readme: ---
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - timit_asr
  - generated_from_trainer
  datasets:
  - timit_asr
  model-index:
  - name: sew-d-small-100k-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. -->
  
  # sew-d-small-100k-ft-timit
  
  This model is a fine-tuned version of [asapp/sew-d-small-100k](https://huggingface.co/asapp/sew-d-small-100k) on the TIMIT_ASR - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7482
  - Wer: 0.7987
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.2068        | 0.69  | 100  | 4.0802          | 1.0    |
  | 2.9805        | 1.38  | 200  | 2.9792          | 1.0    |
  | 2.9781        | 2.07  | 300  | 2.9408          | 1.0    |
  | 2.9655        | 2.76  | 400  | 2.9143          | 1.0    |
  | 2.8953        | 3.45  | 500  | 2.8775          | 1.0    |
  | 2.7719        | 4.14  | 600  | 2.7815          | 0.9999 |
  | 2.6531        | 4.83  | 700  | 2.6375          | 1.0065 |
  | 2.6425        | 5.52  | 800  | 2.5602          | 1.0210 |
  | 2.3963        | 6.21  | 900  | 2.4665          | 1.0591 |
  | 2.1447        | 6.9   | 1000 | 2.2792          | 0.9848 |
  | 2.2719        | 7.59  | 1100 | 2.2237          | 0.9465 |
  | 2.3629        | 8.28  | 1200 | 2.1058          | 0.8907 |
  | 2.0913        | 8.97  | 1300 | 2.0113          | 0.9070 |
  | 1.8334        | 9.66  | 1400 | 1.9466          | 0.8177 |
  | 1.6608        | 10.34 | 1500 | 1.9217          | 0.8698 |
  | 2.2194        | 11.03 | 1600 | 1.9091          | 0.8727 |
  | 1.9002        | 11.72 | 1700 | 1.8746          | 0.8332 |
  | 1.6268        | 12.41 | 1800 | 1.8782          | 0.7951 |
  | 1.6455        | 13.1  | 1900 | 1.8230          | 0.8225 |
  | 2.0308        | 13.79 | 2000 | 1.8067          | 0.8560 |
  | 1.855         | 14.48 | 2100 | 1.8129          | 0.8177 |
  | 1.5901        | 15.17 | 2200 | 1.7891          | 0.8367 |
  | 1.4848        | 15.86 | 2300 | 1.7821          | 0.8201 |
  | 1.8754        | 16.55 | 2400 | 1.7700          | 0.8137 |
  | 1.7975        | 17.24 | 2500 | 1.7795          | 0.8171 |
  | 1.5194        | 17.93 | 2600 | 1.7605          | 0.7977 |
  | 1.4374        | 18.62 | 2700 | 1.7529          | 0.7978 |
  | 1.7498        | 19.31 | 2800 | 1.7522          | 0.8023 |
  | 1.7452        | 20.0  | 2900 | 1.7482          | 0.7987 |
  
  
  ### Framework versions
  
  - Transformers 4.12.0.dev0
  - Pytorch 1.8.1
  - Datasets 1.14.1.dev0
  - Tokenizers 0.10.3
  