---
base_model: openai/whisper-small
datasets:
- common_voice_13_0
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-small-Cantonese-test
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: zh-HK
      split: test
      args: zh-HK
    metrics:
    - type: wer
      value: 71.49724051985046
      name: Wer
---

<!-- 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-Cantonese-test

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3400
- Wer: 71.4972

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5135        | 0.1140 | 100  | 0.4197          | 78.8143 |
| 0.4537        | 0.2281 | 200  | 0.3400          | 71.4972 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1