language, license, tags, datasets, metrics, pipeline_tag, base_model, model-index

language	license	tags	datasets	metrics	pipeline_tag	base_model	model-index
ko	apache-2.0	generated_from_trainer	Bingsu/zeroth-korean	wer	automatic-speech-recognition	openai/whisper-large-v2	name results whisper-large-v2-Ko task dataset metrics type name automatic-speech-recognition Automatic Speech Recognition name type Bingsu/zeroth-korean Bingsu/zeroth-korean type value name wer 2.9 Wer task dataset metrics type name automatic-speech-recognition Automatic Speech Recognition name type config split google/fleurs google/fleurs ko_kr test type value name wer 20.66 WER

whisper-large-v2-Ko

This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0617
• Wer: 2.9

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

***** train metrics *****
epoch = 50.0
train_loss = 0.0234
train_runtime = 16:20:18.00
train_samples = 22262
train_samples_per_second = 19.042
train_steps_per_second = 0.085

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 32
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 7
• total_train_batch_size: 224
• total_eval_batch_size: 112
• 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
0.0299	10.0	1000	0.0745	0.0447
0.0085	20.0	2000	0.0608	0.0353
0.0036	30.0	3000	0.0593	0.0302
0.0013	40.0	4000	0.0609	0.0282
0.0008	50.0	5000	0.0617	0.0290

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.12.1+cu113
• Datasets 2.10.1
• Tokenizers 0.13.2