Emotion Diarization Wavlm Large

🚀 Emotion Diarization with WavLM Large on 5 popular emotional datasets

This repository offers all the essential tools for speech emotion diarization. It utilizes a fine - tuned wavlm (large) model through SpeechBrain. The model is trained on concatenated audios and tested on ZaionEmotionDataset. The evaluation metric is Emotion Diarization Error Rate (EDER). For more details, refer to the paper link.

🚀 Quick Start

For a better experience, we recommend you to learn more about SpeechBrain. The model performance on ZED (test set) is as follows:

✨ Features

This system consists of a wavlm encoder and a downstream frame - wise classifier. The goal of the task is to predict the correct emotion components and their boundaries within a speech recording. Currently, the model is trained with audios that contain only 1 non - neutral emotion event. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling diarize_file if necessary.

📦 Installation

First, install the development version of SpeechBrain using the following commands:

git clone https://github.com/speechbrain/speechbrain.git
cd speechbrain
pip install -r requirements.txt
pip install --editable .

Please note that we encourage you to read our tutorials and learn more about SpeechBrain.

💻 Usage Examples

Basic Usage

from speechbrain.inference.diarization import Speech_Emotion_Diarization
classifier = Speech_Emotion_Diarization.from_hparams(
    source="speechbrain/emotion-diarization-wavlm-large"
)
diary = classifier.diarize_file("speechbrain/emotion-diarization-wavlm-large/example.wav")
print(diary)

# {
#    'speechbrain/emotion-diarization-wavlm-large/example.wav':
#       [
#          {'start': 0.0, 'end': 1.94, 'emotion': 'n'}, # n -> neutral
#          {'start': 1.94, 'end': 4.48, 'emotion': 'h'} # h -> happy
#       ]
# }

diary = classifier.diarize_file("speechbrain/emotion-diarization-wavlm-large/example_sad.wav")
print(diary)

# {
#    'speechbrain/emotion-diarization-wavlm-large/example_sad.wav':
#        [
#          {'start': 0.0, 'end': 3.54, 'emotion': 's'}, # s -> sad
#          {'start': 3.54, 'end': 5.26, 'emotion': 'n'} # n -> neutral
#        ]
# }

The output will be a dictionary containing emotion components and their boundaries.

Advanced Usage

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

Training

The model was trained with SpeechBrain (aa018540). To train it from scratch, follow these steps:

1. Clone SpeechBrain:

git clone https://github.com/speechbrain/speechbrain/

2. Install it:

cd speechbrain
pip install -r requirements.txt
pip install -e .

3. Run Training:

cd  recipes/ZaionEmotionDataset/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

You can find our training results (models, logs, etc) here.

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

📚 Documentation

About Speech Emotion Diarization/Zaion Emotion Dataset

@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}
}

Citing SpeechBrain

Please cite SpeechBrain if you use it for your research or business.

@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}
}

About SpeechBrain

• Website: https://speechbrain.github.io/
• Code: https://github.com/speechbrain/speechbrain/
• HuggingFace: https://huggingface.co/speechbrain/

📄 License

This project is licensed under the "apache - 2.0" license.