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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-chinese-tw-minnan-hanzi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: nan-tw
split: test
args: nan-tw
metrics:
- name: Wer
type: wer
value: 86.71399594320486
---
<!-- 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-small-chinese-tw-minnan-hanzi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2235
- Wer: 86.7140
- Cer: 62.5226
## 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: 8
- eval_batch_size: 8
- 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|
| 0.0802 | 3.6364 | 1000 | 1.1582 | 91.0751 | 80.0120 |
| 0.0014 | 7.2727 | 2000 | 1.1876 | 85.8012 | 61.7399 |
| 0.0002 | 10.9091 | 3000 | 1.1944 | 85.4970 | 61.7098 |
| 0.0002 | 14.5455 | 4000 | 1.2139 | 85.8012 | 61.8603 |
| 0.0001 | 18.1818 | 5000 | 1.2235 | 86.7140 | 62.5226 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|