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# torgo_xlsr_finetune-F01-2

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0297
- Wer: 0.9501

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 23.5657       | 0.81  | 500   | 3.3629          | 1.0    |
| 3.349         | 1.62  | 1000  | 2.9797          | 1.0    |
| 2.6233        | 2.44  | 1500  | 1.9631          | 1.0317 |
| 1.6556        | 3.25  | 2000  | 1.5230          | 1.2971 |
| 1.1942        | 4.06  | 2500  | 1.3185          | 1.1814 |
| 0.9141        | 4.87  | 3000  | 1.3889          | 1.1723 |
| 0.7505        | 5.68  | 3500  | 1.2601          | 1.1247 |
| 0.6246        | 6.49  | 4000  | 1.4765          | 1.0907 |
| 0.5506        | 7.31  | 4500  | 1.3586          | 1.1020 |
| 0.5004        | 8.12  | 5000  | 1.2889          | 1.0454 |
| 0.4591        | 8.93  | 5500  | 1.6057          | 1.0680 |
| 0.4064        | 9.74  | 6000  | 1.4433          | 1.0023 |
| 0.3865        | 10.55 | 6500  | 1.7125          | 1.0567 |
| 0.3422        | 11.36 | 7000  | 1.7667          | 1.0045 |
| 0.3084        | 12.18 | 7500  | 1.6708          | 1.0567 |
| 0.2904        | 12.99 | 8000  | 1.8301          | 1.0045 |
| 0.2826        | 13.8  | 8500  | 1.5566          | 0.9796 |
| 0.2707        | 14.61 | 9000  | 1.6200          | 0.9909 |
| 0.2499        | 15.42 | 9500  | 1.9019          | 1.0045 |
| 0.2292        | 16.23 | 10000 | 1.7184          | 0.9841 |
| 0.2245        | 17.05 | 10500 | 1.7977          | 0.9932 |
| 0.2156        | 17.86 | 11000 | 1.7847          | 0.9705 |
| 0.2152        | 18.67 | 11500 | 1.9312          | 0.9660 |
| 0.2003        | 19.48 | 12000 | 2.1059          | 0.9909 |
| 0.1908        | 20.29 | 12500 | 1.9738          | 0.9683 |
| 0.1638        | 21.1  | 13000 | 1.7796          | 0.9592 |
| 0.1606        | 21.92 | 13500 | 2.0548          | 0.9569 |
| 0.1569        | 22.73 | 14000 | 1.9577          | 0.9274 |
| 0.1458        | 23.54 | 14500 | 2.1357          | 0.9569 |
| 0.1599        | 24.35 | 15000 | 1.9791          | 0.9410 |
| 0.1489        | 25.16 | 15500 | 1.9031          | 0.9456 |
| 0.1473        | 25.97 | 16000 | 1.8522          | 0.9342 |
| 0.1167        | 26.79 | 16500 | 1.8953          | 0.9342 |
| 0.1177        | 27.6  | 17000 | 1.9675          | 0.9342 |
| 0.1268        | 28.41 | 17500 | 1.9905          | 0.9501 |
| 0.1136        | 29.22 | 18000 | 2.0297          | 0.9501 |


### 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-F01-2", "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-F01-2
===========================


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.0297
* Wer: 0.9501


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