#Tacotron

Discover WaveRNN, an open-source neural audio synthesis model implemented in Pytorch. Includes Quick Start TTS features, model training using the LJSpeech dataset, and access to pretrained models. Offers customizable scripts for improved text-to-speech processes, benefiting audio researchers and enthusiasts seeking advanced TTS 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.

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.

The project investigates various deep learning techniques to enhance emotional expression in Text-to-Speech systems. Focusing on Tacotron and DCTTS models, it explores fine-tuning strategies using datasets such as RAVDESS and EMOV-DB to augment speech naturalness and emotional depth. The research involves optimizing model parameters, applying novel training methodologies, and utilizing transfer learning in low-resource settings. The repository offers insights into utilizing neural networks for generating emotionally nuanced speech, along with practical implementations and evaluations of diverse methods.

Explore the TensorFlow implementation of Tacotron for end-to-end text-to-speech synthesis using publicly available datasets like LJ Speech. Gain insights into training processes, including hyperparameter adjustment, data downloading, and synthesis. Key features such as attention plot monitoring and gradient clipping offer valuable learning for TTS system advancements.

The glados-tts project provides a neural network TTS engine that supports local and remote use, offering models trained on diverse datasets such as LJSpeech and the enhanced Ellen McClain dataset. Its multispeaker capability and optimized model performance ensure efficient voice synthesis. Installation involves downloading model files and installing Python dependencies. Suitable for simple local tests or more sophisticated setups, this TTS engine is flexible and powerful.

An offline text-to-speech solution utilizing Kotlin, JetPack Compose, and Tensorflow Lite. Features FastSpeech for rapid audio generation on mid-range devices and Tacotron for superior output needing higher performance. Includes detailed model download directions and optimized Tensorflow Lite size for mobile applications.