readme: ---
  language:
  - ca
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: Whisper Medium Catala 1k steps
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: Common Voice 11.0, Fleurs, SLR69, tb3_parla, parlament_parla
        type: mozilla-foundation/common_voice_11_0, google/fleurs, openslr, collectivat/tv3_parla, projecte-aina/parlament_parla
        config: ca
        split: test
        args: ca
      metrics:
      - name: Wer
        type: wer
        value: 10.9688
  ---
  
  Whisper Md Ca - 1k
  This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
  
  Loss: 0.2554
  Wer: 10.9688
  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: 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: 100
  training_steps: 1000
  mixed_precision_training: Native AMP
  
  Training results
  Training Loss	Epoch	Step	Validation Loss	Wer
  0.2554	1.0	1000	0.2554	10.9688
  Framework versions
  Transformers 4.26.0.dev0
  Pytorch 1.13.1+cu117
  Datasets 2.7.1.dev0
  Tokenizers 0.13.2