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# wav2vec2-base-random-stop-classification-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: 0.4066
- Accuracy: 0.8651

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6949        | 0.99  | 18   | 0.6706          | 0.5906   |
| 0.6753        | 1.97  | 36   | 0.6470          | 0.6383   |
| 0.6231        | 2.96  | 54   | 0.5590          | 0.7302   |
| 0.544         | 4.0   | 73   | 0.4623          | 0.7977   |
| 0.4806        | 4.99  | 91   | 0.4061          | 0.8317   |
| 0.4543        | 5.97  | 109  | 0.5891          | 0.7643   |
| 0.4947        | 6.96  | 127  | 0.3944          | 0.8386   |
| 0.4431        | 8.0   | 146  | 0.4528          | 0.8093   |
| 0.4147        | 8.99  | 164  | 0.4560          | 0.8222   |
| 0.4094        | 9.97  | 182  | 0.4193          | 0.8447   |
| 0.3906        | 10.96 | 200  | 0.3846          | 0.8549   |
| 0.3835        | 12.0  | 219  | 0.3845          | 0.8569   |
| 0.3632        | 12.99 | 237  | 0.3660          | 0.8644   |
| 0.3622        | 13.97 | 255  | 0.4107          | 0.8617   |
| 0.3472        | 14.96 | 273  | 0.3733          | 0.8685   |
| 0.3419        | 16.0  | 292  | 0.4496          | 0.8467   |
| 0.3074        | 16.99 | 310  | 0.3987          | 0.8638   |
| 0.3278        | 17.97 | 328  | 0.3740          | 0.8665   |
| 0.2841        | 18.96 | 346  | 0.3999          | 0.8651   |
| 0.2837        | 20.0  | 365  | 0.3954          | 0.8604   |
| 0.2928        | 20.99 | 383  | 0.3871          | 0.8644   |
| 0.3002        | 21.97 | 401  | 0.4978          | 0.8386   |
| 0.2783        | 22.96 | 419  | 0.4079          | 0.8692   |
| 0.2703        | 24.0  | 438  | 0.3977          | 0.8713   |
| 0.2816        | 24.66 | 450  | 0.4066          | 0.8651   |


### 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-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-stop-classification-1
==========================================


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


* Loss: 0.4066
* Accuracy: 0.8651


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