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# hubert-base-timit-demo-google-colab-ft30ep_v5

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the timit-asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4763
- Wer: 0.3322

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.9596        | 0.87  | 500   | 3.1237          | 1.0    |
| 2.5388        | 1.73  | 1000  | 1.1689          | 0.9184 |
| 1.0448        | 2.6   | 1500  | 0.6106          | 0.5878 |
| 0.6793        | 3.46  | 2000  | 0.4912          | 0.5200 |
| 0.5234        | 4.33  | 2500  | 0.4529          | 0.4798 |
| 0.4368        | 5.19  | 3000  | 0.4239          | 0.4543 |
| 0.3839        | 6.06  | 3500  | 0.4326          | 0.4339 |
| 0.3315        | 6.92  | 4000  | 0.4265          | 0.4173 |
| 0.2878        | 7.79  | 4500  | 0.4304          | 0.4068 |
| 0.25          | 8.65  | 5000  | 0.4130          | 0.3940 |
| 0.242         | 9.52  | 5500  | 0.4310          | 0.3938 |
| 0.2182        | 10.38 | 6000  | 0.4204          | 0.3843 |
| 0.2063        | 11.25 | 6500  | 0.4449          | 0.3816 |
| 0.2099        | 12.11 | 7000  | 0.4016          | 0.3681 |
| 0.1795        | 12.98 | 7500  | 0.4027          | 0.3647 |
| 0.1604        | 13.84 | 8000  | 0.4294          | 0.3664 |
| 0.1683        | 14.71 | 8500  | 0.4412          | 0.3661 |
| 0.1452        | 15.57 | 9000  | 0.4484          | 0.3588 |
| 0.1491        | 16.44 | 9500  | 0.4508          | 0.3515 |
| 0.1388        | 17.3  | 10000 | 0.4240          | 0.3518 |
| 0.1399        | 18.17 | 10500 | 0.4605          | 0.3513 |
| 0.1265        | 19.03 | 11000 | 0.4412          | 0.3485 |
| 0.1137        | 19.9  | 11500 | 0.4520          | 0.3467 |
| 0.106         | 20.76 | 12000 | 0.4873          | 0.3426 |
| 0.1243        | 21.63 | 12500 | 0.4456          | 0.3396 |
| 0.1055        | 22.49 | 13000 | 0.4819          | 0.3406 |
| 0.1124        | 23.36 | 13500 | 0.4613          | 0.3391 |
| 0.1064        | 24.22 | 14000 | 0.4842          | 0.3430 |
| 0.0875        | 25.09 | 14500 | 0.4661          | 0.3348 |
| 0.086         | 25.95 | 15000 | 0.4724          | 0.3371 |
| 0.0842        | 26.82 | 15500 | 0.4982          | 0.3381 |
| 0.0834        | 27.68 | 16000 | 0.4856          | 0.3337 |
| 0.0918        | 28.55 | 16500 | 0.4783          | 0.3344 |
| 0.0773        | 29.41 | 17000 | 0.4763          | 0.3322 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "hubert-base-timit-demo-google-colab-ft30ep_v5", "results": []}]}
transformers pytorch tensorboard hubert automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #hubert #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

hubert-base-timit-demo-google-colab-ft30ep\_v5
==============================================


This model is a fine-tuned version of facebook/hubert-base-ls960 on the timit-asr dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4763
* Wer: 0.3322


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 1.11.0+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 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #hubert #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 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #hubert #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 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1