readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-en-demo
    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-xlsr-en-demo
  
  This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1356
  - Wer: 0.2015
  
  ## 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: 1
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 5.3911        | 0.5   | 500  | 0.5397          | 0.2615 |
  | 0.3413        | 1.01  | 1000 | 0.1423          | 0.2137 |
  | 0.243         | 1.51  | 1500 | 0.1458          | 0.2210 |
  | 0.2232        | 2.01  | 2000 | 0.1380          | 0.2143 |
  | 0.162         | 2.51  | 2500 | 0.1464          | 0.2149 |
  | 0.1384        | 3.02  | 3000 | 0.1348          | 0.2109 |
  | 0.1164        | 3.52  | 3500 | 0.1324          | 0.2040 |
  | 0.1103        | 4.02  | 4000 | 0.1310          | 0.2051 |
  | 0.0857        | 4.53  | 4500 | 0.1356          | 0.2015 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  