readme: ---
  tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  datasets:
  - mozilla-foundation/common_voice_3_0
  model-index:
  - name: wavlm-base-plus-ft-cv3
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: LibriSpeech (clean)
        type: librispeech_asr
        config: clean
        split: test
        args:
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: '8.06'
  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. -->
  
  # wavlm-base-plus-ft-cv3
  
  This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the "mozilla-foundation/common_voice_3_0 english" dataset: "train" and "validation" splits are used for training while "test" split is used for validation.
  It achieves the following results on the validation set:
  - Loss: 0.4365
  - Wer: 0.1801
  
  ## 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: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 11
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | 5.3448        | 0.05  | 500    | 3.2621          | 1.0    |
  | 2.9322        | 0.1   | 1000   | 2.8551          | 1.0    |
  | 1.7692        | 0.16  | 1500   | 1.2653          | 0.7447 |
  | 1.012         | 0.21  | 2000   | 0.9008          | 0.5601 |
  | 0.7129        | 0.26  | 2500   | 0.7684          | 0.4762 |
  | 0.6424        | 0.31  | 3000   | 0.6282          | 0.4276 |
  | 0.6518        | 0.37  | 3500   | 0.5888          | 0.3916 |
  | 0.5142        | 0.42  | 4000   | 0.5428          | 0.3727 |
  | 0.48          | 0.47  | 4500   | 0.5614          | 0.3549 |
  | 0.4523        | 0.52  | 5000   | 0.5334          | 0.3487 |
  | 0.4315        | 0.58  | 5500   | 0.5376          | 0.3317 |
  | 0.4292        | 0.63  | 6000   | 0.4939          | 0.3172 |
  | 0.4229        | 0.68  | 6500   | 0.4977          | 0.3117 |
  | 0.3837        | 0.73  | 7000   | 0.4899          | 0.3056 |
  | 0.385         | 0.78  | 7500   | 0.4571          | 0.2864 |
  | 0.4155        | 0.84  | 8000   | 0.4635          | 0.2866 |
  | 0.3768        | 0.89  | 8500   | 0.4390          | 0.2843 |
  | 0.3864        | 0.94  | 9000   | 0.4529          | 0.2764 |
  | 0.387         | 0.99  | 9500   | 0.4870          | 0.2755 |
  | 0.341         | 1.05  | 10000  | 0.4498          | 0.2696 |
  | 0.3334        | 1.1   | 10500  | 0.4355          | 0.2600 |
  | 0.3039        | 1.15  | 11000  | 0.4634          | 0.2716 |
  | 0.3101        | 1.2   | 11500  | 0.4615          | 0.2582 |
  | 0.4343        | 1.25  | 12000  | 0.4510          | 0.2574 |
  | 0.3002        | 1.31  | 12500  | 0.4313          | 0.2590 |
  | 0.3419        | 1.36  | 13000  | 0.4121          | 0.2493 |
  | 0.3162        | 1.41  | 13500  | 0.4423          | 0.2498 |
  | 0.3134        | 1.46  | 14000  | 0.4260          | 0.2506 |
  | 0.2963        | 1.52  | 14500  | 0.4272          | 0.2556 |
  | 0.3297        | 1.57  | 15000  | 0.4413          | 0.2487 |
  | 0.3199        | 1.62  | 15500  | 0.4260          | 0.2432 |
  | 0.3368        | 1.67  | 16000  | 0.4164          | 0.2464 |
  | 0.2981        | 1.73  | 16500  | 0.4111          | 0.2402 |
  | 0.2887        | 1.78  | 17000  | 0.4372          | 0.2460 |
  | 0.3058        | 1.83  | 17500  | 0.4161          | 0.2397 |
  | 0.2877        | 1.88  | 18000  | 0.4046          | 0.2386 |
  | 0.2904        | 1.93  | 18500  | 0.4108          | 0.2399 |
  | 0.2851        | 1.99  | 19000  | 0.4196          | 0.2385 |
  | 0.2451        | 2.04  | 19500  | 0.4096          | 0.2406 |
  | 0.259         | 2.09  | 20000  | 0.4437          | 0.2374 |
  | 0.2681        | 2.14  | 20500  | 0.4226          | 0.2357 |
  | 0.4371        | 2.2   | 21000  | 0.4301          | 0.2356 |
  | 0.2468        | 2.25  | 21500  | 0.4431          | 0.2326 |
  | 0.2687        | 2.3   | 22000  | 0.4218          | 0.2401 |
  | 0.2571        | 2.35  | 22500  | 0.4131          | 0.2337 |
  | 0.2541        | 2.41  | 23000  | 0.4105          | 0.2312 |
  | 0.2663        | 2.46  | 23500  | 0.4228          | 0.2327 |
  | 0.2777        | 2.51  | 24000  | 0.3960          | 0.2254 |
  | 0.2659        | 2.56  | 24500  | 0.4074          | 0.2289 |
  | 0.2519        | 2.61  | 25000  | 0.4220          | 0.2363 |
  | 0.2607        | 2.67  | 25500  | 0.3912          | 0.2253 |
  | 0.2749        | 2.72  | 26000  | 0.4017          | 0.2214 |
  | 0.2431        | 2.77  | 26500  | 0.3879          | 0.2181 |
  | 0.2557      