readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: 20220412-203254
    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. -->
  
  # 20220412-203254
  
  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: 5.0428
  - Wer: 1.0019
  
  ## 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: 6e-06
  - train_batch_size: 1
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 1200
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch   | Step   | Validation Loss | Wer    |
  |:-------------:|:-------:|:------:|:---------------:|:------:|
  | 16.9455       | 1.5     | 200    | 16.4676         | 1.2534 |
  | 15.444        | 3.01    | 400    | 14.1207         | 1.0    |
  | 9.5452        | 4.51    | 600    | 8.4030          | 1.0    |
  | 6.2565        | 6.02    | 800    | 6.5233          | 1.0    |
  | 5.2827        | 7.52    | 1000   | 5.6058          | 1.0    |
  | 4.7652        | 9.02    | 1200   | 4.9765          | 1.0    |
  | 4.3803        | 10.53   | 1400   | 4.4565          | 1.0    |
  | 4.0005        | 12.03   | 1600   | 4.0224          | 1.0    |
  | 3.7041        | 13.53   | 1800   | 3.6903          | 1.0    |
  | 3.4991        | 15.04   | 2000   | 3.4642          | 1.0    |
  | 3.34          | 16.54   | 2200   | 3.3425          | 1.0    |
  | 3.2352        | 18.05   | 2400   | 3.2617          | 1.0    |
  | 3.1867        | 19.55   | 2600   | 3.2358          | 1.0    |
  | 3.161         | 21.05   | 2800   | 3.2289          | 1.0    |
  | 3.145         | 22.56   | 3000   | 3.2023          | 1.0    |
  | 3.1203        | 24.06   | 3200   | 3.1964          | 1.0    |
  | 3.1109        | 25.56   | 3400   | 3.1844          | 1.0    |
  | 3.0958        | 27.07   | 3600   | 3.1839          | 1.0    |
  | 3.0732        | 28.57   | 3800   | 3.2058          | 1.0    |
  | 3.0535        | 30.08   | 4000   | 3.1843          | 1.0    |
  | 3.0243        | 31.58   | 4200   | 3.1992          | 1.0    |
  | 2.9829        | 33.08   | 4400   | 3.2019          | 1.0    |
  | 2.9219        | 34.59   | 4600   | 3.2346          | 1.0    |
  | 2.8313        | 36.09   | 4800   | 3.2781          | 1.0    |
  | 2.7186        | 37.59   | 5000   | 3.3056          | 1.0    |
  | 2.5745        | 39.1    | 5200   | 3.3554          | 1.0    |
  | 2.4028        | 40.6    | 5400   | 3.4331          | 1.0    |
  | 2.2645        | 42.11   | 5600   | 3.4418          | 1.0    |
  | 2.1303        | 43.61   | 5800   | 3.5584          | 1.0    |
  | 2.0257        | 45.11   | 6000   | 3.5943          | 1.0    |
  | 1.9223        | 46.62   | 6200   | 3.6767          | 1.0    |
  | 1.8344        | 48.12   | 6400   | 3.7363          | 1.0    |
  | 1.7574        | 49.62   | 6600   | 3.8921          | 1.0    |
  | 1.67          | 51.13   | 6800   | 3.9054          | 1.0    |
  | 1.6118        | 52.63   | 7000   | 4.0352          | 1.0    |
  | 1.5372        | 54.14   | 7200   | 3.9742          | 1.0    |
  | 1.4846        | 55.64   | 7400   | 4.1078          | 1.0    |
  | 1.4093        | 57.14   | 7600   | 4.1705          | 1.0    |
  | 1.3379        | 58.65   | 7800   | 4.2737          | 1.0    |
  | 1.28          | 60.15   | 8000   | 4.3662          | 1.0    |
  | 1.2268        | 61.65   | 8200   | 4.4278          | 1.0    |
  | 1.1641        | 63.16   | 8400   | 4.4831          | 1.0    |
  | 1.1058        | 64.66   | 8600   | 4.5354          | 1.0    |
  | 1.0596        | 66.17   | 8800   | 4.5983          | 1.0    |
  | 0.9953        | 67.67   | 9000   | 4.7143          | 1.0    |
  | 0.9406        | 69.17   | 9200   | 4.8536          | 1.0    |
  | 0.9022        | 70.68   | 9400   | 4.7732          | 1.0    |
  | 0.8551        | 72.18   | 9600   | 4.8929          | 1.0    |
  | 0.8103        | 73.68   | 9800   | 4.9513          | 1.0    |
  | 0.7665        | 75.19   | 10000  | 4.9530          | 1.0    |
  | 0.7215        | 76.69   | 10200  | 5.1471          | 1.0    |
  | 0.6906        | 78.2    | 10400  | 5.2295          | 1.0    |
  | 0.6354        | 79.7    | 10600  | 5.1287          | 1.0    |
  | 0.6196        | 81.2    | 10800  | 5.2081          | 1.0    |
  | 0.6026        | 82.71   | 11000  | 5.4323          | 1.0    |
  | 0.5726        | 84.21   | 11200  | 5.3907          | 1.0    |
  | 0.5348        | 85.71   | 11400  | 5.5669          | 1.0    |
  | 0.5344        | 87.22   | 11600  | 5.5685          | 1.0    |
  | 0.4849        | 88.72   | 