https://github.com/Alizeded/BERP
공식 코드BERP: A Blind Estimator of Room Parameters for Single-Channel Noisy Speech Signals
Papers with Code 아카이브
Audio Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/Audio-WestlakeU/VINP
공식 코드https://github.com/Bai-YT/ConsistencyTTA
공식 코드ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
https://github.com/Beilong-Tang/TSELM
공식 코드TSELM: Target Speaker Extraction using Discrete Tokens and Language Models
https://github.com/Chutlhu/dEchorate
공식 코드dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing
https://github.com/DavidDiazGuerra/gpuRIR
공식 코드gpuRIR: A python library for Room Impulse Response simulation with GPU acceleration
https://github.com/DragonLiu1995/Vision-to-Audio-and-Beyond
공식 코드From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation
https://github.com/DragonLiu1995/multimodal-llm-for-audio-gen
공식 코드Tell What You Hear From What You See -- Video to Audio Generation Through Text
https://github.com/Edresson/ZS-TTS-Evaluation
공식 코드XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model
https://github.com/FunAudioLLM/SenseVoice
공식 코드https://github.com/HaydenCaffrey/SIG-VC
공식 코드https://github.com/Lysarthas/StochDiff
공식 코드Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting
https://github.com/NetaGlazer/ADD-GP
공식 코드Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
https://github.com/Newbeeer/Anytime-Auto-Regressive-Model
공식 코드Anytime Sampling for Autoregressive Models via Ordered Autoencoding
https://github.com/PKU-YuanGroup/LLMBind
공식 코드https://github.com/PaddlePaddle/PaddleSpeech
공식 코드ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech
https://github.com/Rongjiehuang/Multi-Singer
공식 코드Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus
https://github.com/SRPOL-AUI/storir
공식 코드StoRIR: Stochastic Room Impulse Response Generation for Audio Data Augmentation
https://github.com/SherifAbdulatif/CMGAN
공식 코드CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement
https://github.com/Text-to-Audio/AudioLCM
공식 코드AudioLCM: Text-to-Audio Generation with Latent Consistency Models
https://github.com/Tomiinek/Multilingual_Text_to_Speech
공식 코드One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech
https://github.com/YoonjinXD/T-foley
공식 코드https://github.com/YoonjinXD/kadtk
공식 코드KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation
https://github.com/acheketa/cwavegan
공식 코드https://github.com/acids-ircam/Expressive_WAE_FADER
공식 코드Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-Encoders
https://github.com/adrienchaton/Expressive_WAE_FADER
공식 코드Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-Encoders
https://github.com/aeromamba-super-resolution/aeromamba
공식 코드https://github.com/amu-cai/cloneval
공식 코드https://github.com/andimarafioti/GACELA
공식 코드GACELA -- A generative adversarial context encoder for long audio inpainting
https://github.com/andreevp/wvmos
공식 코드HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement
https://github.com/anton-jeran/FAST-RIR
공식 코드FAST-RIR: Fast neural diffuse room impulse response generator
https://github.com/archinetai/audio-diffusion-pytorch
공식 코드https://github.com/arraydps/arraydps
공식 코드ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior
https://github.com/audio-agi/wavjourney
공식 코드WavJourney: Compositional Audio Creation with Large Language Models
https://github.com/audio-df-ucb/clonedvoicedetection
공식 코드Single and Multi-Speaker Cloned Voice Detection: From Perceptual to Learned Features
https://github.com/baichuan-inc/Baichuan-Omni-1.5
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.