metadata

language:
  - yue
license: apache-2.0
tags:
  - whisper-event
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - cer
model-index:
  - name: Whisper Large V2 Cantonese
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0
          type: mozilla-foundation/common_voice_11_0
          split: test
          config: yue
        metrics:
          - name: Cer
            type: cer
            value: 6.7274
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice zh-HK
          type: common_voice
          args: zh-HK
        metrics:
          - name: Test CER
            type: cer
            value: 6.7274

Whisper Large V2 Cantonese

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11_0 yue dataset. It achieves the following results on the evaluation set:

Loss: 0.2807
Cer: 6.7274

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: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.0032	13.01	1000	0.2318	6.8569
0.002	26.01	2000	0.2404	7.1524
0.0001	39.02	3000	0.2807	6.7274
0.0001	53.01	4000	0.2912	6.7517
0.0	66.01	5000	0.2957	6.7638

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2