Papers with Code 아카이브
Speech Synthesis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 15
- 평가표
- 5
- 보존된 평가 기록
- 31
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SeanPLeary/dc_tts-transfer-learning
https://github.com/SforAiDl/Neural-Voice-Cloning-With-Few-Samples
https://github.com/ShangHua-Gao/G2L-search
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
https://github.com/ShichengChen/WaveNetSeparateAudio
https://github.com/Shivendra-psc/speechbot
https://github.com/ShotDownDiane/tcn-master
https://github.com/ShuSQ/CCI_AP_PoseLoops
https://github.com/Suhee05/Text-Independent-Speaker-Verification
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/Talk2Levi/DJL
https://github.com/TanUkkii007/wavenet
https://github.com/TartuNLP/deepvoice3_pytorch
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/TensorSpeech/TensorflowTTS
https://github.com/Text-to-Audio/AudioLCM
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech
https://github.com/Vikas-Sony/speech-to-text
https://github.com/WLM1ke/poptimizer
https://github.com/WelkinYang/GradTTS
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
https://github.com/YuanPJ/Voice-in-ASR
https://github.com/YuvalBecker/MelNet
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/YuvalBecker/MelNet-Tensorflow
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/ZTianle/keras-tcn-solar
https://github.com/ZhouYuxuanYX/Wavenet-in-Keras-for-Kaggle-Competition-Web-Traffic-Time-Series-Forecasting
https://github.com/acetylSv/GST-tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/acids-ircam/diffusion_models
https://github.com/adelacvg/ns2vc
NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers
https://github.com/adityaagrawal7/speech-to-text-wavenet
https://github.com/adrienchaton/BERGAN
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/ai-unicamp/tts-objective-metrics
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
https://github.com/ai4bharat/indic-tts
Towards Building Text-To-Speech Systems for the Next Billion Users
https://github.com/albarji/neurowriter
https://github.com/albertfgu/diffwave-sashimi
https://github.com/alpharol/Taco_Collection
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/anandaswarup/TTS
https://github.com/anandaswarup/phrase_break_prediction
https://github.com/anandaswarup/rnn-tts
https://github.com/anandaswarup/waveRNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.