readme: ---
  language:
  - bn
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - bn
  - hf-asr-leaderboard
  - openslr_SLR53
  - robust-speech-event
  datasets:
  - openslr
  - SLR53
  - AI4Bharat/IndicCorp
  metrics:
  - wer
  - cer
  model-index:
  - name: arijitx/wav2vec2-xls-r-300m-bengali
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        type: openslr
        name: Open SLR
        args: SLR53
      metrics:
      - type: wer
        value: 0.21726385291857586
        name: Test WER
      - type: cer
        value: 0.04725010353701041
        name: Test CER
      - type: wer
        value: 0.15322879016421437
        name: Test WER with lm
      - type: cer
        value: 0.03413696666806267
        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: 0.21726385291857586
  - CER: 0.04725010353701041
  
  With 5 gram language model trained on 30M sentences randomly chosen from [AI4Bharat IndicCorp](https://indicnlp.ai4bharat.org/corpora/) dataset : 
  - WER: 0.15322879016421437
  - CER: 0.03413696666806267
  
    
  
  Note : 5% of a total 10935 samples have been used for evaluation. Evaluation set has 10935 examples which was not part of training training was done on first 95% and eval was done on last 5%. Training was stopped after 180k steps. Output predictions are available under files section.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  - dataset_name="openslr"  	
  - model_name_or_path="facebook/wav2vec2-xls-r-300m"  	
  - dataset_config_name="SLR53"  	
  - output_dir="./wav2vec2-xls-r-300m-bengali"  	
  - overwrite_output_dir  	
  - num_train_epochs="50"  	
  - per_device_train_batch_size="32"  	
  - per_device_eval_batch_size="32"  	
  - gradient_accumulation_steps="1"  	
  - learning_rate="7.5e-5"  	
  - warmup_steps="2000"  	
  - length_column_name="input_length"  	
  - evaluation_strategy="steps"  	
  - text_column_name="sentence"  	
  - chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … –  	
  - save_steps="2000"  	
  - eval_steps="3000"  	
  - logging_steps="100"  	
  - layerdrop="0.0"  	
  - activation_dropout="0.1"  	
  - save_total_limit="3"  	
  - freeze_feature_encoder  	
  - feat_proj_dropout="0.0"  	
  - mask_time_prob="0.75"  	
  - mask_time_length="10"  	
  - mask_feature_prob="0.25"  	
  - mask_feature_length="64"      
  - preprocessing_num_workers 32  
  
  ### Framework versions
  
  - Transformers 4.16.0.dev0
  - Pytorch 1.10.1+cu102
  - Datasets 1.17.1.dev0
  - Tokenizers 0.11.0
  
  Notes
  - Training and eval code modified from : 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.
  - Minimum audio duration of 0.5s has been used to filter the training data which excluded may be 10-20 samples.
  - OpenSLR53 transcripts are *not* part of LM training and LM used to evaluate.