zohirjonsharipov/xls-r-uzbek-cv8

XLS-R-300M Uzbek CV8

Ushbu model facebook/wav2vec2-xls-r-300m asosida MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UZ datasetidan foydalangan holda Transfer Learning usuli orqali ngramm modeli asosida o'zbek tili uchun fine-tuning qilingan. Model quydagi natijalarga erishgan:

• Loss: 0.3063
• Wer: 0.3852
• Cer: 0.0777

Model haqida

Model arxitekturasi haqida ko'prom ma'lumot olish uchun ushbu facebook/wav2vec2-xls-r-300m havola orqali o'ting

Ushbu modelning lugʻati oʻzbek tili zamonaviy lotin alifbosidan iborat boʻlib, tinish belgilari olib tashlangan(https://en.wikipedia.org/wiki/Uzbek_alphabet). Shuni ta'kidlash kerakki, <‘> va <’> belgilar tinish belgisi sifatida hisoblanmaydi, qachonki mana shunday belgilar <o> va <g> dan so'ng kelganda ularni <‘> bilan o‘zgartirilgan.

Dekoder common_voice matniga asoslangan kenlm tili modelidan foydalanadi.

Foydalanish yo'nalishilari va cheklovlar

Ushbu model quyidagi foydalanish holatlari uchun foydali bo'lishi kutilmoqda:

• Video subtitr uchun
• yozib olingan eshittirishlarni indekslash

Model jonli efirdagi uchrashuvlar yoki ko'rsatuvlarni subtitrini aniqlash uchun kerakli ravishda mos emas va undan Common Voice maʼlumotlar toʻplamiga yoki boshqa hissa qoʻshuvchilarning shaxsiy hayotini xafvga qo'yadigan holatlar uchun ishlatilmasligi kerak.

Training va baholash ma'lumotlari

The 50% of the train common voice official split was used as training data. The 50% of the official dev split was used as validation data, and the full test set was used for final evaluation of the model without LM, while the model with LM was evaluated only on 500 examples from the test set.

The kenlm language model was compiled from the target sentences of the train + other dataset splits.

Training giperparametrlari

Training jarayonida quyidagi giperparametrlardan foydalanildi:

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training natijalari

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
3.1401	3.25	500	3.1146	1.0	1.0
2.7484	6.49	1000	2.2842	1.0065	0.7069
1.0899	9.74	1500	0.5414	0.6125	0.1351
0.9465	12.99	2000	0.4566	0.5635	0.1223
0.8771	16.23	2500	0.4212	0.5366	0.1161
0.8346	19.48	3000	0.3994	0.5144	0.1102
0.8127	22.73	3500	0.3819	0.4944	0.1051
0.7833	25.97	4000	0.3705	0.4798	0.1011
0.7603	29.22	4500	0.3661	0.4704	0.0992
0.7424	32.47	5000	0.3529	0.4577	0.0957
0.7251	35.71	5500	0.3410	0.4473	0.0928
0.7106	38.96	6000	0.3401	0.4428	0.0919
0.7027	42.21	6500	0.3355	0.4353	0.0905
0.6927	45.45	7000	0.3308	0.4296	0.0885
0.6828	48.7	7500	0.3246	0.4204	0.0863
0.6706	51.95	8000	0.3250	0.4233	0.0868
0.6629	55.19	8500	0.3264	0.4159	0.0849
0.6556	58.44	9000	0.3213	0.4100	0.0835
0.6484	61.69	9500	0.3182	0.4124	0.0837
0.6407	64.93	10000	0.3171	0.4050	0.0825
0.6375	68.18	10500	0.3150	0.4039	0.0822
0.6363	71.43	11000	0.3129	0.3991	0.0810
0.6307	74.67	11500	0.3114	0.3986	0.0807
0.6232	77.92	12000	0.3103	0.3895	0.0790
0.6216	81.17	12500	0.3086	0.3891	0.0790
0.6174	84.41	13000	0.3082	0.3881	0.0785
0.6196	87.66	13500	0.3059	0.3875	0.0782
0.6174	90.91	14000	0.3084	0.3862	0.0780
0.6169	94.16	14500	0.3070	0.3860	0.0779
0.6166	97.4	15000	0.3066	0.3855	0.0778

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.2+cu102
• Datasets 1.18.3
• Tokenizers 0.11.0