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# wav2vec2-base-timit-eng

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: 0.4391
- Wer: 0.3836

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.8306        | 1.0   | 500   | 2.9588          | 1.0    |
| 2.1928        | 2.01  | 1000  | 1.2215          | 0.9355 |
| 1.1547        | 3.01  | 1500  | 0.9228          | 0.7135 |
| 0.9487        | 4.02  | 2000  | 0.7682          | 0.6513 |
| 0.8163        | 5.02  | 2500  | 0.7154          | 0.6164 |
| 0.6642        | 6.02  | 3000  | 0.6160          | 0.5919 |
| 0.6291        | 7.03  | 3500  | 0.6224          | 0.5485 |
| 0.601         | 8.03  | 4000  | 0.5927          | 0.5371 |
| 0.5443        | 9.04  | 4500  | 0.5757          | 0.5240 |
| 0.4798        | 10.04 | 5000  | 0.5673          | 0.5074 |
| 0.5142        | 11.04 | 5500  | 0.6138          | 0.5131 |
| 0.4044        | 12.05 | 6000  | 0.5899          | 0.5120 |
| 0.4214        | 13.05 | 6500  | 0.5443          | 0.4932 |
| 0.377         | 14.06 | 7000  | 0.6055          | 0.5337 |
| 0.3985        | 15.06 | 7500  | 0.5055          | 0.4812 |
| 0.3609        | 16.06 | 8000  | 0.5764          | 0.4600 |
| 0.299         | 17.07 | 8500  | 0.5524          | 0.4635 |
| 0.2984        | 18.07 | 9000  | 0.5272          | 0.4435 |
| 0.2908        | 19.08 | 9500  | 0.5393          | 0.4446 |
| 0.2714        | 20.08 | 10000 | 0.4548          | 0.4463 |
| 0.2285        | 21.08 | 10500 | 0.5126          | 0.4309 |
| 0.2245        | 22.09 | 11000 | 0.4770          | 0.4309 |
| 0.229         | 23.09 | 11500 | 0.4763          | 0.4150 |
| 0.2032        | 24.1  | 12000 | 0.5009          | 0.4127 |
| 0.2125        | 25.1  | 12500 | 0.4698          | 0.4087 |
| 0.1955        | 26.1  | 13000 | 0.4592          | 0.4001 |
| 0.1841        | 27.11 | 13500 | 0.4517          | 0.3898 |
| 0.164         | 28.11 | 14000 | 0.4628          | 0.3927 |
| 0.1687        | 29.12 | 14500 | 0.4391          | 0.3836 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-base-timit-eng", "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-eng
=======================


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: 0.4391
* Wer: 0.3836


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 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2



### 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 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2
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 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2
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 4.25.1
* Pytorch 1.13.0+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2