Speech Emotion Diarization is a technique that focuses on predicting emotions and their corresponding time boundaries within a speech recording. The model, described in the research paper titled "Speech Emotion Diarization" (available here), has been trained using audio samples that include neutral and a non-neutral emotional event. The model's output takes the form of a dictionary comprising emotion components (neutral, happy, angry, and sad) along with their respective start and end boundaries, as exemplified below:
{
'example.wav': [
{'start': 0.0, 'end': 1.94, 'emotion': 'n'}, # 'n' denotes neutral
{'start': 1.94, 'end': 4.48, 'emotion': 'h'} # 'h' denotes happy
]
}Before proceeding, ensure you have installed the necessary additional dependencies. To do this, simply run the following command in your terminal:
pip install -r emotion_diarization/extra_requirements.txt
The test set is Zaion Emotion Dataset (ZED), which can be downloaded via this dropbox link.
-
A fast download can be done by
wget https://dl.dropboxusercontent.com/s/aancfsluvcyrxou/RAVDESS.zip -
A fast download can be done by
wget https://dl.dropboxusercontent.com/s/e05ul8myqb5hkbj/ESD.zip -
A fast download can be done by
wget https://dl.dropboxusercontent.com/s/4t3vlq5cv5e8wv6/JL_corpus.zip -
A fast download can be done by
wget https://dl.dropboxusercontent.com/s/drvn10ph8q6aw8t/EmoV-DB.zip, where onlyAmused, Neutral, Angryemotions are kept.
First download the train/test datasets and unzip them.
To run the code, do:
cd emotion_diarization/
python train.py hparams/train.yaml --zed_folder /path/to/ZED --emovdb_folder /path/to/EmoV-DB --esd_folder /path/to/ESD --iemocap_folder /path/to/IEMOCAP --jlcorpus_folder /path/to/JL_corpus --ravdess_folder /path/to/RAVDESS.
The frame-wise classification result for each utterance can be found in results/eder.txt.
The EDER (Emotion Diarization Error Rate) reported here was averaged on 5 different seeds, results of other models (wav2vec2.0, HuBERT) can be found in the paper. You can find our training results (model, logs, etc) here.
| model | EDER |
|---|---|
| WavLM-large | 30.2 ± 1.60 |
It takes about 40 mins/epoch with 1xRTX8000(40G), reduce the batch size if OOM.
The pretrained models and a easy-inference interface can be found on HuggingFace.
@article{wang2023speech,
title={Speech Emotion Diarization: Which Emotion Appears When?},
author={Wang, Yingzhi and Ravanelli, Mirco and Nfissi, Alaa and Yacoubi, Alya},
journal={arXiv preprint arXiv:2306.12991},
year={2023}
}Please, cite SpeechBrain if you use it for your research or business.
@misc{speechbrainV1,
title={Open-Source Conversational AI with SpeechBrain 1.0},
author={Mirco Ravanelli and Titouan Parcollet and Adel Moumen and Sylvain de Langen and Cem Subakan and Peter Plantinga and Yingzhi Wang and Pooneh Mousavi and Luca Della Libera and Artem Ploujnikov and Francesco Paissan and Davide Borra and Salah Zaiem and Zeyu Zhao and Shucong Zhang and Georgios Karakasidis and Sung-Lin Yeh and Pierre Champion and Aku Rouhe and Rudolf Braun and Florian Mai and Juan Zuluaga-Gomez and Seyed Mahed Mousavi and Andreas Nautsch and Xuechen Liu and Sangeet Sagar and Jarod Duret and Salima Mdhaffar and Gaelle Laperriere and Mickael Rouvier and Renato De Mori and Yannick Esteve},
year={2024},
eprint={2407.00463},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2407.00463},
}
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}