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# wav2vec2-base-vios-v4

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the vivos_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3198
- Wer: 0.2169

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 7.8138        | 0.69  | 500   | 3.5011          | 1.0    |
| 3.4372        | 1.37  | 1000  | 3.3447          | 1.0    |
| 1.9519        | 2.06  | 1500  | 0.8356          | 0.5944 |
| 0.8581        | 2.74  | 2000  | 0.5280          | 0.4038 |
| 0.6405        | 3.43  | 2500  | 0.4410          | 0.3410 |
| 0.5417        | 4.12  | 3000  | 0.3990          | 0.3140 |
| 0.4804        | 4.8   | 3500  | 0.3804          | 0.2973 |
| 0.4384        | 5.49  | 4000  | 0.3644          | 0.2808 |
| 0.4162        | 6.17  | 4500  | 0.3542          | 0.2648 |
| 0.3941        | 6.86  | 5000  | 0.3436          | 0.2529 |
| 0.3733        | 7.54  | 5500  | 0.3355          | 0.2520 |
| 0.3564        | 8.23  | 6000  | 0.3294          | 0.2415 |
| 0.3412        | 8.92  | 6500  | 0.3311          | 0.2332 |
| 0.3266        | 9.6   | 7000  | 0.3217          | 0.2325 |
| 0.3226        | 10.29 | 7500  | 0.3317          | 0.2303 |
| 0.3115        | 10.97 | 8000  | 0.3226          | 0.2279 |
| 0.3094        | 11.66 | 8500  | 0.3157          | 0.2236 |
| 0.2967        | 12.35 | 9000  | 0.3109          | 0.2202 |
| 0.2995        | 13.03 | 9500  | 0.3129          | 0.2156 |
| 0.2895        | 13.72 | 10000 | 0.3195          | 0.2146 |
| 0.3089        | 14.4  | 10500 | 0.3198          | 0.2169 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vivos_dataset"], "model-index": [{"name": "wav2vec2-base-vios-v4", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:vivos_dataset license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-vios-v4
=====================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the vivos\_dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3198
* Wer: 0.2169


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: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-vivos_dataset #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.11.0+cu113
* Datasets 1.18.3
* Tokenizers 0.12.1