EMOTION2VEC

Official PyTorch code for extracting features and training downstream models with
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation

News

[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
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 modelscope (Recommended)

install modelscope and funasr
```
pip install -U funasr modelscope
```

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 modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks

inference_pipeline = pipeline( task=Tasks.emotion_recognition, model="iic/emotion2vec_large") # Alternative: iic/emotion2vec_plus_seed, iic/emotion2vec_plus_base, iic/emotion2vec_plus_large and iic/emotion2vec_base_finetuned

rec_result = inference_pipeline('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav', output_dir="./outputs", granularity="utterance", extract_embedding=False) print(rec_result)

The model will be downloaded automatically.

### Install from FunASR
1. install funasr
```bash
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

from funasr import AutoModel

model = AutoModel(model="iic/emotion2vec_base_finetuned") # Alternative: iic/emotion2vec_plus_seed, iic/emotion2vec_plus_base, iic/emotion2vec_plus_large and iic/emotion2vec_base_finetuned

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](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining/emotion2vec) 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.
![](./src/IEMOCAP.png)

### 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.
![](./src/Languages.png)

### 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. 
![](./src/UMAP.png)

## 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](https://drive.google.com/file/d/1JdQzwDJJEdKZcqSC1TXETvFZ7VpUvLEX/view?usp=sharing) | [Baidu Netdisk](https://pan.baidu.com/s/1FtCwhUwhONaeEos4nLYFWw?pwd=zb3p) (password: zb3p)
- utterance-level: [Google Drive](https://drive.google.com/file/d/1jJVfoEKC8yjwj39F__8jIQayd5PBO0WD/view?usp=sharing) | [Baidu Netdisk](https://pan.baidu.com/s/1AsJHacD6a5h27YJiCSee4w?pwd=qu3u) (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`.
1. git clone repos.
```bash
pip install fairseq
git clone https://github.com/ddlBoJack/emotion2vec.git

download emotion2vec checkpoint from:
- Google Drive
- Baidu Netdisk (password: b9fq)
- modelscope: git clone https://www.modelscope.cn/damo/emotion2vec_base.git
modify and run scripts/extract_features.sh

Install from modelscope (Recommended)

install modelscope and funasr
```
pip install -U funasr modelscope
```

run the code.


'''
Using the emotion representation model
rec_result only contains {'feats'}
granularity="utterance": {'feats': [*768]}
granularity="frame": {feats: [T*768]}
'''

from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks

inference_pipeline = pipeline( task=Tasks.emotion_recognition, model="iic/emotion2vec_base")

rec_result = inference_pipeline('https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav', output_dir="./outputs", granularity="utterance") print(rec_result)

The model will be downloaded automatically.

Refer to model scope of [emotion2vec_base](https://www.modelscope.cn/models/damo/emotion2vec_base/summary) and [emotion2vec_base_finetuned](https://www.modelscope.cn/models/iic/emotion2vec_base_finetuned/summary) for more details.

#### Install from FunASR
1. install funasr
```bash
pip install -U funasr

run the code.


'''
Using the emotion representation model
rec_result only contains {'feats'}
granularity="utterance": {'feats': [*768]}
granularity="frame": {feats: [T*768]}
'''

from funasr import AutoModel

model = AutoModel(model="iic/emotion2vec_base")

wav_file = f"{model.model_path}/example/test.wav" rec_result = model.generate(wav_file, output_dir="./outputs", granularity="utterance") 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](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining/emotion2vec) for more details.

## Training your downstream model
We provide training scripts for IEMOCAP dataset in the `iemocap_downstream` folder. You can modify the scripts to train your downstream model on other datasets.

## Citation
If you find our emotion2vec code and paper useful, please kindly cite:

@article{ma2023emotion2vec, title={emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation}, author={Ma, Ziyang and Zheng, Zhisheng and Ye, Jiaxin and Li, Jinchao and Gao, Zhifu and Zhang, Shiliang and Chen, Xie}, journal={arXiv preprint arXiv:2312.15185}, year={2023} }

ddlBoJack / emotion2vec

readme

EMOTION2VEC

News

Model Card

Overview

emotion2vec+: speech emotion recognition foundation model

Guides

Data Engineering

Performance

Inference with checkpoints

Install from modelscope (Recommended)

Install from modelscope (Recommended)