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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-clean-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: 0.1098
- Wer: 0.0866
- Cer: 0.0228

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 29.0965       | 1.0   | 67   | 8.2084          | 1.0    | 0.9866 |
| 12.5066       | 2.0   | 134  | 5.8509          | 1.0    | 0.9760 |
| 5.3711        | 3.0   | 201  | 3.0401          | 1.0    | 1.0    |
| 5.3711        | 4.0   | 268  | 2.9035          | 1.0    | 1.0    |
| 2.973         | 5.0   | 335  | 2.8834          | 1.0    | 1.0    |
| 2.9146        | 6.0   | 402  | 2.8791          | 1.0    | 1.0    |
| 2.9146        | 7.0   | 469  | 2.8697          | 1.0    | 1.0    |
| 2.8879        | 8.0   | 536  | 2.7420          | 1.0    | 1.0    |
| 2.7039        | 9.0   | 603  | 2.0292          | 0.9997 | 0.8203 |
| 2.7039        | 10.0  | 670  | 0.7833          | 0.9461 | 0.2105 |
| 1.5273        | 11.0  | 737  | 0.4157          | 0.2626 | 0.0603 |
| 0.744         | 12.0  | 804  | 0.2995          | 0.1683 | 0.0441 |
| 0.744         | 13.0  | 871  | 0.2560          | 0.1587 | 0.0422 |
| 0.5569        | 14.0  | 938  | 0.2261          | 0.1425 | 0.0381 |
| 0.462         | 15.0  | 1005 | 0.2143          | 0.1366 | 0.0372 |
| 0.462         | 16.0  | 1072 | 0.1977          | 0.1270 | 0.0339 |
| 0.3805        | 17.0  | 1139 | 0.1815          | 0.1227 | 0.0324 |
| 0.3617        | 18.0  | 1206 | 0.1712          | 0.1118 | 0.0300 |
| 0.3617        | 19.0  | 1273 | 0.1592          | 0.1095 | 0.0288 |
| 0.3152        | 20.0  | 1340 | 0.1549          | 0.1091 | 0.0277 |
| 0.2768        | 21.0  | 1407 | 0.1455          | 0.1015 | 0.0266 |
| 0.2768        | 22.0  | 1474 | 0.1485          | 0.0992 | 0.0262 |
| 0.2779        | 23.0  | 1541 | 0.1418          | 0.1048 | 0.0271 |
| 0.2605        | 24.0  | 1608 | 0.1376          | 0.0926 | 0.0249 |
| 0.2605        | 25.0  | 1675 | 0.1358          | 0.0995 | 0.0253 |
| 0.259         | 26.0  | 1742 | 0.1360          | 0.0956 | 0.0249 |
| 0.2669        | 27.0  | 1809 | 0.1362          | 0.0933 | 0.0251 |
| 0.2669        | 28.0  | 1876 | 0.1304          | 0.0956 | 0.0247 |
| 0.2454        | 29.0  | 1943 | 0.1289          | 0.0959 | 0.0248 |
| 0.2177        | 30.0  | 2010 | 0.1325          | 0.0959 | 0.0251 |
| 0.2177        | 31.0  | 2077 | 0.1260          | 0.0919 | 0.0242 |
| 0.2292        | 32.0  | 2144 | 0.1239          | 0.0919 | 0.0249 |
| 0.2058        | 33.0  | 2211 | 0.1259          | 0.0893 | 0.0243 |
| 0.2058        | 34.0  | 2278 | 0.1217          | 0.0853 | 0.0231 |
| 0.1865        | 35.0  | 2345 | 0.1214          | 0.0883 | 0.0236 |
| 0.204         | 36.0  | 2412 | 0.1195          | 0.0843 | 0.0237 |
| 0.204         | 37.0  | 2479 | 0.1204          | 0.0883 | 0.0243 |
| 0.1856        | 38.0  | 2546 | 0.1210          | 0.0886 | 0.0244 |
| 0.2094        | 39.0  | 2613 | 0.1188          | 0.0866 | 0.0239 |
| 0.2094        | 40.0  | 2680 | 0.1144          | 0.0860 | 0.0231 |
| 0.1772        | 41.0  | 2747 | 0.1137          | 0.0866 | 0.0235 |
| 0.1768        | 42.0  | 2814 | 0.1168          | 0.0886 | 0.0240 |
| 0.1768        | 43.0  | 2881 | 0.1169          | 0.0896 | 0.0240 |
| 0.1868        | 44.0  | 2948 | 0.1148          | 0.0896 | 0.0239 |
| 0.163         | 45.0  | 3015 | 0.1146          | 0.0896 | 0.0235 |
| 0.163         | 46.0  | 3082 | 0.1141          | 0.0896 | 0.0240 |
| 0.1661        | 47.0  | 3149 | 0.1170          | 0.0903 | 0.0239 |
| 0.1626        | 48.0  | 3216 | 0.1149          | 0.0856 | 0.0231 |
| 0.1626        | 49.0  | 3283 | 0.1156          | 0.0843 | 0.0231 |
| 0.1611        | 50.0  | 3350 | 0.1161          | 0.0863 | 0.0235 |
| 0.1707        | 51.0  | 3417 | 0.1140          | 0.0840 | 0.0228 |
| 0.1707        | 52.0  | 3484 | 0.1129          | 0.0863 | 0.0236 |
| 0.1571        | 53.0  | 3551 | 0.1117          | 0.0856 | 0.0228 |
| 0.142         | 54.0  | 3618 | 0.1103          | 0.0810 | 0.0222 |
| 0.142         | 55.0  | 3685 | 0.1098          | 0.0866 | 0.0228 |
| 0.1486        | 56.0  | 3752 | 0.1102          | 0.0813 | 0.0224 |
| 0.1464        | 57.0  | 3819 | 0.1112          | 0.0823 | 0.0224 |
| 0.1464        | 58.0  | 3886 | 0.1149          | 0.0820 | 0.0226 |
| 0.143         | 59.0  | 3953 | 0.1126          | 0.0823 | 0.0227 |
| 0.1533        | 60.0  | 4020 | 0.1125          | 0.0823 | 0.0224 |
| 0.1533        | 61.0  | 4087 | 0.1118          | 0.0800 | 0.0221 |
| 0.1547        | 62.0  | 4154 | 0.1131          | 0.0790 | 0.0219 |
| 0.1313        | 63.0  | 4221 | 0.1111          | 0.0800 | 0.0223 |
| 0.1313        | 64.0  | 4288 | 0.1111          | 0.0810 | 0.0223 |
| 0.1573        | 65.0  | 4355 | 0.1115          | 0.0807 | 0.0222 |
| 0.1358        | 66.0  | 4422 | 0.1107          | 0.0823 | 0.0222 |
| 0.1358        | 67.0  | 4489 | 0.1137          | 0.0820 | 0.0223 |
| 0.1386        | 68.0  | 4556 | 0.1135          | 0.0820 | 0.0225 |
| 0.1351        | 69.0  | 4623 | 0.1123          | 0.0810 | 0.0220 |
| 0.1351        | 70.0  | 4690 | 0.1121          | 0.0830 | 0.0225 |
| 0.1312        | 71.0  | 4757 | 0.1129          | 0.0837 | 0.0226 |
| 0.1295        | 72.0  | 4824 | 0.1118          | 0.0804 | 0.0224 |
| 0.1295        | 73.0  | 4891 | 0.1120          | 0.0797 | 0.0222 |
| 0.15          | 74.0  | 4958 | 0.1127          | 0.0810 | 0.0224 |
| 0.1291        | 75.0  | 5025 | 0.1146          | 0.0790 | 0.0220 |


### 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-clean-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-clean-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: 0.1098
* Wer: 0.0866
* Cer: 0.0228


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