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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-clean-03

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: 0.1367
- Wer: 0.0891
- Cer: 0.0248

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 22.0316       | 1.0   | 67   | 5.8843          | 0.9702 | 0.9853 |
| 9.9692        | 2.0   | 134  | 4.7064          | 0.9589 | 0.9111 |
| 5.1963        | 3.0   | 201  | 4.3124          | 0.9609 | 0.9444 |
| 5.1963        | 4.0   | 268  | 4.1419          | 0.9596 | 0.9452 |
| 4.5756        | 5.0   | 335  | 4.0705          | 0.9765 | 0.9826 |
| 4.5148        | 6.0   | 402  | 3.8180          | 0.9755 | 0.9834 |
| 4.5148        | 7.0   | 469  | 3.8169          | 0.9748 | 0.9835 |
| 4.1354        | 8.0   | 536  | 3.8965          | 0.9742 | 0.9838 |
| 3.8029        | 9.0   | 603  | 3.4946          | 0.9752 | 0.9826 |
| 3.8029        | 10.0  | 670  | 3.3564          | 0.9722 | 0.9834 |
| 3.6281        | 11.0  | 737  | 3.3778          | 0.9778 | 0.9799 |
| 3.4586        | 12.0  | 804  | 3.5996          | 0.9735 | 0.9839 |
| 3.4586        | 13.0  | 871  | 3.0914          | 0.9725 | 0.9843 |
| 3.2715        | 14.0  | 938  | 3.1400          | 0.9758 | 0.9827 |
| 3.3073        | 15.0  | 1005 | 3.1388          | 0.9768 | 0.9808 |
| 3.3073        | 16.0  | 1072 | 3.1733          | 0.9758 | 0.9811 |
| 3.2006        | 17.0  | 1139 | 3.0084          | 0.9891 | 0.9914 |
| 3.1493        | 18.0  | 1206 | 3.0215          | 0.9811 | 0.9827 |
| 3.1493        | 19.0  | 1273 | 3.1791          | 0.9768 | 0.9778 |
| 3.0254        | 20.0  | 1340 | 3.2284          | 0.9791 | 0.9739 |
| 2.9615        | 21.0  | 1407 | 3.7736          | 0.9728 | 0.9824 |
| 2.9615        | 22.0  | 1474 | 3.6841          | 0.9725 | 0.9700 |
| 2.9588        | 23.0  | 1541 | 3.2069          | 0.9821 | 0.9813 |
| 2.9151        | 24.0  | 1608 | 2.9156          | 0.9983 | 0.9988 |
| 2.9151        | 25.0  | 1675 | 2.9429          | 0.9917 | 0.9916 |
| 2.9081        | 26.0  | 1742 | 3.7887          | 0.9719 | 0.9715 |
| 2.9139        | 27.0  | 1809 | 2.8008          | 0.9841 | 0.9725 |
| 2.9139        | 28.0  | 1876 | 2.8962          | 0.9864 | 0.9253 |
| 2.8165        | 29.0  | 1943 | 2.4966          | 0.9907 | 0.8956 |
| 2.5363        | 30.0  | 2010 | 1.7632          | 0.9685 | 0.5402 |
| 2.5363        | 31.0  | 2077 | 0.8292          | 0.5411 | 0.1377 |
| 1.6241        | 32.0  | 2144 | 0.4653          | 0.3255 | 0.0787 |
| 0.8674        | 33.0  | 2211 | 0.3569          | 0.2513 | 0.0604 |
| 0.8674        | 34.0  | 2278 | 0.2987          | 0.1917 | 0.0497 |
| 0.6012        | 35.0  | 2345 | 0.2660          | 0.1864 | 0.0475 |
| 0.5177        | 36.0  | 2412 | 0.2435          | 0.1669 | 0.0434 |
| 0.5177        | 37.0  | 2479 | 0.2229          | 0.1430 | 0.0383 |
| 0.4461        | 38.0  | 2546 | 0.2097          | 0.1474 | 0.0377 |
| 0.3942        | 39.0  | 2613 | 0.2031          | 0.1338 | 0.0355 |
| 0.3942        | 40.0  | 2680 | 0.1968          | 0.1258 | 0.0350 |
| 0.3685        | 41.0  | 2747 | 0.1909          | 0.1295 | 0.0342 |
| 0.3445        | 42.0  | 2814 | 0.1795          | 0.1185 | 0.0327 |
| 0.3445        | 43.0  | 2881 | 0.1807          | 0.1212 | 0.0325 |
| 0.3284        | 44.0  | 2948 | 0.1740          | 0.1205 | 0.0321 |
| 0.3117        | 45.0  | 3015 | 0.1686          | 0.1185 | 0.0316 |
| 0.3117        | 46.0  | 3082 | 0.1693          | 0.1126 | 0.0304 |
| 0.2911        | 47.0  | 3149 | 0.1656          | 0.1159 | 0.0312 |
| 0.2866        | 48.0  | 3216 | 0.1619          | 0.1169 | 0.0309 |
| 0.2866        | 49.0  | 3283 | 0.1638          | 0.1159 | 0.0306 |
| 0.266         | 50.0  | 3350 | 0.1609          | 0.1083 | 0.0297 |
| 0.2591        | 51.0  | 3417 | 0.1589          | 0.1083 | 0.0298 |
| 0.2591        | 52.0  | 3484 | 0.1548          | 0.1043 | 0.0284 |
| 0.2447        | 53.0  | 3551 | 0.1557          | 0.1070 | 0.0284 |
| 0.2596        | 54.0  | 3618 | 0.1544          | 0.1033 | 0.0275 |
| 0.2596        | 55.0  | 3685 | 0.1539          | 0.1060 | 0.0286 |
| 0.2193        | 56.0  | 3752 | 0.1512          | 0.0977 | 0.0266 |
| 0.222         | 57.0  | 3819 | 0.1507          | 0.1023 | 0.0276 |
| 0.222         | 58.0  | 3886 | 0.1478          | 0.1026 | 0.0275 |
| 0.2266        | 59.0  | 3953 | 0.1461          | 0.0974 | 0.0271 |
| 0.2168        | 60.0  | 4020 | 0.1447          | 0.0950 | 0.0265 |
| 0.2168        | 61.0  | 4087 | 0.1458          | 0.0997 | 0.0271 |
| 0.2057        | 62.0  | 4154 | 0.1470          | 0.0957 | 0.0270 |
| 0.2039        | 63.0  | 4221 | 0.1450          | 0.0987 | 0.0270 |
| 0.2039        | 64.0  | 4288 | 0.1429          | 0.0927 | 0.0258 |
| 0.2049        | 65.0  | 4355 | 0.1422          | 0.1003 | 0.0273 |
| 0.222         | 66.0  | 4422 | 0.1426          | 0.0997 | 0.0274 |
| 0.222         | 67.0  | 4489 | 0.1420          | 0.0950 | 0.0261 |
| 0.2024        | 68.0  | 4556 | 0.1417          | 0.0964 | 0.0265 |
| 0.2012        | 69.0  | 4623 | 0.1418          | 0.0947 | 0.0261 |
| 0.2012        | 70.0  | 4690 | 0.1387          | 0.0944 | 0.0260 |
| 0.1833        | 71.0  | 4757 | 0.1380          | 0.0974 | 0.0267 |
| 0.1908        | 72.0  | 4824 | 0.1383          | 0.0924 | 0.0259 |
| 0.1908        | 73.0  | 4891 | 0.1373          | 0.0891 | 0.0253 |
| 0.1773        | 74.0  | 4958 | 0.1370          | 0.0927 | 0.0257 |
| 0.1786        | 75.0  | 5025 | 0.1367          | 0.0891 | 0.0248 |
| 0.1786        | 76.0  | 5092 | 0.1381          | 0.0937 | 0.0260 |
| 0.1844        | 77.0  | 5159 | 0.1380          | 0.0907 | 0.0251 |
| 0.1643        | 78.0  | 5226 | 0.1380          | 0.0921 | 0.0253 |
| 0.1643        | 79.0  | 5293 | 0.1411          | 0.0887 | 0.0253 |
| 0.1786        | 80.0  | 5360 | 0.1418          | 0.0887 | 0.0253 |
| 0.1816        | 81.0  | 5427 | 0.1412          | 0.0891 | 0.0253 |
| 0.1816        | 82.0  | 5494 | 0.1404          | 0.0927 | 0.0256 |
| 0.1819        | 83.0  | 5561 | 0.1385          | 0.0887 | 0.0249 |
| 0.1787        | 84.0  | 5628 | 0.1393          | 0.0891 | 0.0247 |
| 0.1787        | 85.0  | 5695 | 0.1394          | 0.0897 | 0.0253 |
| 0.1762        | 86.0  | 5762 | 0.1386          | 0.0901 | 0.0254 |
| 0.1608        | 87.0  | 5829 | 0.1386          | 0.0901 | 0.0255 |
| 0.1608        | 88.0  | 5896 | 0.1386          | 0.0894 | 0.0252 |
| 0.1751        | 89.0  | 5963 | 0.1398          | 0.0894 | 0.0253 |
| 0.1696        | 90.0  | 6030 | 0.1388          | 0.0897 | 0.0251 |
| 0.1696        | 91.0  | 6097 | 0.1380          | 0.0911 | 0.0253 |
| 0.1816        | 92.0  | 6164 | 0.1378          | 0.0901 | 0.0253 |
| 0.1731        | 93.0  | 6231 | 0.1386          | 0.0921 | 0.0255 |
| 0.1731        | 94.0  | 6298 | 0.1377          | 0.0921 | 0.0255 |
| 0.1794        | 95.0  | 6365 | 0.1378          | 0.0921 | 0.0254 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xlsr-mecita-coraa-portuguese-all-clean-03", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xlsr-mecita-coraa-portuguese-all-clean-03
========================================================


This model is a fine-tuned version of Edresson/wav2vec2-large-xlsr-coraa-portuguese on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1367
* Wer: 0.0891
* Cer: 0.0248


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



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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 ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3