
# wav2vec2-base-Toronto_emotional_speech_set

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: 0.4925
- Accuracy: 0.8804
- Weighted f1: 0.8837
- Micro f1: 0.8804
- Macro f1: 0.8822
- Weighted recall: 0.8804
- Micro recall: 0.8804
- Macro recall: 0.8757
- Weighted precision: 0.9044
- Micro precision: 0.8804
- Macro precision: 0.9059

## Model description

This model classifies the emotion when someone speaks in audio sample.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Audio-Projects/Emotion%20Detection/Toronto%20Emotional%20Speech%20Set%20(TESS)/Toronto%20Emotional%20Speech%20Set%20(TESS).ipynb

## 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/ejlok1/toronto-emotional-speech-set-tess

## 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: 15

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.9517        | 0.97  | 17   | 1.9432          | 0.2411   | 0.1338      | 0.2411   | 0.1201   | 0.2411          | 0.2411       | 0.2168       | 0.1161             | 0.2411          | 0.1049          |
| 1.9517        | 2.0   | 35   | 1.9036          | 0.3375   | 0.3037      | 0.3375   | 0.3082   | 0.3375          | 0.3375       | 0.3533       | 0.5364             | 0.3375          | 0.5379          |
| 1.9517        | 2.97  | 52   | 1.6629          | 0.4518   | 0.4020      | 0.4518   | 0.3936   | 0.4518          | 0.4518       | 0.4503       | 0.6751             | 0.4518          | 0.6555          |
| 1.9517        | 4.0   | 70   | 1.2026          | 0.7357   | 0.7121      | 0.7357   | 0.6989   | 0.7357          | 0.7357       | 0.7240       | 0.7903             | 0.7357          | 0.7848          |
| 1.9517        | 4.97  | 87   | 0.8458          | 0.8839   | 0.8796      | 0.8839   | 0.8767   | 0.8839          | 0.8839       | 0.8845       | 0.8874             | 0.8839          | 0.8807          |
| 1.9517        | 6.0   | 105  | 0.6493          | 0.8946   | 0.8939      | 0.8946   | 0.8914   | 0.8946          | 0.8946       | 0.8937       | 0.9049             | 0.8946          | 0.9014          |
| 1.9517        | 6.97  | 122  | 0.5149          | 0.9089   | 0.9046      | 0.9089   | 0.8989   | 0.9089          | 0.9089       | 0.8957       | 0.9275             | 0.9089          | 0.9327          |
| 1.9517        | 8.0   | 140  | 0.3814          | 0.9536   | 0.9531      | 0.9536   | 0.9501   | 0.9536          | 0.9536       | 0.9474       | 0.9577             | 0.9536          | 0.9583          |
| 1.9517        | 8.97  | 157  | 0.5627          | 0.85     | 0.8459      | 0.85     | 0.8402   | 0.85            | 0.85         | 0.8378       | 0.9100             | 0.85            | 0.9160          |
| 1.9517        | 10.0  | 175  | 0.4702          | 0.8911   | 0.8861      | 0.8911   | 0.8854   | 0.8911          | 0.8911       | 0.8938       | 0.9021             | 0.8911          | 0.8967          |
| 1.9517        | 10.97 | 192  | 0.3362          | 0.9393   | 0.9376      | 0.9393   | 0.9361   | 0.9393          | 0.9393       | 0.9399       | 0.9402             | 0.9393          | 0.9365          |
| 1.9517        | 12.0  | 210  | 0.3808          | 0.9179   | 0.9181      | 0.9179   | 0.9176   | 0.9179          | 0.9179       | 0.9180       | 0.9251             | 0.9179          | 0.9235          |
| 1.9517        | 12.97 | 227  | 0.4546          | 0.9036   | 0.9045      | 0.9036   | 0.9024   | 0.9036          | 0.9036       | 0.8988       | 0.9151             | 0.9036          | 0.9157          |
| 1.9517        | 14.0  | 245  | 0.5065          | 0.8786   | 0.8826      | 0.8786   | 0.8813   | 0.8786          | 0.8786       | 0.8742       | 0.9040             | 0.8786          | 0.9055          |
| 1.9517        | 14.57 | 255  | 0.4925          | 0.8804   | 0.8837      | 0.8804   | 0.8822   | 0.8804          | 0.8804       | 0.8757       | 0.9044             | 0.8804          | 0.9059          |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0
- 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-Toronto_emotional_speech_set", "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-Toronto\_emotional\_speech\_set
=============================================


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: 0.4925
* Accuracy: 0.8804
* Weighted f1: 0.8837
* Micro f1: 0.8804
* Macro f1: 0.8822
* Weighted recall: 0.8804
* Micro recall: 0.8804
* Macro recall: 0.8757
* Weighted precision: 0.9044
* Micro precision: 0.8804
* Macro precision: 0.9059


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


This model classifies the emotion when someone speaks in 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: 15


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* 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: 15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* 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: 15 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3