readme: ---
  tags:
  - automatic-speech-recognition
  - phongdtd/VinDataVLSP
  - generated_from_trainer
  model-index:
  - name: wavLM-VLSP-vi
    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-VLSP-vi
  
  This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the PHONGDTD/VINDATAVLSP - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 45.8892
  - Wer: 0.9999
  - Cer: 0.9973
  
  ## 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: 4
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 8
  - 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: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
  | 3.4482        | 9.41  | 40000  | 3.4480          | 0.9999 | 0.9974 |
  | 3.4619        | 18.81 | 80000  | 3.4514          | 0.9999 | 0.9974 |
  | 3.7961        | 28.22 | 120000 | 3.8732          | 0.9999 | 0.9974 |
  | 24.3843       | 37.62 | 160000 | 22.5457         | 0.9999 | 0.9973 |
  | 48.5691       | 47.03 | 200000 | 45.8892         | 0.9999 | 0.9973 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  