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# wav2vec2-dataset-vios

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: 0.5423
- Wer: 0.4075

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.0963        | 1.1   | 400   | 1.1336          | 0.7374 |
| 0.6576        | 2.2   | 800   | 0.4716          | 0.3727 |
| 0.4099        | 3.3   | 1200  | 0.3907          | 0.3100 |
| 0.3332        | 4.4   | 1600  | 0.3735          | 0.2766 |
| 0.2976        | 5.49  | 2000  | 0.3932          | 0.2801 |
| 0.2645        | 6.59  | 2400  | 0.3628          | 0.2542 |
| 0.2395        | 7.69  | 2800  | 0.3702          | 0.2734 |
| 0.2208        | 8.79  | 3200  | 0.3667          | 0.2467 |
| 0.1974        | 9.89  | 3600  | 0.3688          | 0.2398 |
| 0.1772        | 10.99 | 4000  | 0.3819          | 0.2457 |
| 0.1695        | 12.09 | 4400  | 0.3840          | 0.2451 |
| 0.319         | 13.19 | 4800  | 0.6531          | 0.4084 |
| 0.7305        | 14.29 | 5200  | 0.9883          | 0.6348 |
| 0.5787        | 15.38 | 5600  | 0.5260          | 0.3063 |
| 0.8558        | 16.48 | 6000  | 1.2870          | 0.7692 |
| 1.155         | 17.58 | 6400  | 1.0568          | 0.6353 |
| 0.8393        | 18.68 | 6800  | 0.7360          | 0.4486 |
| 0.6094        | 19.78 | 7200  | 0.6072          | 0.4108 |
| 0.5346        | 20.88 | 7600  | 0.5749          | 0.4095 |
| 0.5073        | 21.98 | 8000  | 0.5588          | 0.4056 |
| 0.4859        | 23.08 | 8400  | 0.5475          | 0.4015 |
| 0.475         | 24.18 | 8800  | 0.5430          | 0.4011 |
| 0.4683        | 25.27 | 9200  | 0.5400          | 0.3990 |
| 0.4673        | 26.37 | 9600  | 0.5407          | 0.4011 |
| 0.4665        | 27.47 | 10000 | 0.5408          | 0.3992 |
| 0.4703        | 28.57 | 10400 | 0.5420          | 0.4070 |
| 0.4709        | 29.67 | 10800 | 0.5423          | 0.4075 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vivos_dataset"], "model-index": [{"name": "wav2vec2-dataset-vios", "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-dataset-vios
=====================


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: 0.5423
* Wer: 0.4075


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: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3
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: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3
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: 0.0003
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu113
* Datasets 1.18.3
* Tokenizers 0.10.3