readme: ---
  language:
  - lt
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  model-index:
  - name: wav2vec2-liepa-1-percent
    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-liepa-1-percent
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - LT dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5774
  - Wer: 0.5079
  
  ## 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: 8
  - 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: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 0.23  | 100  | 3.3596          | 1.0    |
  | No log        | 0.46  | 200  | 2.9280          | 1.0    |
  | No log        | 0.69  | 300  | 1.5091          | 0.9650 |
  | No log        | 0.93  | 400  | 0.9943          | 0.9177 |
  | 3.1184        | 1.16  | 500  | 0.7590          | 0.7793 |
  | 3.1184        | 1.39  | 600  | 0.7336          | 0.7408 |
  | 3.1184        | 1.62  | 700  | 0.7040          | 0.7618 |
  | 3.1184        | 1.85  | 800  | 0.6815          | 0.7233 |
  | 3.1184        | 2.08  | 900  | 0.6457          | 0.6865 |
  | 0.7917        | 2.31  | 1000 | 0.5705          | 0.6813 |
  | 0.7917        | 2.55  | 1100 | 0.5708          | 0.6620 |
  | 0.7917        | 2.78  | 1200 | 0.5888          | 0.6462 |
  | 0.7917        | 3.01  | 1300 | 0.6509          | 0.6970 |
  | 0.7917        | 3.24  | 1400 | 0.5871          | 0.6462 |
  | 0.5909        | 3.47  | 1500 | 0.6199          | 0.6813 |
  | 0.5909        | 3.7   | 1600 | 0.6230          | 0.5919 |
  | 0.5909        | 3.94  | 1700 | 0.5721          | 0.6427 |
  | 0.5909        | 4.17  | 1800 | 0.5331          | 0.5867 |
  | 0.5909        | 4.4   | 1900 | 0.5561          | 0.6007 |
  | 0.4607        | 4.63  | 2000 | 0.5414          | 0.5849 |
  | 0.4607        | 4.86  | 2100 | 0.5390          | 0.5587 |
  | 0.4607        | 5.09  | 2200 | 0.5313          | 0.5569 |
  | 0.4607        | 5.32  | 2300 | 0.5893          | 0.5797 |
  | 0.4607        | 5.56  | 2400 | 0.5507          | 0.5954 |
  | 0.3933        | 5.79  | 2500 | 0.5521          | 0.6025 |
  | 0.3933        | 6.02  | 2600 | 0.5663          | 0.5989 |
  | 0.3933        | 6.25  | 2700 | 0.5636          | 0.5832 |
  | 0.3933        | 6.48  | 2800 | 0.5464          | 0.5919 |
  | 0.3933        | 6.71  | 2900 | 0.5623          | 0.5832 |
  | 0.3367        | 6.94  | 3000 | 0.5324          | 0.5692 |
  | 0.3367        | 7.18  | 3100 | 0.5907          | 0.5394 |
  | 0.3367        | 7.41  | 3200 | 0.5653          | 0.5814 |
  | 0.3367        | 7.64  | 3300 | 0.5707          | 0.5814 |
  | 0.3367        | 7.87  | 3400 | 0.5754          | 0.5429 |
  | 0.2856        | 8.1   | 3500 | 0.5953          | 0.5569 |
  | 0.2856        | 8.33  | 3600 | 0.6275          | 0.5394 |
  | 0.2856        | 8.56  | 3700 | 0.6253          | 0.5569 |
  | 0.2856        | 8.8   | 3800 | 0.5930          | 0.5429 |
  | 0.2856        | 9.03  | 3900 | 0.6082          | 0.5219 |
  | 0.2522        | 9.26  | 4000 | 0.6026          | 0.5447 |
  | 0.2522        | 9.49  | 4100 | 0.6052          | 0.5271 |
  | 0.2522        | 9.72  | 4200 | 0.5871          | 0.5219 |
  | 0.2522        | 9.95  | 4300 | 0.5870          | 0.5236 |
  | 0.2522        | 10.19 | 4400 | 0.5881          | 0.5131 |
  | 0.2167        | 10.42 | 4500 | 0.6122          | 0.5289 |
  | 0.2167        | 10.65 | 4600 | 0.6128          | 0.5166 |
  | 0.2167        | 10.88 | 4700 | 0.6135          | 0.5377 |
  | 0.2167        | 11.11 | 4800 | 0.6055          | 0.5184 |
  | 0.2167        | 11.34 | 4900 | 0.6725          | 0.5569 |
  | 0.1965        | 11.57 | 5000 | 0.6482          | 0.5429 |
  | 0.1965        | 11.81 | 5100 | 0.6037          | 0.5096 |
  | 0.1965        | 12.04 | 5200 | 0.5931          | 0.5131 |
  | 0.1965        | 12.27 | 5300 | 0.5853          | 0.5114 |
  | 0.1965        | 12.5  | 5400 | 0.5798          | 0.5219 |
  | 0.172         | 12.73 | 5500 | 0.5775          | 0.5009 |
  | 0.172         | 12.96 | 5600 | 0.5782          | 0.5044 |
  | 0.172         | 13.19 | 5700 | 0.5804          | 0.5184 |
  | 0.172         | 13.43 | 5800 | 0.5977          | 0.5219 |
  | 0.172         | 13.66 | 5900 | 0.6069          | 0.5236 |
  | 0.1622        | 13.89 | 6000 | 0.5850          | 0.5131 |
  | 0.1622        | 14