readme: ---
  license: apache-2.0
  tags:
  - speech-recognition
  - librispeech_asr
  - generated_from_trainer
  model-index:
  - name: hubert-librispeech-clean-100h-demo-dist
    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-librispeech-clean-100h-demo-dist
  
  This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the LIBRISPEECH_ASR - CLEAN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0984
  - Wer: 0.0883
  
  ## 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: 0.0003
  - train_batch_size: 4
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 32
  - total_eval_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: 3.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.9031        | 0.11  | 100  | 2.9220          | 1.0    |
  | 2.6437        | 0.22  | 200  | 2.6268          | 1.0    |
  | 0.3934        | 0.34  | 300  | 0.4860          | 0.4182 |
  | 0.3531        | 0.45  | 400  | 0.3088          | 0.2894 |
  | 0.2255        | 0.56  | 500  | 0.2568          | 0.2426 |
  | 0.3379        | 0.67  | 600  | 0.2073          | 0.2011 |
  | 0.2419        | 0.78  | 700  | 0.1849          | 0.1838 |
  | 0.2128        | 0.9   | 800  | 0.1662          | 0.1690 |
  | 0.1341        | 1.01  | 900  | 0.1600          | 0.1541 |
  | 0.0946        | 1.12  | 1000 | 0.1431          | 0.1404 |
  | 0.1643        | 1.23  | 1100 | 0.1373          | 0.1304 |
  | 0.0663        | 1.35  | 1200 | 0.1293          | 0.1307 |
  | 0.162         | 1.46  | 1300 | 0.1247          | 0.1266 |
  | 0.1433        | 1.57  | 1400 | 0.1246          | 0.1262 |
  | 0.1581        | 1.68  | 1500 | 0.1219          | 0.1154 |
  | 0.1036        | 1.79  | 1600 | 0.1127          | 0.1081 |
  | 0.1352        | 1.91  | 1700 | 0.1087          | 0.1040 |
  | 0.0471        | 2.02  | 1800 | 0.1085          | 0.1005 |
  | 0.0945        | 2.13  | 1900 | 0.1066          | 0.0973 |
  | 0.0843        | 2.24  | 2000 | 0.1102          | 0.0964 |
  | 0.0774        | 2.35  | 2100 | 0.1079          | 0.0940 |
  | 0.0952        | 2.47  | 2200 | 0.1056          | 0.0927 |
  | 0.0635        | 2.58  | 2300 | 0.1026          | 0.0920 |
  | 0.0665        | 2.69  | 2400 | 0.1012          | 0.0905 |
  | 0.034         | 2.8   | 2500 | 0.1009          | 0.0900 |
  | 0.0251        | 2.91  | 2600 | 0.0993          | 0.0883 |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.9.0+cu111
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  