https://github.com/Edresson/ZS-TTS-Evaluation
공식 코드XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Voice cloning is a highly desired feature for personalized speech interfaces. Neural voice cloning system learns to synthesize a person’s voice from only a few audio samples.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model
Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech
One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech
Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features
Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques
Proactive Detection of Voice Cloning with Localized Watermarking
StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing
LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation
IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
SongGen: A Single Stage Auto-regressive Transformer for Text-to-Song Generation
Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens
Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction
Small-E: Small Language Model with Linear Attention for Efficient Speech Synthesis
PolyGlotFake: A Novel Multilingual and Multimodal DeepFake Dataset
EmoKnob: Enhance Voice Cloning with Fine-Grained Emotion Control
Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.