readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-kika5_my-colab
    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-large-xls-r-300m-kika5_my-colab
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3860
  - Wer: 0.3505
  
  ## 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: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.0007        | 4.82  | 400  | 0.6696          | 0.8283 |
  | 0.2774        | 9.64  | 800  | 0.4231          | 0.5476 |
  | 0.1182        | 14.46 | 1200 | 0.4253          | 0.5102 |
  | 0.0859        | 19.28 | 1600 | 0.4600          | 0.4866 |
  | 0.0693        | 24.1  | 2000 | 0.4030          | 0.4533 |
  | 0.0611        | 28.92 | 2400 | 0.4189          | 0.4412 |
  | 0.0541        | 33.73 | 2800 | 0.4272          | 0.4380 |
  | 0.0478        | 38.55 | 3200 | 0.4537          | 0.4505 |
  | 0.0428        | 43.37 | 3600 | 0.4349          | 0.4181 |
  | 0.038         | 48.19 | 4000 | 0.4562          | 0.4199 |
  | 0.0345        | 53.01 | 4400 | 0.4209          | 0.4310 |
  | 0.0316        | 57.83 | 4800 | 0.4336          | 0.4058 |
  | 0.0288        | 62.65 | 5200 | 0.4004          | 0.3920 |
  | 0.025         | 67.47 | 5600 | 0.4115          | 0.3857 |
  | 0.0225        | 72.29 | 6000 | 0.4296          | 0.3948 |
  | 0.0182        | 77.11 | 6400 | 0.3963          | 0.3772 |
  | 0.0165        | 81.93 | 6800 | 0.3921          | 0.3687 |
  | 0.0152        | 86.75 | 7200 | 0.3969          | 0.3592 |
  | 0.0133        | 91.57 | 7600 | 0.3803          | 0.3527 |
  | 0.0118        | 96.39 | 8000 | 0.3860          | 0.3505 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  