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# wav2vec2-base-timit-small

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5361
- Wer: 0.3380

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.571         | 1.0   | 500   | 1.9252          | 1.0022 |
| 0.8969        | 2.01  | 1000  | 0.5066          | 0.5292 |
| 0.4326        | 3.01  | 1500  | 0.4523          | 0.4562 |
| 0.2993        | 4.02  | 2000  | 0.4228          | 0.4202 |
| 0.2335        | 5.02  | 2500  | 0.4252          | 0.4178 |
| 0.2009        | 6.02  | 3000  | 0.4136          | 0.3910 |
| 0.1552        | 7.03  | 3500  | 0.4747          | 0.3863 |
| 0.1388        | 8.03  | 4000  | 0.4359          | 0.3859 |
| 0.1226        | 9.04  | 4500  | 0.4367          | 0.3879 |
| 0.1109        | 10.04 | 5000  | 0.4360          | 0.3760 |
| 0.0991        | 11.04 | 5500  | 0.4899          | 0.3672 |
| 0.0882        | 12.05 | 6000  | 0.4608          | 0.3653 |
| 0.0792        | 13.05 | 6500  | 0.4882          | 0.3703 |
| 0.0745        | 14.06 | 7000  | 0.4716          | 0.3625 |
| 0.065         | 15.06 | 7500  | 0.4896          | 0.3651 |
| 0.0596        | 16.06 | 8000  | 0.4831          | 0.3659 |
| 0.0563        | 17.07 | 8500  | 0.5092          | 0.3585 |
| 0.0536        | 18.07 | 9000  | 0.5376          | 0.3675 |
| 0.0465        | 19.08 | 9500  | 0.5019          | 0.3534 |
| 0.049         | 20.08 | 10000 | 0.4869          | 0.3723 |
| 0.0423        | 21.08 | 10500 | 0.4947          | 0.3501 |
| 0.0348        | 22.09 | 11000 | 0.5524          | 0.3453 |
| 0.0315        | 23.09 | 11500 | 0.5369          | 0.3499 |
| 0.0312        | 24.1  | 12000 | 0.5283          | 0.3519 |
| 0.0258        | 25.1  | 12500 | 0.5202          | 0.3461 |
| 0.0249        | 26.1  | 13000 | 0.5270          | 0.3449 |
| 0.0236        | 27.11 | 13500 | 0.5388          | 0.3408 |
| 0.0206        | 28.11 | 14000 | 0.5361          | 0.3388 |
| 0.0224        | 29.12 | 14500 | 0.5361          | 0.3380 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-small", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-timit-small
=========================


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


* Loss: 0.5361
* Wer: 0.3380


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
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* 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
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.13.1+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.13.1+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #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
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.13.1+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2