readme: ---
  tags:
  - automatic-speech-recognition
  - phongdtd/VinDataVLSP
  - generated_from_trainer
  datasets:
  - vin_data_vlsp
  model-index:
  - name: wavlm-vindata-demo-dist
    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. -->
  
  # wavlm-vindata-demo-dist
  
  This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the PHONGDTD/VINDATAVLSP - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.4439
  - Wer: 1.0
  
  ## 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: 1
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 2
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer |
  |:-------------:|:-----:|:------:|:---------------:|:---:|
  | 4.0704        | 0.01  | 100    | 3.8768          | 1.0 |
  | 3.6236        | 0.01  | 200    | 3.4611          | 1.0 |
  | 6.597         | 0.02  | 300    | 3.4557          | 1.0 |
  | 3.4744        | 0.03  | 400    | 3.4567          | 1.0 |
  | 5.3992        | 0.04  | 500    | 3.4631          | 1.0 |
  | 4.5348        | 0.04  | 600    | 3.4651          | 1.0 |
  | 3.2457        | 0.05  | 700    | 3.4917          | 1.0 |
  | 3.9245        | 0.06  | 800    | 3.4680          | 1.0 |
  | 3.2904        | 0.07  | 900    | 3.4518          | 1.0 |
  | 3.4768        | 0.07  | 1000   | 3.4506          | 1.0 |
  | 3.2418        | 0.08  | 1100   | 3.4474          | 1.0 |
  | 3.3111        | 0.09  | 1200   | 3.4684          | 1.0 |
  | 3.986         | 0.09  | 1300   | 3.4465          | 1.0 |
  | 4.3206        | 0.1   | 1400   | 3.4723          | 1.0 |
  | 4.682         | 0.11  | 1500   | 3.4732          | 1.0 |
  | 4.858         | 0.12  | 1600   | 3.4416          | 1.0 |
  | 3.2949        | 0.12  | 1700   | 3.4481          | 1.0 |
  | 3.4435        | 0.13  | 1800   | 3.4570          | 1.0 |
  | 5.0695        | 0.14  | 1900   | 3.4448          | 1.0 |
  | 3.4962        | 0.14  | 2000   | 3.4416          | 1.0 |
  | 3.4891        | 0.15  | 2100   | 3.4455          | 1.0 |
  | 4.1281        | 0.16  | 2200   | 3.4447          | 1.0 |
  | 3.5956        | 0.17  | 2300   | 3.4512          | 1.0 |
  | 3.6312        | 0.17  | 2400   | 3.4484          | 1.0 |
  | 4.5383        | 0.18  | 2500   | 3.4435          | 1.0 |
  | 6.1329        | 0.19  | 2600   | 3.4530          | 1.0 |
  | 3.709         | 0.2   | 2700   | 3.4466          | 1.0 |
  | 3.289         | 0.2   | 2800   | 3.4463          | 1.0 |
  | 4.3301        | 0.21  | 2900   | 3.4418          | 1.0 |
  | 4.6656        | 0.22  | 3000   | 3.4447          | 1.0 |
  | 3.4288        | 0.22  | 3100   | 3.4715          | 1.0 |
  | 3.5506        | 0.23  | 3200   | 3.4437          | 1.0 |
  | 3.7497        | 0.24  | 3300   | 3.4910          | 1.0 |
  | 3.5198        | 0.25  | 3400   | 3.4574          | 1.0 |
  | 3.4183        | 0.25  | 3500   | 3.4607          | 1.0 |
  | 4.5573        | 0.26  | 3600   | 3.4421          | 1.0 |
  | 3.5737        | 0.27  | 3700   | 3.4481          | 1.0 |
  | 4.9008        | 0.28  | 3800   | 3.4411          | 1.0 |
  | 4.8725        | 0.28  | 3900   | 3.4422          | 1.0 |
  | 3.5799        | 0.29  | 4000   | 3.4659          | 1.0 |
  | 3.3257        | 0.3   | 4100   | 3.4519          | 1.0 |
  | 3.6887        | 0.3   | 4200   | 3.4827          | 1.0 |
  | 3.3037        | 0.31  | 4300   | 3.4632          | 1.0 |
  | 5.5543        | 0.32  | 4400   | 3.4480          | 1.0 |
  | 3.2898        | 0.33  | 4500   | 3.4404          | 1.0 |
  | 3.2794        | 0.33  | 4600   | 3.4633          | 1.0 |
  | 3.7896        | 0.34  | 4700   | 3.4439          | 1.0 |
  | 3.6662        | 0.35  | 4800   | 3.4587          | 1.0 |
  | 3.588         | 0.35  | 4900   | 3.4520          | 1.0 |
  | 4.0535        | 0.36  | 5000   | 3.4450          | 1.0 |
  | 3.4335        | 0.37  | 5100   | 3.4577          | 1.0 |
  | 3.6317        | 0.38  | 5200   | 3.4443          | 1.0 |
  | 5.2564        | 0.38  | 5300   | 3.4505          | 1.0 |
  | 3.8781        | 0.39  | 5400   | 3.4418          | 1.0 |
  | 4.6269        | 0.4   | 5500   | 3.4425          | 1.0 |
  | 3.6095        | 0.41  | 5600   | 3.4581          | 1.0 |
  | 4.6164        | 0.41  | 5700   | 3.4404          | 1.0 |
  | 3.117         | 0.42  | 5800   | 3.4596          | 1.0 |
  | 4.3939        | 0.43  | 5900   | 3.4401          | 1.0 |
  | 3.5856        | 0.43  | 6000   | 3.4413          | 1.0 |
  | 3.5187        | 0.44  | 6100   | 3.4452          | 1.0 |
  | 4