Papers with Code 아카이브
Text-To-Speech Synthesis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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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/2023-MindSpore-1/ms-code-50
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/fastspeech2_conformer
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/2noise/chattts
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/Berthaniu/LatentOptimalPathsBayesianDP
RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis
https://github.com/CODEJIN/GST_Tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/CSTR-Edinburgh/ophelia
https://github.com/CorentinJ/Real-Time-Voice-Cloning
https://github.com/GNovich/Talk2Me
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/GPUPhobia/vocal-mask
https://github.com/IvKosar/text2speech
https://github.com/KathyReid/opensource-voice-tools
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
https://github.com/KevinMIN95/StyleSpeech
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/KinglittleQ/GST-Tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/Kyubyong/dc_tts
https://github.com/Kyubyong/expressive_tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/Kyubyong/tacotron
https://github.com/M3DV/SimTA
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
https://github.com/MindSpore-paper-code-2/code399/tree/main/wavenet
https://github.com/MindSpore-scientific-2/code-11/tree/main/SING
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
https://github.com/MindSpore-scientific/code-1/tree/main/SING
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
https://github.com/Moon-sung-woo/ParallelWaveGan_korean
https://github.com/MugheesQasim/Voice-Cloning
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/Munna-Manoj/Team6_FastSpeech2_TTS
https://github.com/NVIDIA/radtts
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
https://github.com/NisansaDdS/Some-Languages-are-More-Equal-than-Others
Systematic Inequalities in Language Technology Performance across the World's Languages
https://github.com/Nvillaluenga/DC_TTS
https://github.com/OlaWod/my-fastspeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/OlaWod/my-tacotron2
https://github.com/OlaWod/my-wavernn
https://github.com/PaddlePaddle/DeepSpeech
https://github.com/QData/TextAttack/blob/master/textattack/transformations/sentence_transformations/back_transcription.py
https://github.com/RayeRen/RayeRen
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/Rongjiehuang/Multiband-WaveRNN
https://github.com/RuntimeRacer/rtvc-gcloud
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/SeanPLeary/dc_tts-transfer-learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.