readme: ---
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - facebook/multilingual_librispeech
  metrics:
  - wer
  model-index:
  - name: Whisper largeV2 Spanish MLS
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: facebook/multilingual_librispeech spanish
        type: facebook/multilingual_librispeech
        config: spanish
        split: test
        args: spanish
      metrics:
      - name: Wer
        type: wer
        value: 3.4677966101694913
  ---
  
  <!-- 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 Spanish 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 spanish dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0910
  - Wer: 3.4678
  
  ## Model description
  
  The model is fine-tuned for 4000 updates/steps on multilingual librispeech Spanish train data.
  
  - Zero-shot                        - 4.2 (MLS Spanish test)
  - Fine-tune MLS spanish train      - 3.46 (MLS Spanish test) (-17.61%)
  
  ------------------------------------------------------------------------------
  - Zero-shot                      - 6.3 (CV11 test)
  - Fine-tune MLS spanish train    - 8.38 (CV11 test)
  
  When the model is fine-tuned on specific dataset, the model loose its ability to generalise across datasets.
  Here the model is fine-tuned on MLS Spanish and evaluated on CV11 Spanish test. We can observe the drop in performance on CV11 test data.
  
  ## 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: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.2176        | 0.25  | 1000 | 0.1200          | 5.3932 |
  | 0.1845        | 0.5   | 2000 | 0.1055          | 4.2    |
  | 0.4516        | 0.75  | 3000 | 0.0977          | 3.6768 |
  | 0.1549        | 1.14  | 4000 | 0.0910          | 3.4678 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  