distilhubert-finetuned_distillhubert-ravdess

adhityamw11/distilhubert-finetuned_distillhubert-ravdess

distilhubert-finetuned_distillhubert-ravdess is a audio classification model from adhityamw11. 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: hubert
Access: Public
Created: Mar 10, 2024
Updated: Mar 10, 2024
SHA: 85ff7d9d

Base models

ntu-spml/distilhubert

Datasets

adhityamw11/ravdess_distillhubert

Model card

From the Hugging Face model README

distilhubert-finetuned-ravdess

This model is a fine-tuned version of ntu-spml/distilhubert on the RAVDESS dataset. It achieves the following results on the evaluation set:

Loss: 0.9331
Accuracy: 0.8438

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: 5e-05
train_batch_size: 8
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_ratio: 0.1
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0641	1.0	144	2.0414	0.2778
1.751	2.0	288	1.7801	0.3854
1.5345	3.0	432	1.3610	0.5417
1.1913	4.0	576	1.1896	0.5417
0.8227	5.0	720	0.7924	0.7535
0.6563	6.0	864	0.6772	0.7743
0.4082	7.0	1008	0.6398	0.7847
0.5133	8.0	1152	0.6409	0.7951
0.0467	9.0	1296	0.7356	0.7951
0.0232	10.0	1440	0.8220	0.8160
0.0298	11.0	1584	0.7164	0.8438
0.0021	12.0	1728	0.7578	0.8611
0.0014	13.0	1872	0.6806	0.8507
0.0012	14.0	2016	0.6953	0.8507
0.0009	15.0	2160	0.7311	0.8403
0.0007	16.0	2304	0.7312	0.8472
0.0006	17.0	2448	0.7528	0.8438
0.0005	18.0	2592	0.7748	0.8299
0.0005	19.0	2736	0.7692	0.8472
0.0004	20.0	2880	0.7806	0.8403
0.0003	21.0	3024	0.7907	0.8438
0.0003	22.0	3168	0.7909	0.8438
0.0003	23.0	3312	0.8060	0.8472
0.0003	24.0	3456	0.8302	0.8438
0.0002	25.0	3600	0.8296	0.8438
0.0002	26.0	3744	0.8306	0.8403
0.0002	27.0	3888	0.8399	0.8438
0.0002	28.0	4032	0.8447	0.8438
0.0002	29.0	4176	0.8488	0.8403
0.0002	30.0	4320	0.8564	0.8472
0.0002	31.0	4464	0.8618	0.8472
0.0001	32.0	4608	0.8736	0.8438
0.0001	33.0	4752	0.8793	0.8403
0.0001	34.0	4896	0.8840	0.8438
0.0001	35.0	5040	0.8870	0.8438
0.0001	36.0	5184	0.8882	0.8472
0.0001	37.0	5328	0.9033	0.8403
0.0001	38.0	5472	0.8980	0.8403
0.0001	39.0	5616	0.9081	0.8472
0.0001	40.0	5760	0.9086	0.8472
0.0001	41.0	5904	0.9119	0.8438
0.0001	42.0	6048	0.9106	0.8507
0.0001	43.0	6192	0.9188	0.8438
0.0001	44.0	6336	0.9238	0.8438
0.0001	45.0	6480	0.9282	0.8438
0.0001	46.0	6624	0.9286	0.8438
0.0001	47.0	6768	0.9312	0.8438
0.0001	48.0	6912	0.9296	0.8472
0.0001	49.0	7056	0.9324	0.8438
0.0001	50.0	7200	0.9331	0.8438

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.2

Task: Audio Classification
Library: transformers
Type: hubert
License: apache-2.0
Created: Mar 10, 2024
Updated: Mar 10, 2024