whisper-base-Encode-engmed – AI Model by Hanhpt23 | AlphaNeural AI

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Hanhpt23/
whisper-base-Encode-engmed

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automatic-speech-recognition

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openai/whisper-base

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openai/whisper-base

This model is a fine-tuned version of openai/whisper-base on the pphuc25/EngMed dataset. It achieves the following results on the evaluation set:

Loss: 1.3225
Wer: 27.9808

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
0.7862	1.0	3491	0.8811	43.2441
0.5443	2.0	6982	0.8834	38.7694
0.3319	3.0	10473	0.9247	34.6764
0.2412	4.0	13964	0.9765	34.9636
0.1809	5.0	17455	1.0223	31.3646
0.1112	6.0	20946	1.1013	33.5169
0.0786	7.0	24437	1.1217	34.1563
0.0666	8.0	27928	1.1698	36.5356
0.0487	9.0	31419	1.1934	33.9412
0.0395	10.0	34910	1.2259	31.4509
0.0229	11.0	38401	1.2614	32.0655
0.0181	12.0	41892	1.2823	30.5444
0.0118	13.0	45383	1.2890	30.2773
0.0069	14.0	48874	1.3081	30.3435
0.0073	15.0	52365	1.3085	30.4097
0.0017	16.0	55856	1.3099	29.4145
0.0062	17.0	59347	1.3229	29.5386
0.0013	18.0	62838	1.3162	28.3058
0.0001	19.0	66329	1.3197	28.0387
0.0001	20.0	69820	1.3225	27.9808

Framework versions

Transformers 4.41.1
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1