my_awesome_mind_model

jmjung1997/my_awesome_mind_model

my_awesome_mind_model is a audio classification model from jmjung1997. Use it for the audio classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Audio Classification
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: May 18, 2025
Updated: May 25, 2025
SHA: 38c1ec92

Base models

cogniveon/eeem069_heart_murmur_classification

Model card

From the Hugging Face model README

my_awesome_mind_model

This model is a fine-tuned version of cogniveon/eeem069_heart_murmur_classification on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3779
Accuracy: 0.5204
Precision Macro: 0.5208
Precision Micro: 0.5204
Recall Macro: 0.5205
Recall Micro: 0.5204
F1 Macro: 0.5186
F1 Micro: 0.5204
F1 Weighted: 0.5186
F1 Class 0: 0.4896
F1 Class 1: 0.5477

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use OptimizerNames.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: 60
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision Macro	Precision Micro	Recall Macro	Recall Micro	F1 Macro	F1 Micro	F1 Weighted	F1 Class 0	F1 Class 1
0.6402	1.0	20	0.6875	0.5204	0.5278	0.5204	0.5208	0.5204	0.4885	0.5204	0.4883	0.3609	0.6162
0.6284	2.0	40	0.6876	0.5383	0.7085	0.5383	0.5376	0.5383	0.4188	0.5383	0.4192	0.6824	0.1552
0.632	3.0	60	0.6779	0.5840	0.6080	0.5840	0.5836	0.5840	0.5590	0.5840	0.5592	0.6640	0.4540
0.6242	4.0	80	0.6740	0.5791	0.5897	0.5791	0.5788	0.5791	0.5659	0.5791	0.5660	0.6417	0.4901
0.6321	5.0	100	0.6837	0.5514	0.6863	0.5514	0.5507	0.5514	0.4511	0.5514	0.4515	0.6857	0.2165
0.6477	6.0	120	0.6918	0.4992	0.4996	0.4992	0.4999	0.4992	0.3695	0.4992	0.3690	0.0836	0.6554
0.627	7.0	140	0.6863	0.5742	0.6318	0.5742	0.5737	0.5742	0.5212	0.5742	0.5215	0.6805	0.3619
0.6274	8.0	160	0.6827	0.5677	0.6149	0.5677	0.5672	0.5677	0.5173	0.5677	0.5176	0.6732	0.3614
0.6183	9.0	180	0.6989	0.5302	0.5790	0.5302	0.5295	0.5302	0.4426	0.5302	0.4429	0.6636	0.2216
0.6225	10.0	200	0.6881	0.5644	0.5895	0.5644	0.5640	0.5644	0.5309	0.5644	0.5311	0.6564	0.4053
0.6232	11.0	220	0.6966	0.4812	0.4407	0.4812	0.4819	0.4812	0.3727	0.4812	0.3723	0.1117	0.6336
0.6377	12.0	240	0.6952	0.5204	0.5330	0.5204	0.5209	0.5204	0.4723	0.5204	0.4721	0.3131	0.6316
0.6197	13.0	260	0.7209	0.5318	0.5347	0.5318	0.5320	0.5318	0.5229	0.5318	0.5227	0.4575	0.5882
0.6063	14.0	280	0.6852	0.5840	0.6204	0.5840	0.5836	0.5840	0.5494	0.5840	0.5496	0.6743	0.4244
0.6107	15.0	300	0.7039	0.5155	0.5171	0.5155	0.5157	0.5155	0.5058	0.5155	0.5057	0.4364	0.5751
0.593	16.0	320	0.6930	0.5742	0.5899	0.5742	0.5739	0.5742	0.5542	0.5742	0.5543	0.6487	0.4596
0.5898	17.0	340	0.7005	0.5204	0.5208	0.5204	0.5205	0.5204	0.5184	0.5204	0.5184	0.4878	0.5491
0.5791	18.0	360	0.7313	0.5416	0.5435	0.5416	0.5414	0.5416	0.5359	0.5416	0.5360	0.5874	0.4844
0.5617	19.0	380	0.7126	0.5710	0.6126	0.5710	0.5705	0.5710	0.5264	0.5710	0.5267	0.6717	0.3812
0.5666	20.0	400	0.7046	0.5628	0.5785	0.5628	0.5624	0.5628	0.5391	0.5628	0.5393	0.6436	0.4346
0.5406	21.0	420	0.7530	0.5253	0.5262	0.5253	0.5251	0.5253	0.5204	0.5253	0.5205	0.5689	0.4719
0.5529	22.0	440	0.8603	0.5024	0.5078	0.5024	0.5031	0.5024	0.4141	0.5024	0.4138	0.1867	0.6416
0.4957	23.0	460	0.8393	0.5106	0.5106	0.5106	0.5105	0.5106	0.5092	0.5106	0.5092	0.5356	0.4828
0.4864	24.0	480	0.8082	0.5481	0.5496	0.5481	0.5480	0.5481	0.5443	0.5481	0.5444	0.5859	0.5027
0.4737	25.0	500	0.9035	0.4861	0.4846	0.4861	0.4864	0.4861	0.4709	0.4861	0.4708	0.3811	0.5607
0.4465	26.0	520	0.9034	0.4959	0.4960	0.4959	0.4960	0.4959	0.4933	0.4959	0.4933	0.4569	0.5297
0.4265	27.0	540	0.9025	0.5481	0.5492	0.5481	0.5480	0.5481	0.5453	0.5481	0.5454	0.5809	0.5097
0.4311	28.0	560	1.0398	0.4976	0.4971	0.4976	0.4980	0.4976	0.4552	0.4976	0.4549	0.3032	0.6071
0.4403	29.0	580	1.0356	0.5073	0.5102	0.5073	0.5077	0.5073	0.4755	0.5073	0.4753	0.3463	0.6047
0.3782	30.0	600	0.9161	0.4959	0.4960	0.4959	0.4960	0.4959	0.4938	0.4959	0.4937	0.4607	0.5268
0.3828	31.0	620	0.9565	0.5220	0.5221	0.5220	0.5221	0.5220	0.5218	0.5220	0.5218	0.5109	0.5327
0.3311	32.0	640	1.0187	0.5057	0.5062	0.5057	0.5059	0.5057	0.4999	0.5057	0.4998	0.4461	0.5538
0.3384	33.0	660	1.1069	0.5090	0.5106	0.5090	0.5093	0.5090	0.4932	0.5090	0.4931	0.4040	0.5825
0.3387	34.0	680	0.9807	0.5155	0.5161	0.5155	0.5153	0.5155	0.5098	0.5155	0.5099	0.5626	0.4570
0.2925	35.0	700	1.0492	0.5204	0.5206	0.5204	0.5205	0.5204	0.5197	0.5204	0.5197	0.5017	0.5377
0.2906	36.0	720	1.1563	0.5318	0.5369	0.5318	0.5321	0.5318	0.5162	0.5318	0.5161	0.4294	0.6030
0.2346	37.0	740	1.0991	0.5253	0.5253	0.5253	0.5253	0.5253	0.5252	0.5253	0.5252	0.5190	0.5314
0.2512	38.0	760	1.1686	0.5090	0.5103	0.5090	0.5092	0.5090	0.4961	0.5090	0.4960	0.4155	0.5767
0.2874	39.0	780	1.1907	0.5024	0.5030	0.5024	0.5027	0.5024	0.4910	0.5024	0.4909	0.4146	0.5674
0.2306	40.0	800	1.1613	0.5171	0.5176	0.5171	0.5170	0.5171	0.5129	0.5171	0.5130	0.5582	0.4676
0.2312	41.0	820	1.2137	0.5171	0.5187	0.5171	0.5169	0.5171	0.5052	0.5171	0.5054	0.5819	0.4286
0.1947	42.0	840	1.2258	0.5269	0.5270	0.5269	0.5269	0.5269	0.5269	0.5269	0.5268	0.5215	0.5323
0.2053	43.0	860	1.2047	0.5253	0.5260	0.5253	0.5251	0.5253	0.5213	0.5253	0.5214	0.5650	0.4776
0.1988	44.0	880	1.2851	0.5171	0.5178	0.5171	0.5169	0.5171	0.5109	0.5171	0.5110	0.5660	0.4559
0.196	45.0	900	1.2888	0.5188	0.5204	0.5188	0.5190	0.5188	0.5104	0.5188	0.5103	0.4465	0.5743
0.1706	46.0	920	1.2536	0.5269	0.5271	0.5269	0.5268	0.5269	0.5257	0.5269	0.5257	0.5497	0.5017
0.1732	47.0	940	1.3139	0.5237	0.5240	0.5237	0.5237	0.5237	0.5226	0.5237	0.5225	0.5	0.5452
0.1542	48.0	960	1.3290	0.5188	0.5188	0.5188	0.5188	0.5188	0.5186	0.5188	0.5186	0.5108	0.5265
0.1669	49.0	980	1.3259	0.5139	0.5139	0.5139	0.5138	0.5139	0.5132	0.5139	0.5132	0.5314	0.4949
0.1871	50.0	1000	1.3528	0.5237	0.5237	0.5237	0.5237	0.5237	0.5235	0.5237	0.5235	0.5150	0.5321
0.164	51.0	1020	1.3355	0.5285	0.5290	0.5285	0.5284	0.5285	0.5261	0.5285	0.5262	0.5601	0.4921
0.1833	52.0	1040	1.3069	0.5139	0.5139	0.5139	0.5138	0.5139	0.5138	0.5139	0.5138	0.5209	0.5066
0.1712	53.0	1060	1.3393	0.5237	0.5258	0.5237	0.5239	0.5237	0.5148	0.5237	0.5147	0.4491	0.5805
0.1429	54.0	1080	1.3342	0.5220	0.5227	0.5220	0.5222	0.5220	0.5191	0.5220	0.5190	0.4814	0.5567
0.1518	55.0	1100	1.3593	0.5285	0.5314	0.5285	0.5288	0.5285	0.5186	0.5285	0.5185	0.4495	0.5877
0.1559	56.0	1120	1.3723	0.5188	0.5190	0.5188	0.5188	0.5188	0.5178	0.5188	0.5177	0.4957	0.5398
0.1612	57.0	1140	1.3779	0.5204	0.5208	0.5204	0.5205	0.5204	0.5186	0.5204	0.5186	0.4896	0.5477

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1

Task: Audio Classification
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: May 18, 2025
Updated: May 25, 2025