https://github.com/daniilrobnikov/vits2
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
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/danoneata/xts
https://github.com/dathudeptrai/TensorflowTTS
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/deciding/ParallelWaveGAN
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/deepsound-project/samplernn-pytorch
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
https://github.com/dhgrs/chainer-ClariNet
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
https://github.com/dipjyoti92/SC-WaveRNN
https://github.com/dipjyoti92/TTS-Style-Transfer
https://github.com/dscripka/openwakeword
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
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/facebookresearch/sonar
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
https://github.com/fatchord/MelNet
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/fatchord/WaveRNN
https://github.com/fishaudio/vocoder
EVA-GAN: Enhanced Various Audio Generation via Scalable Generative Adversarial Networks
https://github.com/foamliu/GST-Tacotron-v2
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/francois-rozet/zuko
https://github.com/freedombenLiu/speech-to-text-wavenet
https://github.com/funaudiollm/cv3-eval
CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
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/glakshay/Generating-audio-DL
https://github.com/google/lyra
https://github.com/grammatek/iceprondict
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/huawei-noah/Speech-Backbones/tree/main/Grad-TTS
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
https://github.com/huggingface/transformers
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/iPRoBe-lab/DeepTalk
DeepTalk: Vocal Style Encoding for Speaker Recognition and Speech Synthesis
https://github.com/ibab/tensorflow-wavenet
https://github.com/imdatsolak/wavenet
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.