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# wav2vec2-base-vios-v2

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: 0.6056
- Wer: 0.2442

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 7.8344        | 0.69  | 500   | 3.5012          | 1.0    |
| 3.4505        | 1.37  | 1000  | 3.4081          | 1.0    |
| 2.1426        | 2.06  | 1500  | 0.8761          | 0.6241 |
| 0.8801        | 2.74  | 2000  | 0.5476          | 0.4241 |
| 0.6453        | 3.43  | 2500  | 0.4384          | 0.3495 |
| 0.5449        | 4.12  | 3000  | 0.4055          | 0.3160 |
| 0.4862        | 4.8   | 3500  | 0.3815          | 0.3002 |
| 0.4435        | 5.49  | 4000  | 0.3525          | 0.2776 |
| 0.4205        | 6.17  | 4500  | 0.3660          | 0.2725 |
| 0.3974        | 6.86  | 5000  | 0.3386          | 0.2565 |
| 0.3758        | 7.54  | 5500  | 0.3492          | 0.2607 |
| 0.3595        | 8.23  | 6000  | 0.3391          | 0.2441 |
| 0.3438        | 8.92  | 6500  | 0.3255          | 0.2354 |
| 0.3308        | 9.6   | 7000  | 0.3379          | 0.2422 |
| 0.3265        | 10.29 | 7500  | 0.3375          | 0.2349 |
| 0.311         | 10.97 | 8000  | 0.3356          | 0.2306 |
| 0.3071        | 11.66 | 8500  | 0.3286          | 0.2249 |
| 0.2941        | 12.35 | 9000  | 0.3176          | 0.2211 |
| 0.296         | 13.03 | 9500  | 0.3268          | 0.2257 |
| 0.2852        | 13.72 | 10000 | 0.3265          | 0.2196 |
| 0.3102        | 14.4  | 10500 | 0.3390          | 0.2209 |
| 0.2974        | 15.09 | 11000 | 0.3493          | 0.2199 |
| 0.3433        | 15.78 | 11500 | 0.3687          | 0.2199 |
| 0.3526        | 16.46 | 12000 | 0.3698          | 0.2170 |
| 0.36          | 17.15 | 12500 | 0.4110          | 0.2227 |
| 0.4322        | 17.83 | 13000 | 0.4830          | 0.2290 |
| 0.4973        | 18.52 | 13500 | 0.5280          | 0.2356 |
| 0.5701        | 19.2  | 14000 | 0.5990          | 0.2370 |
| 0.6014        | 19.89 | 14500 | 0.6056          | 0.2442 |


### 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-v2", "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-v2
=====================


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: 0.6056
* Wer: 0.2442


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: 20
* 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: 20
* 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: 20
* 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: 20
* 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