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/zyzpower/gradprune_speaker
공식 코드https://gitlab.com/claudius-krause/caloflow
공식 코드CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
https://gitlab.com/csltstu/sunine
공식 코드The NPU-ASLP System Description for Visual Speech Recognition in CNVSRC 2024
https://gitlab.com/jaco-assistant/corcua
공식 코드Scribosermo: Fast Speech-to-Text models for German and other Languages
https://gitlab.com/jaco-assistant/scribosermo
공식 코드Scribosermo: Fast Speech-to-Text models for German and other Languages
https://gitlab.com/kavyamanohar/ml-subword-asr
공식 코드Syllable Subword Tokens for Open Vocabulary Speech Recognition in Malayalam
https://gitlab.com/milde/sparsespeech
공식 코드Improving Unsupervised Sparsespeech Acoustic Models with Categorical Reparameterization
https://gitlab.com/sburud/master
공식 코드BERT Attends the Conversation: Improving Low-Resource Conversational ASR
https://huggingface.co/datasets/MLCommons/speech-wikimedia
공식 코드https://huggingface.co/fxtentacle/wav2vec2-xls-r-1b-tevr
공식 코드TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
https://huggingface.co/utter-project/mHuBERT-147
공식 코드https://zenodo.org/record/10377492
공식 코드https://github.com/0492wzl/tensorflow_slim_densenet
https://github.com/0fengzi0/tacotron
https://github.com/0jason000/DenseNet
https://github.com/0xSameer/ast
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation
https://github.com/1alexandra/speech
Attention-Based Models for Text-Dependent Speaker Verification
https://github.com/1hb6s7t/contrib
https://github.com/1ytic/warp-rnnt
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/LSTM
https://github.com/2023-MindSpore-1/ms-code-146
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/FastSpeech
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/jasper
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/speech_transformer
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/wavenet
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/3D_DenseNet
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/I3D
I3D: Transformer architectures with input-dependent dynamic depth for speech recognition
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tsn
TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/atae_lstm
https://github.com/2023-MindSpore-1/ms-code-50
https://github.com/2023-MindSpore-1/ms-code-57
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/2023-MindSpore-1/ms-code-63
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
https://github.com/2023-MindSpore-1/ms-code-74
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/2023-MindSpore-4/Code-5/tree/main/I3D
https://github.com/2023-MindSpore-4/Code-5/tree/main/IntegralNeuralNetworks
fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.