
<!-- 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. -->

# addison6

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: 1.4278
- Wer: 0.3784

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0707        | 1.03  | 500   | 2.8972          | 0.9998 |
| 1.8869        | 2.05  | 1000  | 1.3286          | 0.6772 |
| 1.218         | 3.08  | 1500  | 1.0999          | 0.5714 |
| 1.0183        | 4.11  | 2000  | 1.0781          | 0.5523 |
| 0.8743        | 5.13  | 2500  | 0.9991          | 0.4951 |
| 0.7561        | 6.16  | 3000  | 1.0861          | 0.4869 |
| 0.6883        | 7.19  | 3500  | 1.1915          | 0.4704 |
| 0.6185        | 8.21  | 4000  | 1.1553          | 0.4634 |
| 0.5761        | 9.24  | 4500  | 1.1144          | 0.4513 |
| 0.5315        | 10.27 | 5000  | 1.1818          | 0.4298 |
| 0.4982        | 11.29 | 5500  | 1.1973          | 0.4329 |
| 0.4719        | 12.32 | 6000  | 1.1456          | 0.4259 |
| 0.4369        | 13.35 | 6500  | 1.1701          | 0.4292 |
| 0.4083        | 14.37 | 7000  | 1.1929          | 0.4132 |
| 0.3886        | 15.4  | 7500  | 1.3307          | 0.4163 |
| 0.3752        | 16.43 | 8000  | 1.3405          | 0.4081 |
| 0.349         | 17.45 | 8500  | 1.3283          | 0.3952 |
| 0.3261        | 18.48 | 9000  | 1.2956          | 0.4128 |
| 0.3094        | 19.51 | 9500  | 1.2671          | 0.4004 |
| 0.3041        | 20.53 | 10000 | 1.3534          | 0.3964 |
| 0.2796        | 21.56 | 10500 | 1.3730          | 0.3899 |
| 0.25          | 22.59 | 11000 | 1.3952          | 0.3942 |
| 0.2303        | 23.61 | 11500 | 1.4792          | 0.3923 |
| 0.2321        | 24.64 | 12000 | 1.4228          | 0.3847 |
| 0.2           | 25.67 | 12500 | 1.4469          | 0.3837 |
| 0.2009        | 26.69 | 13000 | 1.4532          | 0.3820 |
| 0.195         | 27.72 | 13500 | 1.4329          | 0.3821 |
| 0.1804        | 28.75 | 14000 | 1.4265          | 0.3799 |
| 0.1713        | 29.77 | 14500 | 1.4278          | 0.3784 |


### Framework versions

- Transformers 4.17.0
- Pytorch 2.0.0+cu118
- Datasets 1.18.3
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "addison6", "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 

addison6
========


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: 1.4278
* Wer: 0.3784


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 2.0.0+cu118
* 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 2.0.0+cu118
* 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 4.17.0
* Pytorch 2.0.0+cu118
* 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 4.17.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.2