https://github.com/racai-ai/srla
Tools and resources for Romanian text-to-speech and speech-to-text applications
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/racai-ai/ssla
Tools and resources for Romanian text-to-speech and speech-to-text applications
https://github.com/racinmat/lecture-generator
https://github.com/rafaelmata357/Flower-image-classifier
https://github.com/rajkumargithub/densenet.mura
https://github.com/rampage644/wavenet
https://github.com/randomrandom/deep-atrous-cnn-sentiment
https://github.com/raraz15/DeepTurkish
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/rarefin/TTS_VAE
Hierarchical Generative Modeling for Controllable Speech Synthesis
https://github.com/renyuanL/ry-Speech-commands
https://github.com/retrocirce/hts-audio-transformer
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/revsic/jax-variational-diffwave
https://github.com/revsic/tf-diffwave
https://github.com/revsic/torch-diffusion-wavegan
https://github.com/rf5/diffwave-unconditional
https://github.com/rf5/simple-speech-commands
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/rgarzonj/LSTMs
https://github.com/rhoposit/MOS_Estimation
https://github.com/rhoposit/MOS_Estimation2
https://github.com/richardbaihe/a3t
A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing
https://github.com/rickyHong/ClariNet-WaveNet-repl
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
https://github.com/rickyHong/GAN-TTS-repl2
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
https://github.com/rickyHong/GANTTS-update-repl
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
https://github.com/rickyHong/OpenSeq2Seq-repl
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
https://github.com/rickyHong/gantts-update-repl2
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
https://github.com/rickyHong/segan-pytorch-repl
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks
https://github.com/rishikksh20/FastSpeech2
https://github.com/rishikksh20/HiFi-GAN
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
https://github.com/rishikksh20/LightSpeech
https://github.com/rishikksh20/TalkNet2-pytorch
https://github.com/rishikksh20/UnivNet-pytorch
https://github.com/rishikksh20/VocGAN
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/rishikksh20/iSTFTNet-pytorch
https://github.com/rishikksh20/melgan
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/rishikksh20/tfgan
https://github.com/robmsmt/KerasDeepSpeech
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.