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# wav2vec2-base-timit-ms

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: 0.7589
- Wer: 0.3722

## 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: 80
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0088        | 3.7   | 500   | 2.4873          | 1.0    |
| 1.0451        | 7.41  | 1000  | 0.9286          | 0.5470 |
| 0.4081        | 11.11 | 1500  | 0.5935          | 0.4397 |
| 0.2564        | 14.81 | 2000  | 0.6525          | 0.4292 |
| 0.183         | 18.52 | 2500  | 0.6578          | 0.4486 |
| 0.1481        | 22.22 | 3000  | 0.6786          | 0.4231 |
| 0.1299        | 25.93 | 3500  | 0.6660          | 0.4121 |
| 0.1044        | 29.63 | 4000  | 0.7713          | 0.4209 |
| 0.0953        | 33.33 | 4500  | 0.6728          | 0.4038 |
| 0.0746        | 37.04 | 5000  | 0.7122          | 0.4165 |
| 0.0627        | 40.74 | 5500  | 0.6950          | 0.4126 |
| 0.0554        | 44.44 | 6000  | 0.8237          | 0.4082 |
| 0.0494        | 48.15 | 6500  | 0.7311          | 0.3955 |
| 0.0426        | 51.85 | 7000  | 0.7717          | 0.3899 |
| 0.0368        | 55.56 | 7500  | 0.7490          | 0.3933 |
| 0.0315        | 59.26 | 8000  | 0.7056          | 0.3877 |
| 0.0274        | 62.96 | 8500  | 0.7897          | 0.3850 |
| 0.0237        | 66.67 | 9000  | 0.7715          | 0.3850 |
| 0.0223        | 70.37 | 9500  | 0.7774          | 0.3789 |
| 0.0177        | 74.07 | 10000 | 0.7598          | 0.3744 |
| 0.0182        | 77.78 | 10500 | 0.7589          | 0.3722 |


### Framework versions

- Transformers 4.24.0
- Pytorch 2.0.0+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-base-timit-ms", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-timit-ms
======================


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: 0.7589
* Wer: 0.3722


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: 80
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3



### 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: 80
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 80
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 80
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3