readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-timit-demo-google-colab
    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-demo-google-colab
  
  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: 0.6348
  - Wer: 0.3204
  
  ## 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: 4
  - 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.2767        | 0.5   | 500   | 2.9921          | 1.0    |
  | 1.509         | 1.01  | 1000  | 0.8223          | 0.6031 |
  | 0.7226        | 1.51  | 1500  | 0.6185          | 0.4935 |
  | 0.5777        | 2.01  | 2000  | 0.5600          | 0.4569 |
  | 0.4306        | 2.51  | 2500  | 0.4985          | 0.4229 |
  | 0.3854        | 3.02  | 3000  | 0.5113          | 0.4200 |
  | 0.3161        | 3.52  | 3500  | 0.5197          | 0.4042 |
  | 0.2904        | 4.02  | 4000  | 0.4900          | 0.3936 |
  | 0.2404        | 4.52  | 4500  | 0.5209          | 0.3797 |
  | 0.2546        | 5.03  | 5000  | 0.4836          | 0.3855 |
  | 0.2278        | 5.53  | 5500  | 0.5194          | 0.3676 |
  | 0.2049        | 6.03  | 6000  | 0.5647          | 0.4042 |
  | 0.199         | 6.53  | 6500  | 0.5699          | 0.3932 |
  | 0.1932        | 7.04  | 7000  | 0.5498          | 0.3694 |
  | 0.1633        | 7.54  | 7500  | 0.5918          | 0.3686 |
  | 0.1674        | 8.04  | 8000  | 0.5298          | 0.3716 |
  | 0.1496        | 8.54  | 8500  | 0.5788          | 0.3726 |
  | 0.1488        | 9.05  | 9000  | 0.5603          | 0.3664 |
  | 0.1286        | 9.55  | 9500  | 0.5427          | 0.3550 |
  | 0.1364        | 10.05 | 10000 | 0.5794          | 0.3621 |
  | 0.1177        | 10.55 | 10500 | 0.5587          | 0.3606 |
  | 0.1126        | 11.06 | 11000 | 0.5788          | 0.3519 |
  | 0.1272        | 11.56 | 11500 | 0.5859          | 0.3595 |
  | 0.1414        | 12.06 | 12000 | 0.5852          | 0.3586 |
  | 0.1081        | 12.56 | 12500 | 0.5653          | 0.3727 |
  | 0.1073        | 13.07 | 13000 | 0.5653          | 0.3526 |
  | 0.0922        | 13.57 | 13500 | 0.5758          | 0.3583 |
  | 0.09          | 14.07 | 14000 | 0.5990          | 0.3599 |
  | 0.0987        | 14.57 | 14500 | 0.5837          | 0.3516 |
  | 0.0823        | 15.08 | 15000 | 0.5639          | 0.3454 |
  | 0.0752        | 15.58 | 15500 | 0.5663          | 0.3542 |
  | 0.0714        | 16.08 | 16000 | 0.6273          | 0.3419 |
  | 0.0693        | 16.58 | 16500 | 0.6389          | 0.3441 |
  | 0.0634        | 17.09 | 17000 | 0.6006          | 0.3409 |
  | 0.063         | 17.59 | 17500 | 0.6456          | 0.3444 |
  | 0.0627        | 18.09 | 18000 | 0.6706          | 0.3458 |
  | 0.0519        | 18.59 | 18500 | 0.6370          | 0.3396 |
  | 0.059         | 19.1  | 19000 | 0.6602          | 0.3390 |
  | 0.0495        | 19.6  | 19500 | 0.6642          | 0.3364 |
  | 0.0601        | 20.1  | 20000 | 0.6495          | 0.3408 |
  | 0.07          | 20.6  | 20500 | 0.6526          | 0.3476 |
  | 0.0517        | 21.11 | 21000 | 0.6265          | 0.3401 |
  | 0.0434        | 21.61 | 21500 | 0.6364          | 0.3372 |
  | 0.0383        | 22.11 | 22000 | 0.6742          | 0.3377 |
  | 0.0372        | 22.61 | 22500 | 0.6499          | 0.3330 |
  | 0.0329        | 23.12 | 23000 | 0.6877          | 0.3307 |
  | 0.0366        | 23.62 | 23500 | 0.6351          | 0.3303 |
  | 0.0372        | 24.12 | 24000 | 0.6547          | 0.3286 |
  | 0.031         | 24.62 | 24500 | 0.6757          | 0.3304 |
  | 0.0367        | 25.13 | 25000 | 0.6507          | 0.3312 |
  | 0.0309        | 25.63 | 25500 | 0.6645          | 0.3298 |
  | 0.03          | 26.13 | 26000 | 0.6342          | 0.3325 |
  | 0.0274        | 26.63 | 26500 | 0.6614          | 0.3255 |
  | 0.0236        | 27.14 | 27000 | 0.6614          | 0.3222 |
  | 0.0263        | 27.64 | 27500 | 0.6560          | 0.3242 |
  | 0.0264        | 28.14 | 28000 | 0.6337          | 0.3237 |
  | 0.0234        | 28.64 | 28500 | 0.6322          | 0.3208 |
  | 0.0249        | 29.15 | 29000 | 0.6367          | 0.3218 |
  | 0.0252        | 29.65 | 29500 | 0.6348          | 0.3204 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.8.2+cu111
  - Datasets 1.17.0
  - Tokenizers 0.11.6
  