YunyangZeng
ECE Master's student & Researcher at Carnegie Mellon University
Carnegie Mellon UniversityPittsburgh
Pinned Repositories
Advanced-DSP-Algorithms
Some advanced dsp algorithms written in matlab
CTC-based-Keyword-Spotter
CTC-Phonetics based Keyword Spotter
denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
grok-1
Grok open release
LPCTorch
LPC Utility for Pytorch Library.
MS-SNSD
The Microsoft Scalable Noisy Speech Dataset (MS-SNSD) is a noisy speech dataset that can scale to arbitrary sizes depending on the number of speakers, noise types, and Speech to Noise Ratio (SNR) levels desired.
PAAP
TAPLoss
vall-e
An unofficial PyTorch implementation of the audio LM VALL-E
Wiener-Filter
Wiener Filter in python
YunyangZeng's Repositories
YunyangZeng/TAPLoss
YunyangZeng/Advanced-DSP-Algorithms
Some advanced dsp algorithms written in matlab
YunyangZeng/PAAP
YunyangZeng/Wiener-Filter
Wiener Filter in python
YunyangZeng/CTC-based-Keyword-Spotter
CTC-Phonetics based Keyword Spotter
YunyangZeng/denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
YunyangZeng/grok-1
Grok open release
YunyangZeng/LPCTorch
LPC Utility for Pytorch Library.
YunyangZeng/MS-SNSD
The Microsoft Scalable Noisy Speech Dataset (MS-SNSD) is a noisy speech dataset that can scale to arbitrary sizes depending on the number of speakers, noise types, and Speech to Noise Ratio (SNR) levels desired.
YunyangZeng/vall-e
An unofficial PyTorch implementation of the audio LM VALL-E
YunyangZeng/voicefilter
Unofficial PyTorch implementation of Google AI's VoiceFilter system
YunyangZeng/whisperX
WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)