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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: []
---
<!-- 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. -->
# Whisper Medium Hakka Condenser
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/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
|