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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-02

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: 0.3867
- Wer: 0.2241
- Cer: 0.0617

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 26.8614       | 1.0   | 86   | 4.1075          | 1.0    | 1.0    |
| 9.1211        | 2.0   | 172  | 3.3065          | 0.9589 | 0.9337 |
| 4.4259        | 3.0   | 258  | 3.1101          | 0.9936 | 0.9963 |
| 4.0065        | 4.0   | 344  | 3.1175          | 1.0    | 1.0    |
| 4.1425        | 5.0   | 430  | 2.9843          | 1.0    | 1.0    |
| 3.5388        | 6.0   | 516  | 2.9247          | 0.9816 | 0.9890 |
| 4.1389        | 7.0   | 602  | 2.9091          | 0.9560 | 0.9350 |
| 4.1389        | 8.0   | 688  | 2.9170          | 0.9550 | 0.9197 |
| 3.748         | 9.0   | 774  | 2.8611          | 0.9567 | 0.8971 |
| 3.8952        | 10.0  | 860  | 2.8640          | 0.9616 | 0.8726 |
| 3.6117        | 11.0  | 946  | 2.8944          | 0.9684 | 0.8691 |
| 4.1201        | 12.0  | 1032 | 2.8589          | 0.9704 | 0.8534 |
| 3.8543        | 13.0  | 1118 | 2.8410          | 0.9748 | 0.8257 |
| 3.8936        | 14.0  | 1204 | 2.7883          | 0.9763 | 0.7919 |
| 3.8936        | 15.0  | 1290 | 2.7922          | 0.9660 | 0.7798 |
| 3.5855        | 16.0  | 1376 | 2.7786          | 0.9814 | 0.7712 |
| 3.9366        | 17.0  | 1462 | 2.7948          | 0.9670 | 0.7707 |
| 3.4395        | 18.0  | 1548 | 2.7546          | 0.9533 | 0.7897 |
| 3.66          | 19.0  | 1634 | 2.7427          | 0.9701 | 0.7759 |
| 3.5659        | 20.0  | 1720 | 2.7296          | 0.9665 | 0.7677 |
| 3.3732        | 21.0  | 1806 | 2.6593          | 0.9785 | 0.7440 |
| 3.3732        | 22.0  | 1892 | 2.6909          | 0.9648 | 0.7411 |
| 3.6926        | 23.0  | 1978 | 2.6636          | 0.9697 | 0.7353 |
| 3.5373        | 24.0  | 2064 | 2.6966          | 0.9670 | 0.7250 |
| 3.53          | 25.0  | 2150 | 2.6241          | 0.9741 | 0.7176 |
| 3.3077        | 26.0  | 2236 | 2.6299          | 0.9628 | 0.7247 |
| 3.0885        | 27.0  | 2322 | 2.6890          | 0.9697 | 0.7280 |
| 3.1043        | 28.0  | 2408 | 2.6306          | 0.9594 | 0.7102 |
| 3.1043        | 29.0  | 2494 | 2.5748          | 0.9672 | 0.6988 |
| 3.0444        | 30.0  | 2580 | 2.5896          | 0.9682 | 0.6999 |
| 2.8768        | 31.0  | 2666 | 2.5596          | 0.9746 | 0.6906 |
| 2.9169        | 32.0  | 2752 | 2.5224          | 0.9675 | 0.6819 |
| 2.5564        | 33.0  | 2838 | 2.4848          | 0.9645 | 0.6876 |
| 2.8245        | 34.0  | 2924 | 2.4788          | 0.9621 | 0.6831 |
| 2.5474        | 35.0  | 3010 | 2.4651          | 0.9660 | 0.6664 |
| 2.5474        | 36.0  | 3096 | 2.4165          | 0.9540 | 0.6799 |
| 2.7496        | 37.0  | 3182 | 2.3682          | 0.9621 | 0.6593 |
| 2.5884        | 38.0  | 3268 | 2.3454          | 0.9591 | 0.6501 |
| 2.5919        | 39.0  | 3354 | 2.3438          | 0.9633 | 0.6536 |
| 2.686         | 40.0  | 3440 | 2.2963          | 0.9520 | 0.6457 |
| 2.3805        | 41.0  | 3526 | 2.2551          | 0.9599 | 0.6387 |
| 2.5631        | 42.0  | 3612 | 2.2398          | 0.9530 | 0.6330 |
| 2.5631        | 43.0  | 3698 | 2.1721          | 0.9489 | 0.6122 |
| 2.3876        | 44.0  | 3784 | 2.2873          | 0.9476 | 0.6192 |
| 2.4841        | 45.0  | 3870 | 2.0955          | 0.9457 | 0.6164 |
| 2.4018        | 46.0  | 3956 | 2.0725          | 0.9381 | 0.6160 |
| 2.2015        | 47.0  | 4042 | 2.1368          | 0.9369 | 0.5877 |
| 2.181         | 48.0  | 4128 | 2.0029          | 0.9327 | 0.5861 |
| 2.1738        | 49.0  | 4214 | 2.0337          | 0.9435 | 0.6281 |
| 2.2699        | 50.0  | 4300 | 1.8319          | 0.9320 | 0.5501 |
| 2.2699        | 51.0  | 4386 | 1.7263          | 0.9068 | 0.5057 |
| 1.9857        | 52.0  | 4472 | 1.6162          | 0.8992 | 0.4963 |
| 1.8812        | 53.0  | 4558 | 1.4509          | 0.8505 | 0.4150 |
| 1.8218        | 54.0  | 4644 | 1.3207          | 0.8375 | 0.3834 |
| 1.696         | 55.0  | 4730 | 1.2113          | 0.7827 | 0.3306 |
| 1.613         | 56.0  | 4816 | 1.1816          | 0.7379 | 0.2941 |
| 1.4016        | 57.0  | 4902 | 1.0539          | 0.7000 | 0.2565 |
| 1.4016        | 58.0  | 4988 | 0.9564          | 0.6472 | 0.2200 |
| 1.3268        | 59.0  | 5074 | 0.9172          | 0.6144 | 0.2026 |
| 1.2608        | 60.0  | 5160 | 0.7483          | 0.5664 | 0.1802 |
| 1.1512        | 61.0  | 5246 | 0.7947          | 0.5270 | 0.1609 |
| 1.1152        | 62.0  | 5332 | 0.7427          | 0.4965 | 0.1496 |
| 1.0352        | 63.0  | 5418 | 0.7145          | 0.4593 | 0.1372 |
| 1.0375        | 64.0  | 5504 | 0.5751          | 0.4309 | 0.1266 |
| 1.0375        | 65.0  | 5590 | 0.5847          | 0.4426 | 0.1270 |
| 0.9703        | 66.0  | 5676 | 0.6924          | 0.4177 | 0.1214 |
| 0.9189        | 67.0  | 5762 | 0.6237          | 0.3802 | 0.1097 |
| 0.9436        | 68.0  | 5848 | 0.6301          | 0.3531 | 0.1037 |
| 0.923         | 69.0  | 5934 | 0.5813          | 0.3394 | 0.0968 |
| 0.8563        | 70.0  | 6020 | 0.5515          | 0.3323 | 0.0923 |
| 0.805         | 71.0  | 6106 | 0.4729          | 0.3171 | 0.0888 |
| 0.805         | 72.0  | 6192 | 0.5380          | 0.3017 | 0.0840 |
| 0.7711        | 73.0  | 6278 | 0.4278          | 0.2924 | 0.0819 |
| 0.892         | 74.0  | 6364 | 0.5463          | 0.2765 | 0.0780 |
| 0.7319        | 75.0  | 6450 | 0.5149          | 0.2782 | 0.0771 |
| 0.7468        | 76.0  | 6536 | 0.5249          | 0.2674 | 0.0754 |
| 0.7193        | 77.0  | 6622 | 0.5031          | 0.2542 | 0.0720 |
| 0.6805        | 78.0  | 6708 | 0.3962          | 0.2471 | 0.0692 |
| 0.6805        | 79.0  | 6794 | 0.5201          | 0.2498 | 0.0707 |
| 0.6853        | 80.0  | 6880 | 0.5131          | 0.2408 | 0.0676 |
| 0.6965        | 81.0  | 6966 | 0.4295          | 0.2359 | 0.0658 |
| 0.5952        | 82.0  | 7052 | 0.4917          | 0.2388 | 0.0672 |
| 0.6305        | 83.0  | 7138 | 0.4658          | 0.2356 | 0.0664 |
| 0.6792        | 84.0  | 7224 | 0.4270          | 0.2246 | 0.0639 |
| 0.6227        | 85.0  | 7310 | 0.4015          | 0.2266 | 0.0629 |
| 0.6227        | 86.0  | 7396 | 0.3867          | 0.2241 | 0.0617 |
| 0.6235        | 87.0  | 7482 | 0.4972          | 0.2251 | 0.0627 |
| 0.6418        | 88.0  | 7568 | 0.5182          | 0.2312 | 0.0638 |
| 0.5944        | 89.0  | 7654 | 0.5077          | 0.2224 | 0.0623 |
| 0.6003        | 90.0  | 7740 | 0.5102          | 0.2207 | 0.0617 |
| 0.6756        | 91.0  | 7826 | 0.4906          | 0.2136 | 0.0599 |
| 0.6084        | 92.0  | 7912 | 0.4829          | 0.2200 | 0.0614 |
| 0.6084        | 93.0  | 7998 | 0.4089          | 0.2143 | 0.0600 |
| 0.5737        | 94.0  | 8084 | 0.4178          | 0.2165 | 0.0601 |
| 0.594         | 95.0  | 8170 | 0.3984          | 0.2163 | 0.0602 |
| 0.5532        | 96.0  | 8256 | 0.4473          | 0.2143 | 0.0600 |
| 0.5858        | 97.0  | 8342 | 0.4752          | 0.2165 | 0.0604 |
| 0.5568        | 98.0  | 8428 | 0.4746          | 0.2146 | 0.0602 |
| 0.5664        | 99.0  | 8514 | 0.4429          | 0.2114 | 0.0593 |
| 0.6462        | 100.0 | 8600 | 0.4549          | 0.2143 | 0.0597 |


### 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-02", "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-02
==================================================


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: 0.3867
* Wer: 0.2241
* Cer: 0.0617


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