readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: xlsr-wav2vec2-2
    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. -->
  
  # xlsr-wav2vec2-2
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5884
  - Wer: 0.4301
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 800
  - num_epochs: 60
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 6.6058        | 1.38  | 400   | 3.1894          | 1.0    |
  | 2.3145        | 2.76  | 800   | 0.7193          | 0.7976 |
  | 0.6737        | 4.14  | 1200  | 0.5338          | 0.6056 |
  | 0.4651        | 5.52  | 1600  | 0.5699          | 0.6007 |
  | 0.3968        | 6.9   | 2000  | 0.4608          | 0.5221 |
  | 0.3281        | 8.28  | 2400  | 0.5264          | 0.5209 |
  | 0.2937        | 9.65  | 2800  | 0.5366          | 0.5096 |
  | 0.2619        | 11.03 | 3200  | 0.4902          | 0.5021 |
  | 0.2394        | 12.41 | 3600  | 0.4706          | 0.4908 |
  | 0.2139        | 13.79 | 4000  | 0.5526          | 0.4871 |
  | 0.2034        | 15.17 | 4400  | 0.5396          | 0.5108 |
  | 0.1946        | 16.55 | 4800  | 0.4959          | 0.4866 |
  | 0.1873        | 17.93 | 5200  | 0.4898          | 0.4877 |
  | 0.1751        | 19.31 | 5600  | 0.5488          | 0.4932 |
  | 0.1668        | 20.69 | 6000  | 0.5645          | 0.4986 |
  | 0.1638        | 22.07 | 6400  | 0.5367          | 0.4946 |
  | 0.1564        | 23.45 | 6800  | 0.5282          | 0.4898 |
  | 0.1566        | 24.83 | 7200  | 0.5489          | 0.4841 |
  | 0.1522        | 26.21 | 7600  | 0.5439          | 0.4821 |
  | 0.1378        | 27.59 | 8000  | 0.5796          | 0.4866 |
  | 0.1459        | 28.96 | 8400  | 0.5603          | 0.4875 |
  | 0.1406        | 30.34 | 8800  | 0.6773          | 0.5005 |
  | 0.1298        | 31.72 | 9200  | 0.5858          | 0.4827 |
  | 0.1268        | 33.1  | 9600  | 0.6007          | 0.4790 |
  | 0.1204        | 34.48 | 10000 | 0.5716          | 0.4734 |
  | 0.113         | 35.86 | 10400 | 0.5866          | 0.4748 |
  | 0.1088        | 37.24 | 10800 | 0.5790          | 0.4752 |
  | 0.1074        | 38.62 | 11200 | 0.5966          | 0.4721 |
  | 0.1018        | 40.0  | 11600 | 0.5720          | 0.4668 |
  | 0.0968        | 41.38 | 12000 | 0.5826          | 0.4698 |
  | 0.0874        | 42.76 | 12400 | 0.5937          | 0.4634 |
  | 0.0843        | 44.14 | 12800 | 0.6056          | 0.4640 |
  | 0.0822        | 45.52 | 13200 | 0.5531          | 0.4569 |
  | 0.0806        | 46.9  | 13600 | 0.5669          | 0.4484 |
  | 0.072         | 48.28 | 14000 | 0.5683          | 0.4484 |
  | 0.0734        | 49.65 | 14400 | 0.5735          | 0.4437 |
  | 0.0671        | 51.03 | 14800 | 0.5455          | 0.4394 |
  | 0.0617        | 52.41 | 15200 | 0.5838          | 0.4365 |
  | 0.0607        | 53.79 | 15600 | 0.6233          | 0.4397 |
  | 0.0593        | 55.17 | 16000 | 0.5649          | 0.4340 |
  | 0.0551        | 56.55 | 16400 | 0.5923          | 0.4392 |
  | 0.0503        | 57.93 | 16800 | 0.5858          | 0.4325 |
  | 0.0496        | 59.31 | 17200 | 0.5884          | 0.4301 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  