readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: exp20-M04-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. -->
  
  # exp20-M04-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: 3.3358
  - Wer: 1.1374
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 37.2423       | 0.34  | 500   | 3.2577          | 1.0    |
  | 3.1334        | 0.68  | 1000  | 3.0084          | 1.0    |
  | 2.9616        | 1.02  | 1500  | 2.9946          | 1.0    |
  | 2.8067        | 1.36  | 2000  | 2.6228          | 1.3130 |
  | 2.732         | 1.7   | 2500  | 2.5059          | 1.5013 |
  | 2.3673        | 2.04  | 3000  | 2.1828          | 1.4911 |
  | 2.1378        | 2.38  | 3500  | 2.2066          | 1.4911 |
  | 1.9853        | 2.72  | 4000  | 1.9877          | 1.4580 |
  | 1.8574        | 3.06  | 4500  | 1.8850          | 1.4656 |
  | 1.7085        | 3.4   | 5000  | 1.9121          | 1.4606 |
  | 1.6161        | 3.74  | 5500  | 2.1036          | 1.4326 |
  | 1.5304        | 4.08  | 6000  | 1.9807          | 1.4478 |
  | 1.3531        | 4.42  | 6500  | 2.0211          | 1.4656 |
  | 1.3269        | 4.77  | 7000  | 1.9231          | 1.3893 |
  | 1.2312        | 5.11  | 7500  | 2.2652          | 1.4097 |
  | 1.1161        | 5.45  | 8000  | 1.9543          | 1.4529 |
  | 1.0305        | 5.79  | 8500  | 2.1463          | 1.4071 |
  | 0.9403        | 6.13  | 9000  | 3.7872          | 1.4071 |
  | 0.8723        | 6.47  | 9500  | 2.8466          | 1.4326 |
  | 0.8752        | 6.81  | 10000 | 2.2215          | 1.3766 |
  | 0.7774        | 7.15  | 10500 | 2.0462          | 1.3257 |
  | 0.74          | 7.49  | 11000 | 2.1928          | 1.3333 |
  | 0.7371        | 7.83  | 11500 | 2.8058          | 1.3410 |
  | 0.7075        | 8.17  | 12000 | 2.3100          | 1.3308 |
  | 0.6746        | 8.51  | 12500 | 2.6284          | 1.2875 |
  | 0.6233        | 8.85  | 13000 | 2.2268          | 1.3003 |
  | 0.7172        | 9.19  | 13500 | 2.1980          | 1.2926 |
  | 0.5697        | 9.53  | 14000 | 2.1950          | 1.2468 |
  | 0.5691        | 9.87  | 14500 | 2.1819          | 1.2316 |
  | 0.5062        | 10.21 | 15000 | 2.1426          | 1.2621 |
  | 0.4818        | 10.55 | 15500 | 2.2259          | 1.2545 |
  | 0.5083        | 10.89 | 16000 | 2.1764          | 1.2214 |
  | 0.3901        | 11.23 | 16500 | 2.2412          | 1.2341 |
  | 0.4275        | 11.57 | 17000 | 2.3781          | 1.2290 |
  | 0.4225        | 11.91 | 17500 | 2.1578          | 1.2443 |
  | 0.4106        | 12.25 | 18000 | 2.5651          | 1.2341 |
  | 0.3933        | 12.59 | 18500 | 2.1819          | 1.2265 |
  | 0.3821        | 12.93 | 19000 | 2.0564          | 1.1934 |
  | 0.3584        | 13.27 | 19500 | 2.5475          | 1.2290 |
  | 0.3468        | 13.61 | 20000 | 2.5857          | 1.1781 |
  | 0.3984        | 13.96 | 20500 | 2.2383          | 1.2239 |
  | 0.308         | 14.3  | 21000 | 2.4947          | 1.2137 |
  | 0.3356        | 14.64 | 21500 | 2.6563          | 1.2163 |
  | 0.3406        | 14.98 | 22000 | 2.3337          | 1.2061 |
  | 0.3297        | 15.32 | 22500 | 2.2793          | 1.1908 |
  | 0.3028        | 15.66 | 23000 | 2.6462          | 1.1654 |
  | 0.3226        | 16.0  | 23500 | 2.3785          | 1.1705 |
  | 0.2605        | 16.34 | 24000 | 2.7212          | 1.1858 |
  | 0.2669        | 16.68 | 24500 | 3.0365          | 1.2087 |
  | 0.2967        | 17.02 | 25000 | 2.4898          | 1.1934 |
  | 0.2547        | 17.36 | 25500 | 2.4020          | 1.1832 |
  | 0.2779        | 17.7  | 26000 | 2.5558          | 1.1705 |
  | 0.2341        | 18.04 | 26500 | 2.9406          | 1.1934 |
  | 0.2304        | 18.38 | 27000 | 3.1528          | 1.1603 |
  | 0.226         | 18.72 | 27500 | 3.0001          | 1.2163 |
  | 0.2319        | 19.06 | 28000 | 3.0117          | 1.1603 |
  | 0.1836        | 19.4  | 28500 | 2.8332          | 1.1858 |
  | 0.2085        | 19.74 | 29000 | 2.8757          | 1.1603 |
  | 0.2383        | 20.08 | 29500 | 3.2235          | 1.1934 |
  | 0.2006        | 20.42 | 30000 | 3.0189          | 1.1603 |
  | 0.1722        | 20.76 | 30500 | 2.8001          | 1.1527 |
  | 0.1955        | 21.1  | 31000 | 3.0401          | 1.1578 |
  | 0.1839        | 21.44 | 31500 | 3.2621          | 1.1578 |
  | 0.