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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: 0.5090
- Wer: 0.3435

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5501        | 1.0   | 500   | 1.9752          | 0.9950 |
| 0.8608        | 2.01  | 1000  | 0.5051          | 0.5035 |
| 0.43          | 3.01  | 1500  | 0.4485          | 0.4525 |
| 0.2921        | 4.02  | 2000  | 0.4658          | 0.4332 |
| 0.2248        | 5.02  | 2500  | 0.4262          | 0.4268 |
| 0.1863        | 6.02  | 3000  | 0.4126          | 0.3977 |
| 0.1542        | 7.03  | 3500  | 0.4795          | 0.3987 |
| 0.1374        | 8.03  | 4000  | 0.4882          | 0.3982 |
| 0.1231        | 9.04  | 4500  | 0.4312          | 0.3790 |
| 0.1082        | 10.04 | 5000  | 0.4344          | 0.3679 |
| 0.0949        | 11.04 | 5500  | 0.4720          | 0.3769 |
| 0.0897        | 12.05 | 6000  | 0.5382          | 0.3706 |
| 0.0816        | 13.05 | 6500  | 0.4946          | 0.3618 |
| 0.0726        | 14.06 | 7000  | 0.5383          | 0.3630 |
| 0.0656        | 15.06 | 7500  | 0.4944          | 0.3693 |
| 0.059         | 16.06 | 8000  | 0.5096          | 0.3639 |
| 0.0572        | 17.07 | 8500  | 0.5066          | 0.3572 |
| 0.0559        | 18.07 | 9000  | 0.5366          | 0.3610 |
| 0.0468        | 19.08 | 9500  | 0.5103          | 0.3604 |
| 0.0413        | 20.08 | 10000 | 0.5126          | 0.3496 |
| 0.044         | 21.08 | 10500 | 0.5055          | 0.3524 |
| 0.0351        | 22.09 | 11000 | 0.5526          | 0.3515 |
| 0.0328        | 23.09 | 11500 | 0.4884          | 0.3512 |
| 0.032         | 24.1  | 12000 | 0.5167          | 0.3474 |
| 0.0271        | 25.1  | 12500 | 0.5027          | 0.3495 |
| 0.0229        | 26.1  | 13000 | 0.5076          | 0.3444 |
| 0.0252        | 27.11 | 13500 | 0.5122          | 0.3464 |
| 0.0224        | 28.11 | 14000 | 0.5133          | 0.3447 |
| 0.0236        | 29.12 | 14500 | 0.5090          | 0.3435 |


### 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: 0.5090
* Wer: 0.3435


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