readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - zeroth_korean_asr
  metrics:
  - wer
  model-index:
  - name: hubert_zeroth_gpu_freeze
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: zeroth_korean_asr
        type: zeroth_korean_asr
        config: clean
        split: train
        args: clean
      metrics:
      - name: Wer
        type: wer
        value: 1.0
  ---
  
  <!-- 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. -->
  
  # hubert_zeroth_gpu_freeze
  
  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the zeroth_korean_asr dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.8310
  - 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: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer |
  |:-------------:|:-----:|:-----:|:---------------:|:---:|
  | 26.2877       | 0.14  | 100   | 10.6810         | 1.0 |
  | 6.4696        | 0.29  | 200   | 4.8799          | 1.0 |
  | 4.841         | 0.43  | 300   | 4.8521          | 1.0 |
  | 4.8366        | 0.57  | 400   | 4.8736          | 1.0 |
  | 4.8311        | 0.72  | 500   | 4.8559          | 1.0 |
  | 4.8383        | 0.86  | 600   | 4.8601          | 1.0 |
  | 4.8288        | 1.01  | 700   | 4.8474          | 1.0 |
  | 4.8283        | 1.15  | 800   | 4.8436          | 1.0 |
  | 4.8283        | 1.29  | 900   | 4.8440          | 1.0 |
  | 4.8299        | 1.44  | 1000  | 4.8518          | 1.0 |
  | 4.8274        | 1.58  | 1100  | 4.8406          | 1.0 |
  | 4.8308        | 1.72  | 1200  | 4.8384          | 1.0 |
  | 4.8316        | 1.87  | 1300  | 4.8427          | 1.0 |
  | 4.8298        | 2.01  | 1400  | 4.8423          | 1.0 |
  | 4.8291        | 2.16  | 1500  | 4.8481          | 1.0 |
  | 4.8326        | 2.3   | 1600  | 4.8426          | 1.0 |
  | 4.83          | 2.44  | 1700  | 4.8362          | 1.0 |
  | 4.8286        | 2.59  | 1800  | 4.8424          | 1.0 |
  | 4.8269        | 2.73  | 1900  | 4.8362          | 1.0 |
  | 4.8234        | 2.87  | 2000  | 4.8452          | 1.0 |
  | 4.8179        | 3.02  | 2100  | 4.8416          | 1.0 |
  | 4.825         | 3.16  | 2200  | 4.8519          | 1.0 |
  | 4.8185        | 3.3   | 2300  | 4.8384          | 1.0 |
  | 4.827         | 3.45  | 2400  | 4.8519          | 1.0 |
  | 4.8316        | 3.59  | 2500  | 4.8467          | 1.0 |
  | 4.825         | 3.74  | 2600  | 4.8465          | 1.0 |
  | 4.8246        | 3.88  | 2700  | 4.8422          | 1.0 |
  | 4.8228        | 4.02  | 2800  | 4.8326          | 1.0 |
  | 4.8277        | 4.17  | 2900  | 4.8353          | 1.0 |
  | 4.822         | 4.31  | 3000  | 4.8349          | 1.0 |
  | 4.82          | 4.45  | 3100  | 4.8395          | 1.0 |
  | 4.8252        | 4.6   | 3200  | 4.8350          | 1.0 |
  | 4.8283        | 4.74  | 3300  | 4.8377          | 1.0 |
  | 4.8229        | 4.89  | 3400  | 4.8344          | 1.0 |
  | 4.8264        | 5.03  | 3500  | 4.8352          | 1.0 |
  | 4.8237        | 5.17  | 3600  | 4.8337          | 1.0 |
  | 4.8271        | 5.32  | 3700  | 4.8385          | 1.0 |
  | 4.8332        | 5.46  | 3800  | 4.8392          | 1.0 |
  | 4.8189        | 5.6   | 3900  | 4.8353          | 1.0 |
  | 4.8209        | 5.75  | 4000  | 4.8355          | 1.0 |
  | 4.8179        | 5.89  | 4100  | 4.8297          | 1.0 |
  | 4.821         | 6.03  | 4200  | 4.8505          | 1.0 |
  | 4.8243        | 6.18  | 4300  | 4.8371          | 1.0 |
  | 4.8224        | 6.32  | 4400  | 4.8378          | 1.0 |
  | 4.8261        | 6.47  | 4500  | 4.8368          | 1.0 |
  | 4.8233        | 6.61  | 4600  | 4.8326          | 1.0 |
  | 4.8252        | 6.75  | 4700  | 4.8364          | 1.0 |
  | 4.8247        | 6.9   | 4800  | 4.8438          | 1.0 |
  | 4.8139        | 7.04  | 4900  | 4.8435          | 1.0 |
  | 4.8204        | 7.18  | 5000  | 4.8398          | 1.0 |
  | 4.8197        | 7.33  | 5100  | 4.8382          | 1.0 |
  | 4.82          | 7.47  | 5200  | 4.8371          | 1.0 |
  | 4.8266        | 7.61  | 5300  | 4.8431          | 1.0 |
  | 4.826         | 7.76  | 5400  | 4.8390          | 1.0 |
  | 4.8216        | 7.9   | 5500  | 4.8381          | 1.0 |
  | 4.82          | 8.05  | 5600  | 4.8339          | 1.0 |
  | 4.8281        | 8.19  | 5700  | 4.8316          | 1.0 |
  | 4.8246        | 8.33  | 5800  | 4.8361          | 1.0 |
  | 4.8169 