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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-06

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: 0.1446
- Wer: 0.0908
- Cer: 0.0253

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 32.101        | 1.0   | 86   | 3.3100          | 1.0    | 1.0    |
| 8.4627        | 2.0   | 172  | 3.0630          | 1.0    | 1.0    |
| 3.0813        | 3.0   | 258  | 2.9336          | 1.0    | 1.0    |
| 2.9381        | 4.0   | 344  | 2.9115          | 1.0    | 1.0    |
| 2.9064        | 5.0   | 430  | 2.9012          | 1.0    | 1.0    |
| 2.8703        | 6.0   | 516  | 2.5147          | 0.9978 | 0.9959 |
| 2.0805        | 7.0   | 602  | 0.7929          | 0.3573 | 0.0944 |
| 2.0805        | 8.0   | 688  | 0.4395          | 0.2148 | 0.0570 |
| 0.9196        | 9.0   | 774  | 0.3280          | 0.1848 | 0.0505 |
| 0.6289        | 10.0  | 860  | 0.2805          | 0.1708 | 0.0456 |
| 0.4986        | 11.0  | 946  | 0.2635          | 0.1521 | 0.0422 |
| 0.4212        | 12.0  | 1032 | 0.2375          | 0.1378 | 0.0379 |
| 0.3877        | 13.0  | 1118 | 0.2240          | 0.1316 | 0.0364 |
| 0.366         | 14.0  | 1204 | 0.2124          | 0.1216 | 0.0340 |
| 0.366         | 15.0  | 1290 | 0.2000          | 0.1186 | 0.0328 |
| 0.3107        | 16.0  | 1376 | 0.1929          | 0.1102 | 0.0324 |
| 0.3198        | 17.0  | 1462 | 0.1939          | 0.1152 | 0.0326 |
| 0.3339        | 18.0  | 1548 | 0.1875          | 0.1112 | 0.0317 |
| 0.2773        | 19.0  | 1634 | 0.1811          | 0.1063 | 0.0306 |
| 0.2777        | 20.0  | 1720 | 0.1777          | 0.1097 | 0.0304 |
| 0.2412        | 21.0  | 1806 | 0.1709          | 0.1058 | 0.0297 |
| 0.2412        | 22.0  | 1892 | 0.1645          | 0.1014 | 0.0283 |
| 0.2356        | 23.0  | 1978 | 0.1616          | 0.1006 | 0.0282 |
| 0.2426        | 24.0  | 2064 | 0.1623          | 0.0997 | 0.0287 |
| 0.2372        | 25.0  | 2150 | 0.1655          | 0.0999 | 0.0286 |
| 0.2337        | 26.0  | 2236 | 0.1595          | 0.1041 | 0.0290 |
| 0.2221        | 27.0  | 2322 | 0.1560          | 0.0974 | 0.0270 |
| 0.2281        | 28.0  | 2408 | 0.1549          | 0.0955 | 0.0265 |
| 0.2281        | 29.0  | 2494 | 0.1603          | 0.0997 | 0.0277 |
| 0.2196        | 30.0  | 2580 | 0.1553          | 0.0969 | 0.0276 |
| 0.1983        | 31.0  | 2666 | 0.1557          | 0.0960 | 0.0274 |
| 0.2113        | 32.0  | 2752 | 0.1523          | 0.0942 | 0.0266 |
| 0.189         | 33.0  | 2838 | 0.1551          | 0.0972 | 0.0274 |
| 0.1816        | 34.0  | 2924 | 0.1512          | 0.0950 | 0.0261 |
| 0.1823        | 35.0  | 3010 | 0.1561          | 0.0952 | 0.0269 |
| 0.1823        | 36.0  | 3096 | 0.1539          | 0.0965 | 0.0273 |
| 0.1772        | 37.0  | 3182 | 0.1553          | 0.0910 | 0.0263 |
| 0.1712        | 38.0  | 3268 | 0.1540          | 0.0969 | 0.0270 |
| 0.1712        | 39.0  | 3354 | 0.1577          | 0.0974 | 0.0273 |
| 0.1621        | 40.0  | 3440 | 0.1541          | 0.0930 | 0.0262 |
| 0.1883        | 41.0  | 3526 | 0.1545          | 0.0935 | 0.0260 |
| 0.1648        | 42.0  | 3612 | 0.1533          | 0.0942 | 0.0262 |
| 0.1648        | 43.0  | 3698 | 0.1523          | 0.0965 | 0.0266 |
| 0.1911        | 44.0  | 3784 | 0.1537          | 0.0962 | 0.0267 |
| 0.1724        | 45.0  | 3870 | 0.1497          | 0.0928 | 0.0260 |
| 0.156         | 46.0  | 3956 | 0.1492          | 0.0861 | 0.0254 |
| 0.1638        | 47.0  | 4042 | 0.1474          | 0.0906 | 0.0250 |
| 0.16          | 48.0  | 4128 | 0.1476          | 0.0881 | 0.0249 |
| 0.163         | 49.0  | 4214 | 0.1521          | 0.0891 | 0.0252 |
| 0.16          | 50.0  | 4300 | 0.1488          | 0.0913 | 0.0257 |
| 0.16          | 51.0  | 4386 | 0.1494          | 0.0910 | 0.0253 |
| 0.1492        | 52.0  | 4472 | 0.1509          | 0.0903 | 0.0256 |
| 0.1519        | 53.0  | 4558 | 0.1527          | 0.0923 | 0.0255 |
| 0.1471        | 54.0  | 4644 | 0.1468          | 0.0864 | 0.0247 |
| 0.1601        | 55.0  | 4730 | 0.1475          | 0.0898 | 0.0251 |
| 0.1452        | 56.0  | 4816 | 0.1506          | 0.0854 | 0.0243 |
| 0.1391        | 57.0  | 4902 | 0.1477          | 0.0896 | 0.0247 |
| 0.1391        | 58.0  | 4988 | 0.1467          | 0.0891 | 0.0250 |
| 0.1396        | 59.0  | 5074 | 0.1500          | 0.0913 | 0.0255 |
| 0.1424        | 60.0  | 5160 | 0.1474          | 0.0906 | 0.0248 |
| 0.1161        | 61.0  | 5246 | 0.1532          | 0.0864 | 0.0244 |
| 0.1224        | 62.0  | 5332 | 0.1532          | 0.0869 | 0.0247 |
| 0.1303        | 63.0  | 5418 | 0.1500          | 0.0888 | 0.0253 |
| 0.139         | 64.0  | 5504 | 0.1480          | 0.0869 | 0.0246 |
| 0.139         | 65.0  | 5590 | 0.1491          | 0.0898 | 0.0252 |
| 0.1418        | 66.0  | 5676 | 0.1550          | 0.0938 | 0.0256 |
| 0.1228        | 67.0  | 5762 | 0.1477          | 0.0864 | 0.0245 |
| 0.1258        | 68.0  | 5848 | 0.1482          | 0.0859 | 0.0242 |
| 0.1355        | 69.0  | 5934 | 0.1458          | 0.0878 | 0.0248 |
| 0.1297        | 70.0  | 6020 | 0.1446          | 0.0908 | 0.0253 |
| 0.1289        | 71.0  | 6106 | 0.1489          | 0.0878 | 0.0244 |
| 0.1289        | 72.0  | 6192 | 0.1482          | 0.0920 | 0.0257 |
| 0.1241        | 73.0  | 6278 | 0.1494          | 0.0938 | 0.0257 |
| 0.1233        | 74.0  | 6364 | 0.1478          | 0.0883 | 0.0248 |
| 0.1205        | 75.0  | 6450 | 0.1491          | 0.0874 | 0.0246 |
| 0.1302        | 76.0  | 6536 | 0.1534          | 0.0896 | 0.0248 |
| 0.1238        | 77.0  | 6622 | 0.1514          | 0.0896 | 0.0249 |
| 0.1224        | 78.0  | 6708 | 0.1528          | 0.0876 | 0.0247 |
| 0.1224        | 79.0  | 6794 | 0.1531          | 0.0896 | 0.0248 |
| 0.1177        | 80.0  | 6880 | 0.1537          | 0.0888 | 0.0247 |
| 0.1203        | 81.0  | 6966 | 0.1552          | 0.0896 | 0.0246 |
| 0.1095        | 82.0  | 7052 | 0.1552          | 0.0910 | 0.0250 |
| 0.1234        | 83.0  | 7138 | 0.1538          | 0.0869 | 0.0245 |
| 0.1148        | 84.0  | 7224 | 0.1543          | 0.0859 | 0.0244 |
| 0.122         | 85.0  | 7310 | 0.1545          | 0.0881 | 0.0247 |
| 0.122         | 86.0  | 7396 | 0.1536          | 0.0898 | 0.0248 |
| 0.1201        | 87.0  | 7482 | 0.1527          | 0.0920 | 0.0251 |
| 0.1152        | 88.0  | 7568 | 0.1532          | 0.0903 | 0.0251 |
| 0.1178        | 89.0  | 7654 | 0.1517          | 0.0861 | 0.0244 |
| 0.1164        | 90.0  | 7740 | 0.1541          | 0.0901 | 0.0255 |


### 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-06", "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-06
==================================================


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: 0.1446
* Wer: 0.0908
* Cer: 0.0253


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