readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  model-index:
  - name: wav2vec2-large-xls-r-300m-tr-colab
    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-xls-r-300m-tr-colab
  
  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: 0.4316
  - Wer: 0.2905
  
  ## 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: 0.0003
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.9953        | 3.67  | 400   | 0.7024          | 0.7226 |
  | 0.4046        | 7.34  | 800   | 0.4342          | 0.5343 |
  | 0.201         | 11.01 | 1200  | 0.4396          | 0.5290 |
  | 0.1513        | 14.68 | 1600  | 0.4319          | 0.4108 |
  | 0.1285        | 18.35 | 2000  | 0.4422          | 0.3864 |
  | 0.1086        | 22.02 | 2400  | 0.4568          | 0.3796 |
  | 0.0998        | 25.69 | 2800  | 0.4687          | 0.3732 |
  | 0.0863        | 29.36 | 3200  | 0.4726          | 0.3803 |
  | 0.0809        | 33.03 | 3600  | 0.4479          | 0.3601 |
  | 0.0747        | 36.7  | 4000  | 0.4624          | 0.3525 |
  | 0.0692        | 40.37 | 4400  | 0.4366          | 0.3435 |
  | 0.0595        | 44.04 | 4800  | 0.4204          | 0.3510 |
  | 0.0584        | 47.71 | 5200  | 0.4202          | 0.3402 |
  | 0.0545        | 51.38 | 5600  | 0.4366          | 0.3343 |
  | 0.0486        | 55.05 | 6000  | 0.4492          | 0.3678 |
  | 0.0444        | 58.72 | 6400  | 0.4471          | 0.3301 |
  | 0.0406        | 62.39 | 6800  | 0.4382          | 0.3318 |
  | 0.0341        | 66.06 | 7200  | 0.4295          | 0.3258 |
  | 0.0297        | 69.72 | 7600  | 0.4336          | 0.3205 |
  | 0.0295        | 73.39 | 8000  | 0.4240          | 0.3199 |
  | 0.0261        | 77.06 | 8400  | 0.4316          | 0.3143 |
  | 0.0247        | 80.73 | 8800  | 0.4300          | 0.3165 |
  | 0.0207        | 84.4  | 9200  | 0.4380          | 0.3111 |
  | 0.0203        | 88.07 | 9600  | 0.4218          | 0.2998 |
  | 0.0174        | 91.74 | 10000 | 0.4271          | 0.2973 |
  | 0.015         | 95.41 | 10400 | 0.4330          | 0.2939 |
  | 0.0144        | 99.08 | 10800 | 0.4316          | 0.2905 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu102
  - Datasets 2.5.2
  - Tokenizers 0.13.1
  