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# hubert-base-timit-demo-google-colab-ft35ep

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: 0.4602
- Wer: 0.3466

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.825         | 0.87  | 500   | 2.9521          | 1.0    |
| 2.431         | 1.73  | 1000  | 0.9760          | 0.8013 |
| 1.0089        | 2.6   | 1500  | 0.5934          | 0.5968 |
| 0.6859        | 3.46  | 2000  | 0.5132          | 0.5356 |
| 0.5302        | 4.33  | 2500  | 0.4506          | 0.4894 |
| 0.44          | 5.19  | 3000  | 0.4340          | 0.4670 |
| 0.3926        | 6.06  | 3500  | 0.4506          | 0.4528 |
| 0.3326        | 6.92  | 4000  | 0.4197          | 0.4486 |
| 0.2937        | 7.79  | 4500  | 0.4093          | 0.4193 |
| 0.2568        | 8.65  | 5000  | 0.4098          | 0.4229 |
| 0.2473        | 9.52  | 5500  | 0.4090          | 0.4141 |
| 0.2233        | 10.38 | 6000  | 0.4152          | 0.4125 |
| 0.2108        | 11.25 | 6500  | 0.4586          | 0.4189 |
| 0.2086        | 12.11 | 7000  | 0.4284          | 0.3969 |
| 0.1858        | 12.98 | 7500  | 0.4028          | 0.3946 |
| 0.1641        | 13.84 | 8000  | 0.4679          | 0.4002 |
| 0.1686        | 14.71 | 8500  | 0.4441          | 0.3936 |
| 0.1489        | 15.57 | 9000  | 0.4897          | 0.3828 |
| 0.1541        | 16.44 | 9500  | 0.4953          | 0.3783 |
| 0.1417        | 17.3  | 10000 | 0.4500          | 0.3758 |
| 0.1428        | 18.17 | 10500 | 0.4533          | 0.3796 |
| 0.1306        | 19.03 | 11000 | 0.4474          | 0.3792 |
| 0.1185        | 19.9  | 11500 | 0.4762          | 0.3743 |
| 0.1081        | 20.76 | 12000 | 0.4770          | 0.3699 |
| 0.1253        | 21.63 | 12500 | 0.4749          | 0.3629 |
| 0.1087        | 22.49 | 13000 | 0.4577          | 0.3534 |
| 0.1172        | 23.36 | 13500 | 0.4819          | 0.3525 |
| 0.1086        | 24.22 | 14000 | 0.4709          | 0.3623 |
| 0.089         | 25.09 | 14500 | 0.4852          | 0.3544 |
| 0.086         | 25.95 | 15000 | 0.4602          | 0.3555 |
| 0.086         | 26.82 | 15500 | 0.4861          | 0.3497 |
| 0.086         | 27.68 | 16000 | 0.4527          | 0.3473 |
| 0.0919        | 28.55 | 16500 | 0.4607          | 0.3487 |
| 0.0792        | 29.41 | 17000 | 0.4602          | 0.3466 |


### 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_v4", "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-ft35ep
==========================================


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: 0.4602
* Wer: 0.3466


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