README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
pipeline_tag: audio-classification
base_model: facebook/wav2vec2-base
model-index:
  - name: wav2vec2-base-Toronto_emotional_speech_set
    results: []

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: 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