Official PyTorch code for extracting features and training downstream models with
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
(Logo generated by DALL·E 3)
emotion2vec is the first universal speech emotion representation model. Through self-supervised pre-training, emotion2vec has the ability to extract emotion representation across different tasks, languages, and scenarios.
The paper is coming soon.
emotion2vec achieves SOTA with only linear layers on the mainstream IEMOCAP dataset.
emotion2vec achieves SOTA compared with SOTA SSL models on multiple languages (Mandarin, French, German, Italian, etc.). Refer to the paper for more details.
Refer to the paper for more details.
We provide the extracted features of popular emotion dataset IEMOCAP. The features are extracted from the last layer of emotion2vec. The features are stored in .npy format and the sample rate of the extracted features is 50Hz. The utterance-level features are computed by averaging the frame-level features.
- frame-level: Google Drive | Baidu Netdisk (password: zb3p)
- utterance-level: Google Drive | Baidu Netdisk (password: qu3u)
All wav files are extracted from the original dataset for diverse downstream tasks. If want to train with standard 5531 utterances for 4 emotions classification, please refer to iemocap_downstream.
The minimum environment requirements are python>=3.8 and torch>=1.13. Our testing environments are python=3.8 and torch=2.01.
- git clone repos.
pip install fairseq
git clone https://github.com/ddlBoJack/emotion2vec.git- download emotion2vec checkpoint from:
- Google Drive
- Baidu Netdisk (password: b9fq).
- modify and run
scripts/extract_features.sh
We provide training scripts for IEMOCAP dataset in iemocap_downstream. You can modify the scripts to train your downstream model on other datasets.