BridgetteSong

BridgetteSong

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Location:China Beijing

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BridgetteSong's repositories

ExpressiveTacotron

This repository provides a multi-mode and multi-speaker expressive speech synthesis framework, including multi-attentive Tacotron, DurIAN, Non-attentive Tacotron, GST, VAE, GMVAE, and X-vectors for building prosody encoder.

BunchedLPCnet

This repository provides UNOFFICIAL Bunched LPCNet implementations with Pytorch.

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ddsp

DDSP: Differentiable Digital Signal Processing

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efficient_tts

Pytorch implementation of "Efficienttts: an efficient and high-quality text-to-speech architecture"

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LVCNet

LVCNet: Efficient Condition-Dependent Modeling Network for Waveform Generation

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multiband-hifigan

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

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Parallel-Tacotron2

Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

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ParallelWaveGAN

Unofficial Parallel WaveGAN (+ MelGAN & Multi-band MelGAN & HiFi-GAN & StyleMelGAN) with Pytorch

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Robust_Fine_Grained_Prosody_Control

Pytorch Implementation of Robust and fine-grained prosody control of end-to-end speech synthesis (Unofficial)

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SpeechSplit

Unsupervised Speech Decomposition Via Triple Information Bottleneck

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STYLER

STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech

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StyleSpeech

PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

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TFGAN

TFGAN: Time and Frequency Domain Based Generative Adversarial Network for High-fidelity Speech Synthesis

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TTS_TFLite

This repository is a collection of TTS Models in TFLite

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UnivNet-pytorch

UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

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VocGAN

VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

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VQMIVC

Official implementation of VQMIVC: One-shot Voice Conversion @ Interspeech 2021

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