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# 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: 0.5079
- Wer: 0.3365

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.4933        | 1.0   | 500   | 1.7711          | 0.9978 |
| 0.8658        | 2.01  | 1000  | 0.6262          | 0.5295 |
| 0.4405        | 3.01  | 1500  | 0.4841          | 0.4845 |
| 0.3062        | 4.02  | 2000  | 0.4897          | 0.4215 |
| 0.233         | 5.02  | 2500  | 0.4326          | 0.4101 |
| 0.1896        | 6.02  | 3000  | 0.4924          | 0.4078 |
| 0.1589        | 7.03  | 3500  | 0.4430          | 0.3896 |
| 0.1391        | 8.03  | 4000  | 0.4334          | 0.3889 |
| 0.1216        | 9.04  | 4500  | 0.4691          | 0.3828 |
| 0.1063        | 10.04 | 5000  | 0.4726          | 0.3705 |
| 0.0992        | 11.04 | 5500  | 0.4333          | 0.3690 |
| 0.0872        | 12.05 | 6000  | 0.4986          | 0.3771 |
| 0.0829        | 13.05 | 6500  | 0.4903          | 0.3685 |
| 0.0713        | 14.06 | 7000  | 0.5293          | 0.3655 |
| 0.068         | 15.06 | 7500  | 0.5039          | 0.3612 |
| 0.0621        | 16.06 | 8000  | 0.5314          | 0.3665 |
| 0.0571        | 17.07 | 8500  | 0.5038          | 0.3572 |
| 0.0585        | 18.07 | 9000  | 0.4718          | 0.3550 |
| 0.0487        | 19.08 | 9500  | 0.5482          | 0.3626 |
| 0.0459        | 20.08 | 10000 | 0.5239          | 0.3545 |
| 0.0419        | 21.08 | 10500 | 0.5096          | 0.3473 |
| 0.0362        | 22.09 | 11000 | 0.5222          | 0.3500 |
| 0.0331        | 23.09 | 11500 | 0.5062          | 0.3489 |
| 0.0352        | 24.1  | 12000 | 0.4913          | 0.3459 |
| 0.0315        | 25.1  | 12500 | 0.4701          | 0.3412 |
| 0.028         | 26.1  | 13000 | 0.5178          | 0.3402 |
| 0.0255        | 27.11 | 13500 | 0.5168          | 0.3405 |
| 0.0228        | 28.11 | 14000 | 0.5154          | 0.3368 |
| 0.0232        | 29.12 | 14500 | 0.5079          | 0.3365 |


### 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: 0.5079
* Wer: 0.3365


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
passage: 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