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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-clean-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: 0.1240
- Wer: 0.0822
- Cer: 0.0258

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 25.6341       | 1.0   | 67   | 3.3417          | 1.0    | 1.0    |
| 7.4924        | 2.0   | 134  | 2.9735          | 1.0    | 1.0    |
| 3.0117        | 3.0   | 201  | 2.9203          | 1.0    | 1.0    |
| 3.0117        | 4.0   | 268  | 2.9038          | 1.0    | 1.0    |
| 2.9154        | 5.0   | 335  | 2.8627          | 1.0    | 1.0    |
| 2.7879        | 6.0   | 402  | 2.1177          | 1.0    | 0.6809 |
| 2.7879        | 7.0   | 469  | 0.6995          | 0.3645 | 0.0918 |
| 1.4902        | 8.0   | 536  | 0.4364          | 0.2452 | 0.0628 |
| 0.7612        | 9.0   | 603  | 0.3263          | 0.1858 | 0.0500 |
| 0.7612        | 10.0  | 670  | 0.2701          | 0.1576 | 0.0437 |
| 0.576         | 11.0  | 737  | 0.2434          | 0.1512 | 0.0411 |
| 0.4776        | 12.0  | 804  | 0.2220          | 0.1389 | 0.0392 |
| 0.4776        | 13.0  | 871  | 0.2046          | 0.1318 | 0.0368 |
| 0.4025        | 14.0  | 938  | 0.1913          | 0.1158 | 0.0338 |
| 0.3331        | 15.0  | 1005 | 0.1784          | 0.1124 | 0.0328 |
| 0.3331        | 16.0  | 1072 | 0.1682          | 0.1077 | 0.0320 |
| 0.3441        | 17.0  | 1139 | 0.1603          | 0.1033 | 0.0301 |
| 0.3075        | 18.0  | 1206 | 0.1527          | 0.0975 | 0.0295 |
| 0.3075        | 19.0  | 1273 | 0.1499          | 0.1077 | 0.0299 |
| 0.2895        | 20.0  | 1340 | 0.1416          | 0.0992 | 0.0289 |
| 0.2512        | 21.0  | 1407 | 0.1400          | 0.0985 | 0.0292 |
| 0.2512        | 22.0  | 1474 | 0.1389          | 0.0934 | 0.0290 |
| 0.2711        | 23.0  | 1541 | 0.1339          | 0.0910 | 0.0272 |
| 0.2491        | 24.0  | 1608 | 0.1357          | 0.0917 | 0.0280 |
| 0.2491        | 25.0  | 1675 | 0.1328          | 0.0924 | 0.0282 |
| 0.2176        | 26.0  | 1742 | 0.1320          | 0.0883 | 0.0267 |
| 0.2353        | 27.0  | 1809 | 0.1338          | 0.0904 | 0.0280 |
| 0.2353        | 28.0  | 1876 | 0.1310          | 0.0849 | 0.0273 |
| 0.2177        | 29.0  | 1943 | 0.1240          | 0.0897 | 0.0266 |
| 0.1974        | 30.0  | 2010 | 0.1284          | 0.0859 | 0.0262 |
| 0.1974        | 31.0  | 2077 | 0.1309          | 0.0856 | 0.0262 |
| 0.1956        | 32.0  | 2144 | 0.1296          | 0.0893 | 0.0269 |
| 0.2159        | 33.0  | 2211 | 0.1305          | 0.0842 | 0.0259 |
| 0.2159        | 34.0  | 2278 | 0.1281          | 0.0849 | 0.0258 |
| 0.1882        | 35.0  | 2345 | 0.1284          | 0.0853 | 0.0263 |
| 0.1833        | 36.0  | 2412 | 0.1289          | 0.0863 | 0.0261 |
| 0.1833        | 37.0  | 2479 | 0.1283          | 0.0907 | 0.0274 |
| 0.1646        | 38.0  | 2546 | 0.1282          | 0.0921 | 0.0274 |
| 0.1745        | 39.0  | 2613 | 0.1263          | 0.0815 | 0.0260 |
| 0.1745        | 40.0  | 2680 | 0.1308          | 0.0842 | 0.0263 |
| 0.1891        | 41.0  | 2747 | 0.1287          | 0.0812 | 0.0262 |
| 0.1685        | 42.0  | 2814 | 0.1300          | 0.0825 | 0.0259 |
| 0.1685        | 43.0  | 2881 | 0.1240          | 0.0822 | 0.0258 |
| 0.1569        | 44.0  | 2948 | 0.1301          | 0.0839 | 0.0263 |
| 0.1733        | 45.0  | 3015 | 0.1277          | 0.0808 | 0.0256 |
| 0.1733        | 46.0  | 3082 | 0.1268          | 0.0808 | 0.0254 |
| 0.162         | 47.0  | 3149 | 0.1263          | 0.0802 | 0.0250 |
| 0.1446        | 48.0  | 3216 | 0.1245          | 0.0795 | 0.0247 |
| 0.1446        | 49.0  | 3283 | 0.1249          | 0.0785 | 0.0249 |
| 0.1459        | 50.0  | 3350 | 0.1254          | 0.0825 | 0.0255 |
| 0.1497        | 51.0  | 3417 | 0.1272          | 0.0815 | 0.0256 |
| 0.1497        | 52.0  | 3484 | 0.1253          | 0.0822 | 0.0250 |
| 0.1359        | 53.0  | 3551 | 0.1312          | 0.0805 | 0.0251 |
| 0.1544        | 54.0  | 3618 | 0.1333          | 0.0802 | 0.0253 |
| 0.1544        | 55.0  | 3685 | 0.1319          | 0.0832 | 0.0260 |
| 0.1382        | 56.0  | 3752 | 0.1330          | 0.0832 | 0.0258 |
| 0.1451        | 57.0  | 3819 | 0.1313          | 0.0815 | 0.0251 |
| 0.1451        | 58.0  | 3886 | 0.1310          | 0.0815 | 0.0254 |
| 0.1341        | 59.0  | 3953 | 0.1341          | 0.0785 | 0.0251 |
| 0.1498        | 60.0  | 4020 | 0.1296          | 0.0849 | 0.0260 |
| 0.1498        | 61.0  | 4087 | 0.1303          | 0.0785 | 0.0250 |
| 0.1435        | 62.0  | 4154 | 0.1280          | 0.0798 | 0.0252 |
| 0.1407        | 63.0  | 4221 | 0.1301          | 0.0815 | 0.0248 |


### 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-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-clean-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: 0.1240
* Wer: 0.0822
* Cer: 0.0258


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