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/umd-fire-coml/2020-Speech-Recognition
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/unibe-cns/gle-code
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/unilight/seq2seq-vc
Pretraining Techniques for Sequence-to-Sequence Voice Conversion
https://github.com/upskyy/ContextNet
https://github.com/upskyy/Squeezeformer
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
https://github.com/upskyy/Transformer-Transducer
https://github.com/v-iashin/video_features
https://github.com/vadimkantorov/ctc
Improved training for online end-to-end speech recognition systems
https://github.com/valerielimyh/Intent_Recognition_using_BERT
https://github.com/vaseline555/Federated-Learning-in-PyTorch
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/vaseline555/superfed
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/vasudevgupta7/gsoc-wav2vec2
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/vicky-hnk/time-flex
https://github.com/victor369basu/End2EndAutomaticSpeechRecognition
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/victor45664/espnet
Improved training of end-to-end attention models for speech recognition
https://github.com/vietai/ASR
https://github.com/viig99/mixmatch-freesound
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/vincentherrmann/pytorch-wavenet
https://github.com/vincenzo-scotti/tacotron2
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://github.com/vinitunni/CoupledLoss-LAS-ESPNet
Improved training of end-to-end attention models for speech recognition
https://github.com/vinusankars/ESOLA
Epoch-Synchronous Overlap-Add (ESOLA) for Time- and Pitch-Scale Modification of Speech Signals
https://github.com/vishKurama/audio-adversarial-attack
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
https://github.com/vohoaiviet/voice-vector
https://github.com/walterheymans/pytorch-kaldi-gan
https://github.com/wangbinglin1995/tianchi
https://github.com/wangys102/MindSpore/tree/main/DeepSpeech2
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/wataru-nakata/fastspeech2-jsut
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/wd929/NeMo
https://github.com/weilinie/JARE
Towards a Better Understanding and Regularization of GAN Training Dynamics
https://github.com/wenet-e2e/wenet/tree/main/wenet/ssl/bestrq
Self-supervised Learning with Random-projection Quantizer for Speech Recognition
https://github.com/wenet-e2e/west
An Embarrassingly Simple Approach for LLM with Strong ASR Capacity
https://github.com/whisperspeech/whisperspeech
https://github.com/widmi/widis-lstm-tools
https://github.com/widzemin/audio_project
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/wiebket/bt4vt
SVEva Fair: A Framework for Evaluating Fairness in Speaker Verification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.