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

Deep Learning S2VC

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

Official PyTorch implementation and pretrained models of the paper Self-Supervised Classification Network

Self-Classifier: Self-Supervised Classification Network Official PyTorch implementation and pretrained models of the paper Self-Supervised Classificat

24 Dec 21, 2022

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion

StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion Yinghao Aaron Li, Ali Zare, Nima Mesgarani We pres

282 Jan 1, 2023

A PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-Supervised Learning Framework".

Mugs: A Multi-Granular Self-Supervised Learning Framework This is a PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-

62 Nov 8, 2022

Implementation of Kaneko et al.'s MaskCycleGAN-VC model for non-parallel voice conversion.

MaskCycleGAN-VC Unofficial PyTorch implementation of Kaneko et al.'s MaskCycleGAN-VC (2021) for non-parallel voice conversion. MaskCycleGAN-VC is the

86 Dec 25, 2022

Official implementation of "One-Shot Voice Conversion with Weight Adaptive Instance Normalization".

One-Shot Voice Conversion with Weight Adaptive Instance Normalization By Shengjie Huang, Yanyan Xu*, Dengfeng Ke*, Mingjie Chen, Thomas Hain. This rep

31 Dec 7, 2022

Pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion"

MOSNet pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion" https://arxiv.org/abs/1904.08352 Dependency L

9 Nov 18, 2022

Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

478 Dec 25, 2022

An official reimplementation of the method described in the INTERSPEECH 2021 paper - Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.