This repository contains the source code of the paper Emo-StarGAN: A Semi-Supervised Any-to-Many Non-Parallel Emotion-Preserving Voice Conversion, accepted in Interspeech 2023. An overview of the method and the results can be found here.

• Emo-StarGAN: An emotion-preserving deep semi-supervised voice conversion-based speaker anonymisation method is proposed.
• Emotion supervision techiniques are proposed: (a) Direct: using emotion classifier (b) Indirect: using losses leveraging acoustic features and deep features which represent the emotional content of the source and converted samples.
• The indirect techniques can also be used in the absence of emotion labels.
• Experiments demonstrate its generalizability on the following benchmark datasets, across different accents, genders, emotions and cross-corpus conversions:
  • Emotional Speech Dataset (ESD)
  • Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
  • Centre for Speech Technology Voice Cloning Toolkit (VCTK)

Samples can be found here.

The demo can be found at Demo/EmoStarGAN Demo.ipynb.

1. Python >= 3.7
2. Install the python dependencies mentioned in the requirements.txt

1. Before starting the training, please specify the number of target speakers in num_speaker_domains and other details such as training and validation data in config.yml file.
2. Download VCTK and ESD datasets. For VCTK dataset preprocessing is needed, which can be carried out using Preprocess/getdata.py. The dataset paths need to be adjusted in train train_list.txt and validation val_list.txt lists present in Data/.
3. Download and copy the emotion embeddings weights to the folder Utils/emotion_encoder
4. Download and copy the vocoder weights to the folder Utils/Vocoder

python train.py --config_path ./Configs/speaker_domain_config.yml

The Emo-StarGAN model weight can be downloaded from here.

When the speaker index in train_list.txt or val_list.txt is greater than the number of speakers ( the hyperparameter num_speaker_domains mentioned in speaker_domain_config.yml), the following error is encountered:

[train]:   0%| | 0/66 [00:00<?, ?it/s]../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [0,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed.

Also note that the speaker index starts with 0 (not with 1!) in the training and validation lists.

• Our repository is heavily based on this great repo yl4579/StarGANv2-VC
• kan-bayashi/ParallelWaveGAN
• keums/melodyExtraction_JDC