readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: wav2vec2-xlsr-korean-speech-emotion-recognition
    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. -->
  
  # wav2vec2-xlsr-korean-speech-emotion-recognition
  
  This model is a fine-tuned version of [jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15](https://huggingface.co/jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6651
  - Accuracy: 0.7667
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.7098        | 0.02  | 20   | 1.6849          | 0.1986   |
  | 1.6093        | 0.05  | 40   | 1.6102          | 0.2237   |
  | 1.5673        | 0.07  | 60   | 1.5126          | 0.3696   |
  | 1.554         | 0.1   | 80   | 1.4260          | 0.3624   |
  | 1.3676        | 0.12  | 100  | 1.2504          | 0.4761   |
  | 1.3763        | 0.14  | 120  | 1.3997          | 0.4091   |
  | 1.2727        | 0.17  | 140  | 1.3728          | 0.4211   |
  | 1.4632        | 0.19  | 160  | 1.2427          | 0.4653   |
  | 1.2167        | 0.22  | 180  | 1.1379          | 0.5371   |
  | 1.1789        | 0.24  | 200  | 1.1165          | 0.5514   |
  | 1.1639        | 0.26  | 220  | 1.0029          | 0.5813   |
  | 1.1664        | 0.29  | 240  | 1.1682          | 0.4844   |
  | 1.1223        | 0.31  | 260  | 1.0946          | 0.5526   |
  | 1.2748        | 0.33  | 280  | 1.0023          | 0.5837   |
  | 1.1422        | 0.36  | 300  | 1.0873          | 0.5215   |
  | 0.9999        | 0.38  | 320  | 1.0378          | 0.5742   |
  | 1.0444        | 0.41  | 340  | 1.0049          | 0.5921   |
  | 1.0099        | 0.43  | 360  | 0.9367          | 0.6160   |
  | 0.9251        | 0.45  | 380  | 1.0681          | 0.5694   |
  | 0.8257        | 0.48  | 400  | 1.0516          | 0.5813   |
  | 1.3777        | 0.5   | 420  | 1.0250          | 0.6280   |
  | 0.9415        | 0.53  | 440  | 0.9033          | 0.6316   |
  | 0.9927        | 0.55  | 460  | 0.9360          | 0.6184   |
  | 1.0245        | 0.57  | 480  | 0.9133          | 0.6232   |
  | 1.145         | 0.6   | 500  | 0.8876          | 0.6459   |
  | 0.916         | 0.62  | 520  | 0.9421          | 0.6256   |
  | 0.9833        | 0.65  | 540  | 0.8567          | 0.6400   |
  | 0.7927        | 0.67  | 560  | 0.9217          | 0.6579   |
  | 1.0793        | 0.69  | 580  | 0.8519          | 0.6663   |
  | 0.9642        | 0.72  | 600  | 0.8192          | 0.6758   |
  | 0.9433        | 0.74  | 620  | 0.8412          | 0.6627   |
  | 0.5469        | 0.77  | 640  | 0.8177          | 0.6794   |
  | 0.9734        | 0.79  | 660  | 0.8130          | 0.6794   |
  | 0.7071        | 0.81  | 680  | 0.9579          | 0.6579   |
  | 0.9347        | 0.84  | 700  | 0.7870          | 0.6878   |
  | 0.8024        | 0.86  | 720  | 0.9395          | 0.6411   |
  | 0.9428        | 0.88  | 740  | 0.8817          | 0.6663   |
  | 0.8024        | 0.91  | 760  | 0.7953          | 0.6974   |
  | 0.7028        | 0.93  | 780  | 0.8279          | 0.6890   |
  | 0.8357        | 0.96  | 800  | 0.8291          | 0.6950   |
  | 0.8304        | 0.98  | 820  | 0.7778          | 0.6914   |
  | 0.8157        | 1.0   | 840  | 0.7726          | 0.7081   |
  | 0.5431        | 1.03  | 860  | 0.7628          | 0.7117   |
  | 0.6816        | 1.05  | 880  | 0.7928          | 0.7057   |
  | 0.7498        | 1.08  | 900  | 0.7732          | 0.7069   |
  | 0.6785        | 1.1   | 920  | 0.8298          | 0.7033   |
  | 0.5951        | 1.12  | 940  | 0.8020          | 0.7225   |
  | 0.4782        | 1.15  | 960  | 0.7541          | 0.7297   |
  | 0.6825        | 1.17  | 980  | 0.9647          | 0.6687   |
  | 0.5793        | 1.2   | 1000 | 0.9230          | 0.7057   |
  | 0.4872        | 1.22  | 1020 | 0.8361          | 0.7141   |
  | 0.5422        | 1.24  | 1040 | 0.8629          | 0.7225   |
  | 0.5671        | 1.27  | 1060 | 0.8376          | 0.7261   |
  | 0.6285        | 1.29  | 1080 | 0.7478          | 0.7524   |
  | 0.6647        | 1.32  | 1100 | 0.8001          | 0.7261   |
  | 0.7846        | 1.34  | 1120 | 0.7344          | 0.7428   |
  | 0.6362        | 1.36  | 1140 | 0.6980          | 0.75     |
  | 0.4498        | 1.39  | 1160 | 0.7186          | 0.7536   |
  | 0.5002   