Emotion2vec
[ACL 2024] Official PyTorch code for extracting features and training downstream models with emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
Install / Use
/learn @ddlBoJack/Emotion2vecREADME
News
- [Oct. 2024] 🔧 We update the usage in the FunASR interface with source selection. "ms" or "modelscope" for China mainland users; "hf" or "huggingface" for other overseas users. We recommend using FunASR interface for a smooth landing.
- [Jun. 2024] 🔧 We fix a bug in emotion2vec+. Please re-pull the latest code.
- [May. 2024] 🔥 Speech emotion recognition foundation model: emotion2vec+, with 9-class emotions has been released on Model Scope and Hugging Face. Check out a series of emotion2vec+ (seed, base, large) models for SER with high performance (We recommend this release instead of the Jan. 2024 release).
- [Jan. 2024] 9-class emotion recognition model with iterative fine-tuning from emotion2vec has been released in modelscope and FunASR.
- [Jan. 2024] emotion2vec has been integrated into modelscope and FunASR.
- [Dec. 2023] We release the paper, and create a WeChat group for emotion2vec.
- [Nov. 2023] We release code, checkpoints, and extracted features for emotion2vec.
Model Card
GitHub Repo: emotion2vec |Model|⭐Model Scope|🤗Hugging Face|Fine-tuning Data (Hours)| |:---:|:-------------:|:-----------:|:-------------:| |emotion2vec|Link|Link|/| |emotion2vec+ seed|Link|Link|201| |emotion2vec+ base|Link|Link|4788| |emotion2vec+ large|Link|Link|42526|
Overview
- emotion2vec+: speech emotion recognition foundation model
- emotion2vec: universal speech emotion representation model
emotion2vec+: speech emotion recognition foundation model
Guides
emotion2vec+ is a series of foundational models for speech emotion recognition (SER). We aim to train a "whisper" in the field of speech emotion recognition, overcoming the effects of language and recording environments through data-driven methods to achieve universal, robust emotion recognition capabilities. The performance of emotion2vec+ significantly exceeds other highly downloaded open-source models on Hugging Face.
Data Engineering
We offer 3 versions of emotion2vec+, each derived from the data of its predecessor. If you need a model focusing on spech emotion representation, refer to emotion2vec: universal speech emotion representation model.
- emotion2vec+ seed: Fine-tuned with academic speech emotion data from EmoBox
- emotion2vec+ base: Fine-tuned with filtered large-scale pseudo-labeled data to obtain the base size model (~90M)
- emotion2vec+ large: Fine-tuned with filtered large-scale pseudo-labeled data to obtain the large size model (~300M)
The iteration process is illustrated below, culminating in the training of the emotion2vec+ large model with 40k out of 160k hours of speech emotion data. Details of data engineering will be announced later.
Performance
Performance on EmoBox for 4-class primary emotions (without fine-tuning). Details of model performance will be announced later.
Inference with checkpoints
Install from FunASR
- install funasr
pip install -U funasr
- run the code.
'''
Using the finetuned emotion recognization model
rec_result contains {'feats', 'labels', 'scores'}
extract_embedding=False: 9-class emotions with scores
extract_embedding=True: 9-class emotions with scores, along with features
9-class emotions:
iic/emotion2vec_plus_seed, iic/emotion2vec_plus_base, iic/emotion2vec_plus_large (May. 2024 release)
iic/emotion2vec_base_finetuned (Jan. 2024 release)
0: angry
1: disgusted
2: fearful
3: happy
4: neutral
5: other
6: sad
7: surprised
8: unknown
'''
from funasr import AutoModel
# model="iic/emotion2vec_base"
# model="iic/emotion2vec_base_finetuned"
# model="iic/emotion2vec_plus_seed"
# model="iic/emotion2vec_plus_base"
model_id = "iic/emotion2vec_plus_large"
model = AutoModel(
model=model_id,
hub="ms", # "ms" or "modelscope" for China mainland users; "hf" or "huggingface" for other overseas users
)
wav_file = f"{model.model_path}/example/test.wav"
rec_result = model.generate(wav_file, output_dir="./outputs", granularity="utterance", extract_embedding=False)
print(rec_result)
The model will be downloaded automatically.
FunASR support file list input in wav.scp (kaldi style):
wav_name1 wav_path1.wav
wav_name2 wav_path2.wav
...
Refer to FunASR for more details.
emotion2vec: universal speech emotion representation model
Guides
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.
Performance
Performance on IEMOCAP
emotion2vec achieves SOTA with only linear layers on the mainstream IEMOCAP dataset. Refer to the paper for more details.
Performance on other languages
emotion2vec achieves SOTA compared with SOTA SSL models on multiple languages (Mandarin, French, German, Italian, etc.). Refer to the paper for more details.
Performance on other speech emotion tasks
Refer to the paper for more details.
Visualization
UMAP visualizations of learned features on the IEMOCAP dataset. <span style="color:red;">Red</span> and <span style="color:blue;">Blue</span> tones mean low and high arousal emotional classes, respectively. Refer to the paper for more details.
Extract features
Download extracted features
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 the iemocap_downstream folder.
Extract features from your dataset
Install from the source code
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 h
