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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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