metadata

dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: sentence
      dtype: string
  splits:
    - name: train
      num_bytes: 2458890204.774
      num_examples: 12421
    - name: dev
      num_bytes: 321013046.5
      num_examples: 1700
    - name: test
      num_bytes: 334783172.271
      num_examples: 1359
  download_size: 3025102759
  dataset_size: 3114686423.545
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*
task_categories:
  - translation
language:
  - af

Description

This is speech dataset of Bemba language. This dataset was acquired from (BembaSpeech)[https://github.com/csikasote/BembaSpeech/tree/master]. BembaSpeech is the speech recognition corpus in Bemba [1].

Dataset Structure

DatasetDict({
    train: Dataset({
        features: ['audio', 'sentence'],
        num_rows: 12421
    })
    dev: Dataset({
        features: ['audio', 'sentence'],
        num_rows: 1700
    })
    test: Dataset({
        features: ['audio', 'sentence'],
        num_rows: 1359
    })
})

Citation

1. @InProceedings{sikasote-anastasopoulos:2022:LREC,
    author    = {Sikasote, Claytone  and  Anastasopoulos, Antonios},
    title     = {BembaSpeech: A Speech Recognition Corpus for the Bemba Language},
    booktitle      = {Proceedings of the Language Resources and Evaluation Conference},
    month          = {June},
    year           = {2022},
    address        = {Marseille, France},
    publisher      = {European Language Resources Association},
    pages     = {7277--7283},
    abstract  = {We present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a written but low-resourced language spoken by over 30\% of the population in Zambia. To assess its usefulness for training and testing ASR systems for Bemba, we explored different approaches; supervised pre-training (training from scratch), cross-lingual transfer learning from a monolingual English pre-trained model using DeepSpeech on the portion of the dataset and fine-tuning large scale self-supervised Wav2Vec2.0 based multilingual pre-trained models on the complete BembaSpeech corpus. From our experiments, the 1 billion XLS-R parameter model gives the best results. The model achieves a word error rate (WER) of 32.91\%, results demonstrating that model capacity significantly improves performance and that multilingual pre-trained models transfers cross-lingual acoustic representation better than monolingual pre-trained English model on the BembaSpeech for the Bemba ASR. Lastly, results also show that the corpus can be used for building ASR systems for Bemba language.},
    url       = {https://aclanthology.org/2022.lrec-1.790}
  }