This model is a fine-tuned version of facebook/wav2vec2-base-960h on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3595
Accuracy: 0.8674

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: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9791	1.0	83	0.9290	0.5442
0.6532	2.0	166	0.5495	0.8186
0.5202	3.0	249	0.4569	0.8216
0.4421	4.0	332	0.4378	0.8399
0.4144	5.0	415	0.3853	0.8765
0.4213	6.0	498	0.3835	0.8537
0.3819	7.0	581	0.3647	0.8674
0.3994	7.9119	656	0.3595	0.8674

Framework versions

Transformers 4.49.0
Pytorch 2.0.1+cu118
Datasets 3.3.2
Tokenizers 0.21.0

Vladimirlv/wav2vec2-base-960h-heart-sounds

wav2vec2-base-960h-heart-sounds

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Vladimirlv/
wav2vec2-base-960h-heart-sounds