readme: ---
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - timit_asr
  - generated_from_trainer
  datasets:
  - timit_asr
  model-index:
  - name: wav2vec2-base-timit-fine-tuned
    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-timit-fine-tuned
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT_ASR - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3457
  - Wer: 0.2151
  
  ## 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: 1
  - 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: 20.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.1621        | 0.69  | 100  | 3.1102          | 1.0    |
  | 2.9592        | 1.38  | 200  | 2.9603          | 1.0    |
  | 2.9116        | 2.07  | 300  | 2.8921          | 1.0    |
  | 2.1332        | 2.76  | 400  | 1.9718          | 0.9958 |
  | 0.8477        | 3.45  | 500  | 0.7813          | 0.5237 |
  | 0.4251        | 4.14  | 600  | 0.5166          | 0.3982 |
  | 0.3743        | 4.83  | 700  | 0.4400          | 0.3578 |
  | 0.4194        | 5.52  | 800  | 0.4077          | 0.3370 |
  | 0.23          | 6.21  | 900  | 0.4018          | 0.3142 |
  | 0.1554        | 6.9   | 1000 | 0.3623          | 0.2995 |
  | 0.1511        | 7.59  | 1100 | 0.3433          | 0.2697 |
  | 0.1983        | 8.28  | 1200 | 0.3539          | 0.2715 |
  | 0.1443        | 8.97  | 1300 | 0.3622          | 0.2551 |
  | 0.0971        | 9.66  | 1400 | 0.3580          | 0.2519 |
  | 0.0764        | 10.34 | 1500 | 0.3529          | 0.2437 |
  | 0.1203        | 11.03 | 1600 | 0.3455          | 0.2431 |
  | 0.0881        | 11.72 | 1700 | 0.3648          | 0.2415 |
  | 0.0521        | 12.41 | 1800 | 0.3564          | 0.2320 |
  | 0.0434        | 13.1  | 1900 | 0.3485          | 0.2270 |
  | 0.0864        | 13.79 | 2000 | 0.3517          | 0.2228 |
  | 0.0651        | 14.48 | 2100 | 0.3506          | 0.2285 |
  | 0.0423        | 15.17 | 2200 | 0.3428          | 0.2247 |
  | 0.0302        | 15.86 | 2300 | 0.3372          | 0.2198 |
  | 0.0548        | 16.55 | 2400 | 0.3496          | 0.2196 |
  | 0.0674        | 17.24 | 2500 | 0.3407          | 0.2166 |
  | 0.0291        | 17.93 | 2600 | 0.3512          | 0.2171 |
  | 0.0298        | 18.62 | 2700 | 0.3363          | 0.2158 |
  | 0.0419        | 19.31 | 2800 | 0.3493          | 0.2145 |
  | 0.046         | 20.0  | 2900 | 0.3457          | 0.2151 |
  
  
  ### Framework versions
  
  - Transformers 4.12.0.dev0
  - Pytorch 1.8.1
  - Datasets 1.14.1.dev0
  - Tokenizers 0.10.3
  