readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: Whisper_small_Khmer
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: google/fleurs km_kh
        type: google/fleurs
        config: km_kh
        split: test
      metrics:
      - name: Wer
        type: wer
        value: 84.941730294506
  ---
  
  <!-- 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. -->
  
  # Whisper_small_Khmer
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs km_kh dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9221
  - Wer: 84.9417
  
  ## 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: 8
  - eval_batch_size: 16
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 4
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - total_eval_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 2000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.5571        | 40.0  | 400  | 1.2022          | 89.9564 |
  | 0.0088        | 80.0  | 800  | 1.7980          | 86.6669 |
  | 0.0023        | 120.0 | 1200 | 1.9221          | 84.9417 |
  | 0.0002        | 160.0 | 1600 | 2.0559          | 85.4326 |
  | 0.0002        | 200.0 | 2000 | 2.0787          | 85.6536 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  