Voice Conversion Using Speech-to-Speech Neuro-Style Transfer

Overview

Voice Conversion Using Speech-to-Speech Neuro-Style Transfer

This repo contains the official implementation of the VAE-GAN from the INTERSPEECH 2020 paper Voice Conversion Using Speech-to-Speech Neuro-Style Transfer.

Examples of generated audio using the Flickr8k Audio Corpus: https://ebadawy.github.io/post/speech_style_transfer. Note that these examples are a result of feeding audio reconstructions of this VAE-GAN to an implementation of WaveNet.

1. Data Preperation

Dataset file structure:

/path/to/database
├── spkr_1
│   ├── sample.wav
├── spkr_2
│   ├── sample.wav
│   ...
└── spkr_N
    ├── sample.wav
    ...
# The directory under each speaker cannot be nested.

Here is an example script for setting up data preparation from the Flickr8k Audio Corpus. The speakers of interest are the same as in the paper, but may be modified to other speakers if desirable.

2. Data Preprocessing

The prepared dataset is organised into a train/eval/test split, the audio is preprocessed and melspectrograms are computed.

python preprocess.py --dataset [path/to/dataset] --test-size [float] --eval-size [float]

3. Training

The VAE-GAN model uses the melspectrograms to learn style transfer between two speakers.

python train.py --model_name [name of the model] --dataset [path/to/dataset]

3.1. Visualization

By default, the code plots a batch of input and output melspectrograms every epoch. You may add --plot-interval -1 to the above command to disable it. Alternatively you may add --plot-interval 20 to plot every 20 epochs.

3.2. Saving Models

By default, models are saved every epoch. With smaller datasets than Flickr8k it may be more appropriate to save less frequently by adding --checkpoint_interval 20 for 20 epochs.

3.3. Epochs

The max number of epochs may be set with --n_epochs. For smaller datasets, you may want to increase this to more than the default 100. To load a pretrained model you can use --epoch and set it to the epoch number of the saved model.

3.4. Pretrained Model

You can access pretrained model files here. By downloading and storing them in a directory src/saved_models/pretrained, you may call it for training or inference with:

--model_name pretrained --epoch 99

Note that for inference the discriminator files D1 and D2 are not required (meanwhile for training further they are). Also here, G1 refers to the decoding generator for speaker 1 (female) and G2 for speaker 2 (male).

4. Inference

The trained VAE-GAN is used for inference on a specified audio file. It works by; sliding a window over a full melspectrogram, locally inferring melspectrogram subsamples, and averaging the overlap. The script then uses Griffin-Lim to reconstruct audio from the generated melspectrogram.

python inference.py --model_name [name of the model] --epoch [epoch number] --trg_id [id of target generator] --wav [path/to/source_audio.wav]

For achieving high quality results like the paper you can feed the reconstructed audio to trained vocoders such as WaveNet. An example pipeline of using this model with wavenet can be found here.

4.1. Directory Input

Instead of a single .wav as input you may specify a whole directory of .wav files by using --wavdir instead of --wav.

4.2. Visualization

By default, plotting input and output melspectrograms is enabled. This is useful for a visual comparison between trained models. To disable set --plot -1

4.3. Reconstructive Evaluation

Alongside the process of generating, components for reconstruction and cyclic reconstruction may be enabled by specifying the generator id of the source audio --src_id [id of source generator].

When set, SSIM metrics for reconstructed melspectrograms and cyclically reconstructed melspectrograms are computed and printed at the end of inference.

This is an extra feature to help with comparing the reconstructive capabilities of different models. The higher the SSIM, the higher quality the reconstruction.

References

Citation

If you find this code useful please cite us in your work:

@inproceedings{AlBadawy2020,
  author={Ehab A. AlBadawy and Siwei Lyu},
  title={{Voice Conversion Using Speech-to-Speech Neuro-Style Transfer}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={4726--4730},
  doi={10.21437/Interspeech.2020-3056},
  url={http://dx.doi.org/10.21437/Interspeech.2020-3056}
}

TODO:

  • Rewrite preprocess.py to handle:
    • multi-process feature extraction
    • display error messages for failed cases
  • Create:
    • Notebook for data visualisation
  • Want to add something else? Please feel free to submit a PR with your changes or open an issue for that.
Owner
Ehab AlBadawy
Ehab AlBadawy
EqGAN - Improving GAN Equilibrium by Raising Spatial Awareness

EqGAN - Improving GAN Equilibrium by Raising Spatial Awareness Improving GAN Equilibrium by Raising Spatial Awareness Jianyuan Wang, Ceyuan Yang, Ying

GenForce: May Generative Force Be with You 149 Dec 19, 2022
This project is based on our SIGGRAPH 2021 paper, ROSEFusion: Random Optimization for Online DenSE Reconstruction under Fast Camera Motion .

ROSEFusion 🌹 This project is based on our SIGGRAPH 2021 paper, ROSEFusion: Random Optimization for Online DenSE Reconstruction under Fast Camera Moti

219 Dec 27, 2022
Deep Sketch-guided Cartoon Video Inbetweening

Cartoon Video Inbetweening Paper | DOI | Video The source code of Deep Sketch-guided Cartoon Video Inbetweening by Xiaoyu Li, Bo Zhang, Jing Liao, Ped

Xiaoyu Li 37 Dec 22, 2022
RoadMap and preparation material for Machine Learning and Data Science - From beginner to expert.

ML-and-DataScience-preparation This repository has the goal to create a learning and preparation roadMap for Machine Learning Engineers and Data Scien

33 Dec 29, 2022
Run containerized, rootless applications with podman

Why? restrict scope of file system access run any application without root privileges creates usable "Desktop applications" to integrate into your nor

119 Dec 27, 2022
Pytorch implementation of OCNet series and SegFix.

openseg.pytorch News 2021/09/14 MMSegmentation has supported our ISANet and refer to ISANet for more details. 2021/08/13 We have released the implemen

openseg-group 1.1k Dec 23, 2022
HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep.

HODEmu HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep. and emulates satellite abundance as a function of co

Antonio Ragagnin 1 Oct 13, 2021
Facial Expression Detection In The Realtime

The human's facial expressions is very important to detect thier emotions and sentiment. It can be very efficient to use to make our computers make interviews. Furthermore, we have robots now can det

Adel El-Nabarawy 4 Mar 01, 2022
NeurIPS-2021: Neural Auto-Curricula in Two-Player Zero-Sum Games.

NAC Official PyTorch implementation of NAC from the paper: Neural Auto-Curricula in Two-Player Zero-Sum Games. We release code for: Gradient based ora

Xidong Feng 19 Nov 11, 2022
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
Phy-Q: A Benchmark for Physical Reasoning

Phy-Q: A Benchmark for Physical Reasoning Cheng Xue*, Vimukthini Pinto*, Chathura Gamage* Ekaterina Nikonova, Peng Zhang, Jochen Renz School of Comput

29 Dec 19, 2022
ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

ANEA The goal of Automatic (Named) Entity Annotation is to create a small annotated dataset for NER extracted from German domain-specific texts. Insta

Anastasia Zhukova 2 Oct 07, 2022
GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks This repository implements a capsule model Inten

Joel Huang 15 Dec 24, 2022
Efficient Online Bayesian Inference for Neural Bandits

Efficient Online Bayesian Inference for Neural Bandits By Gerardo Durán-Martín, Aleyna Kara, and Kevin Murphy AISTATS 2022.

Probabilistic machine learning 49 Dec 27, 2022
10th place solution for Google Smartphone Decimeter Challenge at kaggle.

Under refactoring 10th place solution for Google Smartphone Decimeter Challenge at kaggle. Google Smartphone Decimeter Challenge Global Navigation Sat

12 Oct 25, 2022
(AAAI 2021) Progressive One-shot Human Parsing

End-to-end One-shot Human Parsing This is the official repository for our two papers: Progressive One-shot Human Parsing (AAAI 2021) End-to-end One-sh

54 Dec 30, 2022
JudeasRx - graphical app for doing personalized causal medicine using the methods invented by Judea Pearl et al.

JudeasRX Instructions Read the references given in the Theory and Notation section below Fire up the Jupyter Notebook judeas-rx.ipynb The notebook dra

Robert R. Tucci 19 Nov 07, 2022
Pytorch version of SfmLearner from Tinghui Zhou et al.

SfMLearner Pytorch version This codebase implements the system described in the paper: Unsupervised Learning of Depth and Ego-Motion from Video Tinghu

Clément Pinard 909 Dec 22, 2022
Code and data of the Fine-Grained R2R Dataset proposed in paper Sub-Instruction Aware Vision-and-Language Navigation

Fine-Grained R2R Code and data of the Fine-Grained R2R Dataset proposed in the EMNLP2020 paper Sub-Instruction Aware Vision-and-Language Navigation. C

YicongHong 34 Nov 15, 2022
PyTorch Implementation of "Light Field Image Super-Resolution with Transformers"

LFT PyTorch implementation of "Light Field Image Super-Resolution with Transformers", arXiv 2021. [pdf]. Contributions: We make the first attempt to a

Squidward 62 Nov 28, 2022