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wav2vec2-base-arabic_speech_emotion_recognition
ahmmedasaad2772/wav2vec2-base-arabic_speech_emotion_recognition
wav2vec2-base-arabic_speech_emotion_recognition is a audio classification model from ahmmedasaad2772. Use it for the audio classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Audio Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Mar 5, 2024
- Updated
- Mar 5, 2024
- SHA
- 48eaec6c
Datasets
Spaces
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
wav2vec2-base-arabic_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: 0.0430
- Accuracy: 0.9992
- Weighted f1: 0.9992
- Micro f1: 0.9992
- Macro f1: 0.9993
- Weighted recall: 0.9992
- Micro recall: 0.9992
- Macro recall: 0.9992
- Weighted precision: 0.9992
- Micro precision: 0.9992
- Macro precision: 0.9993
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.6078 | 0.98 | 29 | 1.4403 | 0.5227 | 0.4478 | 0.5227 | 0.4426 | 0.5227 | 0.5227 | 0.5187 | 0.4200 | 0.5227 | 0.4145 |
| 1.6078 | 1.98 | 58 | 0.7310 | 0.9808 | 0.9808 | 0.9808 | 0.9809 | 0.9808 | 0.9808 | 0.9799 | 0.9816 | 0.9808 | 0.9827 |
| 1.6078 | 2.98 | 87 | 0.2797 | 0.9992 | 0.9992 | 0.9992 | 0.9993 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9993 |
| 1.6078 | 3.98 | 116 | 0.1361 | 0.9992 | 0.9992 | 0.9992 | 0.9993 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9993 |
| 1.6078 | 4.98 | 145 | 0.0855 | 0.9992 | 0.9992 | 0.9992 | 0.9993 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9993 |
| 1.6078 | 5.98 | 174 | 0.0638 | 0.9992 | 0.9992 | 0.9992 | 0.9993 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9993 |
| 1.6078 | 6.98 | 203 | 0.0529 | 0.9992 | 0.9992 | 0.9992 | 0.9993 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9993 |
| 1.6078 | 7.98 | 232 | 0.0470 | 0.9992 | 0.9992 | 0.9992 | 0.9993 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9993 |
| 1.6078 | 8.98 | 261 | 0.0440 | 0.9992 | 0.9992 | 0.9992 | 0.9993 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9993 |
| 1.6078 | 9.98 | 290 | 0.0430 | 0.9992 | 0.9992 | 0.9992 | 0.9993 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9993 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3
- Task
- Audio Classification
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Mar 5, 2024
- Updated
- Mar 5, 2024