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austinhwang/
whisper-small-cantonese-v1

Name: whisper-small-cantonese-v1
Availability: InStock

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Whisper Small Cantonese

This model is a fine-tuned version of openai/whisper-small on the Multi-Domain Cantonese Corpus (MDCC) dataset. It achieves the following results on the evaluation set:

Loss: 0.1055
Cer: 15.5362

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: 64
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: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.038	0.9606	1000	0.1068	20.1761
0.0436	1.9212	2000	0.1055	15.5362

Framework versions

PEFT 0.13.0
Transformers 4.45.1
Pytorch 2.4.0+cu121
Datasets 3.0.1
Tokenizers 0.20.0

austinhwang/whisper-small-cantonese-v1

Whisper Small Cantonese

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

austinhwang/
whisper-small-cantonese-v1