readme: ---
  language:
  - bn
  license: apache-2.0
  tags:
  - audio
  - speech
  - Bengali
  - Bangla
  - speech-to-text
  datasets:
  - openslr
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
  metrics:
  - wer
  pipeline_tag: automatic-speech-recognition
  model-index:
  - name: Rakib/whisper-small-bn-all
    results:
    - task:
        type: automatic-speech-recognition
        name: Speech Recognition
      dataset:
        name: Common Voice 11.0 & Google-Fleurs Bengali Split
        type: mozilla-foundation/common_voice_11_0 & google/fleurs
        config: bn(common_voice_11_0) & bn_in(google/fleurs)
        split: test
      metrics:
      - type: wer
        value: 13.21539
        name: Test WER
        config: wer
        verified: false
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0
        type: mozilla-foundation/common_voice_11_0
        config: bn
        split: test
      metrics:
      - type: wer
        value: 13.44
        name: WER
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        config: bn_in
        split: test
      metrics:
      - type: wer
        value: 11.78
        name: WER
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # openai/whisper-small
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Bengali split of
  mozilla-foundation/common_voice_11_0, google/fleurs and openslr53 dataset.
  
  It achieves the following results on the evaluation set 
  (Combined test set of the "bn" split of mozilla-foundation/common_voice_11_0 & google/fleurs):
  - Eval Loss: 0.085
  - Eval Wer: 13.21539 
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 4
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 3000
  - training_steps: 30000
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.2355        | 0.13  | 1000  | 0.2900          | 38.5155 |
  | 0.1489        | 0.26  | 2000  | 0.1963          | 27.5883 |
  | 0.1232        | 0.39  | 3000  | 0.1634          | 22.6962 |
  | 0.103         | 0.52  | 4000  | 0.1360          | 19.7700 |
  | 0.0937        | 0.65  | 5000  | 0.1224          | 17.4256 |
  | 0.0768        | 0.78  | 6000  | 0.1127          | 16.4850 |
  | 0.0714        | 0.91  | 7000  | 0.1039          | 15.4131 |
  | 0.0545        | 1.04  | 8000  | 0.1001          | 14.6527 |
  | 0.0486        | 1.17  | 9000  | 0.0980          | 14.8002 |
  | 0.0549        | 1.29  | 10000 | 0.0944          | 14.6506 |
  | 0.0498        | 1.42  | 11000 | 0.0896          | 14.7498 |
  | 0.0506        | 1.55  | 12000 | 0.0873          | 13.8419 |
  | 0.0495        | 1.68  | 13000 | 0.0851          | 13.2154 |
  | 0.0441        | 1.81  | 14000 | 0.0840          | 13.9406 |
  | 0.0435        | 1.94  | 15000 | 0.0812          | 14.0055 |
  | 0.0277        | 2.07  | 16000 | 0.0822          | 14.6130 |
  | 0.03          | 2.2   | 17000 | 0.0803          | 16.6346 |
  | 0.0308        | 2.33  | 18000 | 0.0823          | 19.7865 |
  | 0.0265        | 2.46  | 19000 | 0.0817          | 16.4185 |
  | 0.0247        | 2.59  | 20000 | 0.0784          | 22.3524 |
  | 0.0277        | 2.72  | 21000 | 0.0777          | 19.5960 |
  | 0.0236        | 2.85  | 22000 | 0.0778          | 20.7329 |
  | 0.0263        | 2.98  | 23000 | 0.0765          | 21.3614 |
  | 0.0162        | 3.11  | 24000 | 0.0819          | 25.6551 |
  | 0.0151        | 3.24  | 25000 | 0.0815          | 33.6201 |
  | 0.0162        | 3.37  | 26000 | 0.0813          | 32.6457 |
  | 0.0151        | 3.5   | 27000 | 0.0820          | 30.7683 |
  | 0.0166        | 3.63  | 28000 | 0.0814          | 32.2357 |
  | 0.017         | 3.75  | 29000 | 0.0808          | 31.2332 |
  | 0.0154        | 3.88  | 30000 | 0.0811          | 31.1952 |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 2.0.0.dev20230117+cu117
  - Datasets 2.9.0
  - Tokenizers 0.13.2