Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

Last update: Dec 15, 2022

Related tags

Overview

S2VC

Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In this paper, we proposed S2VC which utilizes Self-Supervised pretrained representation to provide the latent phonetic structure of the utterance from the source speaker and the spectral features of the utterance from the target speaker.

The following is the overall model architecture.

For the audio samples, please refer to our demo page.

Usage

You can download the pretrained model as well as the vocoder following the link under Releases section on the sidebar.

The whole project was developed using Python 3.8, torch 1.7.1, and the pretrained model, as well as the vocoder, were turned to TorchScript, so it's not guaranteed to be backward compatible. You can install the dependencies with

pip install -r requirements.txt

If you encounter any problems while installing fairseq, please refer to pytorch/fairseq for the installation instruction.

Self-Supervised representations

Wav2vec2

In our implementation, we're using Wav2Vec 2.0 Base w/o finetuning which is trained on LibriSpeech. You can download the checkpoint wav2vec_small.pt from pytorch/fairseq.

APC(Autoregressive Predictive Coding), CPC(Contrastive Predictive Coding)

These two representations are extracted using this speech toolkit S3PRL. You can check how to extract various representations from that repo.

Vocoder

The WaveRNN-based neural vocoder is from yistLin/universal-vocoder which is based on the paper, Towards achieving robust universal neural vocoding.

Voice conversion with pretrained models

You can convert an utterance from the source speaker with multiple utterances from the target speaker by preparing a conversion pairs information file in YAML format, like

# pairs_info.yaml
pair1:
    source: VCTK-Corpus/wav48/p225/p225_001.wav
    target:
        - VCTK-Corpus/wav48/p227/p227_001.wav
pair2:
    source: VCTK-Corpus/wav48/p225/p225_001.wav
    target:
        - VCTK-Corpus/wav48/p227/p227_002.wav
        - VCTK-Corpus/wav48/p227/p227_003.wav
        - VCTK-Corpus/wav48/p227/p227_004.wav

And convert multiple pairs at the same time, e.g.

python convert_batch.py \
    -w <WAV2VEC_PATH> \
    -v <VOCODER_PATH> \
    -c <CHECKPOINT_PATH> \
    -s <SOURCE_FEATURE_NAME> \
    -r <REFERENCE_FEATURE_NAME> \
    pairs_info.yaml \
    outputs # the output directory of conversion results

After the conversion, the output directory, outputs, will be containing

pair1.wav
pair1.mel.png
pair1.attn.png
pair2.wav
pair2.mel.png
pair2.attn.png

Train from scratch

Preprocessing

You can preprocess multiple corpora by passing multiple paths. But each path should be the directory that directly contains the speaker directories. And you have to specify the feature you want to extract. Currently, we support apc, cpc, wav2vec2, and timit_posteriorgram. i.e.

python3 preprocess.py
    VCTK-Corpus/wav48 \
    <SECOND_Corpus_PATH> \ # more corpus if you want
    <FEATURE_NAME> \
    <WAV2VEC_PATH> \
    processed/<FEATURE_NAME>  # the output directory of preprocessed features

After preprocessing, the output directory will be containing:

metadata.json
utterance-000x7gsj.tar
utterance-00wq7b0f.tar
utterance-01lpqlnr.tar
...

You may need to preprocess multiple times for different features. i.e.

python3 preprocess.py
    VCTK-Corpus/wav48 apc <WAV2VEC_PATH> processed/apc
python3 preprocess.py
    VCTK-Corpus/wav48 cpc <WAV2VEC_PATH> processed/cpc
    ...

Then merge the metadata of different features.

i.e.

python3 merger.py processed

Training

python train.py processed
    --save_dir ./ckpts \
    -s <SOURCE_FEATURE_NAME> \
    -r <REFERENCE_FEATURE_NAME>

You can further specify --preload for preloading all training data into RAM to boost training speed. If --comment is specified, e.g. --comment CPC-CPC, the training logs will be placed under a newly created directory like, logs/2020-02-02_12:34:56_CPC-CPC, otherwise there won't be any logging. For more details, you can refer to the usage by python train.py -h.

Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC)

ppg-vc Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC) This repo implements different kinds of PPG-based VC models. Pretrained models. More m

227 Dec 28, 2022

The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

92 Nov 30, 2022

Repository providing a wide range of self-supervised pretrained models for computer vision tasks.

Hierarchical Pretraining: Research Repository This is a research repository for reproducing the results from the project "Self-supervised pretraining

53 Nov 9, 2022

The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

249 Dec 22, 2022

Implementation of the method described in the Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di

4 Mar 11, 2022

PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

112 Nov 28, 2022

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University

213 Dec 27, 2022

[CVPR2021] The source code for our paper 《Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning》.

TBE The source code for our paper "Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Le

150 Dec 28, 2022

Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

CorDA Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation Prerequisite Please create and activate the follo

60 Nov 30, 2022

Comments

Cannot find f2114342ff9e813e18a580fa41418aee9925414e in https://github.com/s3prl/s3prl

Running convert_batch.py throws ValueError: Cannot find f2114342ff9e813e18a580fa41418aee9925414e in https://github.com/s3prl/s3prl that originates from https://github.com/howard1337/S2VC/blob/8a6dcebc052424c41c62be0b22cb581258c5b4aa/data/feature_extract.py#L18

File "convert_batch.py", line 61, in main
src_feat_model = FeatureExtractor(src_feat_name, wav2vec_path, device)
File "/deepmind/experiments/howard1337/s2vc/data/feature_extract.py", line 18, in __init__
torch.hub.load("s3prl/s3prl:f2114342ff9e813e18a580fa41418aee9925414e", feature_name, refresh=True).eval().to(device)
File "/storage/usr/conda/envs/s2vc/lib/python3.8/site-packages/torch/hub.py", line 402, in load
repo_or_dir = _get_cache_or_reload(repo_or_dir, force_reload, verbose, skip_validation)
File "/storage/usr/conda/envs/s2vc/lib/python3.8/site-packages/torch/hub.py", line 190, in _get_cache_or_reload
_validate_not_a_forked_repo(repo_owner, repo_name, branch)
File "/storage/usr/conda/envs/s2vc/lib/python3.8/site-packages/torch/hub.py", line 160, in _validate_not_a_forked_repo
raise ValueError(f'Cannot find {branch} in https://github.com/{repo_owner}/{repo_name}. '
ValueError: Cannot find f2114342ff9e813e18a580fa41418aee9925414e in https://github.com/s3prl/s3prl. If it's a commit from a forked repo, please call hub.load() with forked repo directly.

Any idea on how to solve this?

opened by jerrymatjila 1

Could you provide ppg-extracting code?

Dear author,

In your paper, you mentioned you extracted ppg and SSL features by s3prl toolkit. However, I cannot find in s3prl on how to extract ppg. Could you provide the code or guideline on extracting ppgs? Thanks a lot!

opened by hongchengzhu 0

What are vocoder-ckpt-*.pt?
You release the following vocoder checkpoints:

vocoder-ckpt-apc.pt vocoder-ckpt-cpc.pt vocoder-ckpt-wav2vec2.pt

What are they?

Are they vocoders fine-tuned on the output of a particular model? I didn't see that described in the paper. Why is this needed, if the S2VC output is a mel? If it's because different models produce different mels, do you use vocoder-ckpt-cpc.pt when target model is cpc? And if so, how did you do the fine-tuning?
opened by turian 0
Training of other features (apc, timit_posteriorgram etc.) do not work
I have tried training with other than the cpc feature on my prepared corpus. However, the training script fails when the loss function (train.py , line 69). I found that the size of the output vector out is hard-coded, which is inconsistent with the size of the target Mel spectrogram of other features.

The size of some vectors of the model are:

apc case: Input dim: 512, Reference dim: 512, Target dim: 240

cpc case: Input dim: 256, Reference dim: 256, Target dim: 80

I prepared the input feature vectors by using preprocess.py, e.g. python .\preprocess.py (my own corpus) apc .\checkpoints\wav2vec_small.pt processed/apc.

I have modified the model by changing the size of the vectors and can run train.py now. In the model.py, __init__() of S2VC function, I replace 80 with a function argument and pass the size of Mel vector size. But I cannot determine the modification is appropriate, for I am not familiar with NLP.

convert_batch.py with pre-trained models works well as you described in README.md.

Other details of my situation are:

Windows 10, PowerShell

pytorch 1.7.1 + cu110

torchaudio 0.7.1

sox 1.4.1

tqdm 4.42.0

librosa 0.8.1
opened by sage-git 0

Releases(v1.0)

v1.0(Jul 7, 2021)

The pretrained model cpc-cpc.pt utilizes CPC(Contrastive Predictive Coding) as the inputs for both source and target encoders.
Source code(tar.gz)
Source code(zip)
cpc-cpc.pt(151.30 MB)
vocoder-ckpt-apc.pt(19.50 MB)
vocoder-ckpt-cpc.pt(19.50 MB)
vocoder-ckpt-wav2vec2.pt(19.50 MB)

Owner

GitHub Repository

Let Python optimize the best stop loss and take profits for your TradingView strategy.

TradingView Machine Learning TradeView is a free and open source Trading View bot written in Python. It is designed to support all major exchanges. It

473 Jan 09, 2023

A tight inclusion function for continuous collision detection

Tight-Inclusion Continuous Collision Detection A conservative Continuous Collision Detection (CCD) method with support for minimum separation. You can

89 Jan 01, 2023

A simple pytorch pipeline for semantic segmentation.

SegmentationPipeline -- Pytorch A simple pytorch pipeline for semantic segmentation. Requirements : torch=1.9.0 tqdm albumentations=1.0.3 opencv-pyt

4 Feb 22, 2022

Informal Persian Universal Dependency Treebank

Informal Persian Universal Dependency Treebank (iPerUDT) Informal Persian Universal Dependency Treebank, consisting of 3000 sentences and 54,904 token

0 Jan 05, 2022

Dynamic Graph Event Detection

DyGED Dynamic Graph Event Detection Get Started pip install -r requirements.txt TODO Paper link to arxiv, and how to cite. Twitter Weather dataset tra

3 May 09, 2022

Automatic Video Captioning Evaluation Metric --- EMScore

Automatic Video Captioning Evaluation Metric --- EMScore Overview For an illustration, EMScore can be computed as: Installation modify the encode_text

17 Nov 28, 2022

Self-Supervised Deep Blind Video Super-Resolution

Self-Blind-VSR Paper | Discussion Self-Supervised Deep Blind Video Super-Resolution By Haoran Bai and Jinshan Pan Abstract Existing deep learning-base

35 Dec 09, 2022

clustimage is a python package for unsupervised clustering of images.

clustimage The aim of clustimage is to detect natural groups or clusters of images. Image recognition is a computer vision task for identifying and ve

52 Jan 02, 2023

The first dataset of composite images with rationality score indicating whether the object placement in a composite image is reasonable.

Object-Placement-Assessment-Dataset-OPA Object-Placement-Assessment (OPA) is to verify whether a composite image is plausible in terms of the object p

53 Nov 15, 2022

[LREC] MMChat: Multi-Modal Chat Dataset on Social Media

MMChat This repo contains the code and data for the LREC2022 paper MMChat: Multi-Modal Chat Dataset on Social Media. Dataset MMChat is a large-scale d

47 Jan 03, 2023

Lux AI environment interface for RLlib multi-agents

Lux AI interface to RLlib MultiAgentsEnv For Lux AI Season 1 Kaggle competition. LuxAI repo RLlib-multiagents docs Kaggle environments repo Please let

12 Nov 07, 2022

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt.

UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. UltraOpt is a simple and efficient library to minimize expensive

98 Aug 16, 2022

An adaptive hierarchical energy management strategy for hybrid electric vehicles

An adaptive hierarchical energy management strategy This project contains the source code of an adaptive hierarchical EMS combining heuristic equivale

19 Dec 13, 2022

Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Covid-19 Test AI (Deep Learning - NNs) Software I developed a segmentation algorithm to understand whether Covid-19 Test Photos are positive or negati

28 Dec 04, 2021

2021 CCF BDCI 全国信息检索挑战杯（CCIR-Cup）智能人机交互自然语言理解赛道第二名参赛解决方案

2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup) 智能人机交互自然语言理解赛道第二名解决方案比赛网址: CCIR-Cup-智能人机交互自然语言理解 1.依赖环境： python==3.8 torch==1.7.1+cu110 numpy==1.19.2 transformers=

22 Oct 29, 2022

Official repository for "On Generating Transferable Targeted Perturbations" (ICCV 2021)

On Generating Transferable Targeted Perturbations (ICCV'21) Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli Paper:

46 Nov 17, 2022

Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

201 Dec 21, 2022