readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: Whisper Medium MS - Augmented
    results:
    - task:
        type: automatic-speech-recognition
        name: Automatic Speech Recognition
      dataset:
        name: google/fleurs
        type: google/fleurs
        config: ms_my
        split: test
        args: ms_my
      metrics:
      - type: wer
        value: 9.578362255965294
        name: WER
      - type: cer
        value: 2.8109053797929726
        name: CER
  ---
  
  <!-- 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 Medium MS - Augmented
  
  This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2066
  - Wer: 9.5784
  - Cer: 2.8109
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  Training:
  - [google/fleurs](https://huggingface.co/datasets/google/fleurs) (train+validation)
  
  Evaluation:
  - [google/fleurs](https://huggingface.co/datasets/google/fleurs) (test)
  
  ## Training procedure
  
  Datasets were augmented on-the-fly using [audiomentations](https://github.com/iver56/audiomentations) via PitchShift and TimeStretch transformations at `p=0.3`.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - gradient_accumulation_steps: 16
  - 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: 100
  - training_steps: 1000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|
  | 0.0876        | 2.15  | 200  | 0.1949          | 10.3105 | 3.0685 |
  | 0.0064        | 4.3   | 400  | 0.1974          | 9.7004  | 2.9596 |
  | 0.0014        | 6.45  | 600  | 0.2031          | 9.6190  | 2.8955 |
  | 0.001         | 8.6   | 800  | 0.2058          | 9.6055  | 2.8440 |
  | 0.0009        | 10.75 | 1000 | 0.2066          | 9.5784  | 2.8109 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  