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# wav2vec2-base-random-stop-classification-3

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: 0.3662
- Accuracy: 0.8753

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6931        | 0.99  | 18   | 0.6542          | 0.6178   |
| 0.6854        | 1.97  | 36   | 0.6173          | 0.6696   |
| 0.6617        | 2.96  | 54   | 0.5338          | 0.7343   |
| 0.6747        | 4.0   | 73   | 0.6521          | 0.6757   |
| 0.5626        | 4.99  | 91   | 0.4320          | 0.8072   |
| 0.5127        | 5.97  | 109  | 0.4987          | 0.7834   |
| 0.486         | 6.96  | 127  | 0.3753          | 0.8467   |
| 0.4393        | 8.0   | 146  | 0.4076          | 0.8290   |
| 0.4191        | 8.99  | 164  | 0.3877          | 0.8454   |
| 0.4287        | 9.97  | 182  | 0.3613          | 0.8549   |
| 0.4161        | 10.96 | 200  | 0.3714          | 0.8556   |
| 0.3938        | 12.0  | 219  | 0.3561          | 0.8569   |
| 0.3736        | 12.99 | 237  | 0.3914          | 0.8583   |
| 0.3571        | 13.97 | 255  | 0.3917          | 0.8535   |
| 0.3711        | 14.96 | 273  | 0.4288          | 0.8222   |
| 0.3303        | 16.0  | 292  | 0.3680          | 0.8638   |
| 0.3355        | 16.99 | 310  | 0.3724          | 0.8631   |
| 0.3523        | 17.97 | 328  | 0.3741          | 0.8644   |
| 0.3384        | 18.96 | 346  | 0.3726          | 0.8597   |
| 0.3063        | 20.0  | 365  | 0.3705          | 0.8658   |
| 0.2984        | 20.99 | 383  | 0.3866          | 0.8604   |
| 0.2841        | 21.97 | 401  | 0.3897          | 0.8590   |
| 0.3057        | 22.96 | 419  | 0.3662          | 0.8699   |
| 0.2831        | 24.0  | 438  | 0.3627          | 0.8760   |
| 0.2863        | 24.66 | 450  | 0.3662          | 0.8753   |


### 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-3", "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-3
==========================================


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


* Loss: 0.3662
* Accuracy: 0.8753


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