readme: ---
  tags:
  - automatic-speech-recognition
  - timit_asr
  - generated_from_trainer
  datasets:
  - timit_asr
  model-index:
  - name: unispeech-large-1500h-cv-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. -->
  
  # unispeech-large-1500h-cv-timit
  
  This model is a fine-tuned version of [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) on the TIMIT_ASR - NA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3099
  - Wer: 0.2196
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.64          | 0.69  | 100  | 3.9717          | 0.9981 |
  | 2.6793        | 1.38  | 200  | 2.6264          | 1.0    |
  | 1.2221        | 2.07  | 300  | 0.9999          | 0.7167 |
  | 0.9009        | 2.76  | 400  | 0.6509          | 0.5570 |
  | 0.4352        | 3.45  | 500  | 0.4682          | 0.4332 |
  | 0.227         | 4.14  | 600  | 0.3661          | 0.3565 |
  | 0.2169        | 4.83  | 700  | 0.3244          | 0.3203 |
  | 0.2687        | 5.52  | 800  | 0.3137          | 0.2981 |
  | 0.127         | 6.21  | 900  | 0.3220          | 0.2828 |
  | 0.0922        | 6.9   | 1000 | 0.3075          | 0.2708 |
  | 0.0965        | 7.59  | 1100 | 0.2779          | 0.2576 |
  | 0.1298        | 8.28  | 1200 | 0.3111          | 0.2480 |
  | 0.0855        | 8.97  | 1300 | 0.3021          | 0.2421 |
  | 0.0629        | 9.66  | 1400 | 0.3122          | 0.2511 |
  | 0.0471        | 10.34 | 1500 | 0.2965          | 0.2368 |
  | 0.0871        | 11.03 | 1600 | 0.3247          | 0.2387 |
  | 0.0503        | 11.72 | 1700 | 0.3359          | 0.2363 |
  | 0.0402        | 12.41 | 1800 | 0.2976          | 0.2332 |
  | 0.0336        | 13.1  | 1900 | 0.3139          | 0.2321 |
  | 0.0634        | 13.79 | 2000 | 0.3188          | 0.2309 |
  | 0.0429        | 14.48 | 2100 | 0.3145          | 0.2335 |
  | 0.028         | 15.17 | 2200 | 0.3244          | 0.2242 |
  | 0.0255        | 15.86 | 2300 | 0.2914          | 0.2196 |
  | 0.0406        | 16.55 | 2400 | 0.3249          | 0.2202 |
  | 0.0512        | 17.24 | 2500 | 0.3037          | 0.2198 |
  | 0.0269        | 17.93 | 2600 | 0.3218          | 0.2242 |
  | 0.0287        | 18.62 | 2700 | 0.3106          | 0.2185 |
  | 0.0319        | 19.31 | 2800 | 0.3124          | 0.2217 |
  | 0.0494        | 20.0  | 2900 | 0.3099          | 0.2196 |
  
  
  ### Framework versions
  
  - Transformers 4.12.0.dev0
  - Pytorch 1.8.1
  - Datasets 1.14.1.dev0
  - Tokenizers 0.10.3
  