readme: ---
  tags:
  - generated_from_trainer
  - asr
  metrics:
  - wer
  model-index:
  - name: libri-alpha-0.85-Temp-1-processor-change
    results: []
  #   - task:
  #     type: automatic-speech-recognition
  #     name: Speech Recognition
  datasets:
  - librispeech_asr
  ---
  
  <!-- 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. -->
  
  # libri-alpha-0.85-Temp-1-processor-change
  
  This model is a distilled version of [Wav2vec2](https://huggingface.co/) on the 30% of the Librispeech-clean.100 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 78.4467
  - Wer: 0.1153
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  Knowledge distillation of Wav2vec2-base-960h teacher model with 6 attention layers for student model.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  - alpha: 0.75(ignore name of repo)
  - temperature: 1
    
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 493.9213      | 0.75  | 100  | 145.7981        | 0.1515 |
  | 410.8468      | 1.49  | 200  | 119.1579        | 0.1498 |
  | 368.5187      | 2.24  | 300  | 109.7572        | 0.1505 |
  | 329.7762      | 2.99  | 400  | 99.2350         | 0.1439 |
  | 323.7352      | 3.73  | 500  | 92.1173         | 0.1356 |
  | 305.1129      | 4.48  | 600  | 89.3685         | 0.1314 |
  | 294.2529      | 5.22  | 700  | 88.3937         | 0.1287 |
  | 284.5355      | 5.97  | 800  | 87.0589         | 0.1292 |
  | 284.2181      | 6.72  | 900  | 86.4474         | 0.1298 |
  | 273.915       | 7.46  | 1000 | 84.6149         | 0.1265 |
  | 267.7668      | 8.21  | 1100 | 84.1840         | 0.1264 |
  | 262.1592      | 8.96  | 1200 | 83.8678         | 0.1253 |
  | 262.5562      | 9.7   | 1300 | 83.2756         | 0.1207 |
  | 262.9982      | 10.45 | 1400 | 81.8095         | 0.1218 |
  | 256.2891      | 11.19 | 1500 | 82.1241         | 0.1204 |
  | 251.4134      | 11.94 | 1600 | 80.8432         | 0.1207 |
  | 250.0854      | 12.69 | 1700 | 81.1467         | 0.1203 |
  | 250.0077      | 13.43 | 1800 | 80.9370         | 0.1196 |
  | 239.0915      | 14.18 | 1900 | 80.5060         | 0.1201 |
  | 240.9192      | 14.93 | 2000 | 80.4557         | 0.1190 |
  | 241.1668      | 15.67 | 2100 | 80.6453         | 0.1203 |
  | 244.9744      | 16.42 | 2200 | 80.0101         | 0.1192 |
  | 232.4748      | 17.16 | 2300 | 79.4798         | 0.1170 |
  | 237.3503      | 17.91 | 2400 | 79.5743         | 0.1175 |
  | 237.9698      | 18.66 | 2500 | 79.3368         | 0.1178 |
  | 235.8808      | 19.4  | 2600 | 79.5519         | 0.1174 |
  | 230.8314      | 20.15 | 2700 | 79.0367         | 0.1166 |
  | 229.5856      | 20.9  | 2800 | 79.1809         | 0.1172 |
  | 233.1034      | 21.64 | 2900 | 78.9896         | 0.1167 |
  | 231.6986      | 22.39 | 3000 | 78.7184         | 0.1154 |
  | 222.0106      | 23.13 | 3100 | 78.7308         | 0.1160 |
  | 225.1484      | 23.88 | 3200 | 78.6649         | 0.1159 |
  | 232.4254      | 24.63 | 3300 | 78.5096         | 0.1154 |
  | 230.9492      | 25.37 | 3400 | 78.4873         | 0.1153 |
  | 228.3062      | 26.12 | 3500 | 78.5155         | 0.1147 |
  | 225.5572      | 26.87 | 3600 | 78.5693         | 0.1148 |
  | 227.7358      | 27.61 | 3700 | 78.5487         | 0.1149 |
  | 221.2486      | 28.36 | 3800 | 78.4307         | 0.1151 |
  | 231.5915      | 29.1  | 3900 | 78.4270         | 0.1153 |
  | 231.7214      | 29.85 | 4000 | 78.4467         | 0.1153 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.11.0
  