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# wav2vec2-base-finetuned-mednames

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1443
- Accuracy: 1.0

## 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: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2789        | 1.0   | 36   | 2.2639          | 0.2222   |
| 2.1925        | 2.0   | 72   | 2.1404          | 0.3833   |
| 1.8763        | 3.0   | 108  | 1.7607          | 0.5556   |
| 1.5035        | 4.0   | 144  | 1.3599          | 0.7667   |
| 1.0598        | 5.0   | 180  | 0.9607          | 0.8778   |
| 0.8303        | 6.0   | 216  | 0.7327          | 0.8722   |
| 0.6418        | 7.0   | 252  | 0.5544          | 0.8778   |
| 0.4481        | 8.0   | 288  | 0.4377          | 0.9722   |
| 0.3438        | 9.0   | 324  | 0.2963          | 0.9833   |
| 0.1831        | 10.0  | 360  | 0.1443          | 1.0      |
| 0.1129        | 11.0  | 396  | 0.0890          | 1.0      |
| 0.0877        | 12.0  | 432  | 0.0664          | 1.0      |
| 0.0657        | 13.0  | 468  | 0.0509          | 1.0      |
| 0.0528        | 14.0  | 504  | 0.0406          | 1.0      |
| 0.0428        | 15.0  | 540  | 0.0388          | 1.0      |
| 0.0369        | 16.0  | 576  | 0.0526          | 0.9944   |
| 0.0858        | 17.0  | 612  | 0.0539          | 0.9944   |
| 0.0294        | 18.0  | 648  | 0.0231          | 1.0      |
| 0.0272        | 19.0  | 684  | 0.0204          | 1.0      |
| 0.0247        | 20.0  | 720  | 0.0188          | 1.0      |
| 0.0227        | 21.0  | 756  | 0.0172          | 1.0      |
| 0.0211        | 22.0  | 792  | 0.0161          | 1.0      |
| 0.0195        | 23.0  | 828  | 0.0152          | 1.0      |
| 0.0684        | 24.0  | 864  | 0.0144          | 1.0      |
| 0.0175        | 25.0  | 900  | 0.0139          | 1.0      |
| 0.0756        | 26.0  | 936  | 0.0134          | 1.0      |
| 0.017         | 27.0  | 972  | 0.0131          | 1.0      |
| 0.0163        | 28.0  | 1008 | 0.0128          | 1.0      |
| 0.0158        | 29.0  | 1044 | 0.0126          | 1.0      |
| 0.0159        | 30.0  | 1080 | 0.0126          | 1.0      |


### Framework versions

- Transformers 4.28.1
- Pytorch 1.11.0+cu102
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-mednames", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer dataset:audiofolder license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #has_space #region-us 

wav2vec2-base-finetuned-mednames
================================


This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1443
* Accuracy: 1.0


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: 5
* eval\_batch\_size: 5
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 1.11.0+cu102
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.11.0+cu102
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 1.11.0+cu102
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 5
* eval\_batch\_size: 5
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 20
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 1.11.0+cu102
* Datasets 2.11.0
* Tokenizers 0.13.3