---
language:
- uk
license: mit
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-xls-r-300m-uk
  results:
  - task: 
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice uk
      type: common_voice
      args: uk
    metrics:
       - name: Test WER
         type: wer
         value: 12.22
---


<!-- 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. -->

# wav2vec2-xls-r-300m-uk

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0927
- Wer: 0.1222
- Cer: 0.0204

## 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: 3e-05
- train_batch_size: 40
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 9.0008        | 1.68  | 200  | 1.0    | 3.7590          | 1.0    |
| 3.4972        | 3.36  | 400  | 1.0    | 3.3933          | 1.0    |
| 3.3432        | 5.04  | 600  | 1.0    | 3.2617          | 1.0    |
| 3.2421        | 6.72  | 800  | 1.0    | 3.0712          | 1.0    |
| 1.9839        | 7.68  | 1000 | 0.1400 | 0.7204          | 0.6561 |
| 0.8017        | 9.36  | 1200 | 0.0766 | 0.3734          | 0.4159 |
| 0.5554        | 11.04 | 1400 | 0.0583 | 0.2621          | 0.3237 |
| 0.4309        | 12.68 | 1600 | 0.0486 | 0.2085          | 0.2753 |
| 0.3697        | 14.36 | 1800 | 0.0421 | 0.1746          | 0.2427 |
| 0.3293        | 16.04 | 2000 | 0.0388 | 0.1597          | 0.2243 |
| 0.2934        | 17.72 | 2200 | 0.0358 | 0.1428          | 0.2083 |
| 0.2704        | 19.4  | 2400 | 0.0333 | 0.1326          | 0.1949 |
| 0.2547        | 21.08 | 2600 | 0.0322 | 0.1255          | 0.1882 |
| 0.2366        | 22.76 | 2800 | 0.0309 | 0.1211          | 0.1815 |
| 0.2183        | 24.44 | 3000 | 0.0294 | 0.1159          | 0.1727 |
| 0.2115        | 26.13 | 3200 | 0.0280 | 0.1117          | 0.1661 |
| 0.1968        | 27.8  | 3400 | 0.0274 | 0.1063          | 0.1622 |
| 0.1922        | 29.48 | 3600 | 0.0269 | 0.1082          | 0.1598 |
| 0.1847        | 31.17 | 3800 | 0.0260 | 0.1061          | 0.1550 |
| 0.1715        | 32.84 | 4000 | 0.0252 | 0.1014          | 0.1496 |
| 0.1689        | 34.53 | 4200 | 0.0250 | 0.1012          | 0.1492 |
| 0.1655        | 36.21 | 4400 | 0.0243 | 0.0999          | 0.1450 |
| 0.1585        | 37.88 | 4600 | 0.0239 | 0.0967          | 0.1432 |
| 0.1492        | 39.57 | 4800 | 0.0237 | 0.0978          | 0.1421 |
| 0.1491        | 41.25 | 5000 | 0.0236 | 0.0963          | 0.1412 |
| 0.1453        | 42.93 | 5200 | 0.0230 | 0.0979          | 0.1373 |
| 0.1386        | 44.61 | 5400 | 0.0227 | 0.0959          | 0.1353 |
| 0.1387        | 46.29 | 5600 | 0.0226 | 0.0927          | 0.1355 |
| 0.1329        | 47.97 | 5800 | 0.0224 | 0.0951          | 0.1341 |
| 0.1295        | 49.65 | 6000 | 0.0219 | 0.0950          | 0.1306 |
| 0.1287        | 51.33 | 6200 | 0.0216 | 0.0937          | 0.1290 |
| 0.1277        | 53.02 | 6400 | 0.0215 | 0.0963          | 0.1294 |
| 0.1201        | 54.69 | 6600 | 0.0213 | 0.0959          | 0.1282 |
| 0.1199        | 56.38 | 6800 | 0.0215 | 0.0944          | 0.1286 |
| 0.1221        | 58.06 | 7000 | 0.0209 | 0.0938          | 0.1249 |
| 0.1145        | 59.68 | 7200 | 0.0208 | 0.0941          | 0.1254 |
| 0.1143        | 61.36 | 7400 | 0.0209 | 0.0941          | 0.1249 |
| 0.1143        | 63.04 | 7600 | 0.0209 | 0.0940          | 0.1248 |
| 0.1137        | 64.72 | 7800 | 0.0205 | 0.0931          | 0.1234 |
| 0.1125        | 66.4  | 8000 | 0.0204 | 0.0927          | 0.1222 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2