---
language:
- zh
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- formospeech/tat_asr_aligned
model-index:
- name: Whisper Tiny Taiwanese Simulated Android
  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 Tiny Taiwanese Simulated Android

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the TAT ASR Aligned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6140
- Cer: 11.1506

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 681
- training_steps: 6810
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Cer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.345         | 0.9985 | 681  | 0.4722          | 17.2090 |
| 0.2022        | 1.9971 | 1362 | 0.4060          | 13.0439 |
| 0.1228        | 2.9956 | 2043 | 0.4379          | 13.2219 |
| 0.0721        | 3.9941 | 2724 | 0.4696          | 12.3827 |
| 0.0406        | 4.9927 | 3405 | 0.5141          | 12.5748 |
| 0.021         | 5.9912 | 4086 | 0.5437          | 12.2795 |
| 0.0107        | 6.9897 | 4767 | 0.5696          | 11.8216 |
| 0.0034        | 7.9883 | 5448 | 0.5935          | 11.4186 |
| 0.0011        | 8.9868 | 6129 | 0.6080          | 11.2588 |
| 0.0006        | 9.9853 | 6810 | 0.6140          | 11.1506 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1