readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: xlsr-53-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. -->
  
  # xlsr-53-bemba-10hrs
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3190
  - Wer: 0.4032
  
  ## 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: 400
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.3207        | 1.07  | 400  | 0.3720          | 0.5923 |
  | 0.5688        | 2.14  | 800  | 0.3073          | 0.5002 |
  | 0.3927        | 3.22  | 1200 | 0.2678          | 0.4521 |
  | 0.316         | 4.29  | 1600 | 0.2703          | 0.4261 |
  | 0.2531        | 5.36  | 2000 | 0.2663          | 0.4198 |
  | 0.2051        | 6.43  | 2400 | 0.2614          | 0.4037 |
  | 0.1584        | 7.51  | 2800 | 0.2853          | 0.4046 |
  | 0.1343        | 8.58  | 3200 | 0.3072          | 0.4121 |
  | 0.1031        | 9.65  | 3600 | 0.3190          | 0.4032 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  