readme: ---
  language:
  - sv-SE
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - common_voice
  - generated_from_trainer
  model-index:
  - name: wav2vec2-speechdat
    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. -->
  
  # wav2vec2-speechdat
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - SV-SE dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4578
  - Wer: 0.2927
  
  ## 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: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_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: 15.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Wer    |
  |:-------------:|:-----:|:------:|:---------------:|:------:|
  | No log        | 0.01  | 100    | 3.6252          | 1.0    |
  | No log        | 0.02  | 200    | 3.1906          | 1.0    |
  | No log        | 0.03  | 300    | 3.1090          | 1.0    |
  | No log        | 0.04  | 400    | 1.8796          | 0.9955 |
  | 6.2575        | 0.05  | 500    | 1.3515          | 0.9058 |
  | 6.2575        | 0.06  | 600    | 1.1209          | 0.8328 |
  | 6.2575        | 0.07  | 700    | 1.1404          | 0.8309 |
  | 6.2575        | 0.09  | 800    | 1.0599          | 0.8021 |
  | 6.2575        | 0.1   | 900    | 0.9901          | 0.8335 |
  | 0.7737        | 0.11  | 1000   | 0.8846          | 0.7400 |
  | 0.7737        | 0.12  | 1100   | 0.9971          | 0.7820 |
  | 0.7737        | 0.13  | 1200   | 0.8665          | 0.7123 |
  | 0.7737        | 0.14  | 1300   | 0.8490          | 0.7366 |
  | 0.7737        | 0.15  | 1400   | 0.8250          | 0.6765 |
  | 0.6183        | 0.16  | 1500   | 0.8291          | 0.6965 |
  | 0.6183        | 0.17  | 1600   | 0.7946          | 0.6823 |
  | 0.6183        | 0.18  | 1700   | 0.8239          | 0.6894 |
  | 0.6183        | 0.19  | 1800   | 0.8282          | 0.6796 |
  | 0.6183        | 0.2   | 1900   | 0.7645          | 0.6518 |
  | 0.561         | 0.21  | 2000   | 0.7530          | 0.6367 |
  | 0.561         | 0.22  | 2100   | 0.7296          | 0.6177 |
  | 0.561         | 0.24  | 2200   | 0.7527          | 0.6498 |
  | 0.561         | 0.25  | 2300   | 0.7210          | 0.6316 |
  | 0.561         | 0.26  | 2400   | 0.7938          | 0.6757 |
  | 0.5402        | 0.27  | 2500   | 0.7485          | 0.6372 |
  | 0.5402        | 0.28  | 2600   | 0.7146          | 0.6133 |
  | 0.5402        | 0.29  | 2700   | 0.7308          | 0.6626 |
  | 0.5402        | 0.3   | 2800   | 0.7078          | 0.5949 |
  | 0.5402        | 0.31  | 2900   | 0.7679          | 0.6373 |
  | 0.5303        | 0.32  | 3000   | 0.7263          | 0.6502 |
  | 0.5303        | 0.33  | 3100   | 0.6613          | 0.5846 |
  | 0.5303        | 0.34  | 3200   | 0.6784          | 0.5783 |
  | 0.5303        | 0.35  | 3300   | 0.6908          | 0.5833 |
  | 0.5303        | 0.36  | 3400   | 0.6595          | 0.5826 |
  | 0.503         | 0.37  | 3500   | 0.6717          | 0.5938 |
  | 0.503         | 0.39  | 3600   | 0.6938          | 0.5791 |
  | 0.503         | 0.4   | 3700   | 0.6677          | 0.6052 |
  | 0.503         | 0.41  | 3800   | 0.6544          | 0.5554 |
  | 0.503         | 0.42  | 3900   | 0.6514          | 0.5728 |
  | 0.4959        | 0.43  | 4000   | 0.6847          | 0.6188 |
  | 0.4959        | 0.44  | 4100   | 0.6626          | 0.5869 |
  | 0.4959        | 0.45  | 4200   | 0.6670          | 0.5700 |
  | 0.4959        | 0.46  | 4300   | 0.6596          | 0.5846 |
  | 0.4959        | 0.47  | 4400   | 0.6523          | 0.5468 |
  | 0.4824        | 0.48  | 4500   | 0.6392          | 0.5688 |
  | 0.4824        | 0.49  | 4600   | 0.6561          | 0.5687 |
  | 0.4824        | 0.5   | 4700   | 0.6697          | 0.5817 |
  | 0.4824        | 0.51  | 4800   | 0.6348          | 0.5608 |
  | 0.4824        | 0.52  | 4900   | 0.6561          | 0.5600 |
  | 0.4714        | 0.54  | 5000   | 0.6522          | 0.6181 |
  | 0.4714        | 0.55  | 5100   | 0.6858          | 0.5921 |
  | 0.4714        | 0.56  | 5200   | 0.6706          | 0.5497 |
  | 0.4714        | 0.57  | 5300   | 0.7123          | 0.5768 |
  | 0.4714        | 0.58  | 5400   | 0.6599          | 0.6100 |
  | 0.471         | 0.59  | 5500   | 0.6421          | 0.5626 |
  | 0.471         | 0.6   | 5600   | 0.6395          | 0.5753 |
  | 0.471         | 0.61  | 5700   | 0.6788          | 0.5481 |
  | 0.471         | 0.62  | 5800   | 0.6386          | 0.5516 |
  | 0.471         | 0.63  | 