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: 0.5353
  - Wer: 0.3360
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5345        | 1.0   | 500   | 1.8229          | 0.9810 |
  | 0.8731        | 2.01  | 1000  | 0.5186          | 0.5165 |
  | 0.4455        | 3.01  | 1500  | 0.4386          | 0.4572 |
  | 0.3054        | 4.02  | 2000  | 0.4396          | 0.4286 |
  | 0.2354        | 5.02  | 2500  | 0.4454          | 0.4051 |
  | 0.1897        | 6.02  | 3000  | 0.4465          | 0.3925 |
  | 0.1605        | 7.03  | 3500  | 0.4776          | 0.3974 |
  | 0.1413        | 8.03  | 4000  | 0.5254          | 0.4062 |
  | 0.1211        | 9.04  | 4500  | 0.5123          | 0.3913 |
  | 0.1095        | 10.04 | 5000  | 0.4171          | 0.3711 |
  | 0.1039        | 11.04 | 5500  | 0.4258          | 0.3732 |
  | 0.0932        | 12.05 | 6000  | 0.4879          | 0.3701 |
  | 0.0867        | 13.05 | 6500  | 0.4725          | 0.3637 |
  | 0.0764        | 14.06 | 7000  | 0.5041          | 0.3636 |
  | 0.0661        | 15.06 | 7500  | 0.4692          | 0.3646 |
  | 0.0647        | 16.06 | 8000  | 0.4804          | 0.3612 |
  | 0.0576        | 17.07 | 8500  | 0.5545          | 0.3628 |
  | 0.0577        | 18.07 | 9000  | 0.5004          | 0.3557 |
  | 0.0481        | 19.08 | 9500  | 0.5341          | 0.3558 |
  | 0.0466        | 20.08 | 10000 | 0.5056          | 0.3514 |
  | 0.0433        | 21.08 | 10500 | 0.4864          | 0.3481 |
  | 0.0362        | 22.09 | 11000 | 0.4994          | 0.3473 |
  | 0.0325        | 23.09 | 11500 | 0.5327          | 0.3446 |
  | 0.0351        | 24.1  | 12000 | 0.5360          | 0.3445 |
  | 0.0284        | 25.1  | 12500 | 0.5085          | 0.3399 |
  | 0.027         | 26.1  | 13000 | 0.5344          | 0.3426 |
  | 0.0247        | 27.11 | 13500 | 0.5310          | 0.3357 |
  | 0.0251        | 28.11 | 14000 | 0.5201          | 0.3355 |
  | 0.0228        | 29.12 | 14500 | 0.5353          | 0.3360 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  