---
language:
- ug
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- ug
- robust-speech-event
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 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