readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - common_voice_9_0
  metrics:
  - wer
  model-index:
  - name: wav2vec2-300m-swa-r22-200h-lambda-fine-v4
    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: 0.24831349206349207
  ---
  
  <!-- 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-v4
  
  This model is a fine-tuned version of [mutisya/wav2vec2-300m-swa-r22-200h-lambda-pre-v4](https://huggingface.co/mutisya/wav2vec2-300m-swa-r22-200h-lambda-pre-v4) on the common_voice_9_0 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2467
  - Wer: 0.2483
  
  ## 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: 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.8797        | 0.06  | 400   | 3.1519          | 1.0    |
  | 2.2995        | 0.11  | 800   | 1.1376          | 0.8929 |
  | 0.9474        | 0.17  | 1200  | 0.7483          | 0.7234 |
  | 0.7441        | 0.22  | 1600  | 0.6532          | 0.6455 |
  | 0.6677        | 0.28  | 2000  | 0.5766          | 0.6274 |
  | 0.6062        | 0.33  | 2400  | 0.5514          | 0.5731 |
  | 0.5741        | 0.39  | 2800  | 0.5176          | 0.5356 |
  | 0.5664        | 0.44  | 3200  | 0.7080          | 0.5338 |
  | 0.5387        | 0.5   | 3600  | 0.5350          | 0.5053 |
  | 0.5185        | 0.55  | 4000  | 0.4972          | 0.5104 |
  | 0.5015        | 0.61  | 4400  | 0.4306          | 0.4944 |
  | 0.48          | 0.67  | 4800  | 0.4354          | 0.4697 |
  | 0.4617        | 0.72  | 5200  | 0.4355          | 0.4715 |
  | 0.4494        | 0.78  | 5600  | 0.3920          | 0.4532 |
  | 0.4588        | 0.83  | 6000  | 0.4183          | 0.4604 |
  | 0.4441        | 0.89  | 6400  | 0.4371          | 0.4531 |
  | 0.4311        | 0.94  | 6800  | 0.3879          | 0.4456 |
  | 0.436         | 1.0   | 7200  | 0.3980          | 0.4279 |
  | 0.3873        | 1.05  | 7600  | 0.3929          | 0.4261 |
  | 0.3814        | 1.11  | 8000  | 0.5963          | 0.4184 |
  | 0.3792        | 1.16  | 8400  | 0.3615          | 0.4109 |
  | 0.38          | 1.22  | 8800  | 0.7002          | 0.4208 |
  | 0.3904        | 1.27  | 9200  | 0.3725          | 0.4079 |
  | 0.3808        | 1.33  | 9600  | 0.3602          | 0.4111 |
  | 0.3716        | 1.39  | 10000 | 0.3498          | 0.3985 |
  | 0.3828        | 1.44  | 10400 | 0.5355          | 0.4166 |
  | 0.3687        | 1.5   | 10800 | 0.4226          | 0.4056 |
  | 0.3683        | 1.55  | 11200 | 0.4456          | 0.3885 |
  | 0.3656        | 1.61  | 11600 | 0.3599          | 0.3896 |
  | 0.3498        | 1.66  | 12000 | 0.3860          | 0.3823 |
  | 0.349         | 1.72  | 12400 | 0.3684          | 0.3971 |
  | 0.3556        | 1.77  | 12800 | 0.4630          | 0.3870 |
  | 0.3439        | 1.83  | 13200 | 0.3931          | 0.3669 |
  | 0.3423        | 1.88  | 13600 | 0.3394          | 0.3761 |
  | 0.3495        | 1.94  | 14000 | 0.3346          | 0.3755 |
  | 0.3312        | 2.0   | 14400 | 0.4788          | 0.3604 |
  | 0.2909        | 2.05  | 14800 | 0.4135          | 0.3785 |
  | 0.2906        | 2.11  | 15200 | 0.3532          | 0.3634 |
  | 0.2917        | 2.16  | 15600 | 0.3065          | 0.3638 |
  | 0.2905        | 2.22  | 16000 | 0.3478          | 0.3688 |
  | 0.305         | 2.27  | 16400 | 0.3020          | 0.3601 |
  | 0.3056        | 2.33  | 16800 | 0.3404          | 0.3645 |
  | 0.2999        | 2.38  | 17200 | 0.5124          | 0.3671 |
  | 0.3112        | 2.44  | 17600 | 0.5009          | 0.3679 |
  | 0.2961        | 2.49  | 18000 | 0.3015          | 0.3526 |
  | 0.2972        | 2.55  | 18400 | 0.2844          | 0.3512 |
  | 0.297         | 2.6   | 18800 | 0.3018          | 0.3475 |
  | 0.2942        | 2.66  | 19200 | 0.3313          | 0.3554 |
  | 0.2826        | 2.72  | 19600 | 0.3089          | 0.3524 |
  | 0.2962        | 2.77  | 20000 | 0.2972          | 0.3540 |
  | 0.2905        | 2.83  | 20400 | 0.2878          | 0.3461 |
  | 0.2903        | 2.88  | 20800 | 0.3104          | 0.3472 |
  | 0.2805        | 2.94  | 21200 | 0.2828          | 0.3383 |
  | 0.2728        | 2.99  | 21600 | 0.2760          | 0.3351 |
  | 0.26          | 3.05  | 22000 | 0.2912          | 0.3324 |
  | 0.2581 