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# wav2vec2-large-xlsr-mec-ita-coraa-portuguese-all-07

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: 0.2122
- Wer: 0.0947
- Cer: 0.0328

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 30.5249       | 1.0   | 86   | 3.3024          | 1.0    | 1.0    |
| 7.7867        | 2.0   | 172  | 2.9628          | 1.0    | 1.0    |
| 3.0424        | 3.0   | 258  | 2.9183          | 1.0    | 1.0    |
| 2.9279        | 4.0   | 344  | 2.8627          | 1.0    | 1.0    |
| 2.782         | 5.0   | 430  | 1.2256          | 0.9193 | 0.2503 |
| 1.4877        | 6.0   | 516  | 0.5729          | 0.2641 | 0.0747 |
| 0.8015        | 7.0   | 602  | 0.4157          | 0.1947 | 0.0561 |
| 0.8015        | 8.0   | 688  | 0.3609          | 0.1720 | 0.0513 |
| 0.6062        | 9.0   | 774  | 0.3232          | 0.1593 | 0.0477 |
| 0.5305        | 10.0  | 860  | 0.3114          | 0.1516 | 0.0463 |
| 0.4351        | 11.0  | 946  | 0.2865          | 0.1296 | 0.0411 |
| 0.4126        | 12.0  | 1032 | 0.2778          | 0.1267 | 0.0404 |
| 0.3665        | 13.0  | 1118 | 0.2677          | 0.1207 | 0.0391 |
| 0.3415        | 14.0  | 1204 | 0.2625          | 0.1231 | 0.0395 |
| 0.3415        | 15.0  | 1290 | 0.2548          | 0.1154 | 0.0382 |
| 0.3013        | 16.0  | 1376 | 0.2517          | 0.1089 | 0.0367 |
| 0.2869        | 17.0  | 1462 | 0.2377          | 0.1041 | 0.0362 |
| 0.2908        | 18.0  | 1548 | 0.2423          | 0.1014 | 0.0351 |
| 0.2815        | 19.0  | 1634 | 0.2452          | 0.1043 | 0.0357 |
| 0.262         | 20.0  | 1720 | 0.2397          | 0.1024 | 0.0360 |
| 0.2456        | 21.0  | 1806 | 0.2197          | 0.0986 | 0.0342 |
| 0.2456        | 22.0  | 1892 | 0.2307          | 0.0973 | 0.0340 |
| 0.2421        | 23.0  | 1978 | 0.2255          | 0.0976 | 0.0336 |
| 0.2298        | 24.0  | 2064 | 0.2188          | 0.0966 | 0.0332 |
| 0.221         | 25.0  | 2150 | 0.2167          | 0.0935 | 0.0327 |
| 0.2123        | 26.0  | 2236 | 0.2228          | 0.0908 | 0.0312 |
| 0.2182        | 27.0  | 2322 | 0.2154          | 0.0913 | 0.0322 |
| 0.2041        | 28.0  | 2408 | 0.2326          | 0.0928 | 0.0330 |
| 0.2041        | 29.0  | 2494 | 0.2191          | 0.0920 | 0.0320 |
| 0.1971        | 30.0  | 2580 | 0.2241          | 0.0940 | 0.0330 |
| 0.2073        | 31.0  | 2666 | 0.2122          | 0.0947 | 0.0328 |
| 0.2086        | 32.0  | 2752 | 0.2217          | 0.0918 | 0.0313 |
| 0.1828        | 33.0  | 2838 | 0.2163          | 0.0920 | 0.0312 |
| 0.1939        | 34.0  | 2924 | 0.2186          | 0.0952 | 0.0323 |
| 0.1944        | 35.0  | 3010 | 0.2199          | 0.0884 | 0.0306 |
| 0.1944        | 36.0  | 3096 | 0.2127          | 0.0913 | 0.0309 |
| 0.1793        | 37.0  | 3182 | 0.2206          | 0.0899 | 0.0299 |
| 0.1843        | 38.0  | 3268 | 0.2190          | 0.0901 | 0.0305 |
| 0.1875        | 39.0  | 3354 | 0.2215          | 0.0899 | 0.0306 |
| 0.1587        | 40.0  | 3440 | 0.2215          | 0.0911 | 0.0307 |
| 0.1575        | 41.0  | 3526 | 0.2179          | 0.0872 | 0.0304 |
| 0.1853        | 42.0  | 3612 | 0.2183          | 0.0877 | 0.0302 |
| 0.1853        | 43.0  | 3698 | 0.2165          | 0.0865 | 0.0300 |
| 0.1696        | 44.0  | 3784 | 0.2196          | 0.0896 | 0.0305 |
| 0.1507        | 45.0  | 3870 | 0.2160          | 0.0884 | 0.0304 |
| 0.1509        | 46.0  | 3956 | 0.2163          | 0.0882 | 0.0299 |
| 0.1507        | 47.0  | 4042 | 0.2175          | 0.0896 | 0.0307 |
| 0.1596        | 48.0  | 4128 | 0.2234          | 0.0894 | 0.0305 |
| 0.1487        | 49.0  | 4214 | 0.2294          | 0.0877 | 0.0302 |
| 0.1512        | 50.0  | 4300 | 0.2180          | 0.0882 | 0.0306 |
| 0.1512        | 51.0  | 4386 | 0.2354          | 0.0858 | 0.0299 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xlsr-mec-ita-coraa-portuguese-all-07", "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-mec-ita-coraa-portuguese-all-07
===================================================


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: 0.2122
* Wer: 0.0947
* Cer: 0.0328


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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3