readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-base-timit-eng
    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-base-timit-eng
  
  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: 0.4391
  - Wer: 0.3836
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.8306        | 1.0   | 500   | 2.9588          | 1.0    |
  | 2.1928        | 2.01  | 1000  | 1.2215          | 0.9355 |
  | 1.1547        | 3.01  | 1500  | 0.9228          | 0.7135 |
  | 0.9487        | 4.02  | 2000  | 0.7682          | 0.6513 |
  | 0.8163        | 5.02  | 2500  | 0.7154          | 0.6164 |
  | 0.6642        | 6.02  | 3000  | 0.6160          | 0.5919 |
  | 0.6291        | 7.03  | 3500  | 0.6224          | 0.5485 |
  | 0.601         | 8.03  | 4000  | 0.5927          | 0.5371 |
  | 0.5443        | 9.04  | 4500  | 0.5757          | 0.5240 |
  | 0.4798        | 10.04 | 5000  | 0.5673          | 0.5074 |
  | 0.5142        | 11.04 | 5500  | 0.6138          | 0.5131 |
  | 0.4044        | 12.05 | 6000  | 0.5899          | 0.5120 |
  | 0.4214        | 13.05 | 6500  | 0.5443          | 0.4932 |
  | 0.377         | 14.06 | 7000  | 0.6055          | 0.5337 |
  | 0.3985        | 15.06 | 7500  | 0.5055          | 0.4812 |
  | 0.3609        | 16.06 | 8000  | 0.5764          | 0.4600 |
  | 0.299         | 17.07 | 8500  | 0.5524          | 0.4635 |
  | 0.2984        | 18.07 | 9000  | 0.5272          | 0.4435 |
  | 0.2908        | 19.08 | 9500  | 0.5393          | 0.4446 |
  | 0.2714        | 20.08 | 10000 | 0.4548          | 0.4463 |
  | 0.2285        | 21.08 | 10500 | 0.5126          | 0.4309 |
  | 0.2245        | 22.09 | 11000 | 0.4770          | 0.4309 |
  | 0.229         | 23.09 | 11500 | 0.4763          | 0.4150 |
  | 0.2032        | 24.1  | 12000 | 0.5009          | 0.4127 |
  | 0.2125        | 25.1  | 12500 | 0.4698          | 0.4087 |
  | 0.1955        | 26.1  | 13000 | 0.4592          | 0.4001 |
  | 0.1841        | 27.11 | 13500 | 0.4517          | 0.3898 |
  | 0.164         | 28.11 | 14000 | 0.4628          | 0.3927 |
  | 0.1687        | 29.12 | 14500 | 0.4391          | 0.3836 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  