
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wav2vec2-base

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the galsenai/waxal_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7504
- Accuracy: 0.8632
- Precision: 0.9380
- F1: 0.8954

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 4.3647        | 2.53  | 500  | 4.8202          | 0.0117   | 0.0134    | 0.0032 |
| 2.6202        | 5.05  | 1000 | 4.2238          | 0.0625   | 0.0781    | 0.0355 |
| 1.38          | 7.58  | 1500 | 3.6392          | 0.2941   | 0.5211    | 0.3174 |
| 0.8601        | 10.1  | 2000 | 2.7953          | 0.4907   | 0.7446    | 0.5657 |
| 0.5645        | 12.63 | 2500 | 1.9829          | 0.6862   | 0.8363    | 0.7421 |
| 0.4009        | 15.15 | 3000 | 1.4535          | 0.7635   | 0.9000    | 0.8174 |
| 0.3054        | 17.68 | 3500 | 1.1426          | 0.7882   | 0.9058    | 0.8298 |
| 0.2448        | 20.2  | 4000 | 0.9860          | 0.8189   | 0.9206    | 0.8593 |
| 0.2116        | 22.73 | 4500 | 0.8820          | 0.8325   | 0.9261    | 0.8711 |
| 0.1863        | 25.25 | 5000 | 0.8191          | 0.8465   | 0.9366    | 0.8848 |
| 0.1701        | 27.78 | 5500 | 0.7504          | 0.8632   | 0.9380    | 0.8954 |
| 0.1558        | 30.3  | 6000 | 0.7665          | 0.8609   | 0.9398    | 0.8956 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer", "wolof"], "metrics": ["accuracy", "precision", "f1"], "model-index": [{"name": "wav2vec2-base", "results": []}]}
transformers pytorch wav2vec2 audio-classification generated_from_trainer wolof license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #wolof #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base
=============


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


* Loss: 0.7504
* Accuracy: 0.8632
* Precision: 0.9380
* F1: 0.8954


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 #wav2vec2 #audio-classification #generated_from_trainer #wolof #license-apache-2.0 #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 #wav2vec2 #audio-classification #generated_from_trainer #wolof #license-apache-2.0 #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