https://github.com/intelligentmachines/keyword_spotting_transformer
Keyword Transformer: A Self-Attention Model for Keyword Spotting
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/isadrtdinov/wavenet
https://github.com/ishandutta2007/Speech-Denoising-Landscape
Single-Channel Multi-Speaker Separation using Deep Clustering
https://github.com/isi-nlp/Zoph_RNN
https://github.com/ivanvovk/DurIAN
DurIAN: Duration Informed Attention Network For Multimodal Synthesis
https://github.com/ivanvovk/WaveGrad
https://github.com/ivanvovk/durian-pytorch
DurIAN: Duration Informed Attention Network For Multimodal Synthesis
https://github.com/iver56/audiomentations
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/ivrit-ai/ivrit.ai
ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development
https://github.com/izzajalandoni/tts_models
https://github.com/jackaduma/CycleGAN-VC2
https://github.com/jackaduma/LAS_Mandarin_PyTorch
https://github.com/jackjhliu/End-to-End-Mandarin-ASR
https://github.com/jackjhliu/Pytorch-End-to-End-ASR-on-TIMIT
https://github.com/jackson-kang/vqvc-pytorch
VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network
https://github.com/jacksonkaunismaa/neural-music
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/jaejunl/hyface
LRS3-TED: a large-scale dataset for visual speech recognition
https://github.com/jaemin-shin/fedbalancer
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/jahanxb/flcode
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/jain-abhinav02/VoiceFilter
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
https://github.com/jakeju/wav2letter
wav2letter++: The Fastest Open-source Speech Recognition System
https://github.com/jaketae/conformer
Conformer: Convolution-augmented Transformer for Speech Recognition
https://github.com/jamestjw/OCR
Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition
https://github.com/jarfo/gcommands
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/jaryp/confidencebranchnetwok
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/jasminsternkopf/mel_cepstral_distance
Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis
https://github.com/jayant766/MIDAS-IIITD
Very Deep Convolutional Neural Networks for Robust Speech Recognition
https://github.com/jaywalnut310/glow-tts
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
https://github.com/jaywalnut310/melgan-pytorch
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/jbernoz/deeppolyp
https://github.com/jchen98/compression
https://github.com/jeremyrchow/text-generation-kaggle
https://github.com/jessearodriguez/LJ-Audio-dataset-generator
https://github.com/jessefokkinga/MechanismsOfAction
https://github.com/jiali-ms/JLM
Strategies for Training Large Vocabulary Neural Language Models
https://github.com/jiean001/models_m/tree/main/tacotron2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.