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# wav2vec2-base-random-stop-classification-5

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: 0.4239
- Accuracy: 0.8631

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6916        | 0.99  | 18   | 0.6503          | 0.6362   |
| 0.6628        | 1.97  | 36   | 0.5354          | 0.7391   |
| 0.5922        | 2.96  | 54   | 0.4775          | 0.7786   |
| 0.5158        | 4.0   | 73   | 0.4559          | 0.8072   |
| 0.4733        | 4.99  | 91   | 0.4308          | 0.8188   |
| 0.4935        | 5.97  | 109  | 0.5186          | 0.7888   |
| 0.4512        | 6.96  | 127  | 0.4108          | 0.8358   |
| 0.4397        | 8.0   | 146  | 0.4692          | 0.8270   |
| 0.4037        | 8.99  | 164  | 0.4049          | 0.8304   |
| 0.4053        | 9.97  | 182  | 0.4054          | 0.8379   |
| 0.3774        | 10.96 | 200  | 0.4330          | 0.8379   |
| 0.3624        | 12.0  | 219  | 0.3800          | 0.8495   |
| 0.376         | 12.99 | 237  | 0.5123          | 0.8263   |
| 0.3908        | 13.97 | 255  | 0.4049          | 0.8386   |
| 0.3405        | 14.96 | 273  | 0.4200          | 0.8529   |
| 0.3542        | 16.0  | 292  | 0.4040          | 0.8569   |
| 0.3284        | 16.99 | 310  | 0.4578          | 0.8474   |
| 0.3094        | 17.97 | 328  | 0.4465          | 0.8522   |
| 0.2999        | 18.96 | 346  | 0.4126          | 0.8569   |
| 0.3059        | 20.0  | 365  | 0.4139          | 0.8529   |
| 0.2891        | 20.99 | 383  | 0.4101          | 0.8624   |
| 0.2968        | 21.97 | 401  | 0.4589          | 0.8501   |
| 0.2764        | 22.96 | 419  | 0.4263          | 0.8522   |
| 0.2841        | 24.0  | 438  | 0.4350          | 0.8597   |
| 0.2805        | 24.66 | 450  | 0.4239          | 0.8631   |


### 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-5", "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-5
==========================================


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


* Loss: 0.4239
* Accuracy: 0.8631


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