readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: Millad_Customer_RN
    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. -->
  
  # Millad_Customer_RN
  
  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: 4.5635
  - Wer: 0.8113
  - Cer: 0.4817
  
  ## 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: 4000
  - num_epochs: 600
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Wer    | Cer    |
  |:-------------:|:------:|:-----:|:---------------:|:------:|:------:|
  | 1.9257        | 13.33  | 2000  | 2.0606          | 0.9767 | 0.5500 |
  | 1.4828        | 26.67  | 4000  | 2.1161          | 0.9019 | 0.4932 |
  | 1.2582        | 40.0   | 6000  | 2.0589          | 0.8504 | 0.4942 |
  | 0.9804        | 53.33  | 8000  | 2.4633          | 0.8745 | 0.4763 |
  | 0.7862        | 66.67  | 10000 | 2.4794          | 0.8861 | 0.4944 |
  | 0.6492        | 80.0   | 12000 | 2.8693          | 0.8554 | 0.4928 |
  | 0.5375        | 93.33  | 14000 | 2.6125          | 0.8296 | 0.4802 |
  | 0.4462        | 106.67 | 16000 | 2.7591          | 0.8770 | 0.4974 |
  | 0.3873        | 120.0  | 18000 | 3.0325          | 0.8379 | 0.4800 |
  | 0.3445        | 133.33 | 20000 | 2.9965          | 0.8761 | 0.4986 |
  | 0.3087        | 146.67 | 22000 | 3.3437          | 0.8221 | 0.4923 |
  | 0.2755        | 160.0  | 24000 | 3.3022          | 0.8803 | 0.5211 |
  | 0.2467        | 173.33 | 26000 | 3.2348          | 0.8479 | 0.4933 |
  | 0.2281        | 186.67 | 28000 | 3.8010          | 0.8695 | 0.5081 |
  | 0.2119        | 200.0  | 30000 | 3.0446          | 0.8545 | 0.4902 |
  | 0.194         | 213.33 | 32000 | 3.0873          | 0.8454 | 0.4840 |
  | 0.1677        | 226.67 | 34000 | 3.6184          | 0.8645 | 0.5019 |
  | 0.1642        | 240.0  | 36000 | 3.2480          | 0.8412 | 0.4903 |
  | 0.1656        | 253.33 | 38000 | 3.4379          | 0.8362 | 0.4816 |
  | 0.1371        | 266.67 | 40000 | 3.5117          | 0.8479 | 0.5040 |
  | 0.1301        | 280.0  | 42000 | 3.4360          | 0.8404 | 0.4870 |
  | 0.128         | 293.33 | 44000 | 3.6589          | 0.8537 | 0.4977 |
  | 0.1152        | 306.67 | 46000 | 4.2359          | 0.8545 | 0.5051 |
  | 0.1119        | 320.0  | 48000 | 3.5818          | 0.7980 | 0.4882 |
  | 0.1026        | 333.33 | 50000 | 3.7618          | 0.8013 | 0.4865 |
  | 0.0945        | 346.67 | 52000 | 4.2197          | 0.8404 | 0.5028 |
  | 0.0962        | 360.0  | 54000 | 3.9231          | 0.8653 | 0.5030 |
  | 0.088         | 373.33 | 56000 | 3.8400          | 0.8354 | 0.4914 |
  | 0.0743        | 386.67 | 58000 | 3.4924          | 0.8088 | 0.4824 |
  | 0.0811        | 400.0  | 60000 | 3.8370          | 0.8396 | 0.4861 |
  | 0.0696        | 413.33 | 62000 | 4.2808          | 0.8412 | 0.5065 |
  | 0.0692        | 426.67 | 64000 | 4.0161          | 0.8088 | 0.4744 |
  | 0.0622        | 440.0  | 66000 | 3.9080          | 0.8163 | 0.4910 |
  | 0.0591        | 453.33 | 68000 | 3.9838          | 0.8113 | 0.4823 |
  | 0.0527        | 466.67 | 70000 | 3.8067          | 0.8329 | 0.4914 |
  | 0.056         | 480.0  | 72000 | 4.1415          | 0.8096 | 0.4782 |
  | 0.0535        | 493.33 | 74000 | 4.3350          | 0.8229 | 0.4828 |
  | 0.0531        | 506.67 | 76000 | 3.9808          | 0.8071 | 0.4807 |
  | 0.0451        | 520.0  | 78000 | 4.0301          | 0.7988 | 0.4816 |
  | 0.044         | 533.33 | 80000 | 4.4680          | 0.8371 | 0.4921 |
  | 0.0389        | 546.67 | 82000 | 4.1380          | 0.8121 | 0.4819 |
  | 0.0392        | 560.0  | 84000 | 4.3910          | 0.7930 | 0.4763 |
  | 0.0389        | 573.33 | 86000 | 4.5086          | 0.8055 | 0.4802 |
  | 0.0355        | 586.67 | 88000 | 4.6259          | 0.8113 | 0.4821 |
  | 0.0307        | 600.0  | 90000 | 4.5635          | 0.8113 | 0.4817 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  