readme: ---
  language:
    - mn
  license: apache-2.0
  tags:
    - whisper-event
    - hf-asr-leaderboard
    - generated_from_trainer
  datasets:
    - mozilla-foundation/common_voice_11_0
  metrics:
    - wer
  model-index:
    - name: whisper-small-mn-1-bayartsogt
      results:
        - task:
            name: Automatic Speech Recognition
            type: automatic-speech-recognition
          dataset:
            name: Common Voice 11.0
            type: mozilla-foundation/common_voice_11_0
            config: mn
            split: test
            args:
              language: mn
          metrics:
            - name: Wer
              type: wer
              value: 46.68450950404195
  
  ---
  
  <!-- 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-mn-1
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 mn dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4537
  - Wer: 46.6845
  - Cer: 16.1695
  
  ## 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: 32
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 1000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
  | 0.1121        | 4.26  | 1000 | 0.4537          | 46.6845 | 16.1695 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  