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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: 0.5047
- Wer: 0.2233

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5485        | 1.0   | 500   | 1.9954          | 1.0042 |
| 0.9068        | 2.01  | 1000  | 0.6418          | 0.4572 |
| 0.4398        | 3.01  | 1500  | 0.4586          | 0.3629 |
| 0.3023        | 4.02  | 2000  | 0.4464          | 0.3248 |
| 0.2328        | 5.02  | 2500  | 0.4019          | 0.2969 |
| 0.1899        | 6.02  | 3000  | 0.4363          | 0.2961 |
| 0.163         | 7.03  | 3500  | 0.4832          | 0.2872 |
| 0.1442        | 8.03  | 4000  | 0.4421          | 0.2801 |
| 0.1246        | 9.04  | 4500  | 0.4757          | 0.2659 |
| 0.1122        | 10.04 | 5000  | 0.4693          | 0.2648 |
| 0.102         | 11.04 | 5500  | 0.4834          | 0.2549 |
| 0.0919        | 12.05 | 6000  | 0.4558          | 0.2633 |
| 0.0866        | 13.05 | 6500  | 0.4527          | 0.2641 |
| 0.0762        | 14.06 | 7000  | 0.4394          | 0.2565 |
| 0.0705        | 15.06 | 7500  | 0.5240          | 0.2609 |
| 0.0647        | 16.06 | 8000  | 0.4980          | 0.2522 |
| 0.0608        | 17.07 | 8500  | 0.5163          | 0.2589 |
| 0.0576        | 18.07 | 9000  | 0.4991          | 0.2565 |
| 0.0499        | 19.08 | 9500  | 0.4750          | 0.2457 |
| 0.047         | 20.08 | 10000 | 0.5162          | 0.2447 |
| 0.0418        | 21.08 | 10500 | 0.4801          | 0.2413 |
| 0.0383        | 22.09 | 11000 | 0.4961          | 0.2394 |
| 0.0342        | 23.09 | 11500 | 0.5209          | 0.2386 |
| 0.032         | 24.1  | 12000 | 0.4970          | 0.2364 |
| 0.0293        | 25.1  | 12500 | 0.4789          | 0.2309 |
| 0.0265        | 26.1  | 13000 | 0.4948          | 0.2302 |
| 0.0269        | 27.11 | 13500 | 0.4917          | 0.2249 |
| 0.0237        | 28.11 | 14000 | 0.4991          | 0.2238 |
| 0.022         | 29.12 | 14500 | 0.5047          | 0.2233 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.13.0+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-eng", "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-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: 0.5047
* Wer: 0.2233


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 1.13.0+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 4.17.0
* Pytorch 1.13.0+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2
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: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.13.0+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2
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: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.13.0+cu116
* Datasets 1.18.3
* Tokenizers 0.13.2