readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: 20220415-210530
    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. -->
  
  # 20220415-210530
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6544
  - Wer: 0.3881
  
  ## 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: 1e-05
  - train_batch_size: 1
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 400
  - num_epochs: 1200
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch   | Step   | Validation Loss | Wer    |
  |:-------------:|:-------:|:------:|:---------------:|:------:|
  | 6.1495        | 2.27    | 200    | 2.4098          | 1.0    |
  | 0.4347        | 4.54    | 400    | 1.4211          | 0.9914 |
  | 0.2295        | 6.82    | 600    | 1.0229          | 0.9349 |
  | 0.1349        | 9.09    | 800    | 1.0063          | 0.9228 |
  | 0.1001        | 11.36   | 1000   | 1.0333          | 0.9197 |
  | 0.0847        | 13.63   | 1200   | 0.9021          | 0.8725 |
  | 0.0697        | 15.91   | 1400   | 0.9117          | 0.8779 |
  | 0.0634        | 18.18   | 1600   | 0.9550          | 0.8725 |
  | 0.0607        | 20.45   | 1800   | 0.9063          | 0.8303 |
  | 0.0551        | 22.73   | 2000   | 0.8163          | 0.7956 |
  | 0.0536        | 25.0    | 2200   | 0.7385          | 0.7235 |
  | 0.0511        | 27.27   | 2400   | 0.7917          | 0.7215 |
  | 0.0449        | 29.54   | 2600   | 0.7508          | 0.6938 |
  | 0.0417        | 31.82   | 2800   | 0.6892          | 0.6775 |
  | 0.0415        | 34.09   | 3000   | 0.7029          | 0.6790 |
  | 0.0384        | 36.36   | 3200   | 0.6839          | 0.6895 |
  | 0.0392        | 38.63   | 3400   | 0.7067          | 0.6872 |
  | 0.0358        | 40.91   | 3600   | 0.7310          | 0.6763 |
  | 0.0337        | 43.18   | 3800   | 0.7139          | 0.6548 |
  | 0.0362        | 45.45   | 4000   | 0.6975          | 0.6427 |
  | 0.0311        | 47.73   | 4200   | 0.7054          | 0.6412 |
  | 0.0327        | 50.0    | 4400   | 0.6530          | 0.6151 |
  | 0.0286        | 52.27   | 4600   | 0.6565          | 0.6076 |
  | 0.0304        | 54.54   | 4800   | 0.6931          | 0.6283 |
  | 0.0285        | 56.82   | 5000   | 0.6966          | 0.6108 |
  | 0.0279        | 59.09   | 5200   | 0.6473          | 0.5854 |
  | 0.0276        | 61.36   | 5400   | 0.6497          | 0.5920 |
  | 0.0238        | 63.63   | 5600   | 0.6283          | 0.5846 |
  | 0.0237        | 65.91   | 5800   | 0.6871          | 0.5885 |
  | 0.0221        | 68.18   | 6000   | 0.6518          | 0.5593 |
  | 0.0221        | 70.45   | 6200   | 0.6676          | 0.5601 |
  | 0.0215        | 72.73   | 6400   | 0.6299          | 0.5550 |
  | 0.022         | 75.0    | 6600   | 0.6719          | 0.5636 |
  | 0.0198        | 77.27   | 6800   | 0.6082          | 0.5569 |
  | 0.0222        | 79.54   | 7000   | 0.6156          | 0.5589 |
  | 0.0172        | 81.82   | 7200   | 0.6414          | 0.5636 |
  | 0.0188        | 84.09   | 7400   | 0.5874          | 0.5347 |
  | 0.0202        | 86.36   | 7600   | 0.6320          | 0.5421 |
  | 0.0165        | 88.63   | 7800   | 0.6345          | 0.5304 |
  | 0.0164        | 90.91   | 8000   | 0.6243          | 0.5289 |
  | 0.0167        | 93.18   | 8200   | 0.6237          | 0.5285 |
  | 0.015         | 95.45   | 8400   | 0.5937          | 0.5203 |
  | 0.0169        | 97.73   | 8600   | 0.6171          | 0.5343 |
  | 0.0147        | 100.0   | 8800   | 0.6857          | 0.5476 |
  | 0.0164        | 102.27  | 9000   | 0.6099          | 0.5160 |
  | 0.0152        | 104.54  | 9200   | 0.6319          | 0.5285 |
  | 0.0149        | 106.82  | 9400   | 0.6133          | 0.5296 |
  | 0.0155        | 109.09  | 9600   | 0.6237          | 0.5285 |
  | 0.0149        | 111.36  | 9800   | 0.6127          | 0.5012 |
  | 0.0142        | 113.63  | 10000  | 0.6119          | 0.4836 |
  | 0.013         | 115.91  | 10200  | 0.5974          | 0.4746 |
  | 0.012         | 118.18  | 10400  | 0.6296          | 0.5016 |
  | 0.0137        | 120.45  | 10600  | 0.5990          | 0.5023 |
  | 0.0146        | 122.73  | 10800  | 0.5784          | 0.4875 |
  | 0.0117        | 125.0   | 11000  | 0.5436          | 0.4766 |
  | 0.0133        | 127.27  | 11200  | 0.5890          | 0.5020 |
  | 0.0133        | 129.54  | 11400  | 0.6028          | 0.4895 |
  | 0.0119        | 131.82  | 11600  |