---
library_name: transformers
language:
- zh
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper
- cantonese
- zh-hk
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-zh-hk
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: zh-HK
      split: None
      args: 'config: zh-HK, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 200.48085485307215
---

<!-- 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-zh-hk

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: 0.2332
- Wer: 200.4809

## 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3422        | 0.5708 | 1000 | 0.3029          | 237.3286 |
| 0.1806        | 1.1416 | 2000 | 0.2574          | 181.2467 |
| 0.1626        | 1.7123 | 3000 | 0.2383          | 171.2199 |
| 0.0892        | 2.2831 | 4000 | 0.2332          | 200.4809 |


### Framework versions

- Transformers 4.47.0.dev0
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3