---
language:
- ug
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- 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: 36.33
       - name: Test CER
         type: cer
         value: 6.75
---

<!-- 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: 0.2240
- Wer: 0.3693

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.1169        | 2.66  | 500   | 4.0146          | 1.0    |
| 3.2512        | 5.32  | 1000  | 3.2342          | 1.0    |
| 2.5435        | 7.97  | 1500  | 1.8155          | 1.0286 |
| 1.5575        | 10.64 | 2000  | 0.6346          | 0.7058 |
| 1.3979        | 13.3  | 2500  | 0.4885          | 0.6320 |
| 1.2874        | 15.95 | 3000  | 0.4271          | 0.6088 |
| 1.2383        | 18.61 | 3500  | 0.3889          | 0.5869 |
| 1.2054        | 21.28 | 4000  | 0.3609          | 0.5793 |
| 1.1866        | 23.93 | 4500  | 0.3450          | 0.5513 |
| 1.1332        | 26.59 | 5000  | 0.3214          | 0.5379 |
| 1.135         | 29.25 | 5500  | 0.3122          | 0.5384 |
| 1.0992        | 31.91 | 6000  | 0.2948          | 0.5078 |
| 1.0707        | 34.57 | 6500  | 0.2928          | 0.5128 |
| 1.0754        | 37.23 | 7000  | 0.2857          | 0.5017 |
| 1.0461        | 39.89 | 7500  | 0.2791          | 0.5099 |
| 1.0328        | 42.55 | 8000  | 0.2729          | 0.5120 |
| 1.0201        | 45.21 | 8500  | 0.2654          | 0.4720 |
| 1.0035        | 47.87 | 9000  | 0.2623          | 0.4659 |
| 1.0069        | 50.53 | 9500  | 0.2569          | 0.4593 |
| 0.9998        | 53.19 | 10000 | 0.2519          | 0.4405 |
| 0.9762        | 55.85 | 10500 | 0.2505          | 0.4588 |
| 0.9755        | 58.51 | 11000 | 0.2479          | 0.4564 |
| 0.9624        | 61.17 | 11500 | 0.2460          | 0.4298 |
| 0.9494        | 63.83 | 12000 | 0.2402          | 0.4182 |
| 0.948         | 66.49 | 12500 | 0.2412          | 0.4212 |
| 0.9312        | 69.15 | 13000 | 0.2352          | 0.3970 |
| 0.9172        | 71.81 | 13500 | 0.2357          | 0.3926 |
| 0.9101        | 74.47 | 14000 | 0.2305          | 0.3905 |
| 0.9177        | 77.13 | 14500 | 0.2307          | 0.3838 |
| 0.9083        | 79.78 | 15000 | 0.2313          | 0.3800 |
| 0.9068        | 82.45 | 15500 | 0.2275          | 0.3742 |
| 0.9087        | 85.11 | 16000 | 0.2283          | 0.3747 |
| 0.8838        | 87.76 | 16500 | 0.2286          | 0.3777 |
| 0.8868        | 90.42 | 17000 | 0.2269          | 0.3722 |
| 0.8895        | 93.08 | 17500 | 0.2246          | 0.3714 |
| 0.8926        | 95.74 | 18000 | 0.2241          | 0.3705 |
| 0.8856        | 98.4  | 18500 | 0.2242          | 0.3693 |


### Framework versions

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