readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: juancopi81/whisper-medium-es-train-valid
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 11.0
        type: mozilla-foundation/common_voice_11_0
        config: es
        split: test
        args: es
      metrics:
      - name: Wer
        type: wer
        value: 6.15482563276337
  ---
  
  <!-- 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. -->
  
  # juancopi81/whisper-medium-es-train-valid
  
  This model is a fine-tuned version of [juancopi81/whisper-medium-es-train-valid](https://huggingface.co/juancopi81/whisper-medium-es-train-valid) on the common_voice_11_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2227
  - Wer: 6.1548
  
  Using the script provided in the Whisper Sprint (Dec. 2022) the models achieves these results on the evaluation sets (WER):
  - google/fleurs: 6.94
  - mozilla-foundation/common_voice_11_0: XXXX
  
  ## 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: 16
  - seed: 42
  - 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.0539        | 1.01  | 1000 | 0.2100          | 6.4465 |
  | 0.0211        | 2.01  | 2000 | 0.2286          | 6.5082 |
  | 0.0088        | 3.02  | 3000 | 0.2418          | 6.3848 |
  | 0.0205        | 4.02  | 4000 | 0.2288          | 6.6603 |
  | 0.1031        | 5.03  | 5000 | 0.2227          | 6.1548 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1.dev0
  - Tokenizers 0.13.2
  