readme: ---
  language:
  - de
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0,facebook/voxpopuli,google/fleurs
  metrics:
  - wer
  model-index:
  - name: Whisper LargeV2 German
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0,facebook/voxpopuli,google/fleurs
          de,de,de_de
        type: mozilla-foundation/common_voice_11_0,facebook/voxpopuli,google/fleurs
        config: de
        split: test
        args: de
      metrics:
      - name: Wer
        type: wer
        value: 6.313937972585015
  ---
  
  <!-- 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
  
  This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0,facebook/voxpopuli,google/fleurs de,de,de_de dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1307
  - Wer: 6.3139
  
  ## 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: 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: 3000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.127         | 0.17  | 500  | 0.1569          | 7.2588 |
  | 0.1116        | 0.33  | 1000 | 0.1505          | 7.2372 |
  | 0.1132        | 0.5   | 1500 | 0.1435          | 6.8821 |
  | 0.0939        | 0.67  | 2000 | 0.1354          | 6.5343 |
  | 0.0819        | 0.83  | 2500 | 0.1339          | 6.4979 |
  | 0.0892        | 1.0   | 3000 | 0.1307          | 6.3139 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  