
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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: 0.5442
- Wer: 0.3327

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.558         | 1.0   | 500   | 1.9825          | 0.9952 |
| 0.8674        | 2.01  | 1000  | 0.5186          | 0.5141 |
| 0.4291        | 3.01  | 1500  | 0.4576          | 0.4590 |
| 0.3008        | 4.02  | 2000  | 0.4906          | 0.4449 |
| 0.2297        | 5.02  | 2500  | 0.4460          | 0.4242 |
| 0.1848        | 6.02  | 3000  | 0.4410          | 0.4013 |
| 0.1552        | 7.03  | 3500  | 0.4632          | 0.3833 |
| 0.1335        | 8.03  | 4000  | 0.4588          | 0.3870 |
| 0.1209        | 9.04  | 4500  | 0.4553          | 0.3751 |
| 0.108         | 10.04 | 5000  | 0.4463          | 0.3752 |
| 0.1011        | 11.04 | 5500  | 0.4730          | 0.3628 |
| 0.0898        | 12.05 | 6000  | 0.4716          | 0.3739 |
| 0.0822        | 13.05 | 6500  | 0.5299          | 0.3696 |
| 0.0702        | 14.06 | 7000  | 0.5478          | 0.3655 |
| 0.0648        | 15.06 | 7500  | 0.5487          | 0.3631 |
| 0.0595        | 16.06 | 8000  | 0.6031          | 0.3566 |
| 0.0567        | 17.07 | 8500  | 0.5375          | 0.3476 |
| 0.0542        | 18.07 | 9000  | 0.5286          | 0.3540 |
| 0.0467        | 19.08 | 9500  | 0.5743          | 0.3574 |
| 0.0419        | 20.08 | 10000 | 0.5855          | 0.3557 |
| 0.0428        | 21.08 | 10500 | 0.5339          | 0.3459 |
| 0.0346        | 22.09 | 11000 | 0.5261          | 0.3399 |
| 0.0312        | 23.09 | 11500 | 0.5699          | 0.3435 |
| 0.0319        | 24.1  | 12000 | 0.5647          | 0.3442 |
| 0.0288        | 25.1  | 12500 | 0.5419          | 0.3404 |
| 0.0247        | 26.1  | 13000 | 0.5388          | 0.3362 |
| 0.0249        | 27.11 | 13500 | 0.5521          | 0.3357 |
| 0.0214        | 28.11 | 14000 | 0.5515          | 0.3307 |
| 0.0235        | 29.12 | 14500 | 0.5442          | 0.3327 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.0

{"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: 0.5442
* Wer: 0.3327


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 1.12.1+cu113
* Datasets 1.18.3
* Tokenizers 0.13.0



### 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 1.12.1+cu113
* Datasets 1.18.3
* Tokenizers 0.13.0
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 1.12.1+cu113
* Datasets 1.18.3
* Tokenizers 0.13.0
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 1.12.1+cu113
* Datasets 1.18.3
* Tokenizers 0.13.0