https://github.com/hhhaaahhhaa/asr-tta
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech
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/hikaruya8/lstm_model_py
https://github.com/hitz-zentroa/whisper-lm-transformers
Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages
https://github.com/hjc18/language_modeling_lstm
https://github.com/hkakitani/deepspeech.pytorch
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/holgerbovbjerg/data2vec-kws
Keyword Transformer: A Self-Attention Model for Keyword Spotting
https://github.com/hongshenghu/source-inference-fl
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS
Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
https://github.com/hubreb/imitkd_ast
CoVoST 2 and Massively Multilingual Speech-to-Text Translation
https://github.com/huckiyang/QuantumSpeech-QCNN
https://github.com/huggingface/open_asr_leaderboard
ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition
https://github.com/huggingface/transformers/tree/main/src/transformers/models/data2vec
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/huseinzol05/malaya-speech
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/huutuongtu/skd-ctc
Guiding Frame-Level CTC Alignments Using Self-knowledge Distillation
https://github.com/hyang0129/foodclassapp
Regularizing Neural Networks by Penalizing Confident Output Distributions
https://github.com/hycis/TensorGraph
https://github.com/iPRoBe-lab/DeepTalk
DeepTalk: Vocal Style Encoding for Speaker Recognition and Speech Synthesis
https://github.com/iamjanvijay/rnnt_decoder_cuda
https://github.com/iamyuanchung/Autoregressive-Predictive-Coding
Generative Pre-Training for Speech with Autoregressive Predictive Coding
https://github.com/ibab/tensorflow-wavenet
https://github.com/icrto/xML
Evaluating the visualization of what a Deep Neural Network has learned
https://github.com/idiap/apam
TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
https://github.com/idiap/sparch
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/idobronstein/vision_networks
https://github.com/ifrit98/pyramidal-recurrent-layer
https://github.com/ihamdi/Covid-xRay-Classification
https://github.com/iinemo/lm-polygraph
Uncertainty Estimation in Autoregressive Structured Prediction
https://github.com/ikhlestov/vision_networks
https://github.com/imdatsolak/wavenet
https://github.com/inconnu11/Objective-evaluation_speech_synthesis
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data
https://github.com/inesylla/energy-disaggregation-DL
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/innerNULL/simpler-distil-whisper
Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling
https://github.com/inokoj/VAP-Realtime
A Noise-Robust Turn-Taking System for Real-World Dialogue Robots: A Field Experiment
https://github.com/insop/pytorch-hackathon
https://github.com/intel/openfl
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/intellabs/gravi-t
STHG: Spatial-Temporal Heterogeneous Graph Learning for Advanced Audio-Visual Diarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.