https://github.com/2023-MindSpore-4/Code-5/tree/main/IntegralNeuralNetworks
fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Speech synthesis is the task of generating speech from some other modality like text, lip movements etc.
Please note that the leaderboards here are not really comparable between studies - as they use mean opinion score as a metric and collect different samples from Amazon Mechnical Turk.
<span style="color:grey; opacity: 0.6">( Image credit: WaveNet: A generative model for raw audio )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
WaveNODE: A Continuous Normalizing Flow for Speech Synthesis
EfficientNet-Absolute Zero for Continuous Speech Keyword Spotting
RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.