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Sagicc/whisper-small-sr-yodas-v2

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Whisper Small Sr Yodas

This model is a fine-tuned version of openai/whisper-small on merged datasets Common Voice 16 + Fleurs + Juzne vesti (South news) + LBM + (Yodas)[https://huggingface.co/datasets/espnet/yodas] dataset and

Rupnik, Peter and Ljubešić, Nikola, 2022,\ ASR training dataset for Serbian JuzneVesti-SR v1.0, Slovenian language resource repository CLARIN.SI, ISSN 2820-4042,\ http://hdl.handle.net/11356/1679.

It achieves the following results on the evaluation set:

—Loss: 0.3584
—Wer Ortho: 0.2328
—Wer: 0.1220

Model description

Added new dataset Yodas as test and experiment to improve results.

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
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_steps: 50
—num_epochs: 10
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.6958	0.49	1000	0.2114	0.2528	0.1563
0.5941	0.98	2000	0.1857	0.2214	0.1269
0.3985	1.46	3000	0.1729	0.2106	0.1167
0.4187	1.95	4000	0.1745	0.2120	0.1147
0.3446	2.44	5000	0.1770	0.2074	0.1139
0.2992	2.93	6000	0.1710	0.2048	0.1061
0.2074	3.42	7000	0.1887	0.2090	0.1123
0.1958	3.91	8000	0.1871	0.2136	0.1131
0.1707	4.39	9000	0.2069	0.2230	0.1126
0.1403	4.88	10000	0.2092	0.2138	0.1110
0.0871	5.37	11000	0.2345	0.2216	0.1161
0.0856	5.86	12000	0.2384	0.2281	0.1161
0.0496	6.35	13000	0.2657	0.2327	0.1211
0.0542	6.84	14000	0.2760	0.2346	0.1198
0.0274	7.32	15000	0.3024	0.2304	0.1218
0.0281	7.81	16000	0.3134	0.2357	0.1216
0.0151	8.3	17000	0.3328	0.2276	0.1188
0.0165	8.79	18000	0.3417	0.2348	0.1220
0.0094	9.28	19000	0.3545	0.2318	0.1221
0.0125	9.77	20000	0.3584	0.2328	0.1220

Framework versions

—Transformers 4.39.3
—Pytorch 2.0.1+cu117
—Datasets 2.18.0
—Tokenizers 0.15.1