audio_id
stringlengths
14
14
audio
audioduration (s)
1.02
576,461B
speaker_id
stringlengths
6
6
gender
stringclasses
4 values
age
stringclasses
15 values
duration
float32
0
16.8
normalized_text
stringlengths
2
109
016568-0850457
016568
male
10
1.63
en gul
016568-0851450
016568
male
10
2
stúlkan leit snöggt upp
016568-0850696
016568
male
10
1.81
gamli gaur
016568-0851600
016568
male
10
3.44
hvað er nú stefán minn
016568-0851570
016568
male
10
1.72
ekki nema það þó
016568-0850945
016568
male
10
1.49
biður hann að lækka
016568-0851135
016568
male
10
1.53
og bíða
016568-0851155
016568
male
10
1.95
og hvað jón og hvað
016568-0851497
016568
male
10
1.63
hjartað mitt
016568-0851088
016568
male
10
1.49
hún sagði
016568-0851657
016568
male
10
2.46
hann stofnar okkur í hættu
016568-0851525
016568
male
10
2.79
öryggi og eilífir draumar
016568-0851618
016568
male
10
1.11
það batnar
016568-0851634
016568
male
10
1.25
fyrir alla muni
016568-0851470
016568
male
10
2
hann myndi verja sín börn
016910-0917980
016910
female
11
3.93
svenni kemur inn í stofuna í þessum svifum
016910-0915946
016910
female
11
3.71
hann hefur þegið líf sitt af guði
016910-0916845
016910
female
11
3.03
og það yrði læst á milli
016910-0918263
016910
female
11
3.03
það lak blóð úr enninu á mér
016910-0915916
016910
female
11
4.82
það gleður eddu hvað þeim þykir vænt um drenginn hennar
016910-0916330
016910
female
11
3.16
þau höfðu einu sinni verið ung
016910-0916540
016910
female
11
3.37
hann hljóp við fót og út í sjoppu
016910-0916003
016910
female
11
3.54
mía lækkaðu í hátalaranum
016910-0917314
016910
female
11
3.37
finnur fyrir vanmætti hvern einasta dag
016910-0917349
016910
female
11
3.84
hann teygði hendurnar niður til hennar
016910-0916266
016910
female
11
4.44
grunur minn er alltaf á rökum reistur
018352-1194347
018352
male
8
3.97
elsku amma dóra
018352-1192942
018352
male
8
1.49
sigga að gera
018352-1193329
018352
male
8
1.88
þinn magnús andri
018352-1193231
018352
male
8
4.74
þetta helst í hendur
018352-1192513
018352
male
8
2.77
far þú í friði
018352-1194299
018352
male
8
5.33
hún hafi ekki íhugað afsögn
018352-1193082
018352
male
8
2.9
þar á meðal eru helst
018352-1195098
018352
male
8
5.08
það sagðir þú alltaf
018352-1193427
018352
male
8
2.18
nei það finnst mér ekki
018352-1193124
018352
male
8
3.07
skarð sem aldrei verður fyllt
018352-1194426
018352
male
8
4.52
hátíð fer að höndum ein
018352-1192812
018352
male
8
9.47
góða ferð elsku edda
018352-1192588
018352
male
8
2.35
næg eru dæmin
018352-1194321
018352
male
8
5.16
nesti dagsins er búið
018352-1194252
018352
male
8
3.93
blessuð sé minning þín mamma
018352-1194179
018352
male
8
3.29
far vel rembur
018352-1192700
018352
male
8
1.79
halda rónni
018352-1193379
018352
male
8
2.18
og kom víða við
018352-1193571
018352
male
8
3.54
ekki neitt
018352-1193291
018352
male
8
2.09
áttu marga gítara
018352-1193311
018352
male
8
1.66
ég segi bara takk
018352-1193558
018352
male
8
5.29
gínu er annars vegar
018352-1193668
018352
male
8
4.27
hvernig myndu skotar kjósa
018352-1193714
018352
male
8
5.59
þar mætast stálin stinn
018352-1194547
018352
male
8
2.43
svo gúrku
018352-1193357
018352
male
8
2.56
steini kunni ráð við því
016562-0850461
016562
female
10
6.13
þú hefur ekki rakað þig
016562-0852317
016562
female
10
4.04
bjössi svarar ekki strax
016562-0849388
016562
female
10
6.04
viltu kaffi eða te
016562-0852372
016562
female
10
2.09
hvað viltu
016562-0850651
016562
female
10
2.93
fnæsir ekki
016562-0852520
016562
female
10
2.55
ég hendist inn til mín
016562-0850323
016562
female
10
2.41
þeir komu út
016562-0850105
016562
female
10
3.16
ef þess gerist þörf
016562-0850296
016562
female
10
2.46
ég sendi því mat
016562-0852995
016562
female
10
2.93
rólegan æsing elskan
016562-0852336
016562
female
10
2.23
þriggja barna faðir
016562-0852762
016562
female
10
2.23
mun aldrei spyrja
016562-0851136
016562
female
10
1.95
allt í lagi krúttið mitt
016562-0852944
016562
female
10
1.9
ganga á eftir
016562-0850437
016562
female
10
2.88
hvað mundi hún segja
016562-0849676
016562
female
10
2.88
enda nóg komið
016562-0852904
016562
female
10
2.93
ég sótti það mjög fast
016562-0850407
016562
female
10
3.11
allar grétu þær
016562-0849449
016562
female
10
2.69
ég á þau ein
016562-0850557
016562
female
10
4.18
verður að frelsa hann
016562-0850274
016562
female
10
3.72
ég hætti þessu
016562-0852359
016562
female
10
3.39
það er í næstu götu
016562-0849650
016562
female
10
3.44
og honum mun standa
016562-0852416
016562
female
10
2.09
það er nóg komið
016562-0852727
016562
female
10
3.39
öndum og dönsum
016562-0850381
016562
female
10
4.5
svo heyrði ég þau hvísla
016562-0851228
016562
female
10
1.86
og hann er ungur
016562-0850239
016562
female
10
3.16
bíll keyrði hægt framhjá honum
016562-0850491
016562
female
10
2.79
fundist það
016562-0850138
016562
female
10
4.41
agnes varpar öndinni léttar
016562-0852743
016562
female
10
2
segir þú
016562-0849580
016562
female
10
7.52
ég skrifa meira í kvöld
016562-0852977
016562
female
10
3.95
falla á gólfið
016562-0850076
016562
female
10
3.11
hann virðist hafa það gott
016562-0851171
016562
female
10
2.74
en allir í bænum fórust
016562-0852966
016562
female
10
2.74
mæður iðrast
016562-0849984
016562
female
10
2.74
sekk ofan í sætið
016562-0850018
016562
female
10
2.6
viltu fara frá
016562-0851295
016562
female
10
1.95
hún var systir mín
018277-1175219
018277
male
9
5.25
þar skipti hulda máli
018277-1175454
018277
male
9
3.71
hún var henni allt
018277-1175409
018277
male
9
4.1
þau réðu
018277-1173672
018277
male
9
3.33
þín áslaug
018277-1175387
018277
male
9
4.35
ég átti þá bestu
018277-1173728
018277
male
9
4.99
það á um þorpin líka
018277-1175574
018277
male
9
5.89
þar voru þau við nám
018277-1175845
018277
male
9
4.74
eru álfar til
018277-1174539
018277
male
9
7.3
hennar missir er mikill

Dataset Card for samromur_children

Dataset Summary

The Samrómur Children Corpus consists of audio recordings and metadata files containing prompts read by the participants. It contains more than 137000 validated speech-recordings uttered by Icelandic children.

The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021).

Example Usage

The Samrómur Children Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:

from datasets import load_dataset
samromur_children = load_dataset("language-and-voice-lab/samromur_children")

To load an specific split (for example, the validation split) do:

from datasets import load_dataset
samromur_children = load_dataset("language-and-voice-lab/samromur_children",split="validation")

Supported Tasks

automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).

Languages

The audio is in Icelandic. The reading prompts were gathered from a variety of sources, mainly from the Icelandic Gigaword Corpus. The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the Icelandic Web of Science.

Dataset Structure

Data Instances

{
  'audio_id': '015652-0717240', 
  'audio': {
    'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/2c6b0d82de2ef0dc0879732f726809cccbe6060664966099f43276e8c94b03f2/test/015652/015652-0717240.flac', 
    'array': array([ 0.        ,  0.        ,  0.        , ..., -0.00311279,
       -0.0007019 ,  0.00128174], dtype=float32), 
    'sampling_rate': 16000
  }, 
  'speaker_id': '015652', 
  'gender': 'female', 
  'age': '11', 
  'duration': 4.179999828338623, 
  'normalized_text': 'eiginlega var hann hin unga rússneska bylting lifandi komin'
}

Data Fields

  • audio_id (string) - id of audio segment
  • audio (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
  • speaker_id (string) - id of speaker
  • gender (string) - gender of speaker (male or female)
  • age (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+).
  • duration (float32) - duration of the audio file in seconds.
  • normalized_text (string) - normalized audio segment transcription.

Data Splits

The corpus is split into train, dev, and test portions. Lenghts of every portion are: train = 127h25m, test = 1h50m, dev=1h50m.

To load an specific portion please see the above section "Example Usage".

Dataset Creation

Curation Rationale

In the field of Automatic Speech Recognition (ASR) is a known fact that the children's speech is particularly hard to recognise due to its high variability produced by developmental changes in children's anatomy and speech production skills.

For this reason, the criteria of selection for the train/dev/test portions have to take into account the children's age. Nevertheless, the Samrómur Children is an unbalanced corpus in terms of gender and age of the speakers. This means that the corpus has, for example, a total of 1667 female speakers (73h38m) versus 1412 of male speakers (52h26m).

These unbalances impose conditions in the type of the experiments than can be performed with the corpus. For example, a equal number of female and male speakers through certain ranges of age is impossible. So, if one can't have a perfectly balance corpus in the training set, at least one can have it in the test portion.

The test portion of the Samrómur Children was meticulously selected to cover ages between 6 to 16 years in both female and male speakers. Every of these range of age in both genders have a total duration of 5 minutes each.

The development portion of the corpus contains only speakers with an unknown gender information. Both test and dev sets have a total duration of 1h50m each.

In order to perform fairer experiments, speakers in the train and test sets are not shared. Nevertheless, there is only one speaker shared between the train and development set. It can be identified with the speaker ID=010363. However, no audio files are shared between these two sets.

Source Data

Initial Data Collection and Normalization

The data was collected using the website https://samromur.is, code of which is available at https://github.com/cadia-lvl/samromur. The age range selected for this corpus is between 4 and 17 years.

The original audio was collected at 44.1 kHz or 48 kHz sampling rate as *.wav files, which was down-sampled to 16 kHz and converted to *.flac. Each recording contains one read sentence from a script. The script contains 85.080 unique sentences and 90.838 unique tokens.

There was no identifier other than the session ID, which is used as the speaker ID. The corpus is distributed with a metadata file with a detailed information on each utterance and speaker. The madata file is encoded as UTF-8 Unicode.

The prompts were gathered from a variety of sources, mainly from The Icelandic Gigaword Corpus, which is available at http://clarin.is/en/resources/gigaword. The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the Icelandic Web of Science.

Annotations

Annotation process

Prompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the DIM: Database Icelandic Morphology.

There are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device.

Who are the annotators?

The audio files content was manually verified against the prompts by one or more listener (summer students mainly).

Personal and Sensitive Information

The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.

Considerations for Using the Data

Social Impact of Dataset

This is the first ASR corpus of Icelandic children.

Discussion of Biases

  • The utterances were recorded by a smartphone or the web app.

  • Participants self-reported their age group, gender, and the native language.

  • Participants are aged between 4 to 17 years.

  • The corpus contains 137597 utterances from 3175 speakers, totalling 131 hours.

  • The amount of data due to female speakers is 73h38m, the amount of data due to male speakers is 52h26m and the amount of data due to speakers with an unknown gender information is 05h02m

  • The number of female speakers is 1667, the number of male speakers is 1412. The number of speakers with an unknown gender information is 96.

  • The audios due to female speakers are 78993, the audios due to male speakers are 53927 and the audios due to speakers with an unknown gender information are 4677.

Other Known Limitations

"Samrómur Children: Icelandic Speech 21.09" by the Language and Voice Laboratory (LVL) at the Reykjavik University is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Additional Information

Dataset Curators

The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021). The corpus was curated by Carlos Daniel Hernández Mena in 2021.

Licensing Information

CC-BY-4.0

Citation Information

@misc{menasamromurchildren2021,
      title={Samrómur Children Icelandic Speech 1.0}, 
      ldc_catalog_no={LDC2022S11},
      DOI={https://doi.org/10.35111/frrj-qd60},
      author={Hernández Mena, Carlos Daniel and Borsky, Michal and Mollberg, David Erik  and Guðmundsson, Smári Freyr and Hedström, Staffan and Pálsson, Ragnar and Jónsson, Ólafur Helgi and Þorsteinsdóttir, Sunneva and Guðmundsdóttir, Jóhanna Vigdís and Magnúsdóttir, Eydís Huld and Þórhallsdóttir, Ragnheiður and Guðnason, Jón},
      publisher={Reykjavík University},
      journal={Linguistic Data Consortium, Philadelphia},
      year={2021},
      url={https://catalog.ldc.upenn.edu/LDC2022S11},
}

Contributions

This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.

The verification for the dataset was funded by the the Icelandic Directorate of Labour's Student Summer Job Program in 2020 and 2021.

Special thanks for the summer students for all the hard work.

Downloads last month
142
Edit dataset card

Models trained or fine-tuned on language-and-voice-lab/samromur_children