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metadata
license: cc-by-sa-4.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: transcription
      dtype: string
    - name: character_name
      dtype: string
    - name: gender
      dtype: string
    - name: age
      dtype: string
    - name: Dialect
      dtype: string
    - name: Title_ckb_arab
      dtype: string
    - name: Title_ckb_latn
      dtype: string
    - name: Episode
      dtype: string
    - name: Episode_ckb_arab
      dtype: string
    - name: Episode_ckb_latn
      dtype: string
    - name: Type
      dtype: string
    - name: Genre
      dtype: string
  splits:
    - name: train
      num_bytes: 4516986412.81
      num_examples: 186126
  download_size: 4561611360
  dataset_size: 4516986412.81
  tags:
    - asr
    - less-resourced
    - NLP
    - Kurdish
  language:
    - ckb
    - ku
  pretty_name:
    - CORDI

CORDI — Corpus of Dialogues in Central Kurdish

CORDI

➡️ See the repository on GitHub

This repository provides resources for language and speech technology for Central Kurdish varieties discussed in our LREC-COLING 2024 paper, particularly the first annotated corpus of spoken Central Kurdish varieties — CORDI. Given the financial burden of traditional ways of documenting languages and varieties as in fieldwork, we follow a rather novel alternative where movies and series are transcribed. Based on the cities where the content are produced, we categorize the material with the following city names:

  • Sulaymaniyah. Traditionally, the variety spoken in this region is referred to as Babani (بابانی)
  • Sanandaj. Traditionally, the variety spoken in this region is referred to as Ardalani (ئەردەڵانی).
  • Mahabad where the Mukriyani (موکریانی) or Mukri variety is spoken
  • Erbil or Hewlêr (هەولێر)
  • Sardasht (سەردەشت)
  • Kalar (کەلار)

CORDI epicenters

CORDI contains transcriptions of 311 movies and episodes with 186,038 utterances in more than 100 hours of recording. It is openly available and can pave the way for further progress in NLP tasks for Central Kurdish varieties such as Kurdish ASR and machine translation.

More details can be found in the paper: CORDI paper

Samples

Here are a few samples from CORDI (turn audio on to play):

Sulaymaniyah:

  • Sentence (Female): "ئا دە بەو خوایە هەبێ و نەبێ ئەمە بە دایکی مناڵەکان ئەڵێی شەریکی حەیاتم"

https://github.com/sinaahmadi/CORDI/assets/12765529/d77097e2-9ccc-45e9-85e8-bcda7961a9aa

  • Sentence (Male): "نازانی ئەترسن، ئەی نەو بیستوە ئەڵێ ماران گەستە لە گوریسیش ئەترسێ"

https://github.com/sinaahmadi/CORDI/assets/12765529/a67b0ffe-9bd2-411a-ad01-8afca3720686

Erbil:

  • Sentence (Female): "تێگەیشتی بەڵام لەو قازانجەی دەستان دەکەوێ نیوەکی ئی منە"

https://github.com/sinaahmadi/CORDI/assets/12765529/139d14e3-9a5e-474c-a6e4-189e7d204ef6

  • Sentence (Male): "پێی برێن ئەگەر ڕای لێ نییە با بڕواتە مەنتیقەکی دیکە تەلەفۆن بکات موشتەریم زۆرە"

https://github.com/sinaahmadi/CORDI/assets/12765529/9ec4b544-fc65-477c-a1fd-ebd6308b7d8d

Sanandaj:

  • Sentence (Female): "دەی مەیه خوەیشکه کی وتییه بییه کەنیشک ئایت بە شوو، بییه بە کەسێ تر"

https://github.com/sinaahmadi/CORDI/assets/12765529/50491f95-c5b6-4a42-b419-3a51f8722bcf

  • Sentence (Male): "بنیام لە بەر ئەو ئاپارتمانە ئەوێسێ، سوح تا ئێوارە تماشای بکا، تێری لێ ناخوا"

https://github.com/sinaahmadi/CORDI/assets/12765529/43a514f2-0984-440a-9988-597df2f3eb49

Mahabad:

  • Sentence (Female): "حا، خەجیج خانم گیان بە قوربانت بم ئەتووش نفووسی خراپ لێ مەدە مە"

https://github.com/sinaahmadi/CORDI/assets/12765529/2f8440b0-ea38-498f-963e-13f5f01dcfb0

  • Sentence (Male): "بۆ لەبەر چی ئەرێ ئەمن نازانم بۆ ئەو قەدەرەی هەموو لە من قەڵسن بۆ لە من ناڕەحەتن"

https://github.com/sinaahmadi/CORDI/assets/12765529/2051e772-89b2-4b3f-b242-b5ef68c01ed7

Structure

CORDI available here is compatible with Hugging Face audio data format. If, instead, you prefer to use other format, please refer to the following.

The metadata of the corpus is described in JSON and TSV with the following information:

{
    "CORDI": {
        "2": {
            "Dialect": "Silêmanî",
            "Title_ckb_arab": "بەرنامەی بەرنامە",
            "Title_ckb_latn": "Bernamey bername",
            "Episode": "1",
            "Episode_ckb_arab": "عەرەبانەکە",
            "Episode_ckb_latn": "`Erebaneke",
            "Type": "Theatre show",
            "Genre": "Comedy",
            "is_synched": true,
            "Comment": "",
            "text": "content/text_original/2.srt",
            "audio": "content/audio/2.wav",
            "video": "content/video/2.mp4"
        }
    }
}

Each entry in the metadata file points to a movie or episode which is itself described as follows:

    {
        "filename": "2",
        "utterance_ID": "2",
        "gender": "F",
        "age": "A",
        "text": "وەڵا مەخۆ وەڵا ناوی گرانە ئە ئەمە کەی کوولەکەی خۆمانە",
        "audio": "content/segments/2/2.ogg",
        "dialect": "Silêmanî"
    }

Download CORDI

Only text:

Text + audio:

  • Segments (4GB) contains utterances in audio (.ogg) and their transcriptions in JSON. This can be used for ASR.

In case you want to work on the original material, you can find the videos here: link (27GB).

Downstream tasks

In addition to the corpus, we focus on three downstream tasks in this project: automatic speech recognition (ASR), machine translation (MT) and language identification (LID). While the details of the ASR systems are described in the paper, more details can be found about the two other tasks in their folders:

Report problems

Given the size of the projects, it is totally possible that some utterances are not synchronized correctly. We currently don't have much time to each them manually. However, if you notice any serious problem, please consider reporting (in issues) or correct them. We also appreciate any efforts to further clean the transcriptions.

Cite this corpus

If you're using this resource, please cite this paper: 

@inproceedings{ahmadi2024cordi,
  title = "Language and Speech Technology for {Central Kurdish} Varieties",
  author = "Ahmadi, Sina and Q. Jaff, Daban and Ibn Alam, Md Mahfuz and Anastasopoulos, Antonios",
  booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
  month = may,
  year = "2024",
  address = "Torino, Italy",
  publisher = "ELRA Language Resources Association"
}

Acknowledgements

We express our heartfelt gratitude to the volunteers at Koya University who actively participated in the transcription and annotation tasks spanning a period of ten months from June 2021 to April 2022. Many low-resourced languages face financial constraints and Kurdish is regrettably no exception. Nevertheless, the 36 individuals mentioned below (in alphabetical order) showcase how passion for a language can overcome obstacles despite not being remunerated monetarily:

Niyan Abdulla Omer, Sharmin Ahmadi, Shnya Aram Ahmad, Muhammad Aram Jalal, Zaytwn Awny Sabir, Lavin Azwar Omar, Shnyar Bakhtyar Karim, Sazan Barzani Ali, Rayan Bestun Abdulla, Roshna Bestun Abdulla, Halala Edres Omer, Elaf Farhad Muhammad, Sima Farhad Qadr, Ibrahem Ismail Nadr, Chnar Kamal Sleman, Muhamad Kamaran Ahmad, Raman Kazm Hamad, Burhan Luqman Khursheed, Shaima Mikaeel Esmaeel, Lavan Muhammad Smail, Dween Muhammed Jamal, Hana Muhammed Rashid, Amen Muhseen Nasr, Bryar Murshid Mustafa, Rayan Mzafar Tofiq, Taban Omar Mohamad, Nian Qasim Jaff, Dilan Raza Nadr, Razaw S. Bor, Soma Salam Arif, Zulaykha Samad Abdulla, Awdang Saman Abdullqahar, Eman Sardar Hamed, Sakar Star Omar, Nawa Taha Yasin, Triska Zrar Mawlood.

Muhammad Aziz, Harman Hameed and Xaliss Jamal kindly contributed to the translation of sentences of the Erbil variant in the parallel corpus as well.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

CC BY-SA 4.0