---
language:
- zh
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Cantonese - Daniel Chan
  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: Cantonese, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 55.88601959038291
---

<!-- 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 - Daniel Chan

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: 0.2611
- Wer: 55.8860

## 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: 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: 500
- training_steps: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2222        | 1.14  | 1000 | 0.2847          | 63.1879 |
| 0.1146        | 2.28  | 2000 | 0.2592          | 58.2725 |
| 0.0382        | 3.42  | 3000 | 0.2575          | 55.9216 |
| 0.024         | 4.57  | 4000 | 0.2611          | 55.8860 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2