readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: xls-r-300m-bemba-10hrs
    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. -->
  
  # xls-r-300m-bemba-10hrs
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3022
  - Wer: 0.3976
  
  ## 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: 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.4544        | 1.07  | 400  | 0.4912          | 0.6813 |
  | 0.662         | 2.14  | 800  | 0.3667          | 0.5690 |
  | 0.4601        | 3.22  | 1200 | 0.2792          | 0.4819 |
  | 0.3816        | 4.29  | 1600 | 0.2828          | 0.4608 |
  | 0.3012        | 5.36  | 2000 | 0.2881          | 0.4651 |
  | 0.2427        | 6.43  | 2400 | 0.2758          | 0.4219 |
  | 0.1888        | 7.51  | 2800 | 0.2743          | 0.4094 |
  | 0.1559        | 8.58  | 3200 | 0.2893          | 0.4021 |
  | 0.1203        | 9.65  | 3600 | 0.3022          | 0.3976 |
  
  
  ### Framework versions
  
  - Transformers 4.19.0.dev0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  