Edit model card

whisper-large-v3-japanese-4k-steps

This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 16.1 dataset. I followed a post by Sanchit Gandhi, https://huggingface.co/blog/fine-tune-whisper It took 24 hours using an A100 on Google Colab to complete 4000 steps using the Common Voice 16.1 dataset. Training loss dropped over epochs but validation loss increased, so textbook overfitting. Furthermore, WER increased. It achieves the following results on the evaluation set:

  • Loss: 0.4057
  • Wer: 18.2149

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: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • 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
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.1374 1.02 1000 0.3618 11.983182
0.0508 2.04 2000 0.3658 17.554657
0.0206 3.05 3000 0.3904 21.087484
0.0066 4.07 4000 0.4057 18.214909

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.15.2
Downloads last month
32
Safetensors
Model size
1.54B params
Tensor type
F32
·

Finetuned from

Dataset used to train drewschaub/whisper-large-v3-japanese-4k-steps

Evaluation results