
<!-- 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-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: 0.4772
- Wer: 0.2821

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.6949        | 0.87  | 500   | 2.4599          | 0.9999 |
| 0.9858        | 1.73  | 1000  | 0.5249          | 0.4674 |
| 0.4645        | 2.6   | 1500  | 0.4604          | 0.3900 |
| 0.3273        | 3.46  | 2000  | 0.3939          | 0.3612 |
| 0.2474        | 4.33  | 2500  | 0.4150          | 0.3560 |
| 0.2191        | 5.19  | 3000  | 0.3855          | 0.3344 |
| 0.1662        | 6.06  | 3500  | 0.3779          | 0.3258 |
| 0.1669        | 6.92  | 4000  | 0.4841          | 0.3286 |
| 0.151         | 7.79  | 4500  | 0.4182          | 0.3219 |
| 0.1175        | 8.65  | 5000  | 0.4194          | 0.3107 |
| 0.1103        | 9.52  | 5500  | 0.4256          | 0.3129 |
| 0.1           | 10.38 | 6000  | 0.4352          | 0.3089 |
| 0.0949        | 11.25 | 6500  | 0.4649          | 0.3160 |
| 0.0899        | 12.11 | 7000  | 0.4472          | 0.3065 |
| 0.0787        | 12.98 | 7500  | 0.4763          | 0.3128 |
| 0.0742        | 13.84 | 8000  | 0.4321          | 0.3034 |
| 0.067         | 14.71 | 8500  | 0.4562          | 0.3076 |
| 0.063         | 15.57 | 9000  | 0.4541          | 0.3102 |
| 0.0624        | 16.44 | 9500  | 0.5113          | 0.3040 |
| 0.0519        | 17.3  | 10000 | 0.4925          | 0.3008 |
| 0.0525        | 18.17 | 10500 | 0.4710          | 0.2987 |
| 0.046         | 19.03 | 11000 | 0.4781          | 0.2977 |
| 0.0455        | 19.9  | 11500 | 0.4572          | 0.2969 |
| 0.0394        | 20.76 | 12000 | 0.5256          | 0.2966 |
| 0.0373        | 21.63 | 12500 | 0.4723          | 0.2921 |
| 0.0375        | 22.49 | 13000 | 0.4640          | 0.2847 |
| 0.0334        | 23.36 | 13500 | 0.4740          | 0.2917 |
| 0.0304        | 24.22 | 14000 | 0.4817          | 0.2874 |
| 0.0291        | 25.09 | 14500 | 0.4722          | 0.2896 |
| 0.0247        | 25.95 | 15000 | 0.4765          | 0.2870 |
| 0.0223        | 26.82 | 15500 | 0.4728          | 0.2821 |
| 0.0223        | 27.68 | 16000 | 0.4690          | 0.2834 |
| 0.0207        | 28.55 | 16500 | 0.4706          | 0.2825 |
| 0.0186        | 29.41 | 17000 | 0.4772          | 0.2821 |


### 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-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-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: 0.4772
* Wer: 0.2821


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 1.12.0+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 1.12.0+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
* mixed\_precision\_training: Native AMP ### 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
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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