---
language:
- zh
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Cantonese - Marco Cheung
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 13
      type: mozilla-foundation/common_voice_13_0
      config: zh-HK
      split: test
      args: zh-HK
    metrics:
    - name: Wer
      type: wer
      value: 114.19385194479297
---

<!-- 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 - Marco Cheung

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2812
- Wer Ortho: 116.9664
- Wer: 114.1939

## 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|
| 0.3111        | 0.57  | 500  | 0.3072          | 92.3269   | 105.6462 |
| 0.1729        | 1.14  | 1000 | 0.2812          | 116.9664  | 114.1939 |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3