readme: ---
  language:
  - de
  license: apache-2.0
  tags:
  - sbb-asr
  - generated_from_trainer
  datasets:
  - marccgrau/sbbdata_allSNR
  metrics:
  - wer
  model-index:
  - name: Whisper Small German SBB all SNR
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: SBB Dataset 05.01.2023
        type: marccgrau/sbbdata_allSNR
        args: 'config: German, split: train, test, val'
      metrics:
      - name: Wer
        type: wer
        value: 0.02092580849714648
  ---
  
  <!-- 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 German SBB all SNR
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0274
  - Wer: 0.0209
  
  ## 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: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 100
  - training_steps: 800
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 1.3449        | 0.36  | 100  | 0.2161          | 0.0380 |
  | 0.0653        | 0.71  | 200  | 0.0265          | 0.0178 |
  | 0.0315        | 1.07  | 300  | 0.0291          | 0.0197 |
  | 0.0194        | 1.42  | 400  | 0.0273          | 0.0197 |
  | 0.0141        | 1.78  | 500  | 0.0278          | 0.0197 |
  | 0.0088        | 2.14  | 600  | 0.0265          | 0.0159 |
  | 0.0055        | 2.49  | 700  | 0.0273          | 0.0209 |
  | 0.0047        | 2.85  | 800  | 0.0274          | 0.0209 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1
  - Datasets 2.8.0
  - Tokenizers 0.12.1
  