readme: ---
  language:
  - ca
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Small Catalan
    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: 8.569471791798646
    - 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: 10.64
    - 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: 19.0
  ---
  
  <!-- 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 Small Catalan
  
  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-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ca dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1980
  - Wer: 8.5695
  
  ## 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: 16
  - eval_batch_size: 8
  - 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: 20000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer     |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|
  | 0.2128        | 0.1   | 2000  | 0.2644          | 13.0303 |
  | 0.1361        | 1.1   | 4000  | 0.2300          | 10.9568 |
  | 0.0658        | 2.1   | 6000  | 0.2376          | 11.2810 |
  | 0.102         | 3.09  | 8000  | 0.2156          | 9.8730  |
  | 0.0706        | 4.09  | 10000 | 0.2126          | 9.6179  |
  | 0.0428        | 5.09  | 12000 | 0.2178          | 9.3405  |
  | 0.0503        | 6.09  | 14000 | 0.2109          | 9.1356  |
  | 0.0778        | 7.08  | 16000 | 0.2058          | 9.2001  |
  | 0.0082        | 8.08  | 18000 | 0.2173          | 8.9941  |
  | 0.0994        | 9.08  | 20000 | 0.1980          | 8.5695  |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.10.0+cu102
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  