https://github.com/AshwinRJ/Federated-Learning-PyTorch
Communication-Efficient Learning of Deep Networks from Decentralized Data
Papers with Code 아카이브
Speech Recognition
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- 평가표
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- 보존된 평가 기록
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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/Asthestarsfalll/DenseNet-MegEngine
https://github.com/Audio-WestlakeU/RCT-Random-Consistency-Training
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/AzizCode92/Listen-Attend-and-Spell-Pytorch
https://github.com/BRAIN-chain/FRAIN
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/Baichenjia/Tensorflow-TCN
https://github.com/Baileyswu/pytorch-hmm-vae
https://github.com/Benja1972/Speech_recognition_Tensorflow2.0
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/Berthaniu/LatentOptimalPathsBayesianDP
RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis
https://github.com/BirgerMoell/tmh
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/BogiHsu/Tacotron2-PyTorch
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/BugOMan/summary_generator
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/Bunseki2/DeepL
https://github.com/BurnhamG/PyTorch-ScalableFHVAE
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
https://github.com/ByakuyaUncia/DenseNetUncia
https://github.com/CKRC24/Listen-and-Translate
https://github.com/CODEJIN/GST_Tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/Cbhihe/classify_sound_mobile
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/CeLaMbDa/DenseNet_keras
https://github.com/Chasm4359/ProTS
https://github.com/Chung-I/Variational-Recurrent-Autoencoder-Tensorflow
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
https://github.com/Ciprian95/Licenta
https://github.com/Cjl-MedSeg/DenseNet
https://github.com/ClorverCcy/GEDLoss_pytorch
https://github.com/Contamination-Classification/DenseNet
https://github.com/CorentinJ/Real-Time-Voice-Cloning
https://github.com/CorrelAid/codingchallenge1020_team1
https://github.com/Cross-Caps/STFADE
https://github.com/Dahee96/Seq2seq-
https://github.com/DaikiTanak/manifold_mixup
https://github.com/DataScienceNigeria/Automatic-speech-recognition-ASR-systems-from-googleAI-and-ALSTDI
Personalizing ASR for Dysarthric and Accented Speech with Limited Data
https://github.com/Deci-AI/super-gradients
https://github.com/DeepMark/deepmark
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/DeepTrial/Retina-VesselNet
https://github.com/Delta-ML/delta
https://github.com/DemisEom/SpecAugment
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.