readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - librispeech_asr
  model-index:
  - name: wav2vec2-large-xls-r-300m-en-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-large-xls-r-300m-en-colab
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1169
  - Wer: 0.0597
  
  ## 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: 0.0003
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 5.6951        | 0.22  | 100  | 3.1606          | 1.0    |
  | 2.924         | 0.45  | 200  | 2.9297          | 1.0    |
  | 2.5328        | 0.67  | 300  | 1.4339          | 0.8953 |
  | 0.8611        | 0.9   | 400  | 0.6104          | 0.5306 |
  | 0.3714        | 1.12  | 500  | 0.2497          | 0.2150 |
  | 0.2015        | 1.35  | 600  | 0.1853          | 0.1615 |
  | 0.1593        | 1.57  | 700  | 0.1613          | 0.1366 |
  | 0.1436        | 1.79  | 800  | 0.1503          | 0.1311 |
  | 0.1249        | 2.02  | 900  | 0.1374          | 0.1038 |
  | 0.0936        | 2.24  | 1000 | 0.1328          | 0.1016 |
  | 0.0896        | 2.47  | 1100 | 0.1234          | 0.0942 |
  | 0.0872        | 2.69  | 1200 | 0.1148          | 0.0922 |
  | 0.0859        | 2.91  | 1300 | 0.1140          | 0.0892 |
  | 0.0733        | 3.14  | 1400 | 0.1134          | 0.0839 |
  | 0.0633        | 3.36  | 1500 | 0.1085          | 0.0802 |
  | 0.0567        | 3.59  | 1600 | 0.1103          | 0.0807 |
  | 0.0604        | 3.81  | 1700 | 0.1088          | 0.0809 |
  | 0.0586        | 4.04  | 1800 | 0.1113          | 0.0804 |
  | 0.0516        | 4.26  | 1900 | 0.1123          | 0.0808 |
  | 0.055         | 4.48  | 2000 | 0.1130          | 0.0764 |
  | 0.0568        | 4.71  | 2100 | 0.1128          | 0.0807 |
  | 0.0529        | 4.93  | 2200 | 0.1009          | 0.0727 |
  | 0.0455        | 5.16  | 2300 | 0.1050          | 0.0726 |
  | 0.0443        | 5.38  | 2400 | 0.1078          | 0.0720 |
  | 0.0434        | 5.61  | 2500 | 0.1027          | 0.0702 |
  | 0.0418        | 5.83  | 2600 | 0.1009          | 0.0693 |
  | 0.0381        | 6.05  | 2700 | 0.1079          | 0.0689 |
  | 0.0344        | 6.28  | 2800 | 0.1062          | 0.0678 |
  | 0.0353        | 6.5   | 2900 | 0.1054          | 0.0682 |
  | 0.0342        | 6.73  | 3000 | 0.1030          | 0.0661 |
  | 0.0329        | 6.95  | 3100 | 0.1021          | 0.0659 |
  | 0.0316        | 7.17  | 3200 | 0.1085          | 0.0667 |
  | 0.0275        | 7.4   | 3300 | 0.1089          | 0.0645 |
  | 0.0275        | 7.62  | 3400 | 0.1064          | 0.0645 |
  | 0.0268        | 7.85  | 3500 | 0.1109          | 0.0639 |
  | 0.0259        | 8.07  | 3600 | 0.1123          | 0.0636 |
  | 0.024         | 8.3   | 3700 | 0.1169          | 0.0631 |
  | 0.0225        | 8.52  | 3800 | 0.1170          | 0.0617 |
  | 0.0229        | 8.74  | 3900 | 0.1153          | 0.0614 |
  | 0.0214        | 8.97  | 4000 | 0.1143          | 0.0610 |
  | 0.02          | 9.19  | 4100 | 0.1162          | 0.0606 |
  | 0.0194        | 9.42  | 4200 | 0.1173          | 0.0603 |
  | 0.0193        | 9.64  | 4300 | 0.1184          | 0.0601 |
  | 0.0177        | 9.87  | 4400 | 0.1169          | 0.0597 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  