https://github.com/MoonInTheRiver/DiffSinger
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
Papers with Code 아카이브
Speech Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 17
- 보존된 평가 기록
- 48
Speech Emotion Recognition is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker, such as happiness, anger, sadness, or frustration, from their speech patterns, such as prosody, pitch, and rhythm.
For multimodal emotion recognition, please upload your result to Multimodal Emotion Recognition on IEMOCAP
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MoritzKappel/HF-NHMT
High-Fidelity Neural Human Motion Transfer from Monocular Video
https://github.com/MrSyee/pg-is-all-you-need
https://github.com/NERSC/sc21-dl-tutorial
Fast, high-fidelity Lyman $α$ forests with convolutional neural networks
https://github.com/NLPbox/feng-hirst-service
Two-pass Discourse Segmentation with Pairing and Global Features
https://github.com/NOHYC/autonomous_driving_car_project
https://github.com/NSR9/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/NU-LL/lighttrack-
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification
Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification
https://github.com/NVIDIA-Omniverse/IsaacGymEnvs
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/NVIDIA/MinkowskiEngine
High-dimensional Convolutional Networks for Geometric Pattern Recognition
https://github.com/NVIDIA/sentiment-discovery
https://github.com/NVIDIA/unsupervised-video-interpolation
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
https://github.com/NVlabs/PAMTRI
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/NVlabs/industreallib
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/NVlabs/selfsupervised-denoising
https://github.com/Neoanarika/torchexplainer
https://github.com/NervanaSystems/coach
https://github.com/NiallEHunt/MonocularDepth-Using-LightFields
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/NiallJeffrey/DeepMass
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
https://github.com/Nikolai10/MRIC
https://github.com/Nimesh-Patel/BERT
https://github.com/Noopuragr/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Nstats/bert_MRC
https://github.com/Nstats/bert_senti_analysis_ch
https://github.com/Nstats/my_bert
https://github.com/OPAYA/Transformer
https://github.com/Olksndr/FaceShifter
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/Osobarako/alduswarrensewell
https://github.com/OsvaldN/APS360_Project
Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter08
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/PaddlePaddle/PaddleClas
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_translation/transformer
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/PaddlePaddle/PaddleSpeech
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.