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---
language:
- ug
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ug
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R-300M Uyghur CV8
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: ug
metrics:
- name: Test WER
type: wer
value: 28.74
- name: Test CER
type: cer
value: 5.38
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLS-R-300M Uyghur 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 - UG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2036
- WER: 0.2977
## 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 conventionally used 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 CV8 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.2892 | 2.66 | 500 | 3.2415 | 1.0 |
| 2.9206 | 5.32 | 1000 | 2.4381 | 1.0056 |
| 1.4909 | 7.97 | 1500 | 0.5428 | 0.6705 |
| 1.3395 | 10.64 | 2000 | 0.4207 | 0.5995 |
| 1.2718 | 13.3 | 2500 | 0.3743 | 0.5648 |
| 1.1798 | 15.95 | 3000 | 0.3225 | 0.4927 |
| 1.1392 | 18.61 | 3500 | 0.3097 | 0.4627 |
| 1.1143 | 21.28 | 4000 | 0.2996 | 0.4505 |
| 1.0923 | 23.93 | 4500 | 0.2841 | 0.4229 |
| 1.0516 | 26.59 | 5000 | 0.2705 | 0.4113 |
| 1.051 | 29.25 | 5500 | 0.2622 | 0.4078 |
| 1.021 | 31.91 | 6000 | 0.2611 | 0.4009 |
| 0.9886 | 34.57 | 6500 | 0.2498 | 0.3921 |
| 0.984 | 37.23 | 7000 | 0.2521 | 0.3845 |
| 0.9631 | 39.89 | 7500 | 0.2413 | 0.3791 |
| 0.9353 | 42.55 | 8000 | 0.2391 | 0.3612 |
| 0.922 | 45.21 | 8500 | 0.2363 | 0.3571 |
| 0.9116 | 47.87 | 9000 | 0.2285 | 0.3668 |
| 0.8951 | 50.53 | 9500 | 0.2256 | 0.3729 |
| 0.8865 | 53.19 | 10000 | 0.2228 | 0.3663 |
| 0.8792 | 55.85 | 10500 | 0.2221 | 0.3656 |
| 0.8682 | 58.51 | 11000 | 0.2228 | 0.3323 |
| 0.8492 | 61.17 | 11500 | 0.2167 | 0.3446 |
| 0.8365 | 63.83 | 12000 | 0.2156 | 0.3321 |
| 0.8298 | 66.49 | 12500 | 0.2142 | 0.3400 |
| 0.808 | 69.15 | 13000 | 0.2079 | 0.3148 |
| 0.7999 | 71.81 | 13500 | 0.2117 | 0.3225 |
| 0.7871 | 74.47 | 14000 | 0.2088 | 0.3174 |
| 0.7858 | 77.13 | 14500 | 0.2060 | 0.3008 |
| 0.7764 | 79.78 | 15000 | 0.2128 | 0.3146 |
| 0.7684 | 82.45 | 15500 | 0.2086 | 0.3101 |
| 0.7717 | 85.11 | 16000 | 0.2048 | 0.3069 |
| 0.7435 | 87.76 | 16500 | 0.2027 | 0.3055 |
| 0.7378 | 90.42 | 17000 | 0.2059 | 0.2993 |
| 0.7406 | 93.08 | 17500 | 0.2040 | 0.2966 |
| 0.7361 | 95.74 | 18000 | 0.2056 | 0.3000 |
| 0.7379 | 98.4 | 18500 | 0.2031 | 0.2976 |
### 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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