# XLS-R-300M Uyghur CV7

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UG dataset. It achieves the following results on the evaluation set:

• Loss: 0.1772
• Wer: 0.2589

## Model description

For a description of the model architecture, see facebook/wav2vec2-xls-r-300m

The model vocabulary consists of the alphabetic characters of the Perso-Arabic script for the Uyghur language, with punctuation removed.

## Intended uses & limitations

This model is expected to be of some utility for low-fidelity use cases such as:

• Draft video captions

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 combination of train and dev of common voice official splits were used as training data. The official test split was used as validation data as well as for final evaluation.

## Training procedure

The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV7 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 18500 steps (100 epochs).

### Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2000
• num_epochs: 100.0
• mixed_precision_training: Native AMP

### Training results

Training Loss Epoch Step Validation Loss Wer
3.3043 2.73 500 3.2415 1.0
3.0482 5.46 1000 2.9591 1.0
1.4767 8.2 1500 0.4779 0.5777
1.3152 10.93 2000 0.3697 0.4938
1.2246 13.66 2500 0.3084 0.4459
1.1781 16.39 3000 0.2842 0.4154
1.1351 19.13 3500 0.2615 0.3929
1.1052 21.86 4000 0.2462 0.3747
1.0711 24.59 4500 0.2366 0.3652
1.035 27.32 5000 0.2268 0.3557
1.0277 30.05 5500 0.2243 0.3450
1.002 32.79 6000 0.2204 0.3389
0.9837 35.52 6500 0.2156 0.3349
0.9773 38.25 7000 0.2127 0.3289
0.9807 40.98 7500 0.2142 0.3274
0.9582 43.72 8000 0.2004 0.3142
0.9548 46.45 8500 0.2022 0.3050
0.9251 49.18 9000 0.2019 0.3035
0.9103 51.91 9500 0.1964 0.3021
0.915 54.64 10000 0.1970 0.3032
0.8962 57.38 10500 0.2007 0.3046
0.8729 60.11 11000 0.1967 0.2942
0.8744 62.84 11500 0.1952 0.2885
0.874 65.57 12000 0.1894 0.2895
0.8457 68.31 12500 0.1895 0.2828
0.8519 71.04 13000 0.1912 0.2875
0.8301 73.77 13500 0.1878 0.2760
0.8226 76.5 14000 0.1808 0.2701
0.8071 79.23 14500 0.1849 0.2741
0.7999 81.97 15000 0.1808 0.2717
0.7947 84.7 15500 0.1821 0.2716
0.7783 87.43 16000 0.1824 0.2661
0.7729 90.16 16500 0.1773 0.2639
0.7759 92.9 17000 0.1767 0.2629
0.7713 95.63 17500 0.1780 0.2621
0.7628 98.36 18000 0.1773 0.2594

### Framework versions

• Transformers 4.16.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0