
# wav2vec2-base-Speech_Emotion_Recognition

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base).

It achieves the following results on the evaluation set:
- Loss: 0.7264
- Accuracy: 0.7539
- F1
  - Weighted: 0.7514
  - Micro: 0.7539
  - Macro: 0.7529
- Recall
  - Weighted: 0.7539
  - Micro: 0.7539
  - Macro: 0.7577
- Precision
  - Weighted: 0.7565
  - Micro: 0.7539
  - Macro: 0.7558

## Model description

This model predicts the emotion of the person speaking in the audio sample.

For more information on how it was created, check out the following link:  https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/tree/main/Audio-Projects/Emotion%20Detection/Speech%20Emotion%20Detection

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/dmitrybabko/speech-emotion-recognition-en

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.5581        | 0.98  | 43   | 1.4046          | 0.4653   | 0.4080      | 0.4653   | 0.4174   | 0.4653          | 0.4653       | 0.4793       | 0.5008             | 0.4653          | 0.4974          |
| 1.5581        | 1.98  | 86   | 1.1566          | 0.5997   | 0.5836      | 0.5997   | 0.5871   | 0.5997          | 0.5997       | 0.6093       | 0.6248             | 0.5997          | 0.6209          |
| 1.5581        | 2.98  | 129  | 0.9733          | 0.6883   | 0.6845      | 0.6883   | 0.6860   | 0.6883          | 0.6883       | 0.6923       | 0.7012             | 0.6883          | 0.7009          |
| 1.5581        | 3.98  | 172  | 0.8313          | 0.7399   | 0.7392      | 0.7399   | 0.7409   | 0.7399          | 0.7399       | 0.7417       | 0.7415             | 0.7399          | 0.7432          |
| 1.5581        | 4.98  | 215  | 0.8708          | 0.7028   | 0.6963      | 0.7028   | 0.6970   | 0.7028          | 0.7028       | 0.7081       | 0.7148             | 0.7028          | 0.7114          |
| 1.5581        | 5.98  | 258  | 0.7969          | 0.7297   | 0.7267      | 0.7297   | 0.7277   | 0.7297          | 0.7297       | 0.7333       | 0.7393             | 0.7297          | 0.7382          |
| 1.5581        | 6.98  | 301  | 0.7349          | 0.7603   | 0.7613      | 0.7603   | 0.7631   | 0.7603          | 0.7603       | 0.7635       | 0.7699             | 0.7603          | 0.7702          |
| 1.5581        | 7.98  | 344  | 0.7714          | 0.7469   | 0.7444      | 0.7469   | 0.7456   | 0.7469          | 0.7469       | 0.7485       | 0.7554             | 0.7469          | 0.7563          |
| 1.5581        | 8.98  | 387  | 0.7183          | 0.7630   | 0.7615      | 0.7630   | 0.7631   | 0.7630          | 0.7630       | 0.7652       | 0.7626             | 0.7630          | 0.7637          |
| 1.5581        | 9.98  | 430  | 0.7264          | 0.7539   | 0.7514      | 0.7539   | 0.7529   | 0.7539          | 0.7539       | 0.7577       | 0.7565             | 0.7539          | 0.7558          |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "audio-classification", "model-index": [{"name": "wav2vec2-base-Speech_Emotion_Recognition", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer en dataset:audiofolder license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-Speech\_Emotion\_Recognition
==========================================


This model is a fine-tuned version of facebook/wav2vec2-base.


It achieves the following results on the evaluation set:


* Loss: 0.7264
* Accuracy: 0.7539
* F1
	+ Weighted: 0.7514
	+ Micro: 0.7539
	+ Macro: 0.7529
* Recall
	+ Weighted: 0.7539
	+ Micro: 0.7539
	+ Macro: 0.7577
* Precision
	+ Weighted: 0.7565
	+ Micro: 0.7539
	+ Macro: 0.7558


Model description
-----------------


This model predicts the emotion of the person speaking in the audio sample.


For more information on how it was created, check out the following link: URL


Intended uses & limitations
---------------------------


This model is intended to demonstrate my ability to solve a complex problem using technology.


Training and evaluation data
----------------------------


Dataset Source: URL


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #dataset-audiofolder #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3