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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: 0.5501
- Wer: 0.3424

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5448        | 1.0   | 500   | 2.5044          | 1.0    |
| 1.0167        | 2.01  | 1000  | 0.5435          | 0.5278 |
| 0.4453        | 3.01  | 1500  | 0.4450          | 0.4534 |
| 0.3           | 4.02  | 2000  | 0.4401          | 0.4245 |
| 0.2304        | 5.02  | 2500  | 0.4146          | 0.4022 |
| 0.1889        | 6.02  | 3000  | 0.4241          | 0.3927 |
| 0.1573        | 7.03  | 3500  | 0.4545          | 0.3878 |
| 0.1363        | 8.03  | 4000  | 0.4936          | 0.3940 |
| 0.1213        | 9.04  | 4500  | 0.4964          | 0.3806 |
| 0.108         | 10.04 | 5000  | 0.4931          | 0.3826 |
| 0.0982        | 11.04 | 5500  | 0.5373          | 0.3778 |
| 0.0883        | 12.05 | 6000  | 0.4978          | 0.3733 |
| 0.0835        | 13.05 | 6500  | 0.5189          | 0.3728 |
| 0.0748        | 14.06 | 7000  | 0.4608          | 0.3692 |
| 0.068         | 15.06 | 7500  | 0.4827          | 0.3608 |
| 0.0596        | 16.06 | 8000  | 0.5022          | 0.3661 |
| 0.056         | 17.07 | 8500  | 0.5482          | 0.3646 |
| 0.0565        | 18.07 | 9000  | 0.5158          | 0.3573 |
| 0.0487        | 19.08 | 9500  | 0.4910          | 0.3513 |
| 0.0444        | 20.08 | 10000 | 0.5771          | 0.3580 |
| 0.045         | 21.08 | 10500 | 0.5160          | 0.3539 |
| 0.0363        | 22.09 | 11000 | 0.5367          | 0.3503 |
| 0.0313        | 23.09 | 11500 | 0.5773          | 0.3500 |
| 0.0329        | 24.1  | 12000 | 0.5683          | 0.3508 |
| 0.0297        | 25.1  | 12500 | 0.5355          | 0.3464 |
| 0.0272        | 26.1  | 13000 | 0.5317          | 0.3450 |
| 0.0256        | 27.11 | 13500 | 0.5602          | 0.3443 |
| 0.0242        | 28.11 | 14000 | 0.5586          | 0.3419 |
| 0.0239        | 29.12 | 14500 | 0.5501          | 0.3424 |


### 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": "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: 0.5501
* Wer: 0.3424


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 #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 1.11.0+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 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1