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
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Model tree for zohirjonsharipov/xls-r-uzbek-cv8
Base model
facebook/wav2vec2-xls-r-300mDataset used to train zohirjonsharipov/xls-r-uzbek-cv8
Space using zohirjonsharipov/xls-r-uzbek-cv8 1
Evaluation results
- Test WER (with LM) on Common Voice 8self-reported15.065
- Test CER (with LM) on Common Voice 8self-reported3.077
- Test WER (no LM) on Common Voice 8self-reported32.880
- Test CER (no LM) on Common Voice 8self-reported6.530