https://github.com/serp-ai/bark-with-voice-clone
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
Papers with Code 아카이브
Speech Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/seungwonpark/melgan
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
https://github.com/sewplay/demos
Effective parameter estimation methods for an ExcitNet model in generative text-to-speech systems
https://github.com/sh-lee-prml/BigVGAN
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
https://github.com/sh951011/Korean-Speech-Recognition/blob/master/package/feature.py
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/shangeth/wavencoder
https://github.com/shangoma/Hate_Speech
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
https://github.com/shayantalaei/quafl
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
https://github.com/shehzeen/waveguard_defense
WaveGuard: Understanding and Mitigating Audio Adversarial Examples
https://github.com/shelling203/SpecAugment
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/shenweichen/DeepCTR
https://github.com/shenweichen/DeepCTR-Torch
https://github.com/shivam13juna/Sequence_Prediction_LSTM_CHAR
https://github.com/shivamishu/cmpe255
https://github.com/shivammehta25/BetterFastSpeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/shivangi-aneja/FaceTalk
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
https://github.com/shortpoet/Final-Project
https://github.com/shuaijiang/Whisper-Finetune
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
https://github.com/shuheng-li/units-sensory-time-series-classification
https://github.com/siddarth-c/FedGMA
Communication-Efficient Learning of Deep Networks from Decentralized Data
https://github.com/silversparro/wav2letter.pytorch
Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
https://github.com/simon-benigeri/lstm-language-model
https://github.com/simongrest/kaggle-freesound-audio-tagging-2019
Trainable Frontend For Robust and Far-Field Keyword Spotting
https://github.com/sinhat98/adapter-wavlm
Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition
https://github.com/smeetrs/deep_avsr
https://github.com/smoke-trees/Voice-synthesis
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
https://github.com/soarsmu/crossasr
https://github.com/sobhan-moosavi/characterizingdrivingstyleswithdeeplearning
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
https://github.com/soham97/pam
Evaluating Speech Synthesis by Training Recognizers on Synthetic Speech
https://github.com/sonos/nlu-benchmark
https://github.com/soobinseo/transformer-tts
https://github.com/sooftware/End-to-End-Speech-Recognition-Models
https://github.com/sooftware/End-to-end-Speech-Recognition
https://github.com/sooftware/Fairseq-Listen-Attend-Spell
https://github.com/sooftware/OpenSpeech
https://github.com/sooftware/conformer
Conformer: Convolution-augmented Transformer for Speech Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.