readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-MIR_ST500-demo-colab
    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-base-MIR_ST500-demo-colab
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.7360
  - Wer: 0.9837
  
  ## 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: 32
  - eval_batch_size: 16
  - 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: 500
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Wer    |
  |:-------------:|:------:|:----:|:---------------:|:------:|
  | 101.0917      | 16.67  | 100  | 18.8979         | 0.8208 |
  | 15.5054       | 33.33  | 200  | 10.9184         | 0.8208 |
  | 10.1879       | 50.0   | 300  | 7.6480          | 0.8208 |
  | 6.777         | 66.67  | 400  | 3.5386          | 1.0    |
  | 3.0546        | 83.33  | 500  | 2.8794          | 1.0    |
  | 2.8661        | 100.0  | 600  | 2.8405          | 1.0    |
  | 2.847         | 116.67 | 700  | 2.8554          | 1.0    |
  | 2.7661        | 133.33 | 800  | 2.6343          | 1.0    |
  | 2.3474        | 150.0  | 900  | 2.7464          | 1.0    |
  | 2.2464        | 166.67 | 1000 | 2.3565          | 1.0    |
  | 2.207         | 183.33 | 1100 | 2.8854          | 1.0    |
  | 2.3138        | 200.0  | 1200 | 2.5868          | 1.0    |
  | 2.259         | 216.67 | 1300 | 2.6530          | 1.0    |
  | 2.1667        | 233.33 | 1400 | 2.4921          | 1.0    |
  | 2.1268        | 250.0  | 1500 | 2.5435          | 1.0    |
  | 2.1089        | 266.67 | 1600 | 2.5444          | 1.0    |
  | 2.0845        | 283.33 | 1700 | 2.6796          | 1.0    |
  | 2.0672        | 300.0  | 1800 | 2.5824          | 1.0    |
  | 2.055         | 316.67 | 1900 | 2.4631          | 1.0    |
  | 2.0317        | 333.33 | 2000 | 2.5751          | 1.0    |
  | 2.0141        | 350.0  | 2100 | 2.5627          | 1.0    |
  | 1.9914        | 366.67 | 2200 | 2.6132          | 1.0    |
  | 1.9489        | 383.33 | 2300 | 2.7527          | 1.0    |
  | 1.9146        | 400.0  | 2400 | 2.6121          | 0.9935 |
  | 1.893         | 416.67 | 2500 | 2.7110          | 0.9902 |
  | 1.845         | 433.33 | 2600 | 2.7410          | 0.9967 |
  | 1.8095        | 450.0  | 2700 | 2.7013          | 0.9935 |
  | 1.7708        | 466.67 | 2800 | 2.7719          | 0.9935 |
  | 1.7224        | 483.33 | 2900 | 2.7740          | 0.9837 |
  | 1.6961        | 500.0  | 3000 | 2.7360          | 0.9837 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1+cu102
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  