Whisper-small-ru-v2

This model is a fine-tuned version of openai/whisper-small on an Russian part of the Common Voice 15 dataset. It achieves the following results on the evaluation set:

• Loss: 0.1329
• Wer: 12.6750
• Cer: 3.7305
• Learning Rate: 0.0000

Model description

Same as openai/whisper-small.

Intended uses & limitations

Same as openai/whisper-small

Training and evaluation data

Fine-tunned on an Russian part of the Common Voice 15 dataset.

Training procedure

According to the article "Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers"

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-08
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 250
• training_steps: 15000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer	Rate
0.0661	0.09	500	0.1358	12.9097	3.8217	0.0000
0.0616	0.17	1000	0.1357	12.9620	3.8949	0.0000
0.0601	0.26	1500	0.1357	12.8795	3.8225	0.0000
0.0666	0.35	2000	0.1353	12.9481	3.8871	0.0000
0.0669	0.43	2500	0.1352	12.8284	3.8283	0.0000
0.0665	0.52	3000	0.1351	12.8203	3.7833	0.0000
0.0649	0.61	3500	0.1349	12.8098	3.7824	0.0000
0.0607	0.69	4000	0.1347	12.8110	3.8105	0.0000
0.0636	0.78	4500	0.1345	12.7994	3.7893	0.0000
0.063	0.87	5000	0.1342	12.8319	3.8084	0.0000
0.0589	0.95	5500	0.1341	12.8807	3.8551	0.0000
0.0734	1.04	6000	0.1341	12.7691	3.7604	0.0000
0.0577	1.13	6500	0.1340	12.7645	3.7602	0.0000
0.052	1.21	7000	0.1340	12.7610	3.7655	0.0000
0.0626	1.3	7500	0.1339	12.7657	3.7593	0.0000
0.0617	1.39	8000	0.1338	12.7912	3.8268	0.0000
0.063	1.47	8500	0.1337	12.7343	3.7573	0.0000
0.0668	1.56	9000	0.1336	12.7308	3.7198	0.0000
0.0634	1.65	9500	0.1335	12.7215	3.7400	0.0000
0.0604	1.73	10000	0.1333	12.7192	3.7515	0.0000
0.0707	1.82	10500	0.1333	12.7052	3.7568	0.0000
0.0639	1.91	11000	0.1332	12.6983	3.7617	0.0000
0.0617	1.99	11500	0.1331	12.6936	3.7402	0.0000
0.0601	2.08	12000	0.1330	12.6901	3.7586	0.0000
0.0632	2.17	12500	0.1330	12.6785	3.7279	0.0000
0.0626	2.25	13000	0.1330	12.6808	3.7333	0.0000
0.066	2.34	13500	0.1329	12.6704	3.7512	0.0000
0.0674	2.42	14000	0.1329	12.6599	3.7384	0.0000
0.0637	2.51	14500	0.1329	12.6797	3.7428	0.0000
0.0641	2.6	15000	0.1329	12.6750	3.7305	0.0000

Framework versions

• Transformers 4.36.0.dev0
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0