https://github.com/RyanXingQL/RBQE
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Papers with Code 아카이브
Speech Recognition
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Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.
<span style="color:grey; opacity: 0.6">( Image credit: SpecAugment )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SANKHA1/Vehicle-Detection
https://github.com/SEC4SR/SEC4SR
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition
https://github.com/SPEECHCOG/data2vec_maiju
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/SaeedNajafi/OCD-Learning
https://github.com/SaeedNajafi/pytorch-ocd
https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras
https://github.com/Salazar-99/Gravitational-WaveNet
https://github.com/SarthakYadav/audax
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/SeanNaren/deepspeech.pytorch
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/SeanNaren/deepspeech.torch
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/SeanPLeary/dc_tts-transfer-learning
https://github.com/SergiBragos/DLAI-2021-project
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/SforAiDl/Neural-Voice-Cloning-With-Few-Samples
https://github.com/ShangHua-Gao/G2L-search
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
https://github.com/Shantanu48114860/MTL-TCNN3
Using Filter Banks in Convolutional Neural Networks for Texture Classification
https://github.com/Shesh6/Deep-Renormalization
An exact mapping between the Variational Renormalization Group and Deep Learning
https://github.com/ShichengChen/WaveNetSeparateAudio
https://github.com/Shivendra-psc/speechbot
https://github.com/ShiyuLiang/odin-pytorch
https://github.com/ShotDownDiane/tcn-master
https://github.com/ShuSQ/CCI_AP_PoseLoops
https://github.com/SimJeg/FC-DenseNet
https://github.com/Snapchat/snapml-templates
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/SoyBison/gammatone
https://github.com/Stomper10/CheXpert
https://github.com/Suhee05/Text-Independent-Speaker-Verification
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/TMarquet/speech_recognition
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/Talk2Levi/DJL
https://github.com/TanUkkii007/wavenet
https://github.com/TartuNLP/deepvoice3_pytorch
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/TeaPoly/CTC-MWER
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
https://github.com/TeaPoly/CTC-OptimizedLoss
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
https://github.com/TeaPoly/Conformer-Athena
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/TensorSpeech/TensorFlowASR
https://github.com/TensorSpeech/TensorflowTTS
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.