Finetune Whisper on Frisian and English
Collection
Assessing Knowledge-Distillation Based Compression of Whisper Model for Frisian ASR
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Updated
DistilFT-English-1h
This model is a fine-tuned version of distil-small.en on the librispeech dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.2630
- eval_wer: 4.4661
- eval_runtime: 443.0803
- eval_samples_per_second: 5.516
- eval_steps_per_second: 0.691
- epoch: 51.2821
- step: 1000
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: 5e-07
- 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: 300
- training_steps: 1000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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