This model is a fine-tuned version of
facebook/wav2vec2-xls-r-300m on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set.
With 5 gram language model trained on
indic-text dataset :
Note : 10% of a total 218703 samples have been used for evaluation. Evaluation set has 21871 examples. Training was stopped after 30k steps. Output predictions are available under files section.
Note : Training and evaluation script modified from
https://huggingface.co/chmanoj/xls-r-300m-te and
https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event.
Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used.
Note 2 : Minimum audio duration of 0.1s has been used to filter the training data which excluded may be 10-20 samples.
@misc {tahsin_mayeesha_2023,
author = { {Tahsin Mayeesha} },
title = { wav2vec2-bn-300m (Revision e10defc) },
year = 2023,
url = {
https://huggingface.co/Tahsin-Mayeesha/wav2vec2-bn-300m },
doi = { 10.57967/hf/0939 },
publisher = { Hugging Face }
}
