readme: ---
  tags:
  - automatic-speech-recognition
  - librispeech_asr
  - generated_from_trainer
  model-index:
  - name: wavlm-libri-clean-100h-base
    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. -->
  
  # wavlm-libri-clean-100h-base
  
  This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the LIBRISPEECH_ASR - CLEAN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0955
  - Wer: 0.0773
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 1.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.8664        | 0.17  | 300  | 2.8439          | 1.0    |
  | 0.5009        | 0.34  | 600  | 0.2709          | 0.2162 |
  | 0.2056        | 0.5   | 900  | 0.1934          | 0.1602 |
  | 0.1648        | 0.67  | 1200 | 0.1576          | 0.1306 |
  | 0.1922        | 0.84  | 1500 | 0.1358          | 0.1114 |
  | 0.093         | 1.01  | 1800 | 0.1277          | 0.1035 |
  | 0.0652        | 1.18  | 2100 | 0.1251          | 0.1005 |
  | 0.0848        | 1.35  | 2400 | 0.1188          | 0.0964 |
  | 0.0706        | 1.51  | 2700 | 0.1091          | 0.0905 |
  | 0.0846        | 1.68  | 3000 | 0.1018          | 0.0840 |
  | 0.0684        | 1.85  | 3300 | 0.0978          | 0.0809 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.9.1
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  