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: 0.5255
  - Wer: 0.3330
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5942        | 1.0   | 500   | 2.3849          | 1.0011 |
  | 0.9765        | 2.01  | 1000  | 0.5907          | 0.5202 |
  | 0.4424        | 3.01  | 1500  | 0.4547          | 0.4661 |
  | 0.3008        | 4.02  | 2000  | 0.4194          | 0.4228 |
  | 0.2316        | 5.02  | 2500  | 0.3933          | 0.4099 |
  | 0.1921        | 6.02  | 3000  | 0.4532          | 0.3965 |
  | 0.1561        | 7.03  | 3500  | 0.4315          | 0.3777 |
  | 0.1378        | 8.03  | 4000  | 0.4463          | 0.3847 |
  | 0.1222        | 9.04  | 4500  | 0.4402          | 0.3784 |
  | 0.1076        | 10.04 | 5000  | 0.4253          | 0.3735 |
  | 0.0924        | 11.04 | 5500  | 0.4844          | 0.3732 |
  | 0.0866        | 12.05 | 6000  | 0.4758          | 0.3646 |
  | 0.086         | 13.05 | 6500  | 0.6395          | 0.4594 |
  | 0.0763        | 14.06 | 7000  | 0.4951          | 0.3647 |
  | 0.0684        | 15.06 | 7500  | 0.4870          | 0.3577 |
  | 0.0616        | 16.06 | 8000  | 0.5442          | 0.3591 |
  | 0.0594        | 17.07 | 8500  | 0.5305          | 0.3606 |
  | 0.0613        | 18.07 | 9000  | 0.5434          | 0.3546 |
  | 0.0473        | 19.08 | 9500  | 0.4818          | 0.3532 |
  | 0.0463        | 20.08 | 10000 | 0.5086          | 0.3514 |
  | 0.042         | 21.08 | 10500 | 0.5017          | 0.3484 |
  | 0.0365        | 22.09 | 11000 | 0.5129          | 0.3536 |
  | 0.0336        | 23.09 | 11500 | 0.5411          | 0.3433 |
  | 0.0325        | 24.1  | 12000 | 0.5307          | 0.3424 |
  | 0.0282        | 25.1  | 12500 | 0.5261          | 0.3404 |
  | 0.0245        | 26.1  | 13000 | 0.5306          | 0.3388 |
  | 0.0257        | 27.11 | 13500 | 0.5242          | 0.3369 |
  | 0.0234        | 28.11 | 14000 | 0.5216          | 0.3359 |
  | 0.0221        | 29.12 | 14500 | 0.5255          | 0.3330 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  