whisper-small-multimed – AI Model by Hanhpt23 | AlphaNeural AI

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Hanhpt23/
whisper-small-multimed

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automatic-speech-recognition

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openai/whisper-small

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openai/whisper-small

This model is a fine-tuned version of openai/whisper-small on the Hanhpt23/MultiMed dataset. It achieves the following results on the evaluation set:

Loss: 1.2205
Wer: 20.9109
Cer: 14.5195

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: 0.0001
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_steps: 100
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.6311	1.0	4626	0.7523	29.1582	21.4782
0.4966	2.0	9252	0.7386	30.4497	22.0004
0.2482	3.0	13878	0.7919	24.2525	17.3460
0.1989	4.0	18504	0.8427	25.2392	17.5838
0.1419	5.0	23130	0.9057	23.5514	16.6085
0.0828	6.0	27756	0.9730	22.8122	15.8105
0.0609	7.0	32382	1.0176	22.9715	16.0236
0.0452	8.0	37008	1.0531	23.1268	16.1591
0.0354	9.0	41634	1.1008	23.0799	16.0754
0.0228	10.0	46260	1.1070	22.0164	15.3787
0.02	11.0	50886	1.1342	22.4321	15.5082
0.0126	12.0	55512	1.1520	22.0949	15.3324
0.0062	13.0	60138	1.1820	22.0076	15.3419
0.0045	14.0	64764	1.1860	21.7019	15.1221
0.0025	15.0	69390	1.2004	21.2894	14.7874
0.0058	16.0	74016	1.1949	21.6711	15.1412
0.0005	17.0	78642	1.1967	21.4148	14.8903
0.0001	18.0	83268	1.2102	21.1438	14.6915
0.0001	19.0	87894	1.2192	21.0654	14.6546
0.0001	20.0	92520	1.2205	20.9109	14.5195

Framework versions

Transformers 4.41.1
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1