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/willemsenbram/mention-detection-vgd
https://github.com/willyfh/federated-learning-tensorflow
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/wnhsu/FactorizedHierarchicalVAE
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
https://github.com/wponghiran/imp-snns-for-sl
https://github.com/x-lance/kwstreamingsearch
Streaming Keyword Spotting Boosted by Cross-layer Discrimination Consistency
https://github.com/xcmyz/FastSpeech
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/xingchensong/s3tokenizer
https://github.com/xinleiniu/latentoptimalpathsbayesiandp
RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis
https://github.com/xinshengwang/Tacotron-pytorch
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/xpz123/pytorch-kaldi
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/xypan1232/iDeepE
https://github.com/yan-gao-GY/FedScale
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
https://github.com/yanggeng1995/EATS
https://github.com/yanggeng1995/FB-MelGAN
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/yanggeng1995/GAN-TTS
https://github.com/yanggeng1995/WaveGlow
WaveGlow: A Flow-based Generative Network for Speech Synthesis
https://github.com/yanggeng1995/subband_WaveRNN
DurIAN: Duration Informed Attention Network For Multimodal Synthesis
https://github.com/yanggeng1995/vae_tacotron
Learning latent representations for style control and transfer in end-to-end speech synthesis
https://github.com/yangle15/RANet-pytorch
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/yangxueruivs/DFSMN
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
https://github.com/yangyucheng000/Paper-2/tree/main/lstm-gaf
https://github.com/yangyucheng000/University/tree/main/model-2/fastspeech2_conformer
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/yangyucheng000/University/tree/main/model-2/hubert
HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition
https://github.com/yangyucheng000/University/tree/main/model-3/seamless_m4t
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
https://github.com/yangyucheng000/University/tree/main/model-3/speech_encoder_decoder
https://github.com/yangyucheng000/University/tree/main/model-3/speech_to_text
https://github.com/yangyucheng000/University/tree/main/model-3/speecht5
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/yangyucheng000/densenet/blob/main/densenet.py
https://github.com/yangyucheng000/papercode-2/tree/main/lstm-gaf
https://github.com/yebiny/DepthOfAnaesthesia_eeg
https://github.com/yichigo/Chest-X-Ray
https://github.com/yjlee22/FedShare
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/yl4579/StyleTTS2
https://github.com/ylacombe/finetune-hf-vits
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.