#speech synthesis

ChatTTS is an advanced TTS model optimized for natural dialogue with multi-speaker and prosody control features. It supports over 100,000 hours of training data in multiple languages, surpassing many open-source models in quality. Pretrained models are available for educational and research use, enabling seamless integration into AI systems. Discover its features and ethical guidelines.

Meta-StyleSpeech is a cutting-edge Text-to-Speech model that generates personalized, high-quality speech from minimal input. By implementing Style-Adaptive Layer Normalization, it precisely adapts to a speaker's style using a single short audio clip. With enhancements like style prototypes and episodic training, it achieves superior speaker adaptation without extensive fine-tuning, suitable for various applications with available pre-trained models and detailed setup guidance.

This unofficial PyTorch implementation of the VALL-E model uses Neural Codec Language Models for zero-shot text-to-speech synthesis. It supports training on a single GPU, making it accessible for development. Safeguards are implemented to prevent misuse due to its ability to replicate speaker identity. Detailed guides cover installation requirements and training for English and Chinese datasets. The project includes advanced features like NAR Decoder prefix modes for refined synthesis outputs, providing valuable resources for researchers and developers in text-to-speech technology.

Discover the unofficial version of Mega-TTS 2, which integrates advanced techniques for speech synthesis. This project involves a blend of Chinese and English with a planned dataset of approximately 1,000 hours to enhance audio quality using Bigvgan. Through VQ-GAN, ADM, and PLM, it aims to elevate zero-shot TTS technologies. Detailed guidance is provided for dataset preparation, model training with Pytorch-lightning, and inference testing. Released under the MIT license and backed by Simon from ZideAI, this project supports wide-ranging language adaptations.

Parrots provides an efficient solution for Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) with multilingual support in Chinese, English, and Japanese. Utilizing models like 'distilwhisper' and 'GPT-SoVITS', this toolkit facilitates seamless voice recognition and synthesis. It supports straightforward installation, command-line operations, and integration with platforms like Hugging Face, ideal for applications necessitating advanced voice interaction.

P-Flow utilizes a speech-prompted text encoder and flow matching generative decoder for efficient zero-shot TTS, achieving notable speaker adaptation and synthesis speed improvements compared to large-scale models. Trained on the LibriTTS dataset, P-Flow maintains high speaker similarity and pronunciation quality.

Discover MARS5, a novel model using two-stage AR-NAR architecture for generating diverse audio from brief reference inputs. Designed for challenging tasks like sports commentary and anime, MARS5 offers intuitive control over speech prosody through text formatting. Its architecture combines autoregressive and multinomial DDPM methods, ensuring consistent and high-quality results. Access detailed documentation to maximize its application across different languages.

The article introduces StyleTTS 2, a text-to-speech synthesis model leveraging style diffusion and adversarial training with large speech language models. This approach synthesizes varied and natural speech without needing reference audio, employing advanced techniques to enhance naturalness. StyleTTS 2 excels in zero-shot speaker adaptation, surpassing traditional models on the LibriTTS dataset. It performs at or above human-level quality across single and multi-speaker datasets, demonstrating the efficacy of style diffusion combined with adversarial training for TTS advancements.

HiFi-GAN employs GAN technology to efficiently produce 22.05 kHz high-quality speech, running at 167.9 times the real-time speed using a single V100 GPU. It enhances audio quality by modeling periodic patterns and supports both mel-spectrogram inversion and end-to-end speech synthesis. The CPU-efficient version achieves 13.4 times real-time speed with quality comparable to autoregressive models. Open-source tools and pre-trained models offer flexibility in application.

Matcha-TTS offers fast, non-autoregressive speech synthesis using conditional flow matching. Delivering natural sound with minimal memory use, it supports ONNX for efficient inference and pre-trained models via CLI, Gradio, or HuggingFace. Comprehensive training guides and ICASSP 2024 insights make it a practical choice for developers.

RHVoice is an open-source speech synthesizer that employs statistical parametric methods, initially developed for Russian and expanded to include languages like American and Scottish English, and Brazilian Portuguese. It functions across Windows, GNU/Linux, and Android, ensuring smooth integration with existing text-to-speech interfaces. Voices are intelligible and derived from natural recordings, and compatibility extends to tools like NVDA. Comprehensive documentation and active community resources facilitate user interaction and project development.

Discover a Pytorch-based model that provides quicker training durations in speech synthesis using the Transformer Network. This implementation offers comparative audio quality to traditional seq2seq models like Tacotron, with notably faster training times. By employing the CBHG model for post network learning and the Griffin-Lim algorithm for audio transformation, it leverages the LJSpeech dataset to effectively synthesize speech. This makes it a valuable resource for developers and researchers focused on enhancing performance while preserving quality.

xVASynth is an application that uses machine learning to synthesize speech by utilizing voices from video game characters. It is designed for creating modifiable voice assets, employing FastPitch models to support the generation of new voice lines, thus enhancing the gaming experience. Users can precisely adjust voice characteristics such as pitch and duration to produce tailored audio outputs. The app also serves purposes such as machinima creation or for those who enjoy familiar voices in new contexts. Easy to install, it requires additional voice sets alongside the main application. Future plans include broadening voice options by training more models. Available on platforms like Steam and HuggingFace.

This open-source project merges Electron, Vue, ElementPlus, and Vite to create a Microsoft speech synthesis tool tailored for personal learning. Users should delete the software within 24 hours of downloading. It includes guides on project introduction, installation, features, FAQs, and updates. The tool is free, and any requested payment is likely fraudulent. User feedback and updates are managed through specific QQ groups.

The dc_tts project introduces a text-to-speech model that employs deep convolutional networks with guided attention, emphasizing efficient training and quality synthesis. The project examines diverse datasets such as LJ Speech and KSS, incorporating techniques like layer normalization and adaptive learning rates to improve performance. Training scripts are available for users to generate and evaluate synthetic speech, aiming for greater efficiency over Tacotron through exclusive use of convolutional layers.

This PyTorch project facilitates style modeling in speech synthesis, featuring support for the Blizzard dataset and Chinese language generation. It provides essential functions like encoder and decoder setup, hyperparameter configuration, and loss function definition, ensuring a robust approach to multispeaker dataset processing and enhanced speech generation capabilities.

Discover audio examples from the Tacotron project, an advanced speech synthesis model from Google's Sound Understanding and Brain teams. Understand the latest developments in speech technology through related publications. This repository is independent and not an official Google product.

Explore TensorFlow 2's capabilities for state-of-the-art speech synthesis with models like Tacotron-2, FastSpeech, and MelGAN. The project enhances training efficiency and inference speed, making it suitable for real-time use on mobile and embedded systems. It supports multiple languages and offers comprehensive documentation for easy integration. Learn more about innovations such as the HiFi-GAN vocoder and guided attention loss for high-quality speech synthesis.

FastDiff offers a PyTorch implementation of a quick conditional diffusion model for high-fidelity speech synthesis, with pretrained models and dataset support including LJSpeech, LibriTTS, and VCTK. It features multi-GPU support and guidance for text-to-speech synthesis using advanced methods like ProDiff and Tacotron. The project ensures ease of integration with well-documented instructions while emphasizing ethical standards for voice usage.

This tool records synthesized voices from Microsoft Edge into high-quality WAV files. It requires Windows 10 with Chromium-based Microsoft Edge and supports Mandarin voices Xiaoxiao and Yunyang. Simplified steps include downloading, inputting text, adjusting settings, and recording. If necessary components like .NET Framework are missing, the tool installs them for a smooth operation. It also offers features like automatic updates and path configuration for enhanced usability.

Discover Tacotron, an open-source neural model for converting text to speech using TensorFlow. This project includes audio samples from models trained on datasets like LJ Speech and Nancy Corpus, and features enhancements such as location-sensitive attention. Detailed guides for installation, training, and utilizing pre-trained models are provided, along with monitoring tips using Tensorboard and common troubleshooting advice. It is an essential resource for developers exploring speech synthesis.

SummerTTS is a self-contained text-to-speech tool that performs offline voice synthesis for both Chinese and English. By leveraging Eigen for neural network tasks, it operates without relying on frameworks such as PyTorch or TensorFlow. While primarily tested on Ubuntu, it is expected to function on similar systems like Linux-based platforms. The VITS algorithm ensures effective speech processing, and latest updates have enhanced the speed for English single-speaker tasks. Downloadable models support various voice configurations, focusing on ease of use and maintaining good audio quality.

nnmnkwii is a specialized library for building speech synthesis systems suited for efficient prototyping. It facilitates creation and testing of speech models, enhancing development through seamless integration capabilities. The library is Python-optimized and available on PyPI, simplifying installation for those with numpy. Its autograd package, depending on PyTorch, allows further functionality. Drawing inspiration from projects like Merlin and Librosa, nnmnkwii combines adaptability with performance, offering a reliable resource for developers in speech synthesis.

FACodec is an integral part of NaturalSpeech 3, transforming speech synthesis by efficiently converting speech waveforms into separate subspaces including content, prosody, timbre, and acoustic details. By using attribute factorization, it aids in the precise modeling and reconstruction of speech waveforms. FACodec enables the creation of both non-autoregressive and autoregressive TTS models, supporting zero-shot voice conversion. It is suitable for 16KHz audio and generates multiple speech codes, enhancing projects like VALL-E and contributing significantly to advancements in TTS research.

RealtimeTTS is a text-to-speech library designed for real-time applications, providing fast and high-quality audio conversion. It supports various TTS engines including OpenAI, Elevenlabs, and Azure, and offers multilingual capabilities. With a robust fallback mechanism, it ensures reliable performance. Custom installation options and high-quality speech generation make it suitable for professional environments. Its counterpart, RealtimeSTT, offers additional functionalities for comprehensive real-time audio solutions involving large language models.