readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-torgo-demo-m04-nolm
    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-torgo-demo-m04-nolm
  
  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: 3.5735
  - 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: 0.0001
  - train_batch_size: 8
  - 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: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer |
  |:-------------:|:-----:|:-----:|:---------------:|:---:|
  | 3.431         | 0.88  | 500   | 4.5567          | 1.0 |
  | 3.4727        | 1.75  | 1000  | 3.5626          | 1.0 |
  | 3.3879        | 2.63  | 1500  | 3.9274          | 1.0 |
  | 3.3513        | 3.5   | 2000  | 3.4813          | 1.0 |
  | 3.3538        | 4.38  | 2500  | 3.7300          | 1.0 |
  | 3.3539        | 5.25  | 3000  | 3.5714          | 1.0 |
  | 3.339         | 6.13  | 3500  | 3.6732          | 1.0 |
  | 3.3038        | 7.01  | 4000  | 3.6788          | 1.0 |
  | 3.35          | 7.88  | 4500  | 3.6715          | 1.0 |
  | 3.338         | 8.76  | 5000  | 3.5161          | 1.0 |
  | 3.3306        | 9.63  | 5500  | 3.7386          | 1.0 |
  | 3.3266        | 10.51 | 6000  | 3.4908          | 1.0 |
  | 3.3184        | 11.38 | 6500  | 3.7669          | 1.0 |
  | 3.3189        | 12.26 | 7000  | 3.6142          | 1.0 |
  | 3.331         | 13.13 | 7500  | 3.5619          | 1.0 |
  | 3.3139        | 14.01 | 8000  | 3.6632          | 1.0 |
  | 3.3069        | 14.89 | 8500  | 3.6127          | 1.0 |
  | 3.315         | 15.76 | 9000  | 3.5562          | 1.0 |
  | 3.3079        | 16.64 | 9500  | 3.7094          | 1.0 |
  | 3.3077        | 17.51 | 10000 | 3.5412          | 1.0 |
  | 3.3188        | 18.39 | 10500 | 3.6303          | 1.0 |
  | 3.3133        | 19.26 | 11000 | 3.5704          | 1.0 |
  | 3.3428        | 20.14 | 11500 | 3.5662          | 1.0 |
  | 3.3082        | 21.02 | 12000 | 3.6084          | 1.0 |
  | 3.3238        | 21.89 | 12500 | 3.6164          | 1.0 |
  | 3.3119        | 22.77 | 13000 | 3.5787          | 1.0 |
  | 3.2981        | 23.64 | 13500 | 3.6356          | 1.0 |
  | 3.3153        | 24.52 | 14000 | 3.5726          | 1.0 |
  | 3.3065        | 25.39 | 14500 | 3.5908          | 1.0 |
  | 3.3199        | 26.27 | 15000 | 3.5823          | 1.0 |
  | 3.306         | 27.15 | 15500 | 3.5658          | 1.0 |
  | 3.3153        | 28.02 | 16000 | 3.5818          | 1.0 |
  | 3.2762        | 28.9  | 16500 | 3.5810          | 1.0 |
  | 3.3196        | 29.77 | 17000 | 3.5735          | 1.0 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.0.0
  - Tokenizers 0.13.2
  