neural-tts

PyTorch Implementation of DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs

PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech

A complete local production workflow for clean narration, structured learning content, and podcast-ready audio

A Non-Autoregressive Transformer based Text-to-Speech, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS

Official implementation of Meta-StyleSpeech and StyleSpeech

PyTorch implementation of DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism (focused on DiffSpeech)

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

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

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 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.

A cross-platform inference engine for neural TTS models.

PyTorch Implementation of Google Brain's WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis

PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

PyTorch Implementation of Google's Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions. This implementation supports both single-, multi-speaker TTS and several techniques to enforce the robustness and efficiency of the model.

Babylon.cpp is a C and C++ library for grapheme to phoneme conversion and text to speech synthesis. For phonemization a ONNX runtime port of the DeepPhonemizer model is used. For speech synthesis VITS models are used. Piper models are compatible after a conversion script is run.

🎙️ Портативная нейросеть синтеза и клонирования речи для Windows — движок Higgs Audio v3, офлайн, русский интерфейс, батч-режим. Portable TTS & voice cloning, offline, one-click.

Offline neural TTS engine for Android. Registers system-wide, ships 186 voices via sherpa-onnx (Piper / Kokoro / Kitten / Matcha / ZipVoice / Pocket / Supertonic). Material 3 Expressive. Companion to Hayai.

Let your GNOME desktop speak. Reads desktop notifications or selected text aloud with human voice using Piper. Uses a local LLM to summarize, translate, explain or proofread selected text.