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: wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-09
  results: []
---

<!-- 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-large-xlsr-mecita-coraa-portuguese-2-all-clean-09

This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1435
- Wer: 0.0935
- Cer: 0.0274

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 28.9568       | 1.0   | 67   | 4.5342          | 1.0    | 1.0    |
| 9.238         | 2.0   | 134  | 3.3773          | 0.9615 | 0.9278 |
| 3.8131        | 3.0   | 201  | 3.2552          | 0.9713 | 0.9672 |
| 3.8131        | 4.0   | 268  | 2.9818          | 0.9825 | 0.9845 |
| 3.7154        | 5.0   | 335  | 2.9048          | 1.0    | 1.0    |
| 3.2775        | 6.0   | 402  | 2.9244          | 1.0    | 1.0    |
| 3.2775        | 7.0   | 469  | 2.8494          | 1.0    | 1.0    |
| 3.295         | 8.0   | 536  | 2.8737          | 0.9829 | 0.9853 |
| 3.4359        | 9.0   | 603  | 2.8125          | 0.9648 | 0.9515 |
| 3.4359        | 10.0  | 670  | 2.8296          | 0.9704 | 0.9416 |
| 3.1631        | 11.0  | 737  | 2.7952          | 0.9690 | 0.9126 |
| 3.4425        | 12.0  | 804  | 2.7936          | 0.9746 | 0.9    |
| 3.4425        | 13.0  | 871  | 2.8312          | 0.9783 | 0.8831 |
| 3.1405        | 14.0  | 938  | 2.7702          | 0.9717 | 0.8519 |
| 3.1256        | 15.0  | 1005 | 2.7649          | 0.9769 | 0.8511 |
| 3.1256        | 16.0  | 1072 | 2.7452          | 0.9763 | 0.8156 |
| 3.3974        | 17.0  | 1139 | 2.7520          | 0.9769 | 0.8255 |
| 3.378         | 18.0  | 1206 | 2.7433          | 0.9835 | 0.7887 |
| 3.378         | 19.0  | 1273 | 2.7587          | 0.9845 | 0.7957 |
| 3.0588        | 20.0  | 1340 | 2.6809          | 0.9773 | 0.7508 |
| 3.2909        | 21.0  | 1407 | 2.6814          | 0.9812 | 0.7634 |
| 3.2909        | 22.0  | 1474 | 2.6797          | 0.9806 | 0.7662 |
| 3.2279        | 23.0  | 1541 | 2.6761          | 0.9710 | 0.7498 |
| 3.0068        | 24.0  | 1608 | 2.6769          | 0.9727 | 0.7580 |
| 3.0068        | 25.0  | 1675 | 2.6543          | 0.9743 | 0.7394 |
| 3.1098        | 26.0  | 1742 | 2.6652          | 0.9806 | 0.7654 |
| 3.2015        | 27.0  | 1809 | 2.6263          | 0.9707 | 0.7314 |
| 3.2015        | 28.0  | 1876 | 2.6353          | 0.9694 | 0.7274 |
| 2.9707        | 29.0  | 1943 | 2.5955          | 0.9727 | 0.7129 |
| 3.116         | 30.0  | 2010 | 2.5964          | 0.9677 | 0.7270 |
| 3.116         | 31.0  | 2077 | 2.5873          | 0.9704 | 0.7185 |
| 3.0265        | 32.0  | 2144 | 2.5629          | 0.9713 | 0.7012 |
| 3.0762        | 33.0  | 2211 | 2.5520          | 0.9717 | 0.6991 |
| 3.0762        | 34.0  | 2278 | 2.5075          | 0.9733 | 0.6937 |
| 3.0228        | 35.0  | 2345 | 2.5200          | 0.9707 | 0.7011 |
| 2.8856        | 36.0  | 2412 | 2.4967          | 0.9700 | 0.6913 |
| 2.8856        | 37.0  | 2479 | 2.5047          | 0.9717 | 0.7040 |
| 2.8122        | 38.0  | 2546 | 2.4714          | 0.9717 | 0.6900 |
| 2.9134        | 39.0  | 2613 | 2.4488          | 0.9667 | 0.6871 |
| 2.9134        | 40.0  | 2680 | 2.4372          | 0.9621 | 0.6826 |
| 2.7965        | 41.0  | 2747 | 2.4174          | 0.9661 | 0.6831 |
| 2.8698        | 42.0  | 2814 | 2.3973          | 0.9727 | 0.6733 |
| 2.8698        | 43.0  | 2881 | 2.3632          | 0.9681 | 0.6703 |
| 2.6243        | 44.0  | 2948 | 2.3325          | 0.9641 | 0.6784 |
| 2.7087        | 45.0  | 3015 | 2.3252          | 0.9730 | 0.6596 |
| 2.7087        | 46.0  | 3082 | 2.2806          | 0.9651 | 0.6643 |
| 2.6011        | 47.0  | 3149 | 2.2519          | 0.9592 | 0.6458 |
| 2.6838        | 48.0  | 3216 | 2.2211          | 0.9532 | 0.6363 |
| 2.6838        | 49.0  | 3283 | 2.1794          | 0.9433 | 0.6468 |
| 2.5863        | 50.0  | 3350 | 2.1214          | 0.9397 | 0.6294 |
| 2.5353        | 51.0  | 3417 | 2.0826          | 0.9499 | 0.6668 |
| 2.5353        | 52.0  | 3484 | 1.9885          | 0.9351 | 0.6165 |
| 2.3509        | 53.0  | 3551 | 1.8180          | 0.9104 | 0.5729 |
| 2.1853        | 54.0  | 3618 | 1.6297          | 0.8762 | 0.5067 |
| 2.1853        | 55.0  | 3685 | 1.3421          | 0.8603 | 0.4185 |
| 1.9876        | 56.0  | 3752 | 1.0128          | 0.7777 | 0.2953 |
| 1.5594        | 57.0  | 3819 | 0.7699          | 0.6663 | 0.2079 |
| 1.5594        | 58.0  | 3886 | 0.5548          | 0.4792 | 0.1294 |
| 1.1199        | 59.0  | 3953 | 0.4366          | 0.3768 | 0.0975 |
| 0.8571        | 60.0  | 4020 | 0.3568          | 0.3202 | 0.0805 |
| 0.8571        | 61.0  | 4087 | 0.3107          | 0.2556 | 0.0672 |
| 0.7263        | 62.0  | 4154 | 0.2792          | 0.2118 | 0.0573 |
| 0.5792        | 63.0  | 4221 | 0.2560          | 0.1838 | 0.0511 |
| 0.5792        | 64.0  | 4288 | 0.2452          | 0.1742 | 0.0494 |
| 0.5726        | 65.0  | 4355 | 0.2277          | 0.1624 | 0.0442 |
| 0.4892        | 66.0  | 4422 | 0.2126          | 0.1522 | 0.0416 |
| 0.4892        | 67.0  | 4489 | 0.2075          | 0.1456 | 0.0396 |
| 0.4639        | 68.0  | 4556 | 0.1982          | 0.1406 | 0.0382 |
| 0.435         | 69.0  | 4623 | 0.1916          | 0.1321 | 0.0365 |
| 0.435         | 70.0  | 4690 | 0.1888          | 0.1281 | 0.0354 |
| 0.4061        | 71.0  | 4757 | 0.1857          | 0.1215 | 0.0338 |
| 0.383         | 72.0  | 4824 | 0.1800          | 0.1179 | 0.0324 |
| 0.383         | 73.0  | 4891 | 0.1741          | 0.1182 | 0.0322 |
| 0.403         | 74.0  | 4958 | 0.1727          | 0.1159 | 0.0310 |
| 0.3688        | 75.0  | 5025 | 0.1711          | 0.1140 | 0.0312 |
| 0.3688        | 76.0  | 5092 | 0.1676          | 0.1126 | 0.0312 |
| 0.3522        | 77.0  | 5159 | 0.1645          | 0.1097 | 0.0305 |
| 0.3235        | 78.0  | 5226 | 0.1606          | 0.1094 | 0.0300 |
| 0.3235        | 79.0  | 5293 | 0.1615          | 0.1061 | 0.0302 |
| 0.3303        | 80.0  | 5360 | 0.1585          | 0.1041 | 0.0295 |
| 0.319         | 81.0  | 5427 | 0.1567          | 0.1061 | 0.0291 |
| 0.319         | 82.0  | 5494 | 0.1554          | 0.1038 | 0.0294 |
| 0.3257        | 83.0  | 5561 | 0.1557          | 0.1054 | 0.0296 |
| 0.3079        | 84.0  | 5628 | 0.1548          | 0.1005 | 0.0286 |
| 0.3079        | 85.0  | 5695 | 0.1512          | 0.1018 | 0.0289 |
| 0.302         | 86.0  | 5762 | 0.1513          | 0.1001 | 0.0286 |
| 0.2919        | 87.0  | 5829 | 0.1493          | 0.0965 | 0.0281 |
| 0.2919        | 88.0  | 5896 | 0.1479          | 0.0968 | 0.0283 |
| 0.2783        | 89.0  | 5963 | 0.1460          | 0.0968 | 0.0278 |
| 0.2862        | 90.0  | 6030 | 0.1453          | 0.0975 | 0.0280 |
| 0.2862        | 91.0  | 6097 | 0.1466          | 0.0942 | 0.0279 |
| 0.276         | 92.0  | 6164 | 0.1458          | 0.0958 | 0.0275 |
| 0.2854        | 93.0  | 6231 | 0.1462          | 0.0945 | 0.0273 |
| 0.2854        | 94.0  | 6298 | 0.1446          | 0.0935 | 0.0275 |
| 0.2887        | 95.0  | 6365 | 0.1445          | 0.0935 | 0.0275 |
| 0.2907        | 96.0  | 6432 | 0.1435          | 0.0935 | 0.0274 |
| 0.2907        | 97.0  | 6499 | 0.1450          | 0.0949 | 0.0272 |
| 0.298         | 98.0  | 6566 | 0.1454          | 0.0935 | 0.0272 |
| 0.2974        | 99.0  | 6633 | 0.1450          | 0.0939 | 0.0273 |
| 0.304         | 100.0 | 6700 | 0.1447          | 0.0935 | 0.0271 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
