readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: 20220413-210552
    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. -->
  
  # 20220413-210552
  
  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: 3.0348
  - Wer: 1.0006
  
  ## 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    |
  |:-------------:|:-------:|:------:|:---------------:|:------:|
  | 17.1111       | 1.5     | 200    | 16.6792         | 1.0    |
  | 16.0992       | 3.01    | 400    | 15.3947         | 1.0    |
  | 10.7668       | 4.51    | 600    | 10.3625         | 1.0    |
  | 6.2652        | 6.02    | 800    | 7.6849          | 1.0    |
  | 5.1417        | 7.52    | 1000   | 6.0307          | 1.0    |
  | 4.6159        | 9.02    | 1200   | 5.0891          | 1.0    |
  | 4.2444        | 10.53   | 1400   | 4.4120          | 1.0    |
  | 3.8935        | 12.03   | 1600   | 3.9570          | 1.0    |
  | 3.6292        | 13.53   | 1800   | 3.6405          | 1.0    |
  | 3.4535        | 15.04   | 2000   | 3.4523          | 1.0    |
  | 3.3175        | 16.54   | 2200   | 3.3589          | 1.0    |
  | 3.2266        | 18.05   | 2400   | 3.2966          | 1.0    |
  | 3.1825        | 19.55   | 2600   | 3.2658          | 1.0    |
  | 3.1604        | 21.05   | 2800   | 3.2534          | 1.0    |
  | 3.1438        | 22.56   | 3000   | 3.2437          | 1.0    |
  | 3.1176        | 24.06   | 3200   | 3.2169          | 1.0    |
  | 3.1088        | 25.56   | 3400   | 3.2102          | 1.0    |
  | 3.0955        | 27.07   | 3600   | 3.1983          | 1.0    |
  | 3.0763        | 28.57   | 3800   | 3.2092          | 1.0    |
  | 3.0599        | 30.08   | 4000   | 3.2092          | 1.0    |
  | 3.0385        | 31.58   | 4200   | 3.2154          | 1.0    |
  | 2.9996        | 33.08   | 4400   | 3.2120          | 1.0    |
  | 2.9207        | 34.59   | 4600   | 3.2146          | 1.0    |
  | 2.8071        | 36.09   | 4800   | 3.2093          | 1.0    |
  | 2.6412        | 37.59   | 5000   | 3.2282          | 1.0    |
  | 2.4594        | 39.1    | 5200   | 3.2442          | 1.0    |
  | 2.2708        | 40.6    | 5400   | 3.2944          | 1.0    |
  | 2.1279        | 42.11   | 5600   | 3.3260          | 1.0    |
  | 1.9985        | 43.61   | 5800   | 3.3586          | 1.0    |
  | 1.8979        | 45.11   | 6000   | 3.3945          | 1.0    |
  | 1.7838        | 46.62   | 6200   | 3.4761          | 1.0    |
  | 1.6774        | 48.12   | 6400   | 3.4886          | 1.0    |
  | 1.5958        | 49.62   | 6600   | 3.6208          | 1.0    |
  | 1.4957        | 51.13   | 6800   | 3.6501          | 1.0    |
  | 1.4202        | 52.63   | 7000   | 3.6492          | 1.0    |
  | 1.3377        | 54.14   | 7200   | 3.7392          | 1.0    |
  | 1.2872        | 55.64   | 7400   | 3.8624          | 1.0    |
  | 1.1992        | 57.14   | 7600   | 3.8511          | 1.0    |
  | 1.1238        | 58.65   | 7800   | 3.9662          | 1.0    |
  | 1.0775        | 60.15   | 8000   | 3.9267          | 1.0    |
  | 1.011         | 61.65   | 8200   | 4.0933          | 1.0    |
  | 0.962         | 63.16   | 8400   | 4.0941          | 1.0    |
  | 0.9041        | 64.66   | 8600   | 4.1163          | 1.0    |
  | 0.8552        | 66.17   | 8800   | 4.1937          | 1.0    |
  | 0.8054        | 67.67   | 9000   | 4.2277          | 1.0    |
  | 0.7457        | 69.17   | 9200   | 4.3899          | 1.0    |
  | 0.7292        | 70.68   | 9400   | 4.3621          | 1.0    |
  | 0.6635        | 72.18   | 9600   | 4.4706          | 1.0    |
  | 0.6333        | 73.68   | 9800   | 4.4571          | 1.0    |
  | 0.6109        | 75.19   | 10000  | 4.4594          | 1.0    |
  | 0.5611        | 76.69   | 10200  | 4.5672          | 1.0    |
  | 0.5286        | 78.2    | 10400  | 4.4957          | 1.0    |
  | 0.4894        | 79.7    | 10600  | 4.5278          | 1.0    |
  | 0.4831        | 81.2    | 10800  | 4.4604          | 1.0    |
  | 0.4575        | 82.71   | 11000  | 4.7439          | 1.0    |
  | 0.4418        | 84.21   | 11200  | 4.6511          | 1.0    |
  | 0.4085        | 85.71   | 11400  | 4.5008          | 1.0    |
  | 0.4011        | 87.22   | 11600  | 4.7690          | 1.0    |
  | 0.3791        | 88.72   | 