readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - google/fleurs
  metrics:
  - wer
  model-index:
  - name: Whisper small Luxembourgish
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: google/fleurs lb_lu
        type: google/fleurs
        config: lb_lu
        split: test
      metrics:
      - name: Wer
        type: wer
        value: 39.49904580152672
  ---
  
  <!-- 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 Luxembourgish
  
  This model is a fine-tuned version of [bofenghuang/whisper-small-cv11-german-punct](https://huggingface.co/bofenghuang/whisper-small-cv11-german-punct) on the google/fleurs lb_lu dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1857
  - Wer: 39.4990
  
  ## 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: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer     |
  |:-------------:|:------:|:----:|:---------------:|:-------:|
  | 0.0618        | 38.46  | 500  | 1.0104          | 43.2968 |
  | 0.0055        | 76.92  | 1000 | 1.0684          | 40.1288 |
  | 0.0024        | 115.38 | 1500 | 1.1056          | 40.9447 |
  | 0.0014        | 153.85 | 2000 | 1.1280          | 39.7615 |
  | 0.0013        | 192.31 | 2500 | 1.1415          | 39.9857 |
  | 0.0008        | 230.77 | 3000 | 1.1573          | 39.7996 |
  | 0.0006        | 269.23 | 3500 | 1.1682          | 40.0095 |
  | 0.0006        | 307.69 | 4000 | 1.1769          | 39.7233 |
  | 0.0005        | 346.15 | 4500 | 1.1826          | 39.5134 |
  | 0.0004        | 384.62 | 5000 | 1.1857          | 39.4990 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  