whisper-base-frenchmed-v1 – AI Model by Hanhpt23 | AlphaNeural AI

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Hanhpt23/
whisper-base-frenchmed-v1

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automatic-speech-recognition

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openai/whisper-base

endpoints_compatible

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openai/whisper-base

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

Loss: 1.5990
Wer: 44.8680

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
1.1328	1.0	215	1.0727	53.9589
0.7015	2.0	430	1.1185	59.4575
0.3727	3.0	645	1.1580	46.9208
0.1859	4.0	860	1.2757	63.0499
0.0996	5.0	1075	1.3674	43.6950
0.0619	6.0	1290	1.3916	48.2405
0.0459	7.0	1505	1.4305	44.8680
0.0342	8.0	1720	1.4555	47.1408
0.0323	9.0	1935	1.4792	45.0880
0.0141	10.0	2150	1.5414	48.8270
0.0109	11.0	2365	1.5575	44.5748
0.0064	12.0	2580	1.5499	45.5279
0.0061	13.0	2795	1.5439	45.6012
0.0081	14.0	3010	1.5679	46.6276
0.0013	15.0	3225	1.5714	46.9941
0.0028	16.0	3440	1.5777	44.6481
0.0019	17.0	3655	1.5904	45.7478
0.0006	18.0	3870	1.5931	43.9150
0.0004	19.0	4085	1.5981	44.5015
0.0004	20.0	4300	1.5990	44.8680

Framework versions

Transformers 4.41.1
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1