readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-robust-ft-timit
    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-large-robust-ft-timit
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2768
  - Wer: 0.2321
  
  ## 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: 1000
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 6.6175        | 1.0   | 500   | 3.3025          | 1.0    |
  | 3.0746        | 2.01  | 1000  | 2.9598          | 1.0    |
  | 1.967         | 3.01  | 1500  | 0.6760          | 0.5607 |
  | 0.7545        | 4.02  | 2000  | 0.4500          | 0.4567 |
  | 0.5415        | 5.02  | 2500  | 0.3702          | 0.3882 |
  | 0.4445        | 6.02  | 3000  | 0.3421          | 0.3584 |
  | 0.3601        | 7.03  | 3500  | 0.2947          | 0.3096 |
  | 0.3098        | 8.03  | 4000  | 0.2740          | 0.2894 |
  | 0.2606        | 9.04  | 4500  | 0.2725          | 0.2787 |
  | 0.238         | 10.04 | 5000  | 0.2549          | 0.2617 |
  | 0.2142        | 11.04 | 5500  | 0.2485          | 0.2530 |
  | 0.1787        | 12.05 | 6000  | 0.2683          | 0.2514 |
  | 0.1652        | 13.05 | 6500  | 0.2559          | 0.2476 |
  | 0.1569        | 14.06 | 7000  | 0.2777          | 0.2470 |
  | 0.1443        | 15.06 | 7500  | 0.2661          | 0.2431 |
  | 0.1335        | 16.06 | 8000  | 0.2717          | 0.2422 |
  | 0.1291        | 17.07 | 8500  | 0.2672          | 0.2428 |
  | 0.1192        | 18.07 | 9000  | 0.2684          | 0.2395 |
  | 0.1144        | 19.08 | 9500  | 0.2770          | 0.2411 |
  | 0.1052        | 20.08 | 10000 | 0.2831          | 0.2379 |
  | 0.1004        | 21.08 | 10500 | 0.2847          | 0.2375 |
  | 0.1053        | 22.09 | 11000 | 0.2851          | 0.2360 |
  | 0.1005        | 23.09 | 11500 | 0.2807          | 0.2361 |
  | 0.0904        | 24.1  | 12000 | 0.2764          | 0.2346 |
  | 0.0876        | 25.1  | 12500 | 0.2774          | 0.2325 |
  | 0.0883        | 26.1  | 13000 | 0.2768          | 0.2313 |
  | 0.0848        | 27.11 | 13500 | 0.2840          | 0.2307 |
  | 0.0822        | 28.11 | 14000 | 0.2812          | 0.2316 |
  | 0.09          | 29.12 | 14500 | 0.2768          | 0.2321 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.8.2+cu111
  - Datasets 1.17.0
  - Tokenizers 0.11.6
  