readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - hf-asr-leaderboard
  datasets:
  - librispeech_asr
  model-index:
  - name: hubert-base-libri-clean-ft100h-v3
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: LibriSpeech
        type: librispeech_asr
        config: clean
        split: test
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: '8.1938'
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: LibriSpeech
        type: librispeech_asr
        config: other
        split: test
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: '16.9783'
  language:
  - en
  ---
  
  <!-- 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. -->
  
  # hubert-base-libri-clean-ft100h-v3
  
  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the librispeech_asr dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1120
  - Wer: 0.1332
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 600
  - num_epochs: 8
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.201         | 0.14  | 250   | 3.9799          | 1.0    |
  | 2.8893        | 0.28  | 500   | 3.4838          | 1.0    |
  | 2.8603        | 0.42  | 750   | 3.3505          | 1.0    |
  | 2.7216        | 0.56  | 1000  | 2.1194          | 0.9989 |
  | 1.3372        | 0.7   | 1250  | 0.8124          | 0.6574 |
  | 0.8238        | 0.84  | 1500  | 0.5712          | 0.5257 |
  | 0.6449        | 0.98  | 1750  | 0.4442          | 0.4428 |
  | 0.5241        | 1.12  | 2000  | 0.3442          | 0.3672 |
  | 0.4458        | 1.26  | 2250  | 0.2850          | 0.3186 |
  | 0.3959        | 1.4   | 2500  | 0.2507          | 0.2882 |
  | 0.3641        | 1.54  | 2750  | 0.2257          | 0.2637 |
  | 0.3307        | 1.68  | 3000  | 0.2044          | 0.2434 |
  | 0.2996        | 1.82  | 3250  | 0.1969          | 0.2313 |
  | 0.2794        | 1.96  | 3500  | 0.1823          | 0.2193 |
  | 0.2596        | 2.1   | 3750  | 0.1717          | 0.2096 |
  | 0.2563        | 2.24  | 4000  | 0.1653          | 0.2000 |
  | 0.2532        | 2.38  | 4250  | 0.1615          | 0.1971 |
  | 0.2376        | 2.52  | 4500  | 0.1559          | 0.1916 |
  | 0.2341        | 2.66  | 4750  | 0.1494          | 0.1855 |
  | 0.2102        | 2.8   | 5000  | 0.1464          | 0.1781 |
  | 0.2222        | 2.94  | 5250  | 0.1399          | 0.1732 |
  | 0.2081        | 3.08  | 5500  | 0.1450          | 0.1707 |
  | 0.1963        | 3.22  | 5750  | 0.1337          | 0.1655 |
  | 0.2107        | 3.36  | 6000  | 0.1344          | 0.1633 |
  | 0.1866        | 3.5   | 6250  | 0.1339          | 0.1611 |
  | 0.186         | 3.64  | 6500  | 0.1311          | 0.1563 |
  | 0.1703        | 3.78  | 6750  | 0.1307          | 0.1537 |
  | 0.1819        | 3.92  | 7000  | 0.1277          | 0.1555 |
  | 0.176         | 4.06  | 7250  | 0.1280          | 0.1515 |
  | 0.1837        | 4.2   | 7500  | 0.1249          | 0.1504 |
  | 0.1678        | 4.34  | 7750  | 0.1236          | 0.1480 |
  | 0.1624        | 4.48  | 8000  | 0.1194          | 0.1456 |
  | 0.1631        | 4.62  | 8250  | 0.1215          | 0.1462 |
  | 0.1736        | 4.76  | 8500  | 0.1192          | 0.1451 |
  | 0.1752        | 4.9   | 8750  | 0.1206          | 0.1432 |
  | 0.1578        | 5.04  | 9000  | 0.1151          | 0.1415 |
  | 0.1537        | 5.18  | 9250  | 0.1185          | 0.1402 |
  | 0.1771        | 5.33  | 9500  | 0.1165          | 0.1414 |
  | 0.1481        | 5.47  | 9750  | 0.1152          | 0.1413 |
  | 0.1509        | 5.61  | 10000 | 0.1152          | 0.1382 |
  | 0.146         | 5.75  | 10250 | 0.1133          | 0.1385 |
  | 0.1464        | 5.89  | 10500 | 0.1139          | 0.1371 |
  | 0.1442        | 6.03  | 10750 | 0.1162          | 0.1365 |
  | 0.128         | 6.17  | 11000 | 0.1147          | 0.1371 |
  | 0.1381        | 6.31  | 11250 | 0.1148          | 0.1378 |
  | 0.1343        | 6.45  | 11500 | 0.1113          | 0.1363 |
  | 0.1325        | 6.59  | 11750 | 0.1134          | 0.1355 |
  | 0.1442        | 6.73  | 12000 | 0.1142          | 0.1358 |
  | 0.1286        | 6.87  | 12250 | 0.1133          | 0.1352 |
  | 0.1