metadata
language:
- zh
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- formospeech/hat_asr_aligned
model-index:
- name: Whisper Medium Hakka Condenser
results: []
Whisper Medium Hakka Condenser
This model is a fine-tuned version of openai/whisper-medium on the HAT ASR Aligned dataset. It achieves the following results on the evaluation set:
- Loss: 0.0401
- Cer: 1.8101
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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1521
- training_steps: 15215
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.0328 | 0.9997 | 3043 | 0.0681 | 4.6235 |
| 0.0135 | 1.9993 | 6086 | 0.0515 | 2.8839 |
| 0.0045 | 2.9990 | 9129 | 0.0440 | 1.9904 |
| 0.0028 | 3.9987 | 12172 | 0.0403 | 2.0760 |
| 0.0007 | 4.9984 | 15215 | 0.0401 | 1.8101 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1