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hughlan1214/
Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0

Name: Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0
Availability: InStock

generated_from_trainer

hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned1.1

finetune

apache-2.0

endpoints_compatible

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SER_wav2vec2-large-xlsr-53_240304_fine-tuned_2

This model is a fine-tuned version of hughlan1214/SER_wav2vec2-large-xlsr-53_240304_fine-tuned1.1 on a Speech Emotion Recognition (en) dataset.

This dataset includes the 4 most popular datasets in English: Crema, Ravdess, Savee, and Tess, containing a total of over 12,000 .wav audio files. Each of these four datasets includes 6 to 8 different emotional labels.

This achieves the following results on the evaluation set:

Loss: 1.0601
Accuracy: 0.6731
Precision: 0.6761
Recall: 0.6794
F1: 0.6738

Model description

The model was obtained through feature extraction using facebook/wav2vec2-large-xlsr-53 and underwent several rounds of fine-tuning. It predicts the 7 types of emotions contained in speech, aiming to lay the foundation for subsequent use of human micro-expressions on the visual level and context semantics under LLMS to infer user emotions in real-time.

Although the model was trained on purely English datasets, post-release testing showed that it also performs well in predicting emotions in Chinese and French, demonstrating the powerful cross-linguistic capability of the facebook/wav2vec2-large-xlsr-53 pre-trained model.

emotions = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']

Intended uses & limitations

More information needed

Training and evaluation data

70/30 of entire dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.8904	1.0	1048	1.1923	0.5773	0.6162	0.5563	0.5494
1.1394	2.0	2096	1.0143	0.6071	0.6481	0.6189	0.6057
0.9373	3.0	3144	1.0585	0.6126	0.6296	0.6254	0.6119
0.7405	4.0	4192	0.9580	0.6514	0.6732	0.6562	0.6576
1.1638	5.0	5240	0.9940	0.6486	0.6485	0.6627	0.6435
0.6741	6.0	6288	1.0307	0.6628	0.6710	0.6711	0.6646
0.604	7.0	7336	1.0248	0.6667	0.6678	0.6751	0.6682
0.6835	8.0	8384	1.0396	0.6722	0.6803	0.6790	0.6743
0.5421	9.0	9432	1.0493	0.6714	0.6765	0.6785	0.6736
0.5728	10.0	10480	1.0601	0.6731	0.6761	0.6794	0.6738

Framework versions

Transformers 4.38.1
Pytorch 2.2.1
Datasets 2.17.1
Tokenizers 0.15.2

hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0