readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: exp17-F03-both
    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. -->
  
  # exp17-F03-both
  
  This model is a fine-tuned version of [yongjian/wav2vec2-large-a](https://huggingface.co/yongjian/wav2vec2-large-a) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9268
  - Wer: 0.9485
  
  ## 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: 4
  - 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: 1000
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 47.4704       | 0.36  | 500   | 3.3075          | 1.0131 |
  | 3.1649        | 0.71  | 1000  | 3.3442          | 1.0    |
  | 2.9674        | 1.07  | 1500  | 2.6986          | 1.0    |
  | 2.7514        | 1.42  | 2000  | 2.5789          | 1.1299 |
  | 2.6045        | 1.78  | 2500  | 2.3025          | 1.2529 |
  | 2.373         | 2.14  | 3000  | 2.2169          | 1.2698 |
  | 2.1632        | 2.49  | 3500  | 1.9883          | 1.2667 |
  | 2.0942        | 2.85  | 4000  | 1.9294          | 1.2567 |
  | 1.9239        | 3.2   | 4500  | 1.9799          | 1.2467 |
  | 1.7549        | 3.56  | 5000  | 1.7485          | 1.2252 |
  | 1.6973        | 3.91  | 5500  | 1.6799          | 1.2283 |
  | 1.5823        | 4.27  | 6000  | 1.6847          | 1.2267 |
  | 1.4761        | 4.63  | 6500  | 1.6971          | 1.1968 |
  | 1.4381        | 4.98  | 7000  | 1.6280          | 1.2052 |
  | 1.2509        | 5.34  | 7500  | 1.6657          | 1.2060 |
  | 1.3112        | 5.69  | 8000  | 1.5618          | 1.1783 |
  | 1.1851        | 6.05  | 8500  | 1.6555          | 1.1783 |
  | 1.1112        | 6.41  | 9000  | 1.6586          | 1.1752 |
  | 1.0463        | 6.76  | 9500  | 1.6135          | 1.1683 |
  | 1.041         | 7.12  | 10000 | 1.5444          | 1.1522 |
  | 0.9451        | 7.47  | 10500 | 1.5561          | 1.1622 |
  | 0.9454        | 7.83  | 11000 | 1.5044          | 1.1483 |
  | 0.8496        | 8.19  | 11500 | 1.6724          | 1.1330 |
  | 0.825         | 8.54  | 12000 | 1.5950          | 1.1414 |
  | 0.8291        | 8.9   | 12500 | 1.6023          | 1.1384 |
  | 0.7279        | 9.25  | 13000 | 1.6319          | 1.1314 |
  | 0.7394        | 9.61  | 13500 | 1.5478          | 1.1337 |
  | 0.7079        | 9.96  | 14000 | 1.7564          | 1.1453 |
  | 0.609         | 10.32 | 14500 | 1.7671          | 1.1245 |
  | 0.6639        | 10.68 | 15000 | 1.7471          | 1.1314 |
  | 0.648         | 11.03 | 15500 | 1.7694          | 1.2160 |
  | 0.577         | 11.39 | 16000 | 1.6149          | 1.1760 |
  | 0.577         | 11.74 | 16500 | 1.9288          | 1.1238 |
  | 0.5695        | 12.1  | 17000 | 1.7503          | 1.1253 |
  | 0.5326        | 12.46 | 17500 | 1.5635          | 1.1376 |
  | 0.5423        | 12.81 | 18000 | 1.7083          | 1.1668 |
  | 0.4775        | 13.17 | 18500 | 1.7054          | 1.1245 |
  | 0.4772        | 13.52 | 19000 | 1.6455          | 1.1045 |
  | 0.4737        | 13.88 | 19500 | 1.5996          | 1.0968 |
  | 0.4529        | 14.23 | 20000 | 1.9847          | 1.1653 |
  | 0.4461        | 14.59 | 20500 | 1.6845          | 1.1084 |
  | 0.4497        | 14.95 | 21000 | 1.6465          | 1.0938 |
  | 0.4096        | 15.3  | 21500 | 1.5919          | 1.0769 |
  | 0.3897        | 15.66 | 22000 | 1.5637          | 1.0761 |
  | 0.4234        | 16.01 | 22500 | 1.6360          | 1.0953 |
  | 0.3659        | 16.37 | 23000 | 1.7573          | 1.0830 |
  | 0.3352        | 16.73 | 23500 | 1.8474          | 1.0976 |
  | 0.3886        | 17.08 | 24000 | 1.9115          | 1.0953 |
  | 0.3255        | 17.44 | 24500 | 1.8820          | 1.0815 |
  | 0.3405        | 17.79 | 25000 | 1.6862          | 1.0346 |
  | 0.3205        | 18.15 | 25500 | 1.6912          | 1.0500 |
  | 0.322         | 18.51 | 26000 | 1.6253          | 1.0615 |
  | 0.296         | 18.86 | 26500 | 1.7924          | 1.0546 |
  | 0.2869        | 19.22 | 27000 | 1.8204          | 1.0899 |
  | 0.269         | 19.57 | 27500 | 1.7558          | 1.0292 |
  | 0.2844        | 19.93 | 28000 | 1.6038          | 1.0131 |
  | 0.2543        | 20.28 | 28500 | 1.7935          | 1.0161 |
  | 0.3025        | 20.64 | 29000 | 1.8706          | 1.0423 |
  | 0.2707        | 21.0  | 29500 | 2.0011          | 1.0208 |
  | 0.2401        | 21.35 | 30000 | 1.9058          | 1.0161 |
  | 0.2609        | 21.71 | 30500 | 1.7555          | 1.0015 |
  | 0.2403        | 22.06 | 31000 | 1.9301          | 1.0085 |
  | 0.2538        | 22.42 | 31500 | 1.8586          | 0.9969 |
  | 0.