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/fatchord/MelNet
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/fatchord/WaveRNN
https://github.com/fd873630/RNN-Transducer
https://github.com/fd873630/deep_speech_2_korean
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/fengjiqiang/pretrainedmodel_pytorch
https://github.com/ferugit/iterative-pseudo-forced-alignment-ctc
Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptation
https://github.com/fineline179/MEHTA_project
An exact mapping between the Variational Renormalization Group and Deep Learning
https://github.com/fineline179/MEHTA_project_2
An exact mapping between the Variational Renormalization Group and Deep Learning
https://github.com/fishaudio/vocoder
EVA-GAN: Enhanced Various Audio Generation via Scalable Generative Adversarial Networks
https://github.com/fkodom/dilated-attention-pytorch
https://github.com/flashlight/wav2letter
wav2letter++: The Fastest Open-source Speech Recognition System
https://github.com/flexible-fl/flex-nlp
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/floydhub/word-language-model
https://github.com/flukeskywalker/highway-networks
https://github.com/flyyufelix/DenseNet-Keras
https://github.com/foamliu/GST-Tacotron-v2
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/foamliu/Listen-Attend-Spell
https://github.com/foamliu/Listen-Attend-Spell-v2
https://github.com/foamliu/Listen-Attend-and-Spell
https://github.com/forgi86/lru-reduction
Efficiently Modeling Long Sequences with Structured State Spaces
https://github.com/fracorti/reds
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/francois-rozet/zuko
https://github.com/freds0/data_augmentation_for_asr
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/freds0/kabooks
Pansori: ASR Corpus Generation from Open Online Video Contents
https://github.com/freds0/katube
Pansori: ASR Corpus Generation from Open Online Video Contents
https://github.com/freedombenLiu/speech-to-text-wavenet
https://github.com/frenchkrab/is2023-powerset-diarization
Speech Recognition and Multi-Speaker Diarization of Long Conversations
https://github.com/freshtan/deepspeech2
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/funaudiollm/cv3-eval
CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
https://github.com/futabato/FutabatedLearning
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/fvilmos/multi_task_learning_shape_color_position
https://github.com/fxfviolet/Densenet_for_quiz_classification
https://github.com/ga642381/FastSpeech2
https://github.com/gaalszandi/visual_speech_recognition
https://github.com/gaasher/data2vec2.0_vision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.