readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xls-r-300m-th-beta
    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-xls-r-300m-th-beta
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9096
  - Wer: 0.7261
  - Cer: 0.2160
  - Clean Cer: 0.1909
  
  ## Model description
  
  This model is available until Jan 12, 2023
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  We use our custom dataset splited into 70k training dataset, and 7k evaluation dataset
  This is our detailed dataset
  
  1. Common Voice 11
  - filter 5k fifties age males out
  - remain 25k training dataset
  
  2. Botnoi voice
  - 45k training dataset
  
  Both dataset was through our custom cleansing text data.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 16
  - eval_batch_size: 16
  - 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
  - training_steps: 1000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    | Clean Cer |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|
  | 6.9481        | 0.34  | 500  | 3.5952          | 1.0    | 0.9815 | 0.9779    |
  | 2.0387        | 0.68  | 1000 | 0.9096          | 0.7261 | 0.2160 | 0.1909    |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  