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/dmeoli/neuro-sat
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero
https://github.com/dmlc/dgl/tree/master/examples/mxnet/monet
Geometric deep learning on graphs and manifolds using mixture model CNNs
https://github.com/dmlc/dgl/tree/master/examples/pytorch/monet
Geometric deep learning on graphs and manifolds using mixture model CNNs
https://github.com/dreamtheater123/voxeval
https://github.com/drumpt/RotNet-OOD
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/dscripka/openwakeword
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
https://github.com/earthLD/Algorithm
https://github.com/earthspecies/aves
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
https://github.com/eastonYi/wav2vec
https://github.com/ebouteillon/freesound-audio-tagging-2019
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/ecnu-cross-innovation-lab/ent
Factorized Neural Transducer for Efficient Language Model Adaptation
https://github.com/edaiofficial/okwugbe
https://github.com/edresson/yourtts
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
https://github.com/eginhard/cae-utd-utils
Multilingual bottleneck features for subword modeling in zero-resource languages
https://github.com/elgazzarr/fmri-s4
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/elizabethnewman/slimTrain
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection
https://github.com/enhuiz/vall-e
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://github.com/ensta-u2is/torch-uncertainty
https://github.com/erasedwalt/FastSpeech
https://github.com/eric-erki/wav2letter
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
https://github.com/ericjang/odin
https://github.com/espnet/espnet/tree/master/egs2/spgispeech/asr1
https://github.com/etapiahe/adversarial_audio_attack
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
https://github.com/etosworld/etos-keywordspotting
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting
https://github.com/fabiofumarola/ultrayolo
https://github.com/facebookresearch/CrypTen
CrypTen: Secure Multi-Party Computation Meets Machine Learning
https://github.com/facebookresearch/brainmagick
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/facebookresearch/data2vec_vision
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
https://github.com/facebookresearch/denoiser
https://github.com/facebookresearch/fairseq/tree/ust/examples/w2vbert
https://github.com/facebookresearch/salina
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
https://github.com/facebookresearch/sonar
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
https://github.com/facebookresearch/wav2letter
wav2letter++: The Fastest Open-source Speech Recognition System
https://github.com/facebookresearch/wav2letter/tree/master/recipes/models/sota/2019
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures
https://github.com/faneshion/DRMM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.