helenai/wav2vec2-base-superb-ks-jpqd-ov

jpqd-wav2vec2-base-ft-keyword-spotting

This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset, using superb/wav2vec2-base-superb-ks as a teacher model

It was compressed using NNCF with Optimum Intel following the JPQD image classification example.

It achieves the following results on the evaluation set:

• Loss: 0.5632
• Accuracy: 0.9756

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: 7e-05
• train_batch_size: 32
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.5
• num_epochs: 12.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2245	1.0	399	2.2351	0.6209
6.9856	2.0	798	7.0597	0.7354
10.013	3.0	1197	9.8779	0.8069
11.3484	4.0	1596	11.1949	0.8719
11.6849	5.0	1995	11.5479	0.9014
11.5921	6.0	2394	11.4193	0.9495
0.8911	7.0	2793	0.7334	0.9500
0.8965	8.0	3192	0.6553	0.9685
0.7198	9.0	3591	0.6213	0.9669
0.7372	10.0	3990	0.5929	0.9675
0.7004	11.0	4389	0.5720	0.9721
0.6195	12.0	4788	0.5632	0.9756

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1+cu117
• Datasets 2.8.0
• Tokenizers 0.13.2