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: 0.5093
  - Wer: 0.3413
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5009        | 1.0   | 500   | 1.6207          | 0.9471 |
  | 0.8414        | 2.01  | 1000  | 0.5128          | 0.5033 |
  | 0.4366        | 3.01  | 1500  | 0.4449          | 0.4450 |
  | 0.3015        | 4.02  | 2000  | 0.3835          | 0.4108 |
  | 0.2281        | 5.02  | 2500  | 0.3989          | 0.4109 |
  | 0.1914        | 6.02  | 3000  | 0.4286          | 0.3982 |
  | 0.1555        | 7.03  | 3500  | 0.4547          | 0.3889 |
  | 0.1349        | 8.03  | 4000  | 0.3876          | 0.3779 |
  | 0.1252        | 9.04  | 4500  | 0.4460          | 0.3810 |
  | 0.1066        | 10.04 | 5000  | 0.3905          | 0.3772 |
  | 0.0979        | 11.04 | 5500  | 0.4469          | 0.3646 |
  | 0.0883        | 12.05 | 6000  | 0.4547          | 0.3612 |
  | 0.0801        | 13.05 | 6500  | 0.4741          | 0.3645 |
  | 0.0709        | 14.06 | 7000  | 0.4682          | 0.3592 |
  | 0.0665        | 15.06 | 7500  | 0.4689          | 0.3647 |
  | 0.0579        | 16.06 | 8000  | 0.5330          | 0.3622 |
  | 0.0556        | 17.07 | 8500  | 0.4885          | 0.3575 |
  | 0.0547        | 18.07 | 9000  | 0.4936          | 0.3543 |
  | 0.0462        | 19.08 | 9500  | 0.4928          | 0.3524 |
  | 0.0475        | 20.08 | 10000 | 0.5286          | 0.3525 |
  | 0.0426        | 21.08 | 10500 | 0.5100          | 0.3550 |
  | 0.0364        | 22.09 | 11000 | 0.5372          | 0.3493 |
  | 0.0306        | 23.09 | 11500 | 0.5049          | 0.3443 |
  | 0.0314        | 24.1  | 12000 | 0.5223          | 0.3519 |
  | 0.0261        | 25.1  | 12500 | 0.5380          | 0.3486 |
  | 0.0257        | 26.1  | 13000 | 0.5326          | 0.3484 |
  | 0.0252        | 27.11 | 13500 | 0.5299          | 0.3446 |
  | 0.0226        | 28.11 | 14000 | 0.5174          | 0.3424 |
  | 0.0232        | 29.12 | 14500 | 0.5093          | 0.3413 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  