Papers with Code 아카이브
Audio Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 3
- 보존된 평가 기록
- 26
Audio generation (synthesis) is the task of generating raw audio such as speech.
<span style="color:grey; opacity: 0.6">( Image credit: MelNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ag1988/dss
https://github.com/albarji/neurowriter
https://github.com/albertfgu/diffwave-sashimi
https://github.com/alexandervnikitin/tsgm
https://github.com/aluo-x/learning_neural_acoustic_fields
AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis
https://github.com/anandharaju/Basic_TCN
https://github.com/andimarafioti/audioContextEncoder
https://github.com/anton-jeran/AV-RIR
https://github.com/anton-jeran/MESH2IR
FAST-RIR: Fast neural diffuse room impulse response generator
https://github.com/anton-jeran/Speech2RIR
FAST-RIR: Fast neural diffuse room impulse response generator
https://github.com/ashishpatel26/tcn-keras-Examples
https://github.com/awslabs/gluon-ts
https://github.com/azerty-waves/ddsp-real-time
https://github.com/basveeling/wavenet
https://github.com/benmoseley/simple-wavenet
https://github.com/bytedance/make-an-audio-2
https://github.com/carlthome/pmqd
https://github.com/cchinchristopherj/Concert-of-Whales
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
https://github.com/coreyoconnor/tensorderp
https://github.com/cristiprg/wavegan-fork
https://github.com/csiki/v2a
https://github.com/dada-bots/dadabots_sampleRNN
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
https://github.com/daviddiazguerra/icodoa
gpuRIR: A python library for Room Impulse Response simulation with GPU acceleration
https://github.com/deciding/StarGAN-VC
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
https://github.com/deepsound-project/samplernn-pytorch
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
https://github.com/delijingyic/wavegan_phonology
https://github.com/elsalmi/qiskit
https://github.com/erik-buchholz/SoK-TrajGen
https://github.com/ethman/tagbox
Unsupervised Source Separation By Steering Pretrained Music Models
https://github.com/facebookresearch/audiocraft
https://github.com/fatchord/MelNet
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/fishaudio/vocoder
https://github.com/fixie-ai/ultravox
https://github.com/freedombenLiu/speech-to-text-wavenet
https://github.com/fromme0528/pytorch-WaveGAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.