https://github.com/pulp-platform/eae-kws
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Papers with Code 아카이브
Speech Recognition
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- 평가표
- 236
- 보존된 평가 기록
- 398
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/purduedigitaltwin/talk2drive
Personalized Autonomous Driving with Large Language Models: Field Experiments
https://github.com/pwc-1/Paper-10/tree/main/seamless_m4t
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
https://github.com/pwc-1/Paper-10/tree/main/speech_encoder_decoder
A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition
https://github.com/pwc-1/Paper-10/tree/main/speech_to_text
https://github.com/pwc-1/Paper-10/tree/main/speecht5
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/pwc-1/Paper-9/tree/main/1/wav2vec2
https://github.com/pwc-1/Paper-9/tree/main/1/wav2vec2_bert
https://github.com/pwc-1/Paper-9/tree/main/1/wav2vec2_conformer
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/pwc-1/Paper-9/tree/main/1/wavlm
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/pwc-1/Paper-9/tree/main/1/whisper
https://github.com/pwc-1/Paper-9/tree/main/1/xlm_roberta_xl
Unsupervised Cross-lingual Representation Learning for Speech Recognition
https://github.com/pwc-1/Paper-9/tree/main/3/stochastic-attention-head-removal-a-simple
https://github.com/pwc-1/Paper-9/tree/main/4/stochastic-attention-head-removal-a-simple
https://github.com/pwc-1/Paper-9/tree/main/5/wav2vec2_conformer
https://github.com/pwc-1/Paper-9/tree/main/5/wav2vec2_with_lm
https://github.com/pwc-1/Paper-9/tree/main/5/wavlm
https://github.com/pwc-1/Paper-9/tree/main/hubert
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
https://github.com/pyaf/DenseNet-MURA-PyTorch
https://github.com/pyf98/dphubert
Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understanding
https://github.com/pytorch/fairseq/blob/master/fairseq/models/wav2vec.py
https://github.com/pytorch/fairseq/tree/master/examples/wav2vec
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/pytorch/vision
https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py
https://github.com/pzelasko/espnet
Improved training of end-to-end attention models for speech recognition
https://github.com/quantum-fusion/AI-deeplearning
A segmental framework for fully-unsupervised large-vocabulary speech recognition
https://github.com/qute012/Wav2Keyword
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
https://github.com/qute012/las-ko
https://github.com/qwopqwop200/Magneto-pytorch
https://github.com/r9y9/deepvoice3_pytorch
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
https://github.com/r9y9/gantts
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks
https://github.com/r9y9/icassp2020-espnet-tts-merlin-baseline
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
https://github.com/r9y9/tacotron_pytorch
https://github.com/r9y9/wavenet
https://github.com/racai-ai/ROBINDialog
Tools and resources for Romanian text-to-speech and speech-to-text applications
https://github.com/racai-ai/TEPROLIN
Tools and resources for Romanian text-to-speech and speech-to-text applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.