readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: hubert_model
    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. -->
  
  # hubert_model
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.2139
  - Wer: 1.0
  
  ## 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: 1e-05
  - train_batch_size: 64
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 125
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer |
  |:-------------:|:-----:|:-----:|:---------------:|:---:|
  | 55.5107       | 0.11  | 100   | 93.6947         | 1.0 |
  | 29.8329       | 0.22  | 200   | 53.0718         | 1.0 |
  | 22.4958       | 0.32  | 300   | 42.6961         | 1.0 |
  | 19.1734       | 0.43  | 400   | 34.1686         | 1.0 |
  | 15.9615       | 0.54  | 500   | 27.1054         | 1.0 |
  | 13.1077       | 0.65  | 600   | 21.2901         | 1.0 |
  | 11.0162       | 0.76  | 700   | 16.6558         | 1.0 |
  | 9.3359        | 0.87  | 800   | 13.1283         | 1.0 |
  | 8.2754        | 0.97  | 900   | 10.6005         | 1.0 |
  | 7.1321        | 1.08  | 1000  | 8.7120          | 1.0 |
  | 6.2621        | 1.19  | 1100  | 7.4866          | 1.0 |
  | 5.8109        | 1.3   | 1200  | 6.6416          | 1.0 |
  | 5.386         | 1.41  | 1300  | 6.1307          | 1.0 |
  | 5.1782        | 1.51  | 1400  | 5.8103          | 1.0 |
  | 4.9481        | 1.62  | 1500  | 5.6119          | 1.0 |
  | 4.8722        | 1.73  | 1600  | 5.4872          | 1.0 |
  | 4.7617        | 1.84  | 1700  | 5.3270          | 1.0 |
  | 4.717         | 1.95  | 1800  | 5.2877          | 1.0 |
  | 4.6256        | 2.06  | 1900  | 5.6727          | 1.0 |
  | 4.6255        | 2.16  | 2000  | 5.4983          | 1.0 |
  | 4.5977        | 2.27  | 2100  | 5.2167          | 1.0 |
  | 4.5797        | 2.38  | 2200  | 4.9743          | 1.0 |
  | 4.5616        | 2.49  | 2300  | 4.8446          | 1.0 |
  | 4.5476        | 2.6   | 2400  | 4.7885          | 1.0 |
  | 4.5516        | 2.71  | 2500  | 4.7597          | 1.0 |
  | 4.5343        | 2.81  | 2600  | 4.7309          | 1.0 |
  | 4.586         | 2.92  | 2700  | 4.7173          | 1.0 |
  | 4.5813        | 3.03  | 2800  | 4.6650          | 1.0 |
  | 4.4794        | 3.14  | 2900  | 4.5851          | 1.0 |
  | 4.4735        | 3.25  | 3000  | 4.5310          | 1.0 |
  | 4.4748        | 3.35  | 3100  | 4.5285          | 1.0 |
  | 4.4439        | 3.46  | 3200  | 4.4971          | 1.0 |
  | 4.4255        | 3.57  | 3300  | 4.5072          | 1.0 |
  | 4.4232        | 3.68  | 3400  | 4.4936          | 1.0 |
  | 4.4066        | 3.79  | 3500  | 4.5279          | 1.0 |
  | 4.4095        | 3.9   | 3600  | 4.4653          | 1.0 |
  | 4.3148        | 4.0   | 3700  | 4.4542          | 1.0 |
  | 4.2788        | 4.11  | 3800  | 4.3820          | 1.0 |
  | 4.3291        | 4.22  | 3900  | 4.3234          | 1.0 |
  | 4.2173        | 4.33  | 4000  | 4.3068          | 1.0 |
  | 4.1921        | 4.44  | 4100  | 4.2719          | 1.0 |
  | 4.1868        | 4.55  | 4200  | 4.2765          | 1.0 |
  | 4.1734        | 4.65  | 4300  | 4.2349          | 1.0 |
  | 4.1868        | 4.76  | 4400  | 4.2002          | 1.0 |
  | 4.169         | 4.87  | 4500  | 4.1915          | 1.0 |
  | 4.131         | 4.98  | 4600  | 4.1673          | 1.0 |
  | 4.1952        | 5.09  | 4700  | 4.1657          | 1.0 |
  | 4.1067        | 5.19  | 4800  | 4.1650          | 1.0 |
  | 4.1026        | 5.3   | 4900  | 4.1394          | 1.0 |
  | 4.0864        | 5.41  | 5000  | 4.1334          | 1.0 |
  | 4.0745        | 5.52  | 5100  | 4.1138          | 1.0 |
  | 4.0653        | 5.63  | 5200  | 4.1029          | 1.0 |
  | 4.0484        | 5.74  | 5300  | 4.0870          | 1.0 |
  | 4.0474        | 5.84  | 5400  | 4.0693          | 1.0 |
  | 4.0299        | 5.95  | 5500  | 4.0489          | 1.0 |
  | 4.0862        | 6.06  | 5600  | 4.0414          | 1.0 |
  | 3.9986        | 6.17  | 5700  | 4.0316          | 1.0 |
  | 4.0042        | 6.28  | 5800  | 4.0100          | 1.0 |
  | 3.9912        | 6.39  | 5900  | 3.9861          | 1.0 |
  | 3.9625        | 6.49  | 6000  | 3.9676          | 1.0 |
  | 3.96          | 6.6   | 6100  | 3.9469          | 1.0 |
  | 3.9443        | 6.71  | 6200  | 3.9514          | 1.0 |
  | 3.9215        | 6.82  | 6300  | 3.9108          | 1.0 |
  | 3.9176        | 6.93  | 6400  | 3.8880          | 1.0 |
  | 3.9986        | 7.03  | 6500  | 3.8798          | 1.0 |
  | 3.8908        | 7.14  | 6600  | 3.8610          | 1