readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_1_0
  model-index:
  - name: dat259-wav2vec2-en2-copy
    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. -->
  
  # dat259-wav2vec2-en2-copy
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_1_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5429
  - Wer: 0.5569
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 300
  - num_epochs: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.5149        | 1.82  | 200  | 3.0474          | 1.0    |
  | 2.0483        | 3.64  | 400  | 1.8004          | 0.7808 |
  | 0.5464        | 5.45  | 600  | 1.5242          | 0.6529 |
  | 0.3269        | 7.27  | 800  | 1.3820          | 0.6046 |
  | 0.2337        | 9.09  | 1000 | 1.4605          | 0.5866 |
  | 0.1863        | 10.91 | 1200 | 1.5567          | 0.5864 |
  | 0.1531        | 12.73 | 1400 | 1.5787          | 0.5871 |
  | 0.1326        | 14.55 | 1600 | 1.5766          | 0.5679 |
  | 0.1118        | 16.36 | 1800 | 1.4709          | 0.5566 |
  | 0.1017        | 18.18 | 2000 | 1.5455          | 0.5598 |
  | 0.0955        | 20.0  | 2200 | 1.5429          | 0.5569 |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu102
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  