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---
language:
- ug
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- ug
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
base_model: facebook/wav2vec2-xls-r-300m
model-index:
- name: XLS-R-300M Uyghur CV7
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ug
metrics:
- type: wer
value: 25.845
name: Test WER
- type: cer
value: 4.795
name: Test CER
---
# XLS-R-300M Uyghur CV7
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_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](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
The model vocabulary consists of the alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), 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
- 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 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
- gradient_accumulation_steps: 4
- 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
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