https://github.com/0nutation/speechgpt
공식 코드SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation
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.
SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation
Universal Adaptor: Converting Mel-Spectrograms Between Different Configurations for Speech Synthesis
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset
Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition
Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion
Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data
Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation
SafeEar: Content Privacy-Preserving Audio Deepfake Detection
Playing with Voices: Tabletop Role-Playing Game Recordings as a Diarization Challenge
CinC-GAN for Effective F0 prediction for Whisper-to-Normal Speech Conversion
ControlVC: Zero-Shot Voice Conversion with Time-Varying Controls on Pitch and Speed
Phoneme Hallucinator: One-shot Voice Conversion via Set Expansion
Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants
DeID-VC: Speaker De-identification via Zero-shot Pseudo Voice Conversion
Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments
Training-Free Voice Conversion with Factorized Optimal Transport
FMFCC-A: A Challenging Mandarin Dataset for Synthetic Speech Detection
Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion
StarGAN-VC++: Towards Emotion Preserving Voice Conversion Using Deep Embeddings
AraBERT: Transformer-based Model for Arabic Language Understanding
Unsupervised Speech Decomposition via Triple Information Bottleneck
Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech
A unified one-shot prosody and speaker conversion system with self-supervised discrete speech units
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.