readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - audiofolder
  metrics:
  - wer
  model-index:
  - name: wav2vec2-xlsr-53-espeak-cv-ft-mhr-ntsema-colab
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: audiofolder
        type: audiofolder
        config: default
        split: train
        args: default
      metrics:
      - name: Wer
        type: wer
        value: 0.8127090301003345
  ---
  
  <!-- 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-xlsr-53-espeak-cv-ft-mhr-ntsema-colab
  
  This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7728
  - Wer: 0.8127
  
  ## 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: 4
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 8
  - 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: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.8463        | 5.79  | 400  | 1.0428          | 0.9331 |
  | 1.4576        | 11.59 | 800  | 0.6796          | 0.8495 |
  | 0.8054        | 17.39 | 1200 | 0.7131          | 0.8227 |
  | 0.4946        | 23.19 | 1600 | 0.7202          | 0.8194 |
  | 0.3157        | 28.98 | 2000 | 0.7728          | 0.8127 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.14.0.dev20221107+cu116
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  