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# wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-03

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: 0.1260
- Wer: 0.0727
- Cer: 0.0223

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 21.1196       | 1.0   | 67   | 3.2895          | 1.0    | 1.0    |
| 7.1316        | 2.0   | 134  | 2.9795          | 1.0    | 1.0    |
| 3.0301        | 3.0   | 201  | 2.9116          | 1.0    | 1.0    |
| 3.0301        | 4.0   | 268  | 2.9080          | 1.0    | 1.0    |
| 2.9213        | 5.0   | 335  | 2.8834          | 1.0    | 1.0    |
| 2.8343        | 6.0   | 402  | 2.3289          | 0.9990 | 0.8566 |
| 2.8343        | 7.0   | 469  | 0.8053          | 0.4228 | 0.0985 |
| 1.6502        | 8.0   | 536  | 0.4598          | 0.2281 | 0.0585 |
| 0.8221        | 9.0   | 603  | 0.3539          | 0.1888 | 0.0500 |
| 0.8221        | 10.0  | 670  | 0.2864          | 0.1590 | 0.0433 |
| 0.6077        | 11.0  | 737  | 0.2527          | 0.1527 | 0.0415 |
| 0.5105        | 12.0  | 804  | 0.2362          | 0.1428 | 0.0386 |
| 0.5105        | 13.0  | 871  | 0.2161          | 0.1236 | 0.0353 |
| 0.3874        | 14.0  | 938  | 0.1950          | 0.1134 | 0.0318 |
| 0.3879        | 15.0  | 1005 | 0.1857          | 0.1074 | 0.0307 |
| 0.3879        | 16.0  | 1072 | 0.1783          | 0.1015 | 0.0294 |
| 0.3175        | 17.0  | 1139 | 0.1711          | 0.1008 | 0.0292 |
| 0.3038        | 18.0  | 1206 | 0.1633          | 0.0916 | 0.0274 |
| 0.3038        | 19.0  | 1273 | 0.1566          | 0.0883 | 0.0263 |
| 0.2751        | 20.0  | 1340 | 0.1524          | 0.0866 | 0.0263 |
| 0.2763        | 21.0  | 1407 | 0.1475          | 0.0883 | 0.0260 |
| 0.2763        | 22.0  | 1474 | 0.1437          | 0.0793 | 0.0244 |
| 0.2553        | 23.0  | 1541 | 0.1408          | 0.0793 | 0.0238 |
| 0.2475        | 24.0  | 1608 | 0.1407          | 0.0803 | 0.0241 |
| 0.2475        | 25.0  | 1675 | 0.1374          | 0.0787 | 0.0241 |
| 0.2406        | 26.0  | 1742 | 0.1404          | 0.0879 | 0.0251 |
| 0.2251        | 27.0  | 1809 | 0.1387          | 0.0813 | 0.0253 |
| 0.2251        | 28.0  | 1876 | 0.1359          | 0.0797 | 0.0242 |
| 0.2115        | 29.0  | 1943 | 0.1363          | 0.0793 | 0.0243 |
| 0.2141        | 30.0  | 2010 | 0.1341          | 0.0820 | 0.0245 |
| 0.2141        | 31.0  | 2077 | 0.1344          | 0.0797 | 0.0235 |
| 0.2031        | 32.0  | 2144 | 0.1309          | 0.0803 | 0.0244 |
| 0.2076        | 33.0  | 2211 | 0.1343          | 0.0793 | 0.0243 |
| 0.2076        | 34.0  | 2278 | 0.1308          | 0.0830 | 0.0243 |
| 0.1876        | 35.0  | 2345 | 0.1327          | 0.0777 | 0.0245 |
| 0.1901        | 36.0  | 2412 | 0.1347          | 0.0803 | 0.0248 |
| 0.1901        | 37.0  | 2479 | 0.1346          | 0.0813 | 0.0250 |
| 0.1801        | 38.0  | 2546 | 0.1305          | 0.0833 | 0.0251 |
| 0.1674        | 39.0  | 2613 | 0.1289          | 0.0793 | 0.0236 |
| 0.1674        | 40.0  | 2680 | 0.1302          | 0.0767 | 0.0238 |
| 0.1719        | 41.0  | 2747 | 0.1290          | 0.0744 | 0.0235 |
| 0.1664        | 42.0  | 2814 | 0.1321          | 0.0737 | 0.0230 |
| 0.1664        | 43.0  | 2881 | 0.1304          | 0.0764 | 0.0234 |
| 0.167         | 44.0  | 2948 | 0.1293          | 0.0721 | 0.0232 |
| 0.1611        | 45.0  | 3015 | 0.1315          | 0.0754 | 0.0234 |
| 0.1611        | 46.0  | 3082 | 0.1337          | 0.0770 | 0.0242 |
| 0.1575        | 47.0  | 3149 | 0.1314          | 0.0767 | 0.0240 |
| 0.162         | 48.0  | 3216 | 0.1312          | 0.0767 | 0.0236 |
| 0.162         | 49.0  | 3283 | 0.1333          | 0.0760 | 0.0237 |
| 0.1464        | 50.0  | 3350 | 0.1354          | 0.0701 | 0.0232 |
| 0.155         | 51.0  | 3417 | 0.1296          | 0.0737 | 0.0233 |
| 0.155         | 52.0  | 3484 | 0.1282          | 0.0740 | 0.0227 |
| 0.1474        | 53.0  | 3551 | 0.1314          | 0.0770 | 0.0234 |
| 0.1599        | 54.0  | 3618 | 0.1278          | 0.0747 | 0.0229 |
| 0.1599        | 55.0  | 3685 | 0.1306          | 0.0757 | 0.0229 |
| 0.1314        | 56.0  | 3752 | 0.1321          | 0.0747 | 0.0231 |
| 0.1366        | 57.0  | 3819 | 0.1317          | 0.0747 | 0.0231 |
| 0.1366        | 58.0  | 3886 | 0.1315          | 0.0760 | 0.0230 |
| 0.1404        | 59.0  | 3953 | 0.1278          | 0.0721 | 0.0224 |
| 0.1372        | 60.0  | 4020 | 0.1273          | 0.0721 | 0.0225 |
| 0.1372        | 61.0  | 4087 | 0.1292          | 0.0737 | 0.0229 |
| 0.1289        | 62.0  | 4154 | 0.1273          | 0.0711 | 0.0220 |
| 0.1283        | 63.0  | 4221 | 0.1279          | 0.0724 | 0.0225 |
| 0.1283        | 64.0  | 4288 | 0.1275          | 0.0707 | 0.0218 |
| 0.1299        | 65.0  | 4355 | 0.1260          | 0.0727 | 0.0223 |
| 0.1475        | 66.0  | 4422 | 0.1267          | 0.0747 | 0.0223 |
| 0.1475        | 67.0  | 4489 | 0.1282          | 0.0734 | 0.0219 |
| 0.1312        | 68.0  | 4556 | 0.1268          | 0.0731 | 0.0224 |
| 0.1315        | 69.0  | 4623 | 0.1293          | 0.0724 | 0.0219 |
| 0.1315        | 70.0  | 4690 | 0.1269          | 0.0737 | 0.0222 |
| 0.1188        | 71.0  | 4757 | 0.1291          | 0.0737 | 0.0226 |
| 0.1252        | 72.0  | 4824 | 0.1290          | 0.0754 | 0.0228 |
| 0.1252        | 73.0  | 4891 | 0.1310          | 0.0717 | 0.0224 |
| 0.1174        | 74.0  | 4958 | 0.1287          | 0.0737 | 0.0229 |
| 0.1169        | 75.0  | 5025 | 0.1273          | 0.0717 | 0.0225 |
| 0.1169        | 76.0  | 5092 | 0.1283          | 0.0737 | 0.0224 |
| 0.1211        | 77.0  | 5159 | 0.1291          | 0.0721 | 0.0227 |
| 0.1079        | 78.0  | 5226 | 0.1290          | 0.0724 | 0.0228 |
| 0.1079        | 79.0  | 5293 | 0.1286          | 0.0737 | 0.0227 |
| 0.1205        | 80.0  | 5360 | 0.1286          | 0.0724 | 0.0222 |
| 0.1234        | 81.0  | 5427 | 0.1288          | 0.0737 | 0.0225 |
| 0.1234        | 82.0  | 5494 | 0.1276          | 0.0734 | 0.0224 |
| 0.1236        | 83.0  | 5561 | 0.1280          | 0.0757 | 0.0230 |
| 0.121         | 84.0  | 5628 | 0.1288          | 0.0767 | 0.0231 |
| 0.121         | 85.0  | 5695 | 0.1298          | 0.0770 | 0.0229 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-clean-03", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us