readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: north
    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. -->
  
  # north
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3589
  - Wer: 0.1906
  
  ## 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: 0.001
  - train_batch_size: 4
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - 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: 1000
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 4.0712        | 0.44  | 1000  | 3.4003          | 1.0    |
  | 3.319         | 0.89  | 2000  | 3.3339          | 1.0    |
  | 3.286         | 1.33  | 3000  | 2.6424          | 1.0288 |
  | 1.9136        | 1.78  | 4000  | 1.2472          | 0.8778 |
  | 1.3317        | 2.22  | 5000  | 1.0164          | 0.7669 |
  | 1.1469        | 2.67  | 6000  | 0.8924          | 0.7040 |
  | 1.0525        | 3.11  | 7000  | 0.8257          | 0.6627 |
  | 0.9665        | 3.56  | 8000  | 0.7738          | 0.6278 |
  | 0.9089        | 4.0   | 9000  | 0.7111          | 0.6036 |
  | 0.8371        | 4.44  | 10000 | 0.6983          | 0.5889 |
  | 0.8198        | 4.89  | 11000 | 0.6626          | 0.5687 |
  | 0.7587        | 5.33  | 12000 | 0.6386          | 0.5456 |
  | 0.7334        | 5.78  | 13000 | 0.6166          | 0.5295 |
  | 0.6919        | 6.22  | 14000 | 0.6022          | 0.5191 |
  | 0.6655        | 6.67  | 15000 | 0.5738          | 0.5053 |
  | 0.6261        | 7.11  | 16000 | 0.5511          | 0.4907 |
  | 0.5947        | 7.56  | 17000 | 0.5344          | 0.4705 |
  | 0.5823        | 8.0   | 18000 | 0.5019          | 0.4509 |
  | 0.5232        | 8.44  | 19000 | 0.5058          | 0.4438 |
  | 0.5291        | 8.89  | 20000 | 0.4723          | 0.4258 |
  | 0.4799        | 9.33  | 21000 | 0.4783          | 0.4163 |
  | 0.4694        | 9.78  | 22000 | 0.4608          | 0.4098 |
  | 0.4392        | 10.22 | 23000 | 0.4502          | 0.3954 |
  | 0.4149        | 10.67 | 24000 | 0.4414          | 0.3839 |
  | 0.4003        | 11.11 | 25000 | 0.4343          | 0.3700 |
  | 0.3713        | 11.56 | 26000 | 0.4203          | 0.3635 |
  | 0.3714        | 12.0  | 27000 | 0.4070          | 0.3541 |
  | 0.3345        | 12.44 | 28000 | 0.4185          | 0.3436 |
  | 0.3315        | 12.89 | 29000 | 0.3973          | 0.3329 |
  | 0.3008        | 13.33 | 30000 | 0.4006          | 0.3253 |
  | 0.2944        | 13.78 | 31000 | 0.3848          | 0.3161 |
  | 0.278         | 14.22 | 32000 | 0.3982          | 0.3087 |
  | 0.2611        | 14.67 | 33000 | 0.3854          | 0.2999 |
  | 0.2599        | 15.11 | 34000 | 0.3868          | 0.2949 |
  | 0.2398        | 15.56 | 35000 | 0.3605          | 0.2851 |
  | 0.2444        | 16.0  | 36000 | 0.3619          | 0.2805 |
  | 0.2146        | 16.44 | 37000 | 0.3788          | 0.2819 |
  | 0.2172        | 16.89 | 38000 | 0.3768          | 0.2765 |
  | 0.2027        | 17.33 | 39000 | 0.3685          | 0.2673 |
  | 0.1982        | 17.78 | 40000 | 0.3655          | 0.2616 |
  | 0.1903        | 18.22 | 41000 | 0.3653          | 0.2570 |
  | 0.1828        | 18.67 | 42000 | 0.3707          | 0.2563 |
  | 0.1764        | 19.11 | 43000 | 0.3733          | 0.2457 |
  | 0.1679        | 19.56 | 44000 | 0.3607          | 0.2456 |
  | 0.1668        | 20.0  | 45000 | 0.3509          | 0.2384 |
  | 0.1555        | 20.44 | 46000 | 0.3603          | 0.2387 |
  | 0.1589        | 20.89 | 47000 | 0.3417          | 0.2331 |
  | 0.1426        | 21.33 | 48000 | 0.3598          | 0.2287 |
  | 0.145         | 21.78 | 49000 | 0.3657          | 0.2301 |
  | 0.1378        | 22.22 | 50000 | 0.3665          | 0.2222 |
  | 0.1324        | 22.67 | 51000 | 0.3603          | 0.2218 |
  | 0.1297        | 23.11 | 52000 | 0.3586          | 0.2179 |
  | 0.1252        | 23.56 | 53000 | 0.3588          | 0.2175 |
  | 0.1229        | 24.0  | 54000 | 0.3561          | 0.2141 |
  | 0.1176        | 24.44 | 55000 | 0.3636          | 0.2115 |
  | 0.1134        | 24.89 | 56000 | 0.3571          | 0.2103 |
  | 0.1136        | 25.33 | 57000 | 0.3655          | 0.2049 |
  | 0.1095        | 25.78 | 58000 | 0.3626          | 0.2039 |
  | 0.1053        | 26.22 | 59000 | 0.3631          | 0.2025 |
  | 0.1037        | 26.67 | 60000 | 0.3665          | 0.1989 |
  | 0.1025        | 27.11 | 61000 | 0.3616          | 0.1978 