transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: xls-r-300m-model-G
  results: []
---

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# xls-r-300m-model-G

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7710
- Wer: 1.0
- Cer: 0.8800

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:---:|:------:|
| 32.1048       | 1.0   | 47   | 31.2303         | 1.0 | 1.2082 |
| 30.3148       | 2.0   | 94   | 25.3574         | 1.0 | 1.0    |
| 24.0771       | 3.0   | 141  | 14.3321         | 1.0 | 1.0    |
| 15.5757       | 4.0   | 188  | 8.7981          | 1.0 | 1.0    |
| 10.6789       | 5.0   | 235  | 6.3534          | 1.0 | 1.0    |
| 7.5286        | 6.0   | 282  | 5.0039          | 1.0 | 1.0    |
| 5.5091        | 7.0   | 329  | 4.2043          | 1.0 | 1.0    |
| 4.4551        | 8.0   | 376  | 3.7495          | 1.0 | 1.0    |
| 3.9291        | 9.0   | 423  | 3.4757          | 1.0 | 1.0    |
| 3.5432        | 10.0  | 470  | 3.3164          | 1.0 | 1.0    |
| 3.3702        | 11.0  | 517  | 3.2356          | 1.0 | 1.0    |
| 3.2758        | 12.0  | 564  | 3.1968          | 1.0 | 1.0    |
| 3.2236        | 13.0  | 611  | 3.1779          | 1.0 | 1.0    |
| 3.1857        | 14.0  | 658  | 3.1688          | 1.0 | 1.0    |
| 3.1704        | 15.0  | 705  | 3.1606          | 1.0 | 1.0    |
| 3.1714        | 16.0  | 752  | 3.1593          | 1.0 | 1.0    |
| 3.1537        | 17.0  | 799  | 3.1548          | 1.0 | 1.0    |
| 3.1519        | 18.0  | 846  | 3.1532          | 1.0 | 1.0    |
| 3.1496        | 19.0  | 893  | 3.1535          | 1.0 | 1.0    |
| 3.1441        | 20.0  | 940  | 3.1499          | 1.0 | 1.0    |
| 3.1456        | 21.0  | 987  | 3.1489          | 1.0 | 1.0    |
| 3.1409        | 22.0  | 1034 | 3.1480          | 1.0 | 1.0    |
| 3.1438        | 23.0  | 1081 | 3.1459          | 1.0 | 1.0    |
| 3.1382        | 24.0  | 1128 | 3.1447          | 1.0 | 1.0    |
| 3.1377        | 25.0  | 1175 | 3.1451          | 1.0 | 1.0    |
| 3.1306        | 26.0  | 1222 | 3.1409          | 1.0 | 1.0    |
| 3.1381        | 27.0  | 1269 | 3.1407          | 1.0 | 1.0    |
| 3.1358        | 28.0  | 1316 | 3.1393          | 1.0 | 1.0    |
| 3.139         | 29.0  | 1363 | 3.1333          | 1.0 | 1.0    |
| 3.1216        | 30.0  | 1410 | 3.1159          | 1.0 | 1.0    |
| 3.1206        | 31.0  | 1457 | 3.0811          | 1.0 | 1.0    |
| 3.081         | 32.0  | 1504 | 3.0663          | 1.0 | 1.0    |
| 3.0502        | 33.0  | 1551 | 3.1086          | 1.0 | 1.0    |
| 3.0215        | 34.0  | 1598 | 3.0463          | 1.0 | 1.0    |
| 2.9894        | 35.0  | 1645 | 2.9205          | 1.0 | 1.0    |
| 2.9546        | 36.0  | 1692 | 2.9391          | 1.0 | 1.0    |
| 2.9263        | 37.0  | 1739 | 2.8987          | 1.0 | 1.0    |
| 2.9039        | 38.0  | 1786 | 2.8631          | 1.0 | 1.0    |
| 2.8882        | 39.0  | 1833 | 2.8381          | 1.0 | 0.9965 |
| 2.8664        | 40.0  | 1880 | 2.8295          | 1.0 | 0.9936 |
| 2.8564        | 41.0  | 1927 | 2.8181          | 1.0 | 0.9781 |
| 2.8354        | 42.0  | 1974 | 2.8205          | 1.0 | 0.9577 |
| 2.824         | 43.0  | 2021 | 2.8253          | 1.0 | 0.9421 |
| 2.8115        | 44.0  | 2068 | 2.8224          | 1.0 | 0.9337 |
| 2.7896        | 45.0  | 2115 | 2.8131          | 1.0 | 0.9047 |
| 2.7896        | 46.0  | 2162 | 2.8056          | 1.0 | 0.9054 |
| 2.7827        | 47.0  | 2209 | 2.7961          | 1.0 | 0.8800 |
| 2.7782        | 48.0  | 2256 | 2.7957          | 1.0 | 0.9012 |
| 2.7735        | 49.0  | 2303 | 2.7653          | 1.0 | 0.8673 |
| 2.764         | 50.0  | 2350 | 2.7609          | 1.0 | 0.8709 |
| 2.7556        | 51.0  | 2397 | 2.7868          | 1.0 | 0.8793 |
| 2.749         | 52.0  | 2444 | 2.7823          | 1.0 | 0.8687 |
| 2.7536        | 53.0  | 2491 | 2.7569          | 1.0 | 0.8631 |
| 2.7514        | 54.0  | 2538 | 2.7655          | 1.0 | 0.8751 |
| 2.7421        | 55.0  | 2585 | 2.7617          | 1.0 | 0.8652 |
| 2.7364        | 56.0  | 2632 | 2.7755          | 1.0 | 0.8800 |
| 2.732         | 57.0  | 2679 | 2.7698          | 1.0 | 0.8744 |
| 2.7358        | 58.0  | 2726 | 2.7808          | 1.0 | 0.8843 |
| 2.7343        | 59.0  | 2773 | 2.7706          | 1.0 | 0.8793 |
| 2.7345        | 60.0  | 2820 | 2.7710          | 1.0 | 0.8800 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
