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jun-han/
Whisper-Small-Deep-Sparse-squeezeformer

Name: Whisper-Small-Deep-Sparse-squeezeformer
Availability: InStock

automatic-speech-recognition

generated_from_trainer

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Whisper-Small-relposition-Deep-Sparse-squeezeformer

This model is a fine-tuned version of openai/whisper-small on the Voice_Data_Collection_second_edition dataset. It achieves the following results on the evaluation set:

Loss: 0.4170
Cer: 24.9259

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: 3750
training_steps: 52500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss
2.9397	1.0	3750	98.2278	2.9583
1.4576	2.0	7500	77.2506	1.4401
0.6726	3.0	11250	41.7028	0.7099
0.4456	4.0	15000	33.5128	0.5605
0.2892	5.0	18750	29.2529	0.4902
0.1983	6.0	22500	27.8926	0.4687
0.1061	7.0	26250	27.1006	0.4629
0.0686	8.0	30000	26.8677	0.4617
0.4473	9.0	33750	24.1060	0.4217
0.3393	10.0	37500	23.1072	0.3924
0.2278	11.0	41250	21.9071	0.3780
0.3404	12.0	45000	23.9830	0.4051
0.2275	13.0	48750	0.4223	25.3396
0.2079	14.0	52500	0.4170	24.9259

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

jun-han/Whisper-Small-Deep-Sparse-squeezeformer

Whisper-Small-relposition-Deep-Sparse-squeezeformer

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jun-han/
Whisper-Small-Deep-Sparse-squeezeformer