readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_9_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-300m-swa-r22-200h-lambda-fine-v1
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: common_voice_9_0
        type: common_voice_9_0
        config: sw
        split: test
        args: sw
      metrics:
      - name: Wer
        type: wer
        value: 0.9308283730158731
  ---
  
  <!-- 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. -->
  
  # wav2vec2-300m-swa-r22-200h-lambda-fine-v1
  
  This model is a fine-tuned version of [mutisya/wav2vec2-300m-swa-r22-200h-lambda-pre-v1](https://huggingface.co/mutisya/wav2vec2-300m-swa-r22-200h-lambda-pre-v1) on the common_voice_9_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3089
  - Wer: 0.9308
  
  ## 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.1297        | 0.22  | 400   | 2.1090          | 1.0015 |
  | 2.0403        | 0.44  | 800   | 1.9713          | 0.9952 |
  | 1.9199        | 0.66  | 1200  | 1.8805          | 0.9903 |
  | 1.83          | 0.89  | 1600  | 1.7530          | 0.9932 |
  | 1.7841        | 1.11  | 2000  | 1.7245          | 0.9858 |
  | 1.7488        | 1.33  | 2400  | 1.6986          | 0.9834 |
  | 1.7071        | 1.55  | 2800  | 1.6577          | 0.9833 |
  | 1.6903        | 1.77  | 3200  | 1.6619          | 0.9872 |
  | 1.6432        | 1.99  | 3600  | 1.6447          | 0.9815 |
  | 1.6066        | 2.22  | 4000  | 1.6048          | 0.9837 |
  | 1.6083        | 2.44  | 4400  | 1.5862          | 0.9893 |
  | 1.5792        | 2.66  | 4800  | 1.5674          | 0.9772 |
  | 1.5646        | 2.88  | 5200  | 1.5637          | 0.9770 |
  | 1.5465        | 3.1   | 5600  | 1.5230          | 0.9672 |
  | 1.5216        | 3.32  | 6000  | 1.5800          | 0.9697 |
  | 1.5051        | 3.55  | 6400  | 1.5026          | 0.9659 |
  | 1.5014        | 3.77  | 6800  | 1.4893          | 0.9728 |
  | 1.4802        | 3.99  | 7200  | 1.4885          | 0.9670 |
  | 1.4568        | 4.21  | 7600  | 1.4465          | 0.9644 |
  | 1.4451        | 4.43  | 8000  | 1.4713          | 0.9592 |
  | 1.4349        | 4.65  | 8400  | 1.4348          | 0.9566 |
  | 1.4265        | 4.88  | 8800  | 1.4356          | 0.9587 |
  | 1.4033        | 5.1   | 9200  | 1.4220          | 0.9600 |
  | 1.401         | 5.32  | 9600  | 1.4129          | 0.9593 |
  | 1.3898        | 5.54  | 10000 | 1.4409          | 0.9623 |
  | 1.3859        | 5.76  | 10400 | 1.4100          | 0.9615 |
  | 1.3714        | 5.98  | 10800 | 1.4147          | 0.9516 |
  | 1.3366        | 6.2   | 11200 | 1.3775          | 0.9504 |
  | 1.3306        | 6.43  | 11600 | 1.4005          | 0.9506 |
  | 1.3226        | 6.65  | 12000 | 1.3955          | 0.9436 |
  | 1.328         | 6.87  | 12400 | 1.3805          | 0.9461 |
  | 1.3044        | 7.09  | 12800 | 1.3534          | 0.9450 |
  | 1.2822        | 7.31  | 13200 | 1.3580          | 0.9424 |
  | 1.2746        | 7.53  | 13600 | 1.3449          | 0.9419 |
  | 1.2674        | 7.76  | 14000 | 1.3391          | 0.9409 |
  | 1.2677        | 7.98  | 14400 | 1.3288          | 0.9458 |
  | 1.2333        | 8.2   | 14800 | 1.3345          | 0.9419 |
  | 1.2286        | 8.42  | 15200 | 1.3302          | 0.9410 |
  | 1.2257        | 8.64  | 15600 | 1.3189          | 0.9397 |
  | 1.2169        | 8.86  | 16000 | 1.3248          | 0.9361 |
  | 1.1991        | 9.09  | 16400 | 1.3190          | 0.9342 |
  | 1.1959        | 9.31  | 16800 | 1.3133          | 0.9353 |
  | 1.1738        | 9.53  | 17200 | 1.3165          | 0.9336 |
  | 1.1708        | 9.75  | 17600 | 1.3089          | 0.9317 |
  | 1.1786        | 9.97  | 18000 | 1.3091          | 0.9311 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  