Guan-Ting (Daniel) Lin's repositories
DanielLin94144.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Emphasized-Talk
Official release of Emphasized-Talk
DUAL-textless-SQA
Textless (ASR-transcript free) Spoken Question Answering. The official release of NMSQA dataset and the implementation of "DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning" paper.
VISinger2
VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis Enhanced by Digital Signal Processing Synthesizer
tango
Codes and Model of the paper "Text-to-Audio Generation using Instruction Tuned LLM and Latent Diffusion Model"
AudioMAE
This repo hosts the code and models of "Masked Autoencoders that Listen".
Test-time-adaptation-ASR-SUTA
Test-time adaptation for speech recognition model by single utterance. The official implementation of "Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition" paper.
Protect-Your-Voice
Official implementation of Meta-StyleSpeech and StyleSpeech
hubert-cluster-code
Extract clustering feature from hubert
SpeechMix
Explore different way to mix speech model(wav2vec2, hubert) and nlp model(BART,T5,GPT) together
Meta-TTS
Official repository of https://arxiv.org/abs/2111.04040v1
FastSpeech2
An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"
unsupervised_ASR_challenge
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
GlossBERT
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (EMNLP 2019)
tent
ICLR21 Tent: Fully Test-Time Adaptation by Entropy Minimization
CPC_audio
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.
CLUENER2020
CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition
FixMatch-pytorch
Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"
fixmatch
A simple method to perform semi-supervised learning with limited data.