readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: exp15-F01-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. -->
  
  # exp15-F01-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: 2.6149
  - Wer: 1.0154
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 45.2628       | 0.33  | 500   | 2.9838          | 1.0    |
  | 3.1304        | 0.67  | 1000  | 2.8311          | 1.0    |
  | 2.9607        | 1.0   | 1500  | 2.6426          | 1.0039 |
  | 2.7429        | 1.33  | 2000  | 2.5365          | 1.2046 |
  | 2.5496        | 1.66  | 2500  | 2.2169          | 1.3050 |
  | 2.3134        | 2.0   | 3000  | 2.0450          | 1.3127 |
  | 2.1189        | 2.33  | 3500  | 1.8677          | 1.2780 |
  | 2.0075        | 2.66  | 4000  | 1.7450          | 1.2703 |
  | 1.9014        | 3.0   | 4500  | 1.8381          | 1.2664 |
  | 1.7246        | 3.33  | 5000  | 1.7980          | 1.2510 |
  | 1.6783        | 3.66  | 5500  | 1.7269          | 1.2510 |
  | 1.589         | 3.99  | 6000  | 1.5640          | 1.2664 |
  | 1.4085        | 4.33  | 6500  | 1.7296          | 1.2355 |
  | 1.4126        | 4.66  | 7000  | 1.5208          | 1.2317 |
  | 1.3506        | 4.99  | 7500  | 1.6253          | 1.2317 |
  | 1.2276        | 5.33  | 8000  | 1.6222          | 1.2239 |
  | 1.1842        | 5.66  | 8500  | 1.4836          | 1.1969 |
  | 1.1445        | 5.99  | 9000  | 1.5313          | 1.2046 |
  | 1.0254        | 6.32  | 9500  | 1.9130          | 1.2046 |
  | 1.0214        | 6.66  | 10000 | 1.8944          | 1.2085 |
  | 0.9677        | 6.99  | 10500 | 1.9039          | 1.1853 |
  | 0.8822        | 7.32  | 11000 | 1.7036          | 1.1892 |
  | 0.8824        | 7.66  | 11500 | 1.6062          | 1.1815 |
  | 0.8695        | 7.99  | 12000 | 1.7019          | 1.1853 |
  | 0.7536        | 8.32  | 12500 | 1.9117          | 1.1737 |
  | 0.775         | 8.66  | 13000 | 1.8778          | 1.1815 |
  | 0.7409        | 8.99  | 13500 | 1.7534          | 1.1776 |
  | 0.7035        | 9.32  | 14000 | 1.9860          | 1.1853 |
  | 0.6905        | 9.65  | 14500 | 1.9141          | 1.1892 |
  | 0.6536        | 9.99  | 15000 | 1.7848          | 1.1737 |
  | 0.6237        | 10.32 | 15500 | 2.0624          | 1.1544 |
  | 0.5986        | 10.65 | 16000 | 1.9958          | 1.1544 |
  | 0.5838        | 10.99 | 16500 | 1.8005          | 1.1622 |
  | 0.5231        | 11.32 | 17000 | 1.5967          | 1.1351 |
  | 0.5452        | 11.65 | 17500 | 1.8145          | 1.1274 |
  | 0.5446        | 11.98 | 18000 | 2.0214          | 1.1429 |
  | 0.4727        | 12.32 | 18500 | 1.8989          | 1.1313 |
  | 0.4908        | 12.65 | 19000 | 1.7152          | 1.1467 |
  | 0.483         | 12.98 | 19500 | 1.7354          | 1.1429 |
  | 0.4455        | 13.32 | 20000 | 1.9493          | 1.1506 |
  | 0.4456        | 13.65 | 20500 | 2.0869          | 1.1197 |
  | 0.4306        | 13.98 | 21000 | 1.9248          | 1.1236 |
  | 0.3827        | 14.31 | 21500 | 1.9245          | 1.1274 |
  | 0.4059        | 14.65 | 22000 | 1.9478          | 1.1313 |
  | 0.3941        | 14.98 | 22500 | 2.2373          | 1.1197 |
  | 0.4094        | 15.31 | 23000 | 2.0268          | 1.1158 |
  | 0.3584        | 15.65 | 23500 | 1.9292          | 1.1313 |
  | 0.3615        | 15.98 | 24000 | 2.1744          | 1.0965 |
  | 0.3564        | 16.31 | 24500 | 2.4167          | 1.0927 |
  | 0.3202        | 16.64 | 25000 | 2.6332          | 1.1081 |
  | 0.3099        | 16.98 | 25500 | 2.9448          | 1.1004 |
  | 0.3126        | 17.31 | 26000 | 2.4662          | 1.0927 |
  | 0.3189        | 17.64 | 26500 | 2.3619          | 1.0772 |
  | 0.3929        | 17.98 | 27000 | 2.3571          | 1.0618 |
  | 0.27          | 18.31 | 27500 | 2.2457          | 1.0734 |
  | 0.2664        | 18.64 | 28000 | 2.5133          | 1.0772 |
  | 0.2875        | 18.97 | 28500 | 2.2798          | 1.0618 |
  | 0.2336        | 19.31 | 29000 | 2.3515          | 1.0347 |
  | 0.2597        | 19.64 | 29500 | 2.3072          | 1.0463 |
  | 0.2573        | 19.97 | 30000 | 2.1702          | 1.0425 |
  | 0.2431        | 20.31 | 30500 | 2.2727          | 1.0618 |
  | 0.2362        | 20.64 | 31000 | 2.3082          | 1.0772 |
  | 0.2377        | 20.97 | 31500 | 2.5453          | 1.0734 |
  | 0.