readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - librispeech_asr
  model-index:
  - name: hubert-base-libri-clean-ft100h
    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. -->
  
  # hubert-base-libri-clean-ft100h
  
  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: 0.1324
  - Wer: 0.1597
  
  ## 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: 1000
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | No log        | 0.14  | 250  | 4.1508          | 1.0000 |
  | 4.4345        | 0.28  | 500  | 3.8766          | 1.0000 |
  | 4.4345        | 0.42  | 750  | 3.4376          | 1.0000 |
  | 2.8475        | 0.56  | 1000 | 2.7380          | 1.0    |
  | 2.8475        | 0.7   | 1250 | 0.8803          | 0.6766 |
  | 1.1877        | 0.84  | 1500 | 0.5671          | 0.5102 |
  | 1.1877        | 0.98  | 1750 | 0.4537          | 0.4388 |
  | 0.5802        | 1.12  | 2000 | 0.3566          | 0.3740 |
  | 0.5802        | 1.26  | 2250 | 0.2925          | 0.3209 |
  | 0.4301        | 1.4   | 2500 | 0.2613          | 0.2952 |
  | 0.4301        | 1.54  | 2750 | 0.2363          | 0.2715 |
  | 0.3591        | 1.68  | 3000 | 0.2155          | 0.2552 |
  | 0.3591        | 1.82  | 3250 | 0.2062          | 0.2418 |
  | 0.3015        | 1.96  | 3500 | 0.1951          | 0.2308 |
  | 0.3015        | 2.1   | 3750 | 0.1842          | 0.2207 |
  | 0.2698        | 2.24  | 4000 | 0.1900          | 0.2112 |
  | 0.2698        | 2.38  | 4250 | 0.1745          | 0.2048 |
  | 0.2561        | 2.52  | 4500 | 0.1718          | 0.2040 |
  | 0.2561        | 2.66  | 4750 | 0.1625          | 0.1939 |
  | 0.2348        | 2.8   | 5000 | 0.1568          | 0.1867 |
  | 0.2348        | 2.94  | 5250 | 0.1517          | 0.1855 |
  | 0.2278        | 3.08  | 5500 | 0.1501          | 0.1807 |
  | 0.2278        | 3.22  | 5750 | 0.1445          | 0.1772 |
  | 0.2166        | 3.36  | 6000 | 0.1422          | 0.1752 |
  | 0.2166        | 3.5   | 6250 | 0.1418          | 0.1741 |
  | 0.2017        | 3.64  | 6500 | 0.1404          | 0.1695 |
  | 0.2017        | 3.78  | 6750 | 0.1356          | 0.1674 |
  | 0.1922        | 3.92  | 7000 | 0.1350          | 0.1688 |
  | 0.1922        | 4.06  | 7250 | 0.1346          | 0.1638 |
  | 0.1979        | 4.2   | 7500 | 0.1359          | 0.1638 |
  | 0.1979        | 4.34  | 7750 | 0.1336          | 0.1612 |
  | 0.1836        | 4.48  | 8000 | 0.1324          | 0.1613 |
  | 0.1836        | 4.62  | 8250 | 0.1320          | 0.1606 |
  | 0.1891        | 4.76  | 8500 | 0.1325          | 0.1598 |
  | 0.1891        | 4.9   | 8750 | 0.1324          | 0.1597 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  