https://github.com/MindSpore-paper-code-2/code3/tree/main/StarGAN
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
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.
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
Decoupling Speaker-Independent Emotions for Voice Conversion Via Source-Filter Networks
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge
A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
Mel-spectrogram augmentation for sequence to sequence voice conversion
Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations
Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.