readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-multilang-cv-ru-night
    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-multilang-cv-ru-night
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6617
  - Wer: 0.5097
  
  ## 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: 0.0001
  - train_batch_size: 16
  - 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: 1000
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 8.725         | 1.58  | 500   | 3.2788          | 1.0    |
  | 3.1184        | 3.15  | 1000  | 2.4018          | 1.0015 |
  | 1.2393        | 4.73  | 1500  | 0.6213          | 0.7655 |
  | 0.6899        | 6.31  | 2000  | 0.5518          | 0.6811 |
  | 0.5532        | 7.89  | 2500  | 0.5102          | 0.6467 |
  | 0.4604        | 9.46  | 3000  | 0.4887          | 0.6213 |
  | 0.4095        | 11.04 | 3500  | 0.4874          | 0.6042 |
  | 0.3565        | 12.62 | 4000  | 0.4810          | 0.5893 |
  | 0.3238        | 14.2  | 4500  | 0.5028          | 0.5890 |
  | 0.3011        | 15.77 | 5000  | 0.5475          | 0.5808 |
  | 0.2827        | 17.35 | 5500  | 0.5289          | 0.5720 |
  | 0.2659        | 18.93 | 6000  | 0.5496          | 0.5733 |
  | 0.2445        | 20.5  | 6500  | 0.5354          | 0.5737 |
  | 0.2366        | 22.08 | 7000  | 0.5357          | 0.5686 |
  | 0.2181        | 23.66 | 7500  | 0.5491          | 0.5611 |
  | 0.2146        | 25.24 | 8000  | 0.5591          | 0.5597 |
  | 0.2006        | 26.81 | 8500  | 0.5625          | 0.5631 |
  | 0.1912        | 28.39 | 9000  | 0.5577          | 0.5647 |
  | 0.1821        | 29.97 | 9500  | 0.5684          | 0.5519 |
  | 0.1744        | 31.55 | 10000 | 0.5639          | 0.5551 |
  | 0.1691        | 33.12 | 10500 | 0.5596          | 0.5425 |
  | 0.1577        | 34.7  | 11000 | 0.5770          | 0.5551 |
  | 0.1522        | 36.28 | 11500 | 0.5634          | 0.5560 |
  | 0.1468        | 37.85 | 12000 | 0.5815          | 0.5453 |
  | 0.1508        | 39.43 | 12500 | 0.6053          | 0.5490 |
  | 0.1394        | 41.01 | 13000 | 0.6193          | 0.5504 |
  | 0.1291        | 42.59 | 13500 | 0.5930          | 0.5424 |
  | 0.1345        | 44.16 | 14000 | 0.6283          | 0.5442 |
  | 0.1296        | 45.74 | 14500 | 0.6063          | 0.5560 |
  | 0.1286        | 47.32 | 15000 | 0.6248          | 0.5378 |
  | 0.1231        | 48.9  | 15500 | 0.6106          | 0.5405 |
  | 0.1189        | 50.47 | 16000 | 0.6164          | 0.5342 |
  | 0.1127        | 52.05 | 16500 | 0.6269          | 0.5359 |
  | 0.112         | 53.63 | 17000 | 0.6170          | 0.5390 |
  | 0.1113        | 55.21 | 17500 | 0.6489          | 0.5385 |
  | 0.1023        | 56.78 | 18000 | 0.6826          | 0.5490 |
  | 0.1069        | 58.36 | 18500 | 0.6147          | 0.5296 |
  | 0.1008        | 59.94 | 19000 | 0.6414          | 0.5332 |
  | 0.1018        | 61.51 | 19500 | 0.6454          | 0.5288 |
  | 0.0989        | 63.09 | 20000 | 0.6603          | 0.5303 |
  | 0.0944        | 64.67 | 20500 | 0.6350          | 0.5288 |
  | 0.0905        | 66.25 | 21000 | 0.6386          | 0.5247 |
  | 0.0837        | 67.82 | 21500 | 0.6563          | 0.5298 |
  | 0.0868        | 69.4  | 22000 | 0.6375          | 0.5208 |
  | 0.0827        | 70.98 | 22500 | 0.6401          | 0.5271 |
  | 0.0797        | 72.56 | 23000 | 0.6723          | 0.5191 |
  | 0.0847        | 74.13 | 23500 | 0.6610          | 0.5213 |
  | 0.0818        | 75.71 | 24000 | 0.6774          | 0.5254 |
  | 0.0793        | 77.29 | 24500 | 0.6543          | 0.5250 |
  | 0.0758        | 78.86 | 25000 | 0.6607          | 0.5218 |
  | 0.0755        | 80.44 | 25500 | 0.6599          | 0.5160 |
  | 0.0722        | 82.02 | 26000 | 0.6683          | 0.5196 |
  | 0.0714        | 83.6  | 26500 | 0.6941          | 0.5180 |
  | 0.0684        | 85.17 | 27000 | 0.6581          | 0.5167 |
  | 0.0686        | 86.75 | 27500 | 0.6651          | 0.5172 |
  | 0.0712        | 88.33 | 28000 | 0.6547          | 0.5208 |
  | 0.0697        | 89.91 | 28500 | 0.6555          | 0.5162 |
  | 0.0696        | 91.48 | 29000 | 0.6678          | 0.5107 |
  | 0.0686        | 93.06 | 29500 | 0.6630          | 0.5124 |
  | 0.0671        | 94.64 | 30000 | 0.6675          | 0.5143 |
  | 0.0668        | 96.21 | 30500 | 0.6