---
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