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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: 0.5035
- Wer: 0.3346

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.1411        | 1.0   | 500   | 0.6675          | 0.6001 |
| 0.5668        | 2.01  | 1000  | 0.4699          | 0.4973 |
| 0.3773        | 3.01  | 1500  | 0.4475          | 0.4403 |
| 0.2696        | 4.02  | 2000  | 0.4162          | 0.4166 |
| 0.2165        | 5.02  | 2500  | 0.3809          | 0.4011 |
| 0.1849        | 6.02  | 3000  | 0.4120          | 0.3849 |
| 0.1542        | 7.03  | 3500  | 0.4436          | 0.3770 |
| 0.1385        | 8.03  | 4000  | 0.3977          | 0.3732 |
| 0.1224        | 9.04  | 4500  | 0.4530          | 0.3672 |
| 0.1233        | 10.04 | 5000  | 0.3949          | 0.3596 |
| 0.1078        | 11.04 | 5500  | 0.4616          | 0.3539 |
| 0.097         | 12.05 | 6000  | 0.4354          | 0.3697 |
| 0.0821        | 13.05 | 6500  | 0.4370          | 0.3643 |
| 0.0724        | 14.06 | 7000  | 0.4729          | 0.3587 |
| 0.0678        | 15.06 | 7500  | 0.5822          | 0.3742 |
| 0.0632        | 16.06 | 8000  | 0.4460          | 0.3513 |
| 0.0627        | 17.07 | 8500  | 0.5531          | 0.3537 |
| 0.0574        | 18.07 | 9000  | 0.5262          | 0.3575 |
| 0.0515        | 19.08 | 9500  | 0.4794          | 0.3488 |
| 0.0475        | 20.08 | 10000 | 0.4941          | 0.3458 |
| 0.0463        | 21.08 | 10500 | 0.4741          | 0.3377 |
| 0.0392        | 22.09 | 11000 | 0.5390          | 0.3381 |
| 0.0401        | 23.09 | 11500 | 0.4984          | 0.3413 |
| 0.0371        | 24.1  | 12000 | 0.5112          | 0.3460 |
| 0.0305        | 25.1  | 12500 | 0.5255          | 0.3418 |
| 0.0278        | 26.1  | 13000 | 0.5045          | 0.3389 |
| 0.0265        | 27.11 | 13500 | 0.4990          | 0.3371 |
| 0.0248        | 28.11 | 14000 | 0.5242          | 0.3362 |
| 0.0249        | 29.12 | 14500 | 0.5035          | 0.3346 |


### 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: 0.5035
* Wer: 0.3346


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
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