readme: ---
  language:
  - bn
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - openslr_SLR53
  - robust-speech-event
  datasets:
  - openslr
  - SLR53
  - Harveenchadha/indic-text
  metrics:
  - wer
  - cer
  model-index:
  - name: Tahsin-Mayeesha/wav2vec2-bn-300m
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: openslr
        name: Open SLR
        args: SLR66
      metrics:
      - type: wer
        value: 0.31104373941386626
        name: Test WER
      - type: cer
        value: 0.07263099973420006
        name: Test CER
      - type: wer
        value: 0.17776164652632478
        name: Test WER with lm
      - type: cer
        value: 0.04394092712884769
        name: Test CER with lm
  ---
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
  It achieves the following results on the evaluation set. 
  
  Without language model : 
  - Wer: 0.3110
  - Cer : 0.072
  
  With 5 gram language model trained on [indic-text](https://huggingface.co/datasets/Harveenchadha/indic-text/tree/main) dataset : 
  - Wer: 0.17776
  - Cer : 0.04394
    
  
  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.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 7.5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - gradient_accumulation_steps: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - mixed_precision_training: Native AMP
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  
  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. 