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wandererupak/whisper-base-n-demo-50e

sourceHugging Faceapache-2.0updated 8mo agoView on Hugging Face

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Whisper Base N - Fine-Tuned

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

—Loss: 0.8412
—Wer: 60.6884
—Cer: 23.9254

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: 1e-05
—trainbatchsize: 16
—evalbatchsize: 8
—seed: 42
—gradientaccumulationsteps: 2
—totaltrainbatch_size: 32
—optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: cosine
—lrschedulerwarmup_ratio: 0.1
—num_epochs: 50
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
2.2957	1.0	27	1.8628	214.4928	171.5234
1.5812	2.0	54	1.2720	129.3478	84.3552
1.1056	3.0	81	0.9757	92.9348	40.4551
0.8301	4.0	108	0.8126	82.0652	34.3236
0.647	5.0	135	0.7076	80.6159	32.7434
0.5085	6.0	162	0.6479	75.5435	29.9621
0.3937	7.0	189	0.6231	74.0942	29.8673
0.3025	8.0	216	0.5965	72.2826	27.5601
0.2285	9.0	243	0.5880	69.2029	26.4223
0.1701	10.0	270	0.6018	68.8406	26.5171
0.1261	11.0	297	0.6133	66.6667	26.0746
0.0921	12.0	324	0.6170	62.8623	25.3793
0.0649	13.0	351	0.6466	62.1377	25.3477
0.0452	14.0	378	0.6619	63.5870	25.6321
0.0332	15.0	405	0.6742	62.1377	24.2099
0.0228	16.0	432	0.6976	62.3188	24.4311
0.0184	17.0	459	0.7067	63.2246	24.1466
0.014	18.0	486	0.7276	59.9638	24.9684
0.0107	19.0	513	0.7442	61.5942	23.6726
0.0088	20.0	540	0.7347	61.9565	23.9886
0.0072	21.0	567	0.7440	60.8696	24.5575
0.0052	22.0	594	0.7598	59.7826	23.4513
0.0036	23.0	621	0.7754	60.3261	24.0834
0.0034	24.0	648	0.7911	59.7826	24.2731
0.0026	25.0	675	0.7969	60.1449	23.7358
0.0019	26.0	702	0.8012	59.4203	23.7042
0.0016	27.0	729	0.8079	58.8768	23.7674
0.0014	28.0	756	0.8144	59.9638	23.9254
0.0013	29.0	783	0.8201	59.9638	23.7674
0.0012	30.0	810	0.8249	59.0580	23.6726
0.0011	31.0	837	0.8293	60.5072	24.1783
0.0011	32.0	864	0.8325	60.5072	24.1150
0.001	33.0	891	0.8358	59.7826	23.9254
0.001	34.0	918	0.8388	60.1449	24.0834
0.0009	35.0	945	0.8412	60.6884	23.9254

Framework versions

—Transformers 4.57.6
—Pytorch 2.9.0+cu126
—Datasets 4.5.0
—Tokenizers 0.22.2