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
train_batch_size: 16
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: 50
num_epochs: 10
mixed_precision_training: 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

Sagicc
/

whisper-small-sr-yodas-v2