
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wav2vec2-base-timit-demo-google-colab

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: 0.5320
- Wer: 0.3362

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5752        | 1.0   | 500   | 2.2162          | 1.0392 |
| 0.9682        | 2.01  | 1000  | 0.5208          | 0.5191 |
| 0.4414        | 3.01  | 1500  | 0.4623          | 0.4528 |
| 0.2971        | 4.02  | 2000  | 0.4553          | 0.4421 |
| 0.2328        | 5.02  | 2500  | 0.4960          | 0.4208 |
| 0.1891        | 6.02  | 3000  | 0.4213          | 0.3812 |
| 0.1571        | 7.03  | 3500  | 0.4863          | 0.3865 |
| 0.1544        | 8.03  | 4000  | 0.4677          | 0.3910 |
| 0.1302        | 9.04  | 4500  | 0.6305          | 0.4042 |
| 0.1111        | 10.04 | 5000  | 0.5104          | 0.3830 |
| 0.0986        | 11.04 | 5500  | 0.5332          | 0.3808 |
| 0.088         | 12.05 | 6000  | 0.4494          | 0.3674 |
| 0.0827        | 13.05 | 6500  | 0.4779          | 0.3748 |
| 0.074         | 14.06 | 7000  | 0.5315          | 0.3738 |
| 0.0664        | 15.06 | 7500  | 0.5367          | 0.3661 |
| 0.0568        | 16.06 | 8000  | 0.5707          | 0.3817 |
| 0.057         | 17.07 | 8500  | 0.5381          | 0.3719 |
| 0.0561        | 18.07 | 9000  | 0.5353          | 0.3705 |
| 0.0487        | 19.08 | 9500  | 0.5087          | 0.3579 |
| 0.0444        | 20.08 | 10000 | 0.4910          | 0.3596 |
| 0.0433        | 21.08 | 10500 | 0.4931          | 0.3497 |
| 0.0363        | 22.09 | 11000 | 0.5414          | 0.3488 |
| 0.0318        | 23.09 | 11500 | 0.5405          | 0.3472 |
| 0.033         | 24.1  | 12000 | 0.5476          | 0.3449 |
| 0.0262        | 25.1  | 12500 | 0.5529          | 0.3443 |
| 0.0255        | 26.1  | 13000 | 0.5299          | 0.3417 |
| 0.0252        | 27.11 | 13500 | 0.5092          | 0.3363 |
| 0.0221        | 28.11 | 14000 | 0.5309          | 0.3357 |
| 0.0223        | 29.12 | 14500 | 0.5320          | 0.3362 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "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-demo-google-colab
=====================================


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: 0.5320
* Wer: 0.3362


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 1.12.1+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1



### 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 1.12.1+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
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 1.12.1+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
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 1.12.1+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1