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# wav2vec2-large-xlsr-mecita-coraa-portuguese-all-clean-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: 0.1158
- Wer: 0.0938
- Cer: 0.0252

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 25.9355       | 1.0   | 67   | 3.6433          | 1.0    | 1.0    |
| 9.9266        | 2.0   | 134  | 3.0004          | 1.0    | 1.0    |
| 3.0568        | 3.0   | 201  | 2.9226          | 1.0    | 1.0    |
| 3.0568        | 4.0   | 268  | 2.8955          | 1.0    | 1.0    |
| 2.9257        | 5.0   | 335  | 2.8838          | 1.0    | 1.0    |
| 2.8908        | 6.0   | 402  | 2.8047          | 1.0    | 1.0    |
| 2.8908        | 7.0   | 469  | 1.8146          | 1.0    | 0.5476 |
| 2.3697        | 8.0   | 536  | 0.6459          | 0.3310 | 0.0821 |
| 1.0965        | 9.0   | 603  | 0.4066          | 0.2195 | 0.0544 |
| 1.0965        | 10.0  | 670  | 0.3180          | 0.1903 | 0.0469 |
| 0.6221        | 11.0  | 737  | 0.2729          | 0.1719 | 0.0440 |
| 0.5212        | 12.0  | 804  | 0.2349          | 0.1584 | 0.0400 |
| 0.5212        | 13.0  | 871  | 0.2107          | 0.1389 | 0.0367 |
| 0.4267        | 14.0  | 938  | 0.2016          | 0.1365 | 0.0365 |
| 0.371         | 15.0  | 1005 | 0.1938          | 0.1289 | 0.0354 |
| 0.371         | 16.0  | 1072 | 0.1803          | 0.1205 | 0.0323 |
| 0.3495        | 17.0  | 1139 | 0.1746          | 0.1167 | 0.0314 |
| 0.3183        | 18.0  | 1206 | 0.1715          | 0.1105 | 0.0312 |
| 0.3183        | 19.0  | 1273 | 0.1586          | 0.1063 | 0.0297 |
| 0.2914        | 20.0  | 1340 | 0.1537          | 0.1014 | 0.0280 |
| 0.2909        | 21.0  | 1407 | 0.1475          | 0.1004 | 0.0280 |
| 0.2909        | 22.0  | 1474 | 0.1434          | 0.1056 | 0.0284 |
| 0.2614        | 23.0  | 1541 | 0.1428          | 0.1073 | 0.0285 |
| 0.2434        | 24.0  | 1608 | 0.1444          | 0.1025 | 0.0282 |
| 0.2434        | 25.0  | 1675 | 0.1413          | 0.1000 | 0.0275 |
| 0.2423        | 26.0  | 1742 | 0.1412          | 0.1000 | 0.0278 |
| 0.2297        | 27.0  | 1809 | 0.1385          | 0.0948 | 0.0258 |
| 0.2297        | 28.0  | 1876 | 0.1371          | 0.0966 | 0.0268 |
| 0.211         | 29.0  | 1943 | 0.1359          | 0.1000 | 0.0274 |
| 0.2101        | 30.0  | 2010 | 0.1310          | 0.0966 | 0.0260 |
| 0.2101        | 31.0  | 2077 | 0.1319          | 0.0986 | 0.0277 |
| 0.2004        | 32.0  | 2144 | 0.1292          | 0.0924 | 0.0256 |
| 0.2064        | 33.0  | 2211 | 0.1296          | 0.1000 | 0.0268 |
| 0.2064        | 34.0  | 2278 | 0.1313          | 0.0990 | 0.0266 |
| 0.1841        | 35.0  | 2345 | 0.1260          | 0.0952 | 0.0256 |
| 0.1855        | 36.0  | 2412 | 0.1265          | 0.0973 | 0.0263 |
| 0.1855        | 37.0  | 2479 | 0.1283          | 0.0959 | 0.0257 |
| 0.1786        | 38.0  | 2546 | 0.1265          | 0.0966 | 0.0265 |
| 0.1691        | 39.0  | 2613 | 0.1242          | 0.0893 | 0.0246 |
| 0.1691        | 40.0  | 2680 | 0.1264          | 0.0959 | 0.0262 |
| 0.1694        | 41.0  | 2747 | 0.1263          | 0.0945 | 0.0262 |
| 0.1654        | 42.0  | 2814 | 0.1263          | 0.0969 | 0.0261 |
| 0.1654        | 43.0  | 2881 | 0.1262          | 0.0948 | 0.0257 |
| 0.1624        | 44.0  | 2948 | 0.1234          | 0.0920 | 0.0247 |
| 0.1705        | 45.0  | 3015 | 0.1242          | 0.0966 | 0.0257 |
| 0.1705        | 46.0  | 3082 | 0.1224          | 0.0959 | 0.0252 |
| 0.1664        | 47.0  | 3149 | 0.1278          | 0.0962 | 0.0261 |
| 0.1603        | 48.0  | 3216 | 0.1231          | 0.0948 | 0.0258 |
| 0.1603        | 49.0  | 3283 | 0.1273          | 0.0955 | 0.0257 |
| 0.1448        | 50.0  | 3350 | 0.1224          | 0.0924 | 0.0248 |
| 0.1764        | 51.0  | 3417 | 0.1198          | 0.0945 | 0.0247 |
| 0.1764        | 52.0  | 3484 | 0.1227          | 0.0986 | 0.0260 |
| 0.1454        | 53.0  | 3551 | 0.1205          | 0.0952 | 0.0249 |
| 0.1539        | 54.0  | 3618 | 0.1203          | 0.0934 | 0.0245 |
| 0.1539        | 55.0  | 3685 | 0.1225          | 0.0973 | 0.0252 |
| 0.1532        | 56.0  | 3752 | 0.1214          | 0.0948 | 0.0255 |
| 0.142         | 57.0  | 3819 | 0.1194          | 0.0941 | 0.0253 |
| 0.142         | 58.0  | 3886 | 0.1200          | 0.0969 | 0.0250 |
| 0.1512        | 59.0  | 3953 | 0.1178          | 0.0917 | 0.0249 |
| 0.1276        | 60.0  | 4020 | 0.1158          | 0.0938 | 0.0252 |
| 0.1276        | 61.0  | 4087 | 0.1189          | 0.0896 | 0.0249 |
| 0.1436        | 62.0  | 4154 | 0.1223          | 0.0931 | 0.0254 |
| 0.1418        | 63.0  | 4221 | 0.1216          | 0.0938 | 0.0254 |
| 0.1418        | 64.0  | 4288 | 0.1205          | 0.0917 | 0.0252 |
| 0.1449        | 65.0  | 4355 | 0.1222          | 0.0900 | 0.0247 |
| 0.1392        | 66.0  | 4422 | 0.1215          | 0.0955 | 0.0256 |
| 0.1392        | 67.0  | 4489 | 0.1222          | 0.0955 | 0.0255 |
| 0.1293        | 68.0  | 4556 | 0.1204          | 0.0934 | 0.0254 |
| 0.1329        | 69.0  | 4623 | 0.1259          | 0.0924 | 0.0250 |
| 0.1329        | 70.0  | 4690 | 0.1274          | 0.0952 | 0.0256 |
| 0.1264        | 71.0  | 4757 | 0.1247          | 0.0903 | 0.0252 |
| 0.1396        | 72.0  | 4824 | 0.1205          | 0.0924 | 0.0252 |
| 0.1396        | 73.0  | 4891 | 0.1202          | 0.0945 | 0.0252 |
| 0.1399        | 74.0  | 4958 | 0.1192          | 0.0938 | 0.0252 |
| 0.1285        | 75.0  | 5025 | 0.1194          | 0.0962 | 0.0254 |
| 0.1285        | 76.0  | 5092 | 0.1193          | 0.0945 | 0.0254 |
| 0.1245        | 77.0  | 5159 | 0.1186          | 0.0910 | 0.0246 |
| 0.1254        | 78.0  | 5226 | 0.1181          | 0.0917 | 0.0245 |
| 0.1254        | 79.0  | 5293 | 0.1187          | 0.0903 | 0.0241 |
| 0.1334        | 80.0  | 5360 | 0.1189          | 0.0917 | 0.0247 |


### 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-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-clean-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: 0.1158
* Wer: 0.0938
* Cer: 0.0252


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