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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
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UZ dataset.
It achieves the following results on the validation set:
- Loss: 0.3063
- Wer: 0.3852
- Cer: 0.0777
## Model description
For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
The model vocabulary consists of the [Modern Latin alphabet for Uzbek](https://en.wikipedia.org/wiki/Uzbek_alphabet), with punctuation removed.
Note that the characters <‘> and <’> do not count as punctuation, as <‘> modifies \<o\> and \<g\>, and <’> indicates the glottal stop or a long vowel.
The decoder uses a kenlm language model built on common_voice text.
## Intended uses & limitations
This model is expected to be of some utility for low-fidelity use cases such as:
- Draft video captions
- Indexing of recorded broadcasts
The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.
## Training and evaluation data
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 hyperparameters
The following hyperparameters were used during training:
- 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 results
| 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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