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---
language:
- uz
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
base_model: facebook/wav2vec2-xls-r-300m
model-index:
- name: XLS-R-300M Uzbek CV8
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: uz
metrics:
- type: wer
value: 15.065
name: Test WER (with LM)
- type: cer
value: 3.077
name: Test CER (with LM)
- type: wer
value: 32.88
name: Test WER (no LM)
- type: cer
value: 6.53
name: Test CER (no LM)
---
# XLS-R-300M Uzbek CV8
Ushbu model [facebook/wav2vec2-xls-r-300m](https://huggingface.co/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](https://huggingface.co/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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