Presenting, recommending and categorizing the audio libraries or other media in general based on detected mood/preferences via user's speech or user's aural environment. A mood lighting system, in addition to the aforementioned features, can be implemented to make user's environment a bit more user-friendly, and and so contribute a little to maintaining the user's mental health and overall welfare. [Goal 3- SDG]
Additionally, the model can be trained on data with more class labels in order to be useful particularly in detecting brawls, and any other uneventful scenario. An audio classifier can be integrated in a surveillance system to detect brawls and other unsettling events that can be recognized using "sound." [Goal 16 -SDG]

Limitations

-The open-source MESD dataset was used to fine-tune the Wav2Vec2 base model, which contains ~1200 audio recordings, all of which were recorded in professional studios and were only one second long. Out of ~1200 audio recordings only 890 of the recordings were utilized for training. Due to these factors, the model and hence this Gradio application may not be able to perform well in noisy environments or audio with background music or noise. It's also worth mentioning that this model performs poorly when it comes to audio recordings from the class "Fear," which the model often misclassifies.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 64
eval_batch_size: 40
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.86	3	1.7516	0.3846
1.9428	1.86	6	1.6859	0.4308
1.9428	2.86	9	1.5575	0.4692
1.9629	3.86	12	1.4160	0.4846
1.5678	4.86	15	1.2979	0.5308
1.5678	5.86	18	1.2294	0.5308
1.4728	6.86	21	1.0703	0.5923
1.4728	7.86	24	0.9926	0.6308
1.2588	8.86	27	0.9202	0.6846
0.991	9.86	30	0.8537	0.6846
0.991	10.86	33	0.8816	0.6769
0.9059	11.86	36	0.7149	0.7769
0.9059	12.86	39	0.7676	0.7462
0.7901	13.86	42	0.6971	0.7538
0.6278	14.86	45	0.6671	0.7923
0.6278	15.86	48	0.5681	0.8231
0.5678	16.86	51	0.5535	0.8154
0.5678	17.86	54	0.5947	0.8077
0.5157	18.86	57	0.6396	0.7692
0.4189	19.86	60	0.5291	0.8077
0.4189	20.86	63	0.4600	0.8538
0.3885	21.86	66	0.5188	0.8308
0.3885	22.86	69	0.5959	0.7923
0.3255	23.86	72	0.5240	0.8462
0.2711	24.86	75	0.5105	0.8385
0.2711	25.86	78	0.5177	0.8231
0.2748	26.86	81	0.3302	0.8923
0.2748	27.86	84	0.4774	0.8538
0.2379	28.86	87	0.4204	0.8769
0.1982	29.86	90	0.6540	0.7692
0.1982	30.86	93	0.5664	0.8308
0.2171	31.86	96	0.5100	0.8462
0.2171	32.86	99	0.3924	0.8769
0.17	33.86	102	0.6002	0.8231
0.1761	34.86	105	0.4364	0.8538
0.1761	35.86	108	0.4166	0.8692
0.1703	36.86	111	0.4374	0.8692
0.1703	37.86	114	0.3872	0.8615
0.1569	38.86	117	0.3941	0.8538
0.1149	39.86	120	0.4004	0.8538
0.1149	40.86	123	0.4360	0.8385
0.1087	41.86	126	0.4387	0.8615
0.1087	42.86	129	0.4352	0.8692
0.1039	43.86	132	0.4018	0.8846
0.099	44.86	135	0.4019	0.8846
0.099	45.86	138	0.4083	0.8923
0.1043	46.86	141	0.4594	0.8692
0.1043	47.86	144	0.4478	0.8769
0.0909	48.86	147	0.5025	0.8538
0.1024	49.86	150	0.5442	0.8692
0.1024	50.86	153	0.3827	0.8769
0.1457	51.86	156	0.6816	0.8231
0.1457	52.86	159	0.3435	0.8923
0.1233	53.86	162	0.4418	0.8769
0.101	54.86	165	0.4629	0.8846
0.101	55.86	168	0.4616	0.8692
0.0969	56.86	171	0.3608	0.8923
0.0969	57.86	174	0.4867	0.8615
0.0981	58.86	177	0.4493	0.8692
0.0642	59.86	180	0.3841	0.8538
0.0642	60.86	183	0.4509	0.8769
0.0824	61.86	186	0.4477	0.8769
0.0824	62.86	189	0.4649	0.8615
0.0675	63.86	192	0.3492	0.9231
0.0839	64.86	195	0.3763	0.8846
0.0839	65.86	198	0.4475	0.8769
0.0677	66.86	201	0.4104	0.8923
0.0677	67.86	204	0.3071	0.9308
0.0626	68.86	207	0.3598	0.9077
0.0412	69.86	210	0.3771	0.8923
0.0412	70.86	213	0.4043	0.8846
0.0562	71.86	216	0.3696	0.9077
0.0562	72.86	219	0.3295	0.9077
0.0447	73.86	222	0.3616	0.8923
0.0727	74.86	225	0.3495	0.8923
0.0727	75.86	228	0.4330	0.8846
0.0576	76.86	231	0.5179	0.8923
0.0576	77.86	234	0.5544	0.8846
0.0489	78.86	237	0.4630	0.9
0.0472	79.86	240	0.4513	0.9
0.0472	80.86	243	0.4207	0.9077
0.0386	81.86	246	0.4118	0.8769
0.0386	82.86	249	0.4764	0.8769
0.0372	83.86	252	0.4167	0.8769
0.0344	84.86	255	0.3744	0.9077
0.0344	85.86	258	0.3712	0.9077
0.0459	86.86	261	0.4249	0.8846
0.0459	87.86	264	0.4687	0.8846
0.0364	88.86	267	0.4194	0.8923
0.0283	89.86	270	0.3963	0.8923
0.0283	90.86	273	0.3982	0.8923
0.0278	91.86	276	0.3838	0.9077
0.0278	92.86	279	0.3731	0.9
0.0352	93.86	282	0.3736	0.9
0.0297	94.86	285	0.3702	0.9
0.0297	95.86	288	0.3521	0.9154
0.0245	96.86	291	0.3522	0.9154
0.0245	97.86	294	0.3600	0.9077
0.0241	98.86	297	0.3636	0.9077
0.0284	99.86	300	0.3639	0.9077

Framework versions

Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6

somosnlp-hackathon-2022/wav2vec2-base-finetuned-sentiment-classification-MESD

wav2vec2-base-finetuned-sentiment-mesd-v11

Model description

Intended uses

Limitations

Training procedure

Training hyperparameters

Training results

Framework versions

somosnlp-hackathon-2022/
wav2vec2-base-finetuned-sentiment-classification-MESD