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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: 0.5707
- Wer: 0.3388

## 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5072        | 1.0   | 500   | 1.8786          | 0.9741 |
| 0.8836        | 2.01  | 1000  | 0.5147          | 0.5317 |
| 0.4576        | 3.01  | 1500  | 0.4774          | 0.4591 |
| 0.3056        | 4.02  | 2000  | 0.4393          | 0.4343 |
| 0.2349        | 5.02  | 2500  | 0.4404          | 0.4022 |
| 0.1946        | 6.02  | 3000  | 0.4564          | 0.3991 |
| 0.1624        | 7.03  | 3500  | 0.4428          | 0.3947 |
| 0.1421        | 8.03  | 4000  | 0.4312          | 0.3878 |
| 0.131         | 9.04  | 4500  | 0.4345          | 0.3853 |
| 0.1115        | 10.04 | 5000  | 0.4318          | 0.3753 |
| 0.1024        | 11.04 | 5500  | 0.5053          | 0.3798 |
| 0.0895        | 12.05 | 6000  | 0.5044          | 0.3782 |
| 0.0856        | 13.05 | 6500  | 0.4893          | 0.3665 |
| 0.0755        | 14.06 | 7000  | 0.4868          | 0.3662 |
| 0.0724        | 15.06 | 7500  | 0.5084          | 0.3681 |
| 0.0635        | 16.06 | 8000  | 0.5367          | 0.3530 |
| 0.0603        | 17.07 | 8500  | 0.5255          | 0.3604 |
| 0.0609        | 18.07 | 9000  | 0.5407          | 0.3678 |
| 0.0486        | 19.08 | 9500  | 0.5312          | 0.3630 |
| 0.047         | 20.08 | 10000 | 0.5498          | 0.3518 |
| 0.0437        | 21.08 | 10500 | 0.5326          | 0.3571 |
| 0.0379        | 22.09 | 11000 | 0.5644          | 0.3608 |
| 0.035         | 23.09 | 11500 | 0.5956          | 0.3539 |
| 0.0333        | 24.1  | 12000 | 0.5967          | 0.3517 |
| 0.0289        | 25.1  | 12500 | 0.5274          | 0.3399 |
| 0.0268        | 26.1  | 13000 | 0.5609          | 0.3406 |
| 0.0256        | 27.11 | 13500 | 0.5451          | 0.3448 |
| 0.0249        | 28.11 | 14000 | 0.5804          | 0.3413 |
| 0.0236        | 29.12 | 14500 | 0.5707          | 0.3388 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.12.0+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: 0.5707
* Wer: 0.3388


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


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.0+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 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.0+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 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.0+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### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.12.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1