberg@nb.no)
51
+
52
+ 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.
53
+
54
+ 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.
55
+
56
+ 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.
57
+
58
+ 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/).
59
+
60
+ ## How to Use (This needs to be edited of course)
61
+ ```python
62
+ from datasets import load_dataset
63
+ data = load_dataset("nb/NPSC", streaming=True)
64
+ ```
65
+
66
+ ## Dataset Summary
67
+ The NPSC dataset contains json lines with language training data. Here is an example json line:
68
+ ```json
69
+
70
+ {
71
+ "sentence_id": 49853,
72
+ "sentence_order": 0,
73
+ "speaker_id": 32,
74
+ "speaker_name": "Olemic Thommessen",
75
+ "sentence_text": "Stortingets møte er lovlig satt",
76
+ "sentence_language_code": "nb-NO",
77
+ "text": "Stortingets møte er lovlig satt",
78
+ "start_time": 320246, "end_time": 323590,
79
+ "normsentence_text": "Stortingets møte er lovlig satt",
80
+ "transsentence_text": "Stortingets møte er lovleg sett",
81
+ "translated": 1,
82
+ "audio": {"path": "audio/20170110-095504_320246_323590.wav",
83
+ "array": [.......]
84
+ }
85
+
86
+ }
87
+
88
+
89
+ ```
90
+ ## Data Fields
91
+ Currently there are two versions included in this repo.
92
+
93
+ ### Version A
94
+ This verison has a short list of the metadata and includes the audio (48k mp3) encoded as a float32 array in the dataset itself.
95
+
96
+ The current dataloader script is associated with this version.
97
+
98
+ |**id:** | String with id to source of line and a unique identifier|
99
+ |:-----------|:------------|
100
+ |**sentence_order** | String with order of sentence |
101
+ |**speaker id** | Integer id of speaker |
102
+ | **speaker_name** | String name of speaker |
103
+ | **sentence_text** | String sentence text |
104
+ | **sentence_language_code** | String sentence text |
105
+ | **text** | String sentence text |
106
+ | **start_time** | int start time |
107
+ | **end_time** | int end time |
108
+ | **normsentence_text** | String normalised sentence text |
109
+ | **transsentence_text** | String translated sentence text |
110
+ | **translated** | int text translated |
111
+ | **audio** | audio audio record with 'path',(mp3) 'array','sampling_rate' (48000) |
112
+
113
+
114
+ ### Version B
115
+ 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.
116
+
117
+
118
+ ### Dataset Creation
119
+ We are providing a **train**, **dev** and **test** split. These are the same as in the orginal corpus.
120
+
121
+ Build date: 20012022
122
+
123
+ #### Initial Data Collection and Curation
124
+ The procedure for the dataset creation is described in detail in the paper.
125
+
126
+
127
+ ## Statistics
128
+ | Feature | Value |
129
+ |:---------|-----------:|
130
+ | Duration, pauses included | 140,3 hours|
131
+ | Duration, pauses not included | 125,7 hours |
132
+ | Word count | 1,2 million |
133
+ | Sentence count | 64.531 |
134
+ | Language distribution | Nynorsk: 12,8%|
135
+ | | Bokmål: 87,2%%|
136
+ | Gender distribution | Female: 38,3% |
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+ | | Male: 61.7% |
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+
139
+ ## Considerations for Using the Data
140
+ This corpus contains speech data and is allowed to be used outside the National Library of Norway for speech recognition technology purposes.
141
+
142
+ ### Discussion of Biases
143
+ Please refer to our paper.
144
+
145
+ ### Dataset Curators
146
+ [Per Erik Solberg](mailto:per.solberg@nb.no)
147
+
148
+ [Freddy Wetjen](mailto:Freddy.wetjen@nb.no), [Andre Kaasen](mailto:andre.kasen@nb.no) and [Per Egil Kummervold](mailto: per.kummervold@nb.no) has contributed to porting it to Hugging Face.
149
+
150
+ ### Licensing Information
151
+ Licensed for use outside the National Library of Norway.
152
+
153
+ ## License
154
+ CC-ZERO(https://creativecommons.org/publicdomain/zero/1.0/)
155
+
156
+ ### Citation Information
157
+ 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:
158
+ ```
159
+ ANDRE: TO BE DONE
160
+
161
+ ```
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