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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-clean-01

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: 0.1551
- Wer: 0.0980
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 28.9237       | 1.0   | 67   | 9.6707          | 1.0    | 0.9866 |
| 12.6709       | 2.0   | 134  | 3.2494          | 1.0    | 1.0    |
| 3.5707        | 3.0   | 201  | 2.9468          | 1.0    | 1.0    |
| 3.5707        | 4.0   | 268  | 2.9034          | 1.0    | 1.0    |
| 2.9301        | 5.0   | 335  | 2.9156          | 1.0    | 1.0    |
| 2.8833        | 6.0   | 402  | 2.7990          | 1.0    | 1.0    |
| 2.8833        | 7.0   | 469  | 2.1014          | 0.9969 | 0.9049 |
| 2.4442        | 8.0   | 536  | 0.7351          | 0.5621 | 0.1277 |
| 1.092         | 9.0   | 603  | 0.4375          | 0.2230 | 0.0596 |
| 1.092         | 10.0  | 670  | 0.3387          | 0.1916 | 0.0521 |
| 0.6709        | 11.0  | 737  | 0.3004          | 0.1727 | 0.0468 |
| 0.5134        | 12.0  | 804  | 0.2680          | 0.1612 | 0.0447 |
| 0.5134        | 13.0  | 871  | 0.2513          | 0.1413 | 0.0396 |
| 0.4219        | 14.0  | 938  | 0.2376          | 0.1315 | 0.0373 |
| 0.3635        | 15.0  | 1005 | 0.2272          | 0.1298 | 0.0365 |
| 0.3635        | 16.0  | 1072 | 0.2160          | 0.1301 | 0.0355 |
| 0.3612        | 17.0  | 1139 | 0.2079          | 0.1183 | 0.0339 |
| 0.3203        | 18.0  | 1206 | 0.2084          | 0.1165 | 0.0335 |
| 0.3203        | 19.0  | 1273 | 0.1959          | 0.1137 | 0.0318 |
| 0.2709        | 20.0  | 1340 | 0.1930          | 0.1099 | 0.0308 |
| 0.2879        | 21.0  | 1407 | 0.1952          | 0.1096 | 0.0312 |
| 0.2879        | 22.0  | 1474 | 0.1923          | 0.1096 | 0.0314 |
| 0.2493        | 23.0  | 1541 | 0.1877          | 0.1078 | 0.0315 |
| 0.2349        | 24.0  | 1608 | 0.1761          | 0.1085 | 0.0316 |
| 0.2349        | 25.0  | 1675 | 0.1789          | 0.1085 | 0.0310 |
| 0.2401        | 26.0  | 1742 | 0.1766          | 0.1005 | 0.0298 |
| 0.235         | 27.0  | 1809 | 0.1730          | 0.1019 | 0.0297 |
| 0.235         | 28.0  | 1876 | 0.1836          | 0.1061 | 0.0304 |
| 0.2073        | 29.0  | 1943 | 0.1726          | 0.1050 | 0.0295 |
| 0.1965        | 30.0  | 2010 | 0.1795          | 0.1008 | 0.0294 |
| 0.1965        | 31.0  | 2077 | 0.1822          | 0.1022 | 0.0297 |
| 0.2129        | 32.0  | 2144 | 0.1686          | 0.1005 | 0.0286 |
| 0.1918        | 33.0  | 2211 | 0.1575          | 0.0998 | 0.0282 |
| 0.1918        | 34.0  | 2278 | 0.1687          | 0.1022 | 0.0292 |
| 0.1974        | 35.0  | 2345 | 0.1576          | 0.1019 | 0.0281 |
| 0.1935        | 36.0  | 2412 | 0.1626          | 0.0998 | 0.0281 |
| 0.1935        | 37.0  | 2479 | 0.1572          | 0.0970 | 0.0282 |
| 0.1881        | 38.0  | 2546 | 0.1552          | 0.0984 | 0.0278 |
| 0.1743        | 39.0  | 2613 | 0.1692          | 0.1001 | 0.0289 |
| 0.1743        | 40.0  | 2680 | 0.1582          | 0.0970 | 0.0282 |
| 0.1667        | 41.0  | 2747 | 0.1616          | 0.0991 | 0.0288 |
| 0.1588        | 42.0  | 2814 | 0.1729          | 0.0984 | 0.0294 |
| 0.1588        | 43.0  | 2881 | 0.1601          | 0.0956 | 0.0282 |
| 0.1834        | 44.0  | 2948 | 0.1664          | 0.0942 | 0.0275 |
| 0.1769        | 45.0  | 3015 | 0.1578          | 0.1015 | 0.0282 |
| 0.1769        | 46.0  | 3082 | 0.1612          | 0.0956 | 0.0276 |
| 0.1677        | 47.0  | 3149 | 0.1551          | 0.0980 | 0.0274 |
| 0.1515        | 48.0  | 3216 | 0.1552          | 0.0956 | 0.0271 |
| 0.1515        | 49.0  | 3283 | 0.1554          | 0.0973 | 0.0277 |
| 0.1558        | 50.0  | 3350 | 0.1631          | 0.0977 | 0.0282 |
| 0.1566        | 51.0  | 3417 | 0.1657          | 0.0984 | 0.0281 |
| 0.1566        | 52.0  | 3484 | 0.1680          | 0.0984 | 0.0278 |
| 0.1384        | 53.0  | 3551 | 0.1689          | 0.0977 | 0.0282 |
| 0.1414        | 54.0  | 3618 | 0.1688          | 0.0980 | 0.0280 |
| 0.1414        | 55.0  | 3685 | 0.1667          | 0.0980 | 0.0279 |
| 0.1357        | 56.0  | 3752 | 0.1693          | 0.0973 | 0.0277 |
| 0.1473        | 57.0  | 3819 | 0.1644          | 0.0960 | 0.0277 |
| 0.1473        | 58.0  | 3886 | 0.1695          | 0.0980 | 0.0281 |
| 0.138         | 59.0  | 3953 | 0.1750          | 0.0998 | 0.0284 |
| 0.1319        | 60.0  | 4020 | 0.1707          | 0.0977 | 0.0279 |
| 0.1319        | 61.0  | 4087 | 0.1705          | 0.0984 | 0.0277 |
| 0.1381        | 62.0  | 4154 | 0.1590          | 0.1001 | 0.0278 |
| 0.1278        | 63.0  | 4221 | 0.1656          | 0.1012 | 0.0281 |
| 0.1278        | 64.0  | 4288 | 0.1658          | 0.0967 | 0.0277 |
| 0.1387        | 65.0  | 4355 | 0.1680          | 0.1001 | 0.0284 |
| 0.1342        | 66.0  | 4422 | 0.1689          | 0.0967 | 0.0277 |
| 0.1342        | 67.0  | 4489 | 0.1736          | 0.0980 | 0.0281 |


### 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-01", "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-01
========================================================


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: 0.1551
* Wer: 0.0980
* 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



### 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