readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-tcrs
    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-tcrs
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.9550
  - Wer: 1.0657
  
  ## 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: 1
  - 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: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 13.6613       | 3.38  | 500   | 3.2415          | 1.0    |
  | 2.9524        | 6.76  | 1000  | 3.0199          | 1.0    |
  | 2.9425        | 10.14 | 1500  | 3.0673          | 1.0    |
  | 2.9387        | 13.51 | 2000  | 3.0151          | 1.0    |
  | 2.9384        | 16.89 | 2500  | 3.0320          | 1.0    |
  | 2.929         | 20.27 | 3000  | 2.9691          | 1.0    |
  | 2.9194        | 23.65 | 3500  | 2.9596          | 1.0    |
  | 2.9079        | 27.03 | 4000  | 2.9279          | 1.0    |
  | 2.8957        | 30.41 | 4500  | 2.9647          | 1.0    |
  | 2.8385        | 33.78 | 5000  | 2.8114          | 1.0193 |
  | 2.6546        | 37.16 | 5500  | 2.6744          | 1.0983 |
  | 2.5866        | 40.54 | 6000  | 2.6192          | 1.1071 |
  | 2.5475        | 43.92 | 6500  | 2.5777          | 1.0950 |
  | 2.5177        | 47.3  | 7000  | 2.5845          | 1.1220 |
  | 2.482         | 50.68 | 7500  | 2.5730          | 1.1264 |
  | 2.4343        | 54.05 | 8000  | 2.5722          | 1.0955 |
  | 2.3754        | 57.43 | 8500  | 2.5781          | 1.1353 |
  | 2.3055        | 60.81 | 9000  | 2.6177          | 1.0972 |
  | 2.2446        | 64.19 | 9500  | 2.6351          | 1.1027 |
  | 2.1625        | 67.57 | 10000 | 2.6924          | 1.0756 |
  | 2.1078        | 70.95 | 10500 | 2.6817          | 1.0795 |
  | 2.0366        | 74.32 | 11000 | 2.7629          | 1.0657 |
  | 1.9899        | 77.7  | 11500 | 2.7972          | 1.0845 |
  | 1.9309        | 81.08 | 12000 | 2.8450          | 1.0734 |
  | 1.8861        | 84.46 | 12500 | 2.8703          | 1.0668 |
  | 1.8437        | 87.84 | 13000 | 2.9308          | 1.0917 |
  | 1.8192        | 91.22 | 13500 | 2.9298          | 1.0701 |
  | 1.7952        | 94.59 | 14000 | 2.9488          | 1.0685 |
  | 1.7745        | 97.97 | 14500 | 2.9550          | 1.0657 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  