Papers with Code 아카이브
Text-To-Speech Synthesis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 21
Text-To-Speech Synthesis is a machine learning task that involves converting written text into spoken words. The goal is to generate synthetic speech that sounds natural and resembles human speech as closely as possible.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/deciding/ParallelWaveGAN
https://github.com/dimasikson/Audio-cleaning-with-Deep-Learning
https://github.com/dipjyoti92/SC-WaveRNN
https://github.com/dipjyoti92/TTS-Style-Transfer
https://github.com/edresson/yourtts
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
https://github.com/enhuiz/vall-e
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/erasedwalt/FastSpeech
https://github.com/facebookresearch/denoiser
https://github.com/fatchord/MelNet
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/fatchord/WaveRNN
https://github.com/foamliu/GST-Tacotron-v2
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/ga642381/FastSpeech2
https://github.com/galaxycong/hpmdubbing
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/google/lyra
https://github.com/hash2430/pitchtron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/hcy71o/autovocoder
https://github.com/heatz123/naturalspeech
NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality
https://github.com/hhguo/msmc-tts
https://github.com/inconnu11/Objective-evaluation_speech_synthesis
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data
https://github.com/ivanvovk/WaveGrad
https://github.com/izzajalandoni/tts_models
https://github.com/jacksonkaunismaa/neural-music
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/jessearodriguez/LJ-Audio-dataset-generator
https://github.com/jinhan/tacotron2-gst
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/jkyunnng/happyquokka_system_for_eeg_challenge
https://github.com/kdaip/stabletts
https://github.com/keithito/tacotron
https://github.com/keonlee9420/Comprehensive-E2E-TTS
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/keonlee9420/Comprehensive-Tacotron2
https://github.com/keonlee9420/Comprehensive-Transformer-TTS
https://github.com/keonlee9420/Cross-Speaker-Emotion-Transfer
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/keonlee9420/DailyTalk
https://github.com/keonlee9420/DiffGAN-TTS
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
https://github.com/keonlee9420/DiffSinger
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/keonlee9420/Expressive-FastSpeech2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.