readme: ---
  tags:
  - automatic-speech-recognition
  - librispeech_asr
  - generated_from_trainer
  - wavlm_libri_finetune
  model-index:
  - name: wavlm-libri-clean-100h-base-plus
    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-plus
  
  This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the LIBRISPEECH_ASR - CLEAN dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0819
  - Wer: 0.0683
  
  ## 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: 4
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - total_train_batch_size: 32
  - total_eval_batch_size: 32
  - 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 2.8877        | 0.34  | 300  | 2.8649          | 1.0    |
  | 0.2852        | 0.67  | 600  | 0.2196          | 0.1830 |
  | 0.1198        | 1.01  | 900  | 0.1438          | 0.1273 |
  | 0.0906        | 1.35  | 1200 | 0.1145          | 0.1035 |
  | 0.0729        | 1.68  | 1500 | 0.1055          | 0.0955 |
  | 0.0605        | 2.02  | 1800 | 0.0936          | 0.0859 |
  | 0.0402        | 2.35  | 2100 | 0.0885          | 0.0746 |
  | 0.0421        | 2.69  | 2400 | 0.0848          | 0.0700 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0.dev0
  - Pytorch 1.9.0+cu111
  - Datasets 1.16.2.dev0
  - Tokenizers 0.10.3
  