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# wav2vec2-base-random-stopvoicing-1

This model is a fine-tuned version of [](https://huggingface.co/) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3669
- Accuracy: 0.8702

## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 24

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6871        | 0.99  | 20   | 0.6530          | 0.6213   |
| 0.6757        | 1.98  | 40   | 0.6507          | 0.6104   |
| 0.6193        | 2.96  | 60   | 0.4827          | 0.7691   |
| 0.5511        | 4.0   | 81   | 0.4494          | 0.7950   |
| 0.5076        | 4.99  | 101  | 0.4027          | 0.8283   |
| 0.4882        | 5.98  | 121  | 0.5145          | 0.7813   |
| 0.4728        | 6.96  | 141  | 0.4394          | 0.8120   |
| 0.4351        | 8.0   | 162  | 0.4163          | 0.8270   |
| 0.4432        | 8.99  | 182  | 0.3823          | 0.8392   |
| 0.4165        | 9.98  | 202  | 0.4307          | 0.8263   |
| 0.3947        | 10.96 | 222  | 0.3569          | 0.8604   |
| 0.4186        | 12.0  | 243  | 0.4431          | 0.8283   |
| 0.3948        | 12.99 | 263  | 0.3836          | 0.8522   |
| 0.3627        | 13.98 | 283  | 0.3778          | 0.8569   |
| 0.3922        | 14.96 | 303  | 0.3523          | 0.8624   |
| 0.3668        | 16.0  | 324  | 0.3543          | 0.8631   |
| 0.3676        | 16.99 | 344  | 0.3485          | 0.8610   |
| 0.3118        | 17.98 | 364  | 0.3838          | 0.8638   |
| 0.328         | 18.96 | 384  | 0.3509          | 0.8685   |
| 0.3387        | 20.0  | 405  | 0.3593          | 0.8685   |
| 0.3088        | 20.99 | 425  | 0.3596          | 0.8631   |
| 0.2942        | 21.98 | 445  | 0.3585          | 0.8713   |
| 0.3027        | 22.96 | 465  | 0.3644          | 0.8651   |
| 0.2913        | 23.7  | 480  | 0.3575          | 0.8692   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-random-stopvoicing-1", "results": []}]}
transformers pytorch wav2vec2 audio-classification generated_from_trainer dataset:audiofolder endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #endpoints_compatible #region-us 

wav2vec2-base-random-stopvoicing-1
==================================


This model is a fine-tuned version of [](URL on the audiofolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3669
* Accuracy: 0.8702


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: 3e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 24


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.7.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 24 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 24 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 24### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.7.1
* Tokenizers 0.13.2