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# wavlm-base

This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on the galsenai/waxal_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1345
- Accuracy: 0.6783
- Precision: 0.8774
- F1: 0.7615

## 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: 30
- eval_batch_size: 30
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 120
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 32.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 4.4506        | 2.53  | 500  | 4.8601          | 0.0224   | 0.0136    | 0.0066 |
| 3.0523        | 5.05  | 1000 | 4.6674          | 0.0720   | 0.0460    | 0.0394 |
| 1.949         | 7.58  | 1500 | 4.1533          | 0.1156   | 0.1847    | 0.1064 |
| 1.3427        | 10.1  | 2000 | 3.8173          | 0.1448   | 0.2382    | 0.1347 |
| 1.0064        | 12.63 | 2500 | 3.5546          | 0.2183   | 0.4464    | 0.2385 |
| 0.7985        | 15.15 | 3000 | 3.1172          | 0.3842   | 0.6336    | 0.4258 |
| 0.6505        | 17.68 | 3500 | 2.9231          | 0.5165   | 0.7677    | 0.5995 |
| 0.5367        | 20.2  | 4000 | 2.4935          | 0.5961   | 0.8182    | 0.6755 |
| 0.465         | 22.73 | 4500 | 2.2411          | 0.6412   | 0.8624    | 0.7272 |
| 0.4075        | 25.25 | 5000 | 2.1345          | 0.6783   | 0.8774    | 0.7615 |
| 0.3793        | 27.78 | 5500 | 2.2535          | 0.6681   | 0.8792    | 0.7543 |
| 0.3418        | 30.3  | 6000 | 2.3390          | 0.6662   | 0.8905    | 0.7576 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2

{"tags": ["audio-classification", "generated_from_trainer", "wolof"], "metrics": ["accuracy", "precision", "f1"], "model-index": [{"name": "wavlm-base", "results": []}]}
transformers pytorch wavlm audio-classification generated_from_trainer wolof endpoints_compatible region:us
TAGS
#transformers #pytorch #wavlm #audio-classification #generated_from_trainer #wolof #endpoints_compatible #region-us 

wavlm-base
==========


This model is a fine-tuned version of microsoft/wavlm-base on the galsenai/waxal\_dataset dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1345
* Accuracy: 0.6783
* Precision: 0.8774
* F1: 0.7615


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: 30
* eval\_batch\_size: 30
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 120
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 32.0


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 120
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 32.0 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wavlm #audio-classification #generated_from_trainer #wolof #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 120
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 32.0 ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #wavlm #audio-classification #generated_from_trainer #wolof #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 30
* eval\_batch\_size: 30
* seed: 0
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 120
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 32.0### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.9.1.dev0
* Tokenizers 0.13.2