readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53-Total2e-4_3
    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-xlsr-53-Total2e-4_3
  
  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: 0.2893
  - Wer: 0.1863
  
  ## 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: 0.0002
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.16          | 0.1   | 200   | 2.9123          | 0.9707 |
  | 2.4599        | 0.2   | 400   | 0.8145          | 0.6906 |
  | 1.0523        | 0.3   | 600   | 0.5247          | 0.4823 |
  | 0.8965        | 0.4   | 800   | 0.4391          | 0.4416 |
  | 0.7994        | 0.5   | 1000  | 0.3889          | 0.3773 |
  | 0.7491        | 0.6   | 1200  | 0.3604          | 0.3305 |
  | 0.7425        | 0.7   | 1400  | 0.3543          | 0.3277 |
  | 0.7253        | 0.8   | 1600  | 0.3397          | 0.3143 |
  | 0.7221        | 0.9   | 1800  | 0.3341          | 0.2979 |
  | 0.6853        | 1.0   | 2000  | 0.3244          | 0.2906 |
  | 0.6107        | 1.1   | 2200  | 0.3127          | 0.2771 |
  | 0.6233        | 1.2   | 2400  | 0.3116          | 0.2721 |
  | 0.6214        | 1.3   | 2600  | 0.3256          | 0.2671 |
  | 0.6511        | 1.4   | 2800  | 0.3019          | 0.2570 |
  | 0.6491        | 1.5   | 3000  | 0.2961          | 0.2576 |
  | 0.6411        | 1.6   | 3200  | 0.2963          | 0.2535 |
  | 0.5963        | 1.7   | 3400  | 0.2939          | 0.2526 |
  | 0.6146        | 1.8   | 3600  | 0.2908          | 0.2490 |
  | 0.6291        | 1.9   | 3800  | 0.2851          | 0.2448 |
  | 0.6154        | 2.0   | 4000  | 0.2861          | 0.2424 |
  | 0.5652        | 2.1   | 4200  | 0.2852          | 0.2411 |
  | 0.5648        | 2.2   | 4400  | 0.2856          | 0.2350 |
  | 0.5365        | 2.3   | 4600  | 0.2802          | 0.2395 |
  | 0.5855        | 2.4   | 4800  | 0.2883          | 0.2374 |
  | 0.5978        | 2.5   | 5000  | 0.2855          | 0.2364 |
  | 0.5863        | 2.6   | 5200  | 0.2736          | 0.2277 |
  | 0.5569        | 2.7   | 5400  | 0.2746          | 0.2293 |
  | 0.5628        | 2.8   | 5600  | 0.2719          | 0.2249 |
  | 0.5655        | 2.9   | 5800  | 0.2653          | 0.2224 |
  | 0.5578        | 3.0   | 6000  | 0.2685          | 0.2243 |
  | 0.5303        | 3.1   | 6200  | 0.2696          | 0.2204 |
  | 0.5316        | 3.2   | 6400  | 0.2733          | 0.2247 |
  | 0.5476        | 3.3   | 6600  | 0.2716          | 0.2203 |
  | 0.5326        | 3.4   | 6800  | 0.2697          | 0.2209 |
  | 0.5375        | 3.5   | 7000  | 0.2701          | 0.2197 |
  | 0.5364        | 3.6   | 7200  | 0.2655          | 0.2165 |
  | 0.503         | 3.7   | 7400  | 0.2650          | 0.2125 |
  | 0.5284        | 3.8   | 7600  | 0.2672          | 0.2162 |
  | 0.5251        | 3.9   | 7800  | 0.2669          | 0.2172 |
  | 0.5299        | 4.0   | 8000  | 0.2632          | 0.2081 |
  | 0.4904        | 4.1   | 8200  | 0.2674          | 0.2099 |
  | 0.496         | 4.2   | 8400  | 0.2700          | 0.2143 |
  | 0.5067        | 4.3   | 8600  | 0.2648          | 0.2090 |
  | 0.506         | 4.4   | 8800  | 0.2595          | 0.2069 |
  | 0.4795        | 4.5   | 9000  | 0.2653          | 0.2072 |
  | 0.5149        | 4.6   | 9200  | 0.2618          | 0.2073 |
  | 0.4786        | 4.7   | 9400  | 0.2632          | 0.2058 |
  | 0.5056        | 4.8   | 9600  | 0.2674          | 0.2123 |
  | 0.5059        | 4.9   | 9800  | 0.2642          | 0.2115 |
  | 0.5119        | 5.0   | 10000 | 0.2672          | 0.2089 |
  | 0.4619        | 5.1   | 10200 | 0.2658          | 0.2062 |
  | 0.4647        | 5.2   | 10400 | 0.2664          | 0.2025 |
  | 0.4707        | 5.3   | 10600 | 0.2656          | 0.2084 |
  | 0.486         | 5.4   | 10800 | 0.2728          | 0.2029 |
  | 0.4785        | 5.5   | 11000 | 0.2653          | 0.2004 |
  | 0.4895        | 5.6   | 11200 | 0.2835          | 0.2119 |
  | 0.4519        | 5.7   | 11400 | 0.2715          | 0.2061 |
  | 0.484         | 5.8   | 11600 | 0.2663          | 0.2071 |
  | 0.4734        | 5.9   | 11800 | 0.2615          | 0.2023 |
  | 0.4563        | 6.0   | 12000 | 0.2604          | 0.1997 |
  | 0.4193       