readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-base-timit-finetune-4-additonal-train
    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-base-timit-finetune-4-additonal-train
  
  This model is a fine-tuned version of [rohitp1/wav2vec2-base-timit-finetune-3](https://huggingface.co/rohitp1/wav2vec2-base-timit-finetune-3) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4123
  - 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: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 60
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.1543        | 2.77  | 200  | 0.3600          | 0.3153 |
  | 0.1277        | 5.55  | 400  | 0.3621          | 0.3110 |
  | 0.106         | 8.33  | 600  | 0.3862          | 0.3050 |
  | 0.095         | 11.11 | 800  | 0.4055          | 0.3042 |
  | 0.0862        | 13.88 | 1000 | 0.4001          | 0.3083 |
  | 0.0834        | 16.66 | 1200 | 0.4228          | 0.2993 |
  | 0.0676        | 19.44 | 1400 | 0.4245          | 0.2955 |
  | 0.0593        | 22.22 | 1600 | 0.4123          | 0.2905 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1
  - Datasets 2.7.0
  - Tokenizers 0.11.0
  