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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-clean-05

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: 0.1089
- Wer: 0.0833
- Cer: 0.0226

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 28.3067       | 1.0   | 67   | 3.2489          | 1.0    | 1.0    |
| 7.0285        | 2.0   | 134  | 2.9648          | 1.0    | 1.0    |
| 3.012         | 3.0   | 201  | 2.9002          | 1.0    | 1.0    |
| 3.012         | 4.0   | 268  | 2.8732          | 1.0    | 1.0    |
| 2.91          | 5.0   | 335  | 2.7571          | 1.0    | 1.0    |
| 2.6396        | 6.0   | 402  | 1.4049          | 1.0    | 0.2939 |
| 2.6396        | 7.0   | 469  | 0.5538          | 0.2890 | 0.0712 |
| 1.2247        | 8.0   | 536  | 0.3766          | 0.2199 | 0.0541 |
| 0.7133        | 9.0   | 603  | 0.2976          | 0.1875 | 0.0479 |
| 0.7133        | 10.0  | 670  | 0.2570          | 0.1610 | 0.0424 |
| 0.5354        | 11.0  | 737  | 0.2390          | 0.1531 | 0.0403 |
| 0.4555        | 12.0  | 804  | 0.2139          | 0.1356 | 0.0363 |
| 0.4555        | 13.0  | 871  | 0.1968          | 0.1303 | 0.0340 |
| 0.4168        | 14.0  | 938  | 0.1846          | 0.1270 | 0.0341 |
| 0.3717        | 15.0  | 1005 | 0.1720          | 0.1118 | 0.0304 |
| 0.3717        | 16.0  | 1072 | 0.1735          | 0.1104 | 0.0311 |
| 0.3164        | 17.0  | 1139 | 0.1564          | 0.1121 | 0.0306 |
| 0.311         | 18.0  | 1206 | 0.1542          | 0.1055 | 0.0287 |
| 0.311         | 19.0  | 1273 | 0.1484          | 0.1071 | 0.0286 |
| 0.2748        | 20.0  | 1340 | 0.1460          | 0.1012 | 0.0271 |
| 0.2443        | 21.0  | 1407 | 0.1411          | 0.0979 | 0.0266 |
| 0.2443        | 22.0  | 1474 | 0.1397          | 0.0959 | 0.0267 |
| 0.2502        | 23.0  | 1541 | 0.1379          | 0.0923 | 0.0253 |
| 0.2362        | 24.0  | 1608 | 0.1346          | 0.0899 | 0.0251 |
| 0.2362        | 25.0  | 1675 | 0.1336          | 0.0989 | 0.0268 |
| 0.2399        | 26.0  | 1742 | 0.1290          | 0.0956 | 0.0255 |
| 0.2546        | 27.0  | 1809 | 0.1256          | 0.0906 | 0.0244 |
| 0.2546        | 28.0  | 1876 | 0.1254          | 0.0956 | 0.0256 |
| 0.2274        | 29.0  | 1943 | 0.1230          | 0.0903 | 0.0240 |
| 0.2035        | 30.0  | 2010 | 0.1199          | 0.0890 | 0.0239 |
| 0.2035        | 31.0  | 2077 | 0.1198          | 0.0893 | 0.0241 |
| 0.2137        | 32.0  | 2144 | 0.1185          | 0.0886 | 0.0246 |
| 0.1963        | 33.0  | 2211 | 0.1219          | 0.0883 | 0.0244 |
| 0.1963        | 34.0  | 2278 | 0.1193          | 0.0873 | 0.0241 |
| 0.1755        | 35.0  | 2345 | 0.1222          | 0.0853 | 0.0242 |
| 0.1956        | 36.0  | 2412 | 0.1172          | 0.0860 | 0.0240 |
| 0.1956        | 37.0  | 2479 | 0.1179          | 0.0850 | 0.0238 |
| 0.1777        | 38.0  | 2546 | 0.1175          | 0.0860 | 0.0234 |
| 0.2008        | 39.0  | 2613 | 0.1142          | 0.0837 | 0.0226 |
| 0.2008        | 40.0  | 2680 | 0.1124          | 0.0870 | 0.0234 |
| 0.1703        | 41.0  | 2747 | 0.1129          | 0.0804 | 0.0223 |
| 0.1713        | 42.0  | 2814 | 0.1128          | 0.0820 | 0.0227 |
| 0.1713        | 43.0  | 2881 | 0.1126          | 0.0797 | 0.0223 |
| 0.1827        | 44.0  | 2948 | 0.1126          | 0.0800 | 0.0225 |
| 0.1582        | 45.0  | 3015 | 0.1128          | 0.0807 | 0.0228 |
| 0.1582        | 46.0  | 3082 | 0.1109          | 0.0784 | 0.0220 |
| 0.1644        | 47.0  | 3149 | 0.1115          | 0.0840 | 0.0230 |
| 0.1598        | 48.0  | 3216 | 0.1119          | 0.0823 | 0.0231 |
| 0.1598        | 49.0  | 3283 | 0.1095          | 0.0820 | 0.0227 |
| 0.159         | 50.0  | 3350 | 0.1106          | 0.0790 | 0.0223 |
| 0.1645        | 51.0  | 3417 | 0.1089          | 0.0833 | 0.0226 |
| 0.1645        | 52.0  | 3484 | 0.1113          | 0.0787 | 0.0222 |
| 0.1518        | 53.0  | 3551 | 0.1106          | 0.0830 | 0.0230 |
| 0.137         | 54.0  | 3618 | 0.1092          | 0.0790 | 0.0222 |
| 0.137         | 55.0  | 3685 | 0.1100          | 0.0813 | 0.0227 |
| 0.1454        | 56.0  | 3752 | 0.1105          | 0.0794 | 0.0226 |
| 0.1405        | 57.0  | 3819 | 0.1112          | 0.0777 | 0.0223 |
| 0.1405        | 58.0  | 3886 | 0.1130          | 0.0800 | 0.0225 |
| 0.1369        | 59.0  | 3953 | 0.1111          | 0.0794 | 0.0223 |
| 0.1513        | 60.0  | 4020 | 0.1122          | 0.0797 | 0.0218 |
| 0.1513        | 61.0  | 4087 | 0.1106          | 0.0797 | 0.0218 |
| 0.1513        | 62.0  | 4154 | 0.1123          | 0.0777 | 0.0216 |
| 0.1285        | 63.0  | 4221 | 0.1097          | 0.0787 | 0.0223 |
| 0.1285        | 64.0  | 4288 | 0.1116          | 0.0784 | 0.0223 |
| 0.1538        | 65.0  | 4355 | 0.1118          | 0.0804 | 0.0225 |
| 0.1325        | 66.0  | 4422 | 0.1106          | 0.0800 | 0.0222 |
| 0.1325        | 67.0  | 4489 | 0.1125          | 0.0771 | 0.0219 |
| 0.138         | 68.0  | 4556 | 0.1123          | 0.0804 | 0.0225 |
| 0.1339        | 69.0  | 4623 | 0.1111          | 0.0813 | 0.0229 |
| 0.1339        | 70.0  | 4690 | 0.1115          | 0.0804 | 0.0232 |
| 0.1307        | 71.0  | 4757 | 0.1117          | 0.0820 | 0.0234 |


### 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-05", "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-05
========================================================


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: 0.1089
* Wer: 0.0833
* Cer: 0.0226


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