IEMOCAP

Emotion recognition experiments with IEMOCAP (speech only)

This folder contains scripts for running emotion recognition experiments with the IEMOCAP dataset (https://paperswithcode.com/dataset/iemocap).

Training ECAPA-TDNN/wav2vec 2.0/HuBERT

Run the following command to train the model: python train.py hparams/train.yaml or with wav2vec2 model: python train_with_wav2vec2.py hparams/train_with_wav2vec2.yaml

Results

The results reported here use random splits

Release	hyperparams file	Val. Acc.	Test Acc.	Model link	GPUs
2021-07-04	train.yaml	65.3	65.7	model	1xV100 16GB
2021-10-17	train_with_wav2vec2.yaml (wav2vec2 base)	best 78.1	best: 78.7 (avg 77.0)	model	1xV100 32GB
2021-10-17	train_with_wav2vec2.yaml (voxpopuli base)	best 73.3	best: 73.3 (avg 70.5)	model	1xV100 32GB
2021-10-17	train_with_wav2vec2.yaml (hubert base)	best 74.9	best: 79.1 (avg 76,6)	model	1xV100 32GB

Training Time

About 40 sec for each epoch with a TESLA V100 (with ECAPA-TDNN). About 3min 14 sec for each epoch with a TESLA V100 (with wav2vec2 BASE encoder).

Note on Data Preparation

We here use only the audio part of the dataset.

Our iemocap_prepare.py will:

1. Do labelling transformation to 4 emotions [neural, happy, sad, anger]
2. Prepare IEMOCAP data with random split if different_speakers is False. (Note for benchmarking: you need to run 5 folds)
3. Prepare IEMOCAP data with speaker-independent split if different_speakers is True. (Note for benchmarking: you need to run 10 folds with test_spk_id from 1 to 10)

PreTrained Model + Easy-Inference

You can find the wav2vec2 pre-trained model with an easy-inference function on HuggingFace:

• https://huggingface.co/speechbrain/emotion-recognition-wav2vec2-IEMOCAP/

About IEMOCAP

@article{Busso2008IEMOCAPIE,
  title={IEMOCAP: interactive emotional dyadic motion capture database},
  author={C. Busso and M. Bulut and Chi-Chun Lee and Ebrahim Kazemzadeh and Emily Mower Provost and Samuel Kim and J. N. Chang and Sungbok Lee and Shrikanth S. Narayanan},
  journal={Language Resources and Evaluation},
  year={2008},
  volume={42},
  pages={335-359}
}

About SpeechBrain

• Website: https://speechbrain.github.io/
• Code: https://github.com/speechbrain/speechbrain/

Citing SpeechBrain

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}
}