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/cosmoquester/speech-recognition
https://github.com/craigmacartney/Wave-U-Net-For-Speech-Enhancement
https://github.com/creotiv/RussianTTS-Tacotron2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/csukuangfj/optimized_transducer
Improving RNN Transducer Modeling for End-to-End Speech Recognition
https://github.com/cui0523/Code6/tree/main/atae_lstm
https://github.com/cui0523/Code6/tree/main/speech_transformer
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
https://github.com/custodio78/Speech_Recognition
https://github.com/cyhuang-tw/speechcaps
PromptTTS: Controllable Text-to-Speech with Text Descriptions
https://github.com/da-moon/classifiers-monorepo
https://github.com/dabsdamoon/MNIST-Auxiliary-Decoder
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
https://github.com/dada-bots/dadabots_sampleRNN
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
https://github.com/danenergetics/returnn
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
https://github.com/daniilrobnikov/vits2
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
https://github.com/dannima/pae_probe_experiments
https://github.com/danoneata/espnet
Improved training of end-to-end attention models for speech recognition
https://github.com/danoneata/xts
https://github.com/dapeter/nas-for-kws
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/dathudeptrai/TensorflowTTS
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/datoboat/Vehicle-Detection
https://github.com/deciding/ParallelWaveGAN
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/deepsound-project/samplernn-pytorch
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
https://github.com/deepspike/snn-for-asr
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
https://github.com/desh2608/dover-lap
https://github.com/develooper1994/MasterThesis
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks
https://github.com/dhanya-e/Google_Indic
Improved training of end-to-end attention models for speech recognition
https://github.com/dhatchi711/espnet-emotional-news
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
https://github.com/dhecloud/simple_language_modelling
https://github.com/dhgrs/chainer-ClariNet
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
https://github.com/diggerdu/AudioMamba
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/diggerdu/audiorwkv
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/dipjyoti92/SC-WaveRNN
https://github.com/dipjyoti92/TTS-Style-Transfer
https://github.com/disooqi/DNN
https://github.com/disooqi/Natasy
https://github.com/dkman94/FastrApp-Android
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.