readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: w2v2-libri
    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. -->
  
  # w2v2-libri
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5387
  - Wer: 0.5380
  
  ## 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
  - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1500
  - training_steps: 2500
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 8.8253        | 50.0  | 200  | 3.1879          | 1.0    |
  | 3.0174        | 100.0 | 400  | 2.9619          | 1.0    |
  | 2.8589        | 150.0 | 600  | 2.9499          | 1.0    |
  | 1.8086        | 200.0 | 800  | 1.0896          | 0.7123 |
  | 0.2145        | 250.0 | 1000 | 1.1973          | 0.6321 |
  | 0.0641        | 300.0 | 1200 | 1.3631          | 0.6100 |
  | 0.0391        | 350.0 | 1400 | 1.4521          | 0.5837 |
  | 0.0258        | 400.0 | 1600 | 1.3671          | 0.5781 |
  | 0.0185        | 450.0 | 1800 | 1.3828          | 0.5698 |
  | 0.0107        | 500.0 | 2000 | 1.4402          | 0.5463 |
  | 0.0099        | 550.0 | 2200 | 1.5724          | 0.5477 |
  | 0.0058        | 600.0 | 2400 | 1.5387          | 0.5380 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  