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# wav2vec2-base-random-stop-classification-4

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: 0.3843
- Accuracy: 0.8706

## 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: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6928        | 0.99  | 18   | 0.6588          | 0.6267   |
| 0.6746        | 1.97  | 36   | 0.5702          | 0.6969   |
| 0.5823        | 2.96  | 54   | 0.5035          | 0.7772   |
| 0.5573        | 4.0   | 73   | 0.4111          | 0.8188   |
| 0.5324        | 4.99  | 91   | 0.4359          | 0.7997   |
| 0.6058        | 5.97  | 109  | 0.4688          | 0.7875   |
| 0.4805        | 6.96  | 127  | 0.4055          | 0.8351   |
| 0.4641        | 8.0   | 146  | 0.4024          | 0.8351   |
| 0.4292        | 8.99  | 164  | 0.3913          | 0.8474   |
| 0.4217        | 9.97  | 182  | 0.3975          | 0.8522   |
| 0.3892        | 10.96 | 200  | 0.3808          | 0.8460   |
| 0.4056        | 12.0  | 219  | 0.4126          | 0.8515   |
| 0.3848        | 12.99 | 237  | 0.3602          | 0.8508   |
| 0.3698        | 13.97 | 255  | 0.3913          | 0.8488   |
| 0.3893        | 14.96 | 273  | 0.3611          | 0.8692   |
| 0.3341        | 16.0  | 292  | 0.3791          | 0.8624   |
| 0.3376        | 16.99 | 310  | 0.3578          | 0.8624   |
| 0.3331        | 17.97 | 328  | 0.3660          | 0.8658   |
| 0.3215        | 18.96 | 346  | 0.3817          | 0.8535   |
| 0.2982        | 20.0  | 365  | 0.4000          | 0.8658   |
| 0.2885        | 20.99 | 383  | 0.3674          | 0.8658   |
| 0.3124        | 21.97 | 401  | 0.3770          | 0.8672   |
| 0.2926        | 22.96 | 419  | 0.3779          | 0.8651   |
| 0.2941        | 24.0  | 438  | 0.3775          | 0.8733   |
| 0.2699        | 24.66 | 450  | 0.3843          | 0.8706   |


### 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-stop-classification-4", "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-stop-classification-4
==========================================


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


* Loss: 0.3843
* Accuracy: 0.8706


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: 25


### 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: 25 ### 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: 25 ### 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: 25### Training results### Framework versions


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