readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-large-teacher-en-asr-timit
    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. -->
  
  # wav2vec2-large-teacher-en-asr-timit
  
  This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4160
  - Wer: 0.2984
  
  ## 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: 64
  - 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.4952        | 3.17  | 200  | 3.0555          | 1.0    |
  | 2.5341        | 6.35  | 400  | 0.8144          | 0.7441 |
  | 0.694         | 9.52  | 600  | 0.4154          | 0.4572 |
  | 0.3593        | 12.7  | 800  | 0.4260          | 0.3890 |
  | 0.2567        | 15.87 | 1000 | 0.4166          | 0.3614 |
  | 0.1988        | 19.05 | 1200 | 0.3912          | 0.3346 |
  | 0.1338        | 22.22 | 1400 | 0.4000          | 0.3178 |
  | 0.1044        | 25.4  | 1600 | 0.4425          | 0.3071 |
  | 0.0786        | 28.57 | 1800 | 0.4160          | 0.2984 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  