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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-08

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: 0.1455
- Wer: 0.0896
- Cer: 0.0251

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 27.8108       | 1.0   | 86   | 3.2125          | 1.0    | 1.0    |
| 7.0217        | 2.0   | 172  | 2.9839          | 1.0    | 1.0    |
| 3.033         | 3.0   | 258  | 2.9245          | 1.0    | 1.0    |
| 2.9324        | 4.0   | 344  | 2.8842          | 1.0    | 1.0    |
| 2.8713        | 5.0   | 430  | 2.1173          | 1.0    | 0.7026 |
| 1.963         | 6.0   | 516  | 0.6336          | 0.3492 | 0.0867 |
| 0.9394        | 7.0   | 602  | 0.4107          | 0.2192 | 0.0562 |
| 0.9394        | 8.0   | 688  | 0.3376          | 0.1913 | 0.0507 |
| 0.6379        | 9.0   | 774  | 0.2888          | 0.1698 | 0.0451 |
| 0.5164        | 10.0  | 860  | 0.2622          | 0.1460 | 0.0416 |
| 0.4629        | 11.0  | 946  | 0.2398          | 0.1301 | 0.0375 |
| 0.4032        | 12.0  | 1032 | 0.2196          | 0.1170 | 0.0344 |
| 0.3776        | 13.0  | 1118 | 0.2118          | 0.1120 | 0.0332 |
| 0.3289        | 14.0  | 1204 | 0.1965          | 0.1139 | 0.0331 |
| 0.3289        | 15.0  | 1290 | 0.1912          | 0.1064 | 0.0314 |
| 0.3063        | 16.0  | 1376 | 0.1836          | 0.1074 | 0.0306 |
| 0.3058        | 17.0  | 1462 | 0.1839          | 0.1027 | 0.0309 |
| 0.3           | 18.0  | 1548 | 0.1702          | 0.0985 | 0.0296 |
| 0.2867        | 19.0  | 1634 | 0.1802          | 0.0994 | 0.0296 |
| 0.2717        | 20.0  | 1720 | 0.1669          | 0.1034 | 0.0296 |
| 0.2546        | 21.0  | 1806 | 0.1648          | 0.1018 | 0.0298 |
| 0.2546        | 22.0  | 1892 | 0.1658          | 0.0975 | 0.0290 |
| 0.2293        | 23.0  | 1978 | 0.1652          | 0.0973 | 0.0291 |
| 0.2404        | 24.0  | 2064 | 0.1651          | 0.0943 | 0.0278 |
| 0.2184        | 25.0  | 2150 | 0.1671          | 0.0933 | 0.0282 |
| 0.2105        | 26.0  | 2236 | 0.1626          | 0.0950 | 0.0285 |
| 0.2229        | 27.0  | 2322 | 0.1577          | 0.0957 | 0.0283 |
| 0.215         | 28.0  | 2408 | 0.1608          | 0.0943 | 0.0280 |
| 0.215         | 29.0  | 2494 | 0.1633          | 0.0922 | 0.0272 |
| 0.1959        | 30.0  | 2580 | 0.1567          | 0.0926 | 0.0270 |
| 0.2024        | 31.0  | 2666 | 0.1645          | 0.0980 | 0.0281 |
| 0.201         | 32.0  | 2752 | 0.1540          | 0.0917 | 0.0270 |
| 0.1932        | 33.0  | 2838 | 0.1563          | 0.0940 | 0.0274 |
| 0.1945        | 34.0  | 2924 | 0.1542          | 0.0954 | 0.0272 |
| 0.1883        | 35.0  | 3010 | 0.1562          | 0.0947 | 0.0274 |
| 0.1883        | 36.0  | 3096 | 0.1558          | 0.0945 | 0.0272 |
| 0.1852        | 37.0  | 3182 | 0.1593          | 0.0891 | 0.0261 |
| 0.1609        | 38.0  | 3268 | 0.1558          | 0.0957 | 0.0271 |
| 0.1801        | 39.0  | 3354 | 0.1576          | 0.0917 | 0.0265 |
| 0.1611        | 40.0  | 3440 | 0.1591          | 0.0917 | 0.0263 |
| 0.1879        | 41.0  | 3526 | 0.1520          | 0.0903 | 0.0258 |
| 0.169         | 42.0  | 3612 | 0.1545          | 0.0971 | 0.0269 |
| 0.169         | 43.0  | 3698 | 0.1553          | 0.0896 | 0.0259 |
| 0.1676        | 44.0  | 3784 | 0.1574          | 0.0884 | 0.0259 |
| 0.1512        | 45.0  | 3870 | 0.1577          | 0.0926 | 0.0269 |
| 0.1457        | 46.0  | 3956 | 0.1611          | 0.0922 | 0.0263 |
| 0.1498        | 47.0  | 4042 | 0.1569          | 0.0905 | 0.0258 |
| 0.1848        | 48.0  | 4128 | 0.1560          | 0.0903 | 0.0263 |
| 0.1455        | 49.0  | 4214 | 0.1584          | 0.0903 | 0.0265 |
| 0.1549        | 50.0  | 4300 | 0.1566          | 0.0903 | 0.0258 |
| 0.1549        | 51.0  | 4386 | 0.1513          | 0.0908 | 0.0259 |
| 0.1454        | 52.0  | 4472 | 0.1513          | 0.0894 | 0.0253 |
| 0.1482        | 53.0  | 4558 | 0.1553          | 0.0894 | 0.0255 |
| 0.1407        | 54.0  | 4644 | 0.1535          | 0.0926 | 0.0263 |
| 0.169         | 55.0  | 4730 | 0.1525          | 0.0912 | 0.0263 |
| 0.1489        | 56.0  | 4816 | 0.1516          | 0.0910 | 0.0257 |
| 0.1489        | 57.0  | 4902 | 0.1567          | 0.0889 | 0.0258 |
| 0.1489        | 58.0  | 4988 | 0.1525          | 0.0894 | 0.0257 |
| 0.1267        | 59.0  | 5074 | 0.1540          | 0.0865 | 0.0249 |
| 0.1411        | 60.0  | 5160 | 0.1521          | 0.0908 | 0.0258 |
| 0.1357        | 61.0  | 5246 | 0.1485          | 0.0910 | 0.0255 |
| 0.1379        | 62.0  | 5332 | 0.1503          | 0.0898 | 0.0257 |
| 0.1348        | 63.0  | 5418 | 0.1500          | 0.0898 | 0.0253 |
| 0.1417        | 64.0  | 5504 | 0.1455          | 0.0896 | 0.0251 |
| 0.1417        | 65.0  | 5590 | 0.1478          | 0.0880 | 0.0249 |
| 0.1419        | 66.0  | 5676 | 0.1515          | 0.0884 | 0.0254 |
| 0.1417        | 67.0  | 5762 | 0.1516          | 0.0894 | 0.0252 |
| 0.127         | 68.0  | 5848 | 0.1487          | 0.0880 | 0.0250 |
| 0.1337        | 69.0  | 5934 | 0.1463          | 0.0910 | 0.0256 |
| 0.1208        | 70.0  | 6020 | 0.1508          | 0.0929 | 0.0256 |
| 0.127         | 71.0  | 6106 | 0.1586          | 0.0896 | 0.0251 |
| 0.127         | 72.0  | 6192 | 0.1542          | 0.0905 | 0.0255 |
| 0.1209        | 73.0  | 6278 | 0.1567          | 0.0891 | 0.0251 |
| 0.1187        | 74.0  | 6364 | 0.1544          | 0.0915 | 0.0255 |
| 0.1244        | 75.0  | 6450 | 0.1597          | 0.0908 | 0.0251 |
| 0.1261        | 76.0  | 6536 | 0.1554          | 0.0887 | 0.0248 |
| 0.1387        | 77.0  | 6622 | 0.1554          | 0.0889 | 0.0246 |
| 0.1262        | 78.0  | 6708 | 0.1559          | 0.0901 | 0.0251 |
| 0.1262        | 79.0  | 6794 | 0.1514          | 0.0887 | 0.0246 |
| 0.115         | 80.0  | 6880 | 0.1522          | 0.0882 | 0.0246 |
| 0.1127        | 81.0  | 6966 | 0.1519          | 0.0889 | 0.0247 |
| 0.1191        | 82.0  | 7052 | 0.1530          | 0.0861 | 0.0243 |
| 0.116         | 83.0  | 7138 | 0.1535          | 0.0877 | 0.0244 |
| 0.1217        | 84.0  | 7224 | 0.1528          | 0.0882 | 0.0249 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+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-08", "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-08
==================================================


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: 0.1455
* Wer: 0.0896
* Cer: 0.0251


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 2.0.1+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 2.0.1+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 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3