ZEST

Zero-Shot Emotion Style Transfer (Zero Shot Audio to Audio Emotion Transfer With Speaker Disentanglement accepted at ICASSP 2024)

Codes are adapted from ZEST

Ablations

We perform two ablations where we remove the two CNN-BiLSTM networks operating on the F0 contour and the HuBERT tokens. The results for these cases are provided in the diagram below. Note that there are two more entries (ZEST-no-F0-enc and ZEST-no-unit-enc) in the table than what was provided in the paper.

EASE Embeddings

We first show the utility of the adversarial learning in the speaker embedding module.

We show the comparison between the x-vectors(left of each group) and the EASE embeddings(right of each group) side-by-side for two different speakers in ESD dataset. The colours in the t-SNE plots are according to the 5 emotions present in the dataset. We note that x-vectors show clear emotion clusters while EASE embeddings are ignorant of the emotion information.

SACE Embeddings

We next show the emotion latent information learnt by the SACE module. We show t-SNE visualization of this latent space and colour them based on the 10 speakers and 5 emotions separately.

Emotion clusters	Speaker clusters

Reconstruction of F0 contour

We show 4 examples of the ability of the F0 predictor module to reconstruct the ground truth pitch contour.

Angry	Happy	Sad	Surprise

Examples of F0 conversion

We show three examples of how the F0 conversion works in ZEST. We show three examples from three test settings - DSDT (source and reference speaker seen but different with unseen text), USS (Unseen source speaker with seen reference speaker/emotion and unseen text) and UTE (seen source speaker with unseen reference speaker/emotion and unseen text). The first row indicates the F0 contour of the source speech signal, the second row indicates pitch contour of the reference speech signal while the last row indicates the converted F0 contour.

DSDT	USS	UTE

Training Pipeline

PLEASE NOTE THAT: ALL CODE SHOULD BE RUN AT BASE FOLDER

please checking out code/config.yml.

EASE
1. run get_seapker_embedding.py, get embeddings of ECAPA.
2. run speaker_classifier.py, train EASE. We provided EASE.pth.
SACE
1. run get_emotion_embedding.py, get embeddings of wav2vec2. We freeze all layers in wav2vec2
2. run emotion_classifier.py, train SACE. We provided SACE.pth.
F0
1. run pitch_attention_adv.py in ddp way, training F0_predictor.
```
python -m torch.distributed.launch --nproc_per_node=4 code/F0_predictor/pitch_attention_adv.py
```
2. reconstruct F0 by pitch_inference.py.

HiFi-GAN

run train.py in ddp way.

python -m torch.distributed.launch --nproc_per_node=4 code/HiFi-GAN/train.py

Reconstruct Pipeline

run pitch_convert.py to get DSDT F0 contours.

run inference.py in HiFi-GAN.

python code/HiFi-GAN/inference.py --convert

chxu2000 / ZEST