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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-09

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: 0.2055
- Wer: 0.1135
- Cer: 0.0374

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 27.8255       | 1.0   | 86   | 6.4686          | 1.0    | 1.0    |
| 9.4861        | 2.0   | 172  | 6.1452          | 0.9703 | 0.9624 |
| 4.0433        | 3.0   | 258  | 6.5288          | 0.9552 | 0.9417 |
| 3.757         | 4.0   | 344  | 6.4359          | 1.0    | 1.0    |
| 3.5579        | 5.0   | 430  | 6.5739          | 1.0    | 1.0    |
| 3.4195        | 6.0   | 516  | 6.5713          | 0.9768 | 0.9849 |
| 3.6813        | 7.0   | 602  | 6.5877          | 0.9749 | 0.9137 |
| 3.6813        | 8.0   | 688  | 6.6049          | 0.9588 | 0.8732 |
| 3.3744        | 9.0   | 774  | 6.5856          | 0.9770 | 0.8666 |
| 3.5373        | 10.0  | 860  | 6.6468          | 0.9583 | 0.8608 |
| 3.1637        | 11.0  | 946  | 6.6958          | 0.9720 | 0.8476 |
| 3.8044        | 12.0  | 1032 | 6.6167          | 0.9703 | 0.8258 |
| 2.8859        | 13.0  | 1118 | 6.4369          | 0.9773 | 0.7946 |
| 3.4141        | 14.0  | 1204 | 6.4624          | 0.9770 | 0.7793 |
| 3.4141        | 15.0  | 1290 | 6.4052          | 0.9775 | 0.7566 |
| 3.4378        | 16.0  | 1376 | 6.3971          | 0.9787 | 0.7434 |
| 3.1892        | 17.0  | 1462 | 6.2175          | 0.9739 | 0.7629 |
| 3.1787        | 18.0  | 1548 | 4.7706          | 0.9739 | 0.7491 |
| 3.3283        | 19.0  | 1634 | 6.2475          | 0.9823 | 0.7578 |
| 3.0856        | 20.0  | 1720 | 5.3353          | 0.9698 | 0.7202 |
| 3.1698        | 21.0  | 1806 | 5.3445          | 0.9703 | 0.7256 |
| 3.1698        | 22.0  | 1892 | 3.5694          | 0.9749 | 0.7154 |
| 3.0717        | 23.0  | 1978 | 4.7239          | 0.9739 | 0.7159 |
| 3.142         | 24.0  | 2064 | 3.2030          | 0.9722 | 0.7019 |
| 2.9683        | 25.0  | 2150 | 4.7049          | 0.9622 | 0.7023 |
| 3.135         | 26.0  | 2236 | 3.2585          | 0.9605 | 0.6932 |
| 3.1443        | 27.0  | 2322 | 2.8654          | 0.9655 | 0.6901 |
| 2.7771        | 28.0  | 2408 | 3.3484          | 0.9693 | 0.6844 |
| 2.7771        | 29.0  | 2494 | 2.5680          | 0.9607 | 0.6808 |
| 2.7258        | 30.0  | 2580 | 2.6370          | 0.9634 | 0.6768 |
| 2.8003        | 31.0  | 2666 | 2.4710          | 0.9636 | 0.6717 |
| 2.8051        | 32.0  | 2752 | 2.6271          | 0.9605 | 0.6689 |
| 2.5177        | 33.0  | 2838 | 2.3597          | 0.9509 | 0.6670 |
| 2.7207        | 34.0  | 2924 | 2.3109          | 0.9571 | 0.6564 |
| 2.4472        | 35.0  | 3010 | 2.3818          | 0.9646 | 0.6348 |
| 2.4472        | 36.0  | 3096 | 2.2100          | 0.9514 | 0.6569 |
| 2.6165        | 37.0  | 3182 | 2.1373          | 0.9526 | 0.6497 |
| 2.4184        | 38.0  | 3268 | 2.0397          | 0.9459 | 0.6180 |
| 2.5079        | 39.0  | 3354 | 1.8301          | 0.9349 | 0.5742 |
| 2.3417        | 40.0  | 3440 | 1.5497          | 0.9004 | 0.4828 |
| 1.853         | 41.0  | 3526 | 1.2096          | 0.7804 | 0.3135 |
| 1.4946        | 42.0  | 3612 | 0.8933          | 0.7035 | 0.2430 |
| 1.4946        | 43.0  | 3698 | 0.7703          | 0.5532 | 0.1646 |
| 1.1791        | 44.0  | 3784 | 0.5455          | 0.4614 | 0.1339 |
| 0.9583        | 45.0  | 3870 | 0.4740          | 0.3726 | 0.1059 |
| 0.8472        | 46.0  | 3956 | 0.4110          | 0.3099 | 0.0878 |
| 0.6646        | 47.0  | 4042 | 0.3723          | 0.2900 | 0.0796 |
| 0.6322        | 48.0  | 4128 | 0.3474          | 0.2493 | 0.0701 |
| 0.5803        | 49.0  | 4214 | 0.3237          | 0.2404 | 0.0681 |
| 0.5056        | 50.0  | 4300 | 0.3052          | 0.2079 | 0.0619 |
| 0.5056        | 51.0  | 4386 | 0.2910          | 0.1892 | 0.0573 |
| 0.4646        | 52.0  | 4472 | 0.2821          | 0.1796 | 0.0542 |
| 0.4439        | 53.0  | 4558 | 0.2698          | 0.1578 | 0.0497 |
| 0.3897        | 54.0  | 4644 | 0.2598          | 0.1573 | 0.0488 |
| 0.4143        | 55.0  | 4730 | 0.2558          | 0.1456 | 0.0468 |
| 0.3624        | 56.0  | 4816 | 0.2467          | 0.1487 | 0.0465 |
| 0.3814        | 57.0  | 4902 | 0.2456          | 0.1408 | 0.0449 |
| 0.3814        | 58.0  | 4988 | 0.2434          | 0.1401 | 0.0445 |
| 0.3546        | 59.0  | 5074 | 0.2400          | 0.1315 | 0.0426 |
| 0.3368        | 60.0  | 5160 | 0.2426          | 0.1329 | 0.0427 |
| 0.3561        | 61.0  | 5246 | 0.2380          | 0.1298 | 0.0420 |
| 0.3077        | 62.0  | 5332 | 0.2306          | 0.1284 | 0.0417 |
| 0.2969        | 63.0  | 5418 | 0.2290          | 0.1257 | 0.0411 |
| 0.2857        | 64.0  | 5504 | 0.2220          | 0.1226 | 0.0398 |
| 0.2857        | 65.0  | 5590 | 0.2245          | 0.1262 | 0.0411 |
| 0.2834        | 66.0  | 5676 | 0.2223          | 0.1238 | 0.0399 |
| 0.3022        | 67.0  | 5762 | 0.2174          | 0.1226 | 0.0397 |
| 0.2479        | 68.0  | 5848 | 0.2239          | 0.1226 | 0.0395 |
| 0.2648        | 69.0  | 5934 | 0.2193          | 0.1195 | 0.0384 |
| 0.2546        | 70.0  | 6020 | 0.2124          | 0.1212 | 0.0388 |
| 0.2645        | 71.0  | 6106 | 0.2175          | 0.1219 | 0.0394 |
| 0.2645        | 72.0  | 6192 | 0.2135          | 0.1195 | 0.0383 |
| 0.2397        | 73.0  | 6278 | 0.2119          | 0.1202 | 0.0385 |
| 0.2508        | 74.0  | 6364 | 0.2118          | 0.1157 | 0.0385 |
| 0.2588        | 75.0  | 6450 | 0.2109          | 0.1207 | 0.0385 |
| 0.2556        | 76.0  | 6536 | 0.2090          | 0.1123 | 0.0376 |
| 0.2376        | 77.0  | 6622 | 0.2080          | 0.1135 | 0.0378 |
| 0.2441        | 78.0  | 6708 | 0.2100          | 0.1133 | 0.0377 |
| 0.2441        | 79.0  | 6794 | 0.2090          | 0.1133 | 0.0380 |
| 0.2158        | 80.0  | 6880 | 0.2075          | 0.1140 | 0.0377 |
| 0.227         | 81.0  | 6966 | 0.2081          | 0.1142 | 0.0375 |
| 0.2196        | 82.0  | 7052 | 0.2088          | 0.1135 | 0.0377 |
| 0.2266        | 83.0  | 7138 | 0.2127          | 0.1116 | 0.0371 |
| 0.2055        | 84.0  | 7224 | 0.2087          | 0.1128 | 0.0373 |
| 0.22          | 85.0  | 7310 | 0.2062          | 0.1147 | 0.0376 |
| 0.22          | 86.0  | 7396 | 0.2059          | 0.1137 | 0.0374 |
| 0.2055        | 87.0  | 7482 | 0.2074          | 0.1149 | 0.0376 |
| 0.2282        | 88.0  | 7568 | 0.2055          | 0.1135 | 0.0374 |
| 0.2266        | 89.0  | 7654 | 0.2078          | 0.1090 | 0.0367 |
| 0.2054        | 90.0  | 7740 | 0.2062          | 0.1111 | 0.0371 |
| 0.2164        | 91.0  | 7826 | 0.2058          | 0.1125 | 0.0373 |
| 0.2047        | 92.0  | 7912 | 0.2087          | 0.1078 | 0.0368 |
| 0.2047        | 93.0  | 7998 | 0.2074          | 0.1075 | 0.0363 |
| 0.2125        | 94.0  | 8084 | 0.2068          | 0.1099 | 0.0370 |
| 0.213         | 95.0  | 8170 | 0.2064          | 0.1102 | 0.0367 |
| 0.1905        | 96.0  | 8256 | 0.2056          | 0.1099 | 0.0366 |
| 0.1947        | 97.0  | 8342 | 0.2063          | 0.1085 | 0.0364 |
| 0.2015        | 98.0  | 8428 | 0.2064          | 0.1094 | 0.0366 |
| 0.2028        | 99.0  | 8514 | 0.2062          | 0.1099 | 0.0368 |
| 0.2001        | 100.0 | 8600 | 0.2063          | 0.1097 | 0.0368 |


### 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-09", "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-09
==================================================


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: 0.2055
* Wer: 0.1135
* Cer: 0.0374


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