Papers with Code 아카이브
Accented Speech Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kinimod23/ATS_Project
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/ksw0306/ClariNet
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
https://github.com/lakahaga/dc-comix-tts
https://github.com/lifeiteng/naturalspeech3_facodec
NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models
https://github.com/lifeiteng/vall-e
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/liguigui/speech-to-text-wavenet
https://github.com/lissyx/STT
https://github.com/ljuvela/GELP
GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram
https://github.com/lmnt-com/diffwave
https://github.com/lmnt-com/wavegrad
https://github.com/lorenlugosch/pretrain_speech_model
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
https://github.com/lturing/Tools
Hierarchical Generative Modeling for Controllable Speech Synthesis
https://github.com/lucidrains/naturalspeech2-pytorch
NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers
https://github.com/lucidrains/voicebox-pytorch
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
https://github.com/majidAdibian77/ResGrad
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/majidAdibian77/persian-SV2TTS
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/mamhoud/CapstoneProject
A Critical Review of Recurrent Neural Networks for Sequence Learning
https://github.com/mangelroman/audio2score
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/mangushev/deep_speech
https://github.com/martinlenglet/avtacotron2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/maum-ai/phaseaug
PhaseAug: A Differentiable Augmentation for Speech Synthesis to Simulate One-to-Many Mapping
https://github.com/maum-ai/univnet
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
https://github.com/maum-ai/wavegrad2
https://github.com/mindmapper15/Voice-Converter
https://github.com/mindslab-ai/univnet
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
https://github.com/mindslab-ai/wavegrad2
https://github.com/mindspore-ai/models/tree/master/official/audio/lpcnet
A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet
https://github.com/mindspore-ai/models/tree/master/official/audio/melgan
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/mindspore-ai/models/tree/master/official/cv/warpctc
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/mindspore-ai/models/tree/master/research/audio/deepspeech2
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/mindspore-ai/models/tree/master/research/audio/tacotron2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/mindspore-ai/models/tree/master/research/audio/wavenet
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/4-neural_poet_RNN
A Critical Review of Recurrent Neural Networks for Sequence Learning
https://github.com/mitsu-h/deepvoice3
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/mkotha/WaveRNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.