readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: libri-alpha-0-Temp-1-kl
    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. -->
  
  # libri-alpha-0-Temp-1-kl
  
  This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 101.1091
  - Wer: 0.1503
  
  ## 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: 2e-05
  - train_batch_size: 28
  - eval_batch_size: 28
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 56
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 714.4386      | 0.65  | 100  | 206.0585        | 0.1904 |
  | 455.7244      | 1.31  | 200  | 150.6093        | 0.2306 |
  | 397.5524      | 1.96  | 300  | 134.8838        | 0.2301 |
  | 364.2438      | 2.61  | 400  | 125.3695        | 0.2193 |
  | 336.6832      | 3.27  | 500  | 120.0034        | 0.2168 |
  | 337.777       | 3.92  | 600  | 117.2567        | 0.2011 |
  | 315.4012      | 4.58  | 700  | 115.6776        | 0.1879 |
  | 316.353       | 5.23  | 800  | 114.1418        | 0.1826 |
  | 312.6547      | 5.88  | 900  | 112.3635        | 0.1872 |
  | 289.6309      | 6.54  | 1000 | 111.3456        | 0.1741 |
  | 301.9763      | 7.19  | 1100 | 110.7650        | 0.1714 |
  | 289.428       | 7.84  | 1200 | 109.2358        | 0.1709 |
  | 275.0852      | 8.5   | 1300 | 108.4552        | 0.1725 |
  | 297.2818      | 9.15  | 1400 | 108.1707        | 0.1657 |
  | 273.9007      | 9.8   | 1500 | 107.2497        | 0.1616 |
  | 283.6222      | 10.46 | 1600 | 107.5092        | 0.1599 |
  | 279.0072      | 11.11 | 1700 | 106.6930        | 0.1612 |
  | 270.9222      | 11.76 | 1800 | 105.6633        | 0.1635 |
  | 284.9869      | 12.42 | 1900 | 105.7896        | 0.1625 |
  | 271.7268      | 13.07 | 2000 | 105.5718        | 0.1586 |
  | 268.3919      | 13.73 | 2100 | 105.5480        | 0.1591 |
  | 269.6929      | 14.38 | 2200 | 104.9802        | 0.1596 |
  | 260.1387      | 15.03 | 2300 | 104.3509        | 0.1547 |
  | 267.38        | 15.69 | 2400 | 103.6222        | 0.1573 |
  | 264.2958      | 16.34 | 2500 | 103.7570        | 0.1524 |
  | 253.4907      | 16.99 | 2600 | 102.8282        | 0.1580 |
  | 270.6275      | 17.65 | 2700 | 103.1811        | 0.1566 |
  | 257.2584      | 18.3  | 2800 | 102.8419        | 0.1522 |
  | 264.0412      | 18.95 | 2900 | 102.2693        | 0.1551 |
  | 264.1277      | 19.61 | 3000 | 102.4851        | 0.1535 |
  | 250.8255      | 20.26 | 3100 | 101.7220        | 0.1550 |
  | 265.9928      | 20.92 | 3200 | 101.8507        | 0.1541 |
  | 251.8845      | 21.57 | 3300 | 102.0956        | 0.1508 |
  | 256.2606      | 22.22 | 3400 | 101.8346        | 0.1524 |
  | 255.3002      | 22.88 | 3500 | 101.6648        | 0.1495 |
  | 252.5451      | 23.53 | 3600 | 101.2943        | 0.1524 |
  | 256.3488      | 24.18 | 3700 | 101.3040        | 0.1521 |
  | 252.4813      | 24.84 | 3800 | 101.3680        | 0.1502 |
  | 249.9003      | 25.49 | 3900 | 101.1692        | 0.1522 |
  | 254.8601      | 26.14 | 4000 | 101.2368        | 0.1489 |
  | 247.9549      | 26.8  | 4100 | 101.0358        | 0.1516 |
  | 254.5287      | 27.45 | 4200 | 101.1500        | 0.1502 |
  | 255.1176      | 28.1  | 4300 | 101.0062        | 0.1511 |
  | 246.2381      | 28.76 | 4400 | 100.9802        | 0.1505 |
  | 262.6459      | 29.41 | 4500 | 101.1091        | 0.1503 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.11.0
  