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# torgo_xlsr_finetune-F04-2

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the Torgo dataset. With language model added.
It achieves the following results on the evaluation set:
- Loss: 0.3123
- Wer: 0.5055

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 23.0192       | 0.89  | 500   | 3.3685          | 1.0    |
| 3.3848        | 1.79  | 1000  | 3.1230          | 1.0    |
| 2.8977        | 2.68  | 1500  | 2.7814          | 1.0    |
| 2.6545        | 3.57  | 2000  | 2.4289          | 1.0    |
| 2.0706        | 4.46  | 2500  | 1.3831          | 1.2573 |
| 1.4296        | 5.36  | 3000  | 0.8670          | 1.1277 |
| 1.0995        | 6.25  | 3500  | 0.6462          | 1.0274 |
| 0.8746        | 7.14  | 4000  | 0.5206          | 0.9240 |
| 0.7191        | 8.04  | 4500  | 0.3925          | 0.8352 |
| 0.6227        | 8.93  | 5000  | 0.4340          | 0.8741 |
| 0.5577        | 9.82  | 5500  | 0.3835          | 0.7524 |
| 0.4712        | 10.71 | 6000  | 0.3221          | 0.7208 |
| 0.4373        | 11.61 | 6500  | 0.3851          | 0.7062 |
| 0.3994        | 12.5  | 7000  | 0.3124          | 0.6843 |
| 0.3614        | 13.39 | 7500  | 0.2716          | 0.6509 |
| 0.3278        | 14.29 | 8000  | 0.3251          | 0.6210 |
| 0.3056        | 15.18 | 8500  | 0.2820          | 0.6144 |
| 0.3043        | 16.07 | 9000  | 0.2554          | 0.5876 |
| 0.2831        | 16.96 | 9500  | 0.2745          | 0.6022 |
| 0.2485        | 17.86 | 10000 | 0.2667          | 0.5675 |
| 0.2536        | 18.75 | 10500 | 0.2789          | 0.5620 |
| 0.2134        | 19.64 | 11000 | 0.3169          | 0.5487 |
| 0.2095        | 20.54 | 11500 | 0.3009          | 0.5499 |
| 0.1958        | 21.43 | 12000 | 0.3615          | 0.5499 |
| 0.2035        | 22.32 | 12500 | 0.3107          | 0.5328 |
| 0.1849        | 23.21 | 13000 | 0.2891          | 0.5225 |
| 0.191         | 24.11 | 13500 | 0.2867          | 0.5189 |
| 0.1505        | 25.0  | 14000 | 0.2713          | 0.5170 |
| 0.1583        | 25.89 | 14500 | 0.3168          | 0.5170 |
| 0.1607        | 26.79 | 15000 | 0.2835          | 0.5097 |
| 0.1438        | 27.68 | 15500 | 0.2848          | 0.5109 |
| 0.1446        | 28.57 | 16000 | 0.3030          | 0.5079 |
| 0.1362        | 29.46 | 16500 | 0.3123          | 0.5055 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "torgo_xlsr_finetune-F04-2", "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 

torgo\_xlsr\_finetune-F04-2
===========================


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the Torgo dataset. With language model added.
It achieves the following results on the evaluation set:


* Loss: 0.3123
* Wer: 0.5055


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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2
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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2
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: 0.0001
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2