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whisper-small-ru-v2

artyomboyko/whisper-small-ru-v2

whisper-small-ru-v2 is a automatic speech recognition model from artyomboyko. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: whisper
Access: Public
Created: Dec 7, 2023
Updated: Dec 13, 2023
SHA: e87b7b2a

Base models

openai/whisper-small

Datasets

artyomboyko/common_voice_15_0_RU

Spaces

elyutoy/club-kinopodbor-backend

Model card

From the Hugging Face model README

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

Task: Automatic Speech Recognition
Library: transformers
Type: whisper
License: apache-2.0
Languages: ru
Created: Dec 7, 2023
Updated: Dec 13, 2023