jed351/whisper_medium_cantonese_cm_voice

whisper-large-v2-zh-hk-2gpu

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

• Loss: 0.2237
• Wer: 0.4573

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: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 16
• total_eval_batch_size: 16
• 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	Wer
0.1544	1.14	1000	0.2260	0.5485
0.0745	2.28	2000	0.2132	0.4967
0.0213	3.42	3000	0.2114	0.4718
0.0117	4.57	4000	0.2196	0.4643
0.0014	5.71	5000	0.2237	0.4573

Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.13.1
• Datasets 2.8.0
• Tokenizers 0.13.2