readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: wavlm-large-timit-punctuation
    results: []
  ---
  
  <!-- 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. -->
  
  # wavlm-large-timit-punctuation
  
  This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3368
  - Wer: 0.2601
  
  ## 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: 0.0001
  - train_batch_size: 8
  - 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: 1000
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.2379        | 1.0   | 500   | 3.1228          | 1.0    |
  | 2.5847        | 2.01  | 1000  | 1.1550          | 0.9147 |
  | 1.0034        | 3.01  | 1500  | 0.5856          | 0.5180 |
  | 0.5868        | 4.02  | 2000  | 0.4238          | 0.4229 |
  | 0.3892        | 5.02  | 2500  | 0.3356          | 0.3665 |
  | 0.2926        | 6.02  | 3000  | 0.3196          | 0.3360 |
  | 0.2294        | 7.03  | 3500  | 0.3046          | 0.3170 |
  | 0.1976        | 8.03  | 4000  | 0.3032          | 0.3111 |
  | 0.1644        | 9.04  | 4500  | 0.2946          | 0.2954 |
  | 0.1574        | 10.04 | 5000  | 0.3211          | 0.2998 |
  | 0.1391        | 11.04 | 5500  | 0.2986          | 0.2922 |
  | 0.1124        | 12.05 | 6000  | 0.2948          | 0.2837 |
  | 0.1003        | 13.05 | 6500  | 0.2928          | 0.2788 |
  | 0.1031        | 14.06 | 7000  | 0.3230          | 0.2805 |
  | 0.0901        | 15.06 | 7500  | 0.3081          | 0.2749 |
  | 0.0842        | 16.06 | 8000  | 0.3075          | 0.2726 |
  | 0.0809        | 17.07 | 8500  | 0.3215          | 0.2717 |
  | 0.0747        | 18.07 | 9000  | 0.3272          | 0.2721 |
  | 0.0735        | 19.08 | 9500  | 0.3242          | 0.2684 |
  | 0.0631        | 20.08 | 10000 | 0.3216          | 0.2640 |
  | 0.0632        | 21.08 | 10500 | 0.3149          | 0.2646 |
  | 0.0625        | 22.09 | 11000 | 0.3196          | 0.2630 |
  | 0.0611        | 23.09 | 11500 | 0.3244          | 0.2638 |
  | 0.0532        | 24.1  | 12000 | 0.3271          | 0.2641 |
  | 0.0503        | 25.1  | 12500 | 0.3368          | 0.2636 |
  | 0.0534        | 26.1  | 13000 | 0.3393          | 0.2627 |
  | 0.049         | 27.11 | 13500 | 0.3389          | 0.2626 |
  | 0.0441        | 28.11 | 14000 | 0.3375          | 0.2605 |
  | 0.0522        | 29.12 | 14500 | 0.3368          | 0.2601 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.8.2+cu111
  - Datasets 1.17.0
  - Tokenizers 0.11.6
  