This is a template for the Non-autoregressive Deep Learning-Based TTS model (in PyTorch).
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Updated
Jun 15, 2021 - Python
This is a template for the Non-autoregressive Deep Learning-Based TTS model (in PyTorch).
Implementation of 2021 EACL paper Enconter
Codes for our paper "Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation" (EMNLP 2023 Findings)
Non-autoregressive sequence-to-sequence voice conversion
M.Sc. thesis on Continual Learning for Non-Autoregressive Neural Machine Translation
BERT-based pre-trained non-autoregressive sequence-to-sequence model
Code for the paper "Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models"
Reparameterized Discrete Diffusion Models for Text Generation
[EMNLP'23] Code for "Non-autoregressive Text Editing with Copy-aware Latent Alignments".
[AAAI 2024] GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
A length-controllable and non-autoregressive image captioning model.
Official repository of DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech, ICASSP 2023 (Oral)
PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis
PyTorch Implementation of Google Brain's WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
Paper Lists, Notes and Slides, Focus on NLP. For summarization, please refer to https://github.com/xcfcode/Summarization-Papers
A Non-Autoregressive End-to-End Text-to-Speech (text-to-wav), supporting a family of SOTA unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate E2E-TTS
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech
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