---
library_name: transformers
language:
- zh
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- formospeech/hat_asr_aligned
model-index:
- name: Whisper Base 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 Base Hakka Condenser

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the HAT ASR Aligned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1321
- Cer: 7.6600

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 488
- training_steps: 4880
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Cer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1299        | 0.9980 | 488  | 0.2820          | 17.9474 |
| 0.0685        | 1.9959 | 976  | 0.1976          | 12.9309 |
| 0.0314        | 2.9939 | 1464 | 0.1912          | 11.2525 |
| 0.0169        | 3.9918 | 1952 | 0.1744          | 13.9527 |
| 0.009         | 4.9898 | 2440 | 0.1621          | 9.9198  |
| 0.0027        | 5.9877 | 2928 | 0.1494          | 10.6526 |
| 0.0016        | 6.9857 | 3416 | 0.1451          | 8.7107  |
| 0.0006        | 7.9836 | 3904 | 0.1357          | 7.6889  |
| 0.0004        | 8.9816 | 4392 | 0.1329          | 7.6242  |
| 0.0001        | 9.9796 | 4880 | 0.1321          | 7.6600  |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.3.0
- Datasets 3.4.0
- Tokenizers 0.21.0