readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: base-on-torgo0003
    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. -->
  
  # base-on-torgo0003
  
  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: 0.6579
  - Wer: 0.7547
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 28.1611       | 0.46  | 500   | 3.4550          | 1.0163 |
  | 3.2238        | 0.92  | 1000  | 2.8781          | 1.0411 |
  | 2.8617        | 1.39  | 1500  | 2.9896          | 1.0028 |
  | 2.5841        | 1.85  | 2000  | 2.3744          | 1.2896 |
  | 2.2029        | 2.31  | 2500  | 1.8598          | 1.2722 |
  | 1.9976        | 2.77  | 3000  | 1.6505          | 1.2513 |
  | 1.7817        | 3.23  | 3500  | 1.5291          | 1.2294 |
  | 1.6484        | 3.69  | 4000  | 1.4635          | 1.2106 |
  | 1.56          | 4.16  | 4500  | 1.4251          | 1.1989 |
  | 1.417         | 4.62  | 5000  | 1.4040          | 1.1904 |
  | 1.2884        | 5.08  | 5500  | 1.2734          | 1.1568 |
  | 1.2788        | 5.54  | 6000  | 1.2242          | 1.1384 |
  | 1.2159        | 6.0   | 6500  | 1.0561          | 1.1349 |
  | 1.1125        | 6.46  | 7000  | 1.1001          | 1.1175 |
  | 1.1495        | 6.93  | 7500  | 1.0409          | 1.1112 |
  | 1.0222        | 7.39  | 8000  | 1.0525          | 1.0952 |
  | 1.0104        | 7.85  | 8500  | 1.0184          | 1.1048 |
  | 0.9956        | 8.31  | 9000  | 1.0438          | 1.1196 |
  | 0.8747        | 8.77  | 9500  | 1.0736          | 1.1005 |
  | 0.8437        | 9.23  | 10000 | 1.0041          | 1.0768 |
  | 0.861         | 9.7   | 10500 | 0.9407          | 1.0496 |
  | 0.8238        | 10.16 | 11000 | 0.9237          | 1.0697 |
  | 0.7806        | 10.62 | 11500 | 0.8706          | 1.0343 |
  | 0.7475        | 11.08 | 12000 | 0.9576          | 1.0407 |
  | 0.6963        | 11.54 | 12500 | 0.9195          | 1.0159 |
  | 0.7624        | 12.0  | 13000 | 0.8102          | 1.0060 |
  | 0.6311        | 12.47 | 13500 | 0.8208          | 0.9897 |
  | 0.6649        | 12.93 | 14000 | 0.7699          | 0.9968 |
  | 0.6025        | 13.39 | 14500 | 0.7834          | 0.9547 |
  | 0.5691        | 13.85 | 15000 | 0.7414          | 0.9632 |
  | 0.532         | 14.31 | 15500 | 0.7056          | 0.9473 |
  | 0.5572        | 14.77 | 16000 | 0.8136          | 0.9929 |
  | 0.5455        | 15.24 | 16500 | 0.7355          | 0.9264 |
  | 0.5369        | 15.7  | 17000 | 0.7531          | 0.9352 |
  | 0.4771        | 16.16 | 17500 | 0.7527          | 0.9228 |
  | 0.4778        | 16.62 | 18000 | 0.7312          | 0.9218 |
  | 0.4384        | 17.08 | 18500 | 0.6774          | 0.8913 |
  | 0.4619        | 17.54 | 19000 | 0.6888          | 0.8896 |
  | 0.4341        | 18.01 | 19500 | 0.7068          | 0.9030 |
  | 0.4164        | 18.47 | 20000 | 0.6484          | 0.8754 |
  | 0.3883        | 18.93 | 20500 | 0.6388          | 0.8676 |
  | 0.4135        | 19.39 | 21000 | 0.6732          | 0.8683 |
  | 0.4121        | 19.85 | 21500 | 0.6354          | 0.8591 |
  | 0.3694        | 20.31 | 22000 | 0.6751          | 0.8581 |
  | 0.367         | 20.78 | 22500 | 0.6487          | 0.8411 |
  | 0.3798        | 21.24 | 23000 | 0.5955          | 0.8312 |
  | 0.3249        | 21.7  | 23500 | 0.6209          | 0.8230 |
  | 0.3182        | 22.16 | 24000 | 0.7341          | 0.8212 |
  | 0.3196        | 22.62 | 24500 | 0.6533          | 0.8106 |
  | 0.297         | 23.08 | 25000 | 0.7163          | 0.8177 |
  | 0.3021        | 23.55 | 25500 | 0.7209          | 0.8149 |
  | 0.3248        | 24.01 | 26000 | 0.6268          | 0.8018 |
  | 0.3013        | 24.47 | 26500 | 0.7014          | 0.7915 |
  | 0.2986        | 24.93 | 27000 | 0.7306          | 0.8028 |
  | 0.2913        | 25.39 | 27500 | 0.6866          | 0.7912 |
  | 0.2706        | 25.85 | 28000 | 0.6860          | 0.7851 |
  | 0.2572        | 26.32 | 28500 | 0.6478          | 0.7752 |
  | 0.2794        | 26.78 | 29000 | 0.6308          | 0.7703 |
  | 0.2796        | 27.24 | 29500 | 0.6302          | 0.7653 |
  | 0.2604        | 27.7  | 30000 | 0.6638          | 0.7621 |
  | 0.2367        | 28.16 | 30500 | 0.6492          | 0.7593 |
  | 0.2383        | 28.62 | 31000 | 0.6560          | 0.7614 |
  | 0.2495        | 29.09 | 31500 | 0.6577          | 0.7593 