Update README.md

README.md

@@ -26,4 +26,49 @@ configs:
     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}
+  }
+```
+