https://github.com/MindSpore-scientific-2/code-11/tree/main/ZoneoutRNN
A Comparative Study on Transformer vs RNN in Speech Applications
Papers with Code 아카이브
Speech Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 236
- 보존된 평가 기록
- 398
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/MindSpore-scientific-2/code-14/tree/main/hubert
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
https://github.com/MindSpore-scientific-2/code-5/tree/main/Deep-Gradient-Compression
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
https://github.com/MindSpore-scientific-2/code-8/tree/main/shallow-rnns
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
https://github.com/MindSpore-scientific-2/code-9/tree/main/Deep-Gradient-Compression
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
https://github.com/MindSpore-scientific/code-1/tree/main/shallow-rnns
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
https://github.com/MindSpore-scientific/code-12/tree/main/Deep-Gradient-Compression
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
https://github.com/MindSpore-scientific/code-14/tree/main/ReSeg
https://github.com/MindSpore-scientific/code-3/tree/main/contextual-learning
https://github.com/MindSpore-scientific/code-4/tree/main/Datasets-Models-main
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/MindSpore-scientific/code-5/tree/main/ReSeg
https://github.com/MiuLab/SlotGated-SLU
https://github.com/Mixergi/MelGAN
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/Mnpr/OC-DeepLearning
https://github.com/Mnpr/Thoractic-Diseases-Detection
https://github.com/Mnpr/Thoractic_Disease_Detection
https://github.com/Moon-sung-woo/ParallelWaveGan_korean
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/MrMao/wav2letter
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
https://github.com/MugheesQasim/Voice-Cloning
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/Munna-Manoj/Team6_FastSpeech2_TTS
https://github.com/MysteryVaibhav/LAS
https://github.com/NOEPG/pytorch-kaldi
https://github.com/NVIDIA/DeepLearningExamples
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
https://github.com/NVIDIA/NeMo/tree/main/examples/asr/conf/squeezeformer
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
https://github.com/NVIDIA/nv-wavenet
https://github.com/NVIDIA/radtts
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
https://github.com/NVIDIA/tacotron2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/NVIDIA/waveglow
WaveGlow: A Flow-based Generative Network for Speech Synthesis
https://github.com/Nilanshrajput/Intent_classification
https://github.com/NirHeaven/D3D
LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild
https://github.com/NisansaDdS/Some-Languages-are-More-Equal-than-Others
Systematic Inequalities in Language Technology Performance across the World's Languages
https://github.com/OdysseasKr/online-nilm
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
https://github.com/OlaWod/my-fastspeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/OlaWod/my-tacotron2
https://github.com/OlaWod/my-wavernn
https://github.com/OpenSourceAI/sota-server
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.