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---
library_name: transformers
language:
- lg
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- yogera
metrics:
- wer
model-index:
- name: wav2vec2-bert
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Yogera
type: yogera
metrics:
- name: Wer
type: wer
value: 0.12906588824020016
---
<!-- 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-bert
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Yogera dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2157
- Wer: 0.1291
- Cer: 0.0296
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.6428 | 1.0 | 257 | 0.1958 | 0.2392 | 0.0488 |
| 0.1608 | 2.0 | 514 | 0.1623 | 0.1868 | 0.0393 |
| 0.1216 | 3.0 | 771 | 0.1471 | 0.1663 | 0.0368 |
| 0.1001 | 4.0 | 1028 | 0.1483 | 0.1601 | 0.0351 |
| 0.0859 | 5.0 | 1285 | 0.1471 | 0.1497 | 0.0332 |
| 0.0742 | 6.0 | 1542 | 0.1478 | 0.1468 | 0.0315 |
| 0.0641 | 7.0 | 1799 | 0.1642 | 0.1476 | 0.0326 |
| 0.0544 | 8.0 | 2056 | 0.1520 | 0.1461 | 0.0322 |
| 0.0489 | 9.0 | 2313 | 0.1596 | 0.1386 | 0.0312 |
| 0.0452 | 10.0 | 2570 | 0.1521 | 0.1408 | 0.0320 |
| 0.04 | 11.0 | 2827 | 0.1754 | 0.1395 | 0.0306 |
| 0.0371 | 12.0 | 3084 | 0.1703 | 0.1405 | 0.0309 |
| 0.0329 | 13.0 | 3341 | 0.1657 | 0.1447 | 0.0318 |
| 0.0323 | 14.0 | 3598 | 0.1695 | 0.1327 | 0.0298 |
| 0.0282 | 15.0 | 3855 | 0.1852 | 0.1356 | 0.0310 |
| 0.0237 | 16.0 | 4112 | 0.1728 | 0.1399 | 0.0308 |
| 0.0229 | 17.0 | 4369 | 0.1810 | 0.1301 | 0.0291 |
| 0.02 | 18.0 | 4626 | 0.1781 | 0.1367 | 0.0304 |
| 0.0204 | 19.0 | 4883 | 0.2039 | 0.1329 | 0.0293 |
| 0.0186 | 20.0 | 5140 | 0.1929 | 0.1366 | 0.0302 |
| 0.0164 | 21.0 | 5397 | 0.2022 | 0.1356 | 0.0301 |
| 0.0154 | 22.0 | 5654 | 0.1787 | 0.1307 | 0.0293 |
| 0.0127 | 23.0 | 5911 | 0.2086 | 0.1296 | 0.0290 |
| 0.0129 | 24.0 | 6168 | 0.2094 | 0.1281 | 0.0287 |
| 0.0108 | 25.0 | 6425 | 0.2148 | 0.1254 | 0.0280 |
| 0.0122 | 26.0 | 6682 | 0.2091 | 0.1339 | 0.0305 |
| 0.0106 | 27.0 | 6939 | 0.2030 | 0.1315 | 0.0295 |
| 0.0102 | 28.0 | 7196 | 0.2092 | 0.1241 | 0.0282 |
| 0.0088 | 29.0 | 7453 | 0.2078 | 0.1290 | 0.0287 |
| 0.008 | 30.0 | 7710 | 0.2112 | 0.1298 | 0.0282 |
| 0.0084 | 31.0 | 7967 | 0.1972 | 0.1305 | 0.0295 |
| 0.0074 | 32.0 | 8224 | 0.2130 | 0.1337 | 0.0293 |
| 0.0062 | 33.0 | 8481 | 0.2141 | 0.1308 | 0.0297 |
| 0.0065 | 34.0 | 8738 | 0.2151 | 0.1319 | 0.0296 |
| 0.0079 | 35.0 | 8995 | 0.2070 | 0.1253 | 0.0279 |
| 0.0059 | 36.0 | 9252 | 0.2229 | 0.1267 | 0.0285 |
| 0.0071 | 37.0 | 9509 | 0.2218 | 0.1295 | 0.0297 |
| 0.0066 | 38.0 | 9766 | 0.2157 | 0.1291 | 0.0296 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.1.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1
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