readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - whisper-event
  metrics:
  - wer
  model-index:
  - name: openai/whisper-medium
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0 ca
        type: mozilla-foundation/common_voice_11_0
        args: 'config: ml, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 16.15101446793939
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: google/fleurs ca
        type: google/fleurs
        args: 'config: ml, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 20.4
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: projecte-aina/parlament_parla clean
        type: projecte-aina/parlament_parla
        args: 'config: ml, split: test'
      metrics:
      - name: Wer
        type: wer
        value: 21.14
  ---
  
  <!-- 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. -->
  
  # openai/whisper-base
  
  This is an automatic speech recognition model that also does punctuation and casing. Vegeu [projecte de millora dels models Whisper](https://www.softcatala.org/projectes/millora-del-catala-dels-models-del-reconeixement-de-la-parla-whisper/) (Catalan).
  
  This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 ca dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3608
  - Wer: 16.1510
  
  ## 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: 2
  - eval_batch_size: 1
  - 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: 40000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.4841        | 0.1   | 4000  | 0.5078          | 26.7974 |
  | 0.3116        | 0.2   | 8000  | 0.4524          | 22.9455 |
  | 0.3971        | 0.3   | 12000 | 0.4281          | 21.5427 |
  | 0.2965        | 0.4   | 16000 | 0.4037          | 20.3082 |
  | 0.2634        | 1.09  | 20000 | 0.3875          | 18.7980 |
  | 0.2163        | 1.19  | 24000 | 0.3754          | 17.8170 |
  | 0.3182        | 1.29  | 28000 | 0.3695          | 16.8587 |
  | 0.2201        | 1.39  | 32000 | 0.3613          | 16.5785 |
  | 0.155         | 2.08  | 36000 | 0.3633          | 16.3959 |
  | 0.0904        | 2.18  | 40000 | 0.3608          | 16.1510 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.10.0+cu102
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  