README.md

metadata

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

wav2vec2-base-Speech_Emotion_Recognition

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: 0.7264
• Accuracy: 0.7539
• Weighted f1: 0.7514
• Micro f1: 0.7539
• Macro f1: 0.7529
• Weighted recall: 0.7539
• Micro recall: 0.7539
• Macro recall: 0.7577
• Weighted precision: 0.7565
• Micro precision: 0.7539
• Macro precision: 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