https://github.com/winddori2002/TriAAN-VC
공식 코드TriAAN-VC: Triple Adaptive Attention Normalization for Any-to-Any Voice Conversion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
I remember all the summer days Drinking wine in the sunshine I hope it never leaves And I remember all the summer nights Staring at you in the moonlight I hope you never leave 'cause baby You're so good to me You have all that all that I ever need It's easy to love you So easy to love you Ooh you know it's true The best part of being with you To know you're with me It's not so hard to say It's easy to love you I remember all those winter days frozen In the cold tryin' to get you home Should I be moving in, we can be together then Remember spending all those winter nights Stayin' inside by the warm fire Yeah you gotta know that I can never let you go You and I have the rest of our lives to say It's easy to love you So easy to love you Ooh you know it's true The best part of being with you To know you're with me It's not so hard to say It's easy to love you Can anybody else see it? Mm, can anybody else see what I do? Can anybody else feel it? Oh, can anybody else feel the way I do? But now I'm with you Hard to forget all the moments when We'd be sitting there hoping it would never end 'Cause this is meant to be So baby, will you marry me? It's easy to love you So easy to love you Ooh, you know it's true The best part of being with you To know you are with me It's not so hard to say It's easy to love you You and me will be together I know our love will last forever You and me will be together I know our love will last forever You know it's true The best part of being with you You're easy to love
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
TriAAN-VC: Triple Adaptive Attention Normalization for Any-to-Any Voice Conversion
Scalable Factorized Hierarchical Variational Autoencoder Training
End-to-End Zero-Shot Voice Conversion with Location-Variable Convolutions
An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion
StyleTTS-VC: One-Shot Voice Conversion by Knowledge Transfer from Style-Based TTS Models
One-class learning towards generalized voice spoofing detection
UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021
Utilizing Self-supervised Representations for MOS Prediction
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
Scalable Factorized Hierarchical Variational Autoencoder Training
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Unsupervised Speech Decomposition via Triple Information Bottleneck
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
Unsupervised Speech Decomposition via Triple Information Bottleneck
MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames
Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder
Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset
Non-Parallel Voice Conversion with Cyclic Variational Autoencoder
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
Utilizing Self-supervised Representations for MOS Prediction
Utilizing Self-supervised Representations for MOS Prediction
Utilizing Self-supervised Representations for MOS Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.