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---
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
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