Whisper large-v2, KsponSpeech Partial 5 epochs
This model is a fine-tuned version of openai/whisper-large-v2 on the KsponSpeech dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0150
- eval_wer: 25.4322
- eval_runtime: 1298.665
- eval_samples_per_second: 0.689
- eval_steps_per_second: 0.689
- epoch: 5.07
- step: 300
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- 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: 50
- training_steps: 300
Framework versions
- Transformers 4.31.0
- Pytorch 1.12.1+cu116
- Datasets 2.14.0
- Tokenizers 0.12.1
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Model tree for Jungwonchang/whisper_finetune_ksponspeech_partial
Base model
openai/whisper-large-v2