readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - facebook/multilingual_librispeech
  metrics:
  - wer
  model-index:
  - name: Whisper largeV2 German MLS
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: facebook/multilingual_librispeech german
        type: facebook/multilingual_librispeech
        config: german
        split: test
        args: german
      metrics:
      - name: Wer
        type: wer
        value: 6.048320913895545
  ---
  
  <!-- 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 largeV2 German MLS
  
  This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the facebook/multilingual_librispeech german dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1370
  - Wer: 6.0483
  
  ## Model description
  
  
  The model is fine-tuned for 4000 updates/steps on multilingual librispeech German train data.
  
  - Zero-shot                        - 5.5 (MLS German test)
  - Fine-tune MLS German train      - 6.04 (MLS German test)
  
  Even after fine-tuning the model is doing slightly worse than the zero-shot.
  
  ## 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: 8
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_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: 4000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.1755        | 0.25  | 1000 | 0.1844          | 7.7118 |
  | 0.1185        | 0.5   | 2000 | 0.1636          | 7.0659 |
  | 0.1081        | 0.75  | 3000 | 0.1396          | 6.0844 |
  | 0.1222        | 1.0   | 4000 | 0.1370          | 6.0483 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  