Search Results for "raw"

Readest is a project meant to facilitate reading, studying, or consuming content by integrating reading tools with AI-powered assistance. Although the repository is not as widely documented or popular as some, the idea is that Readest supports features to help with reading comprehension — likely combining OCR / text retrieval, translation, note-taking, or summarization for reading materials (eBooks, articles, PDFs). The goal appears to be to let users feed in arbitrary reading material and...

TTS-Vue is a desktop text-to-speech application built with Electron, Vue, ElementPlus, and Vite, focused on using Microsoft’s official Speech API for high-quality neural synthesis. It wraps the Microsoft TTS WebSocket interface in a clean UI so users can paste or load text, choose voices, tweak parameters, and export audio without touching raw API calls. The app supports SSML (Speech Synthesis Markup Language), letting power users specify fine-grained control over pronunciation, pauses, prosody, and emphasis using XML-like markup. It includes batch conversion: users can select multiple .txt files and convert them into audio in one go, making it handy for large text collections or repetitive tasks. ...

WaveRNN is a PyTorch implementation of DeepMind’s WaveRNN vocoder, bundled with a Tacotron-style TTS front end to form a complete text-to-speech stack. As a vocoder, WaveRNN models raw audio with a compact recurrent neural network that can generate high-quality waveforms more efficiently than many traditional autoregressive models. The repository includes scripts and code for preprocessing datasets such as LJSpeech, training Tacotron to produce mel spectrograms, training WaveRNN on those spectrograms (with optional GTA data), and finally generating audio. ...

Tacotron-2 is a TensorFlow implementation of DeepMind’s Tacotron-2 end-to-end text-to-speech architecture, which predicts mel spectrograms from raw text and then feeds them to a neural vocoder such as WaveNet. It reproduces the original paper’s hyperparameters exactly via paper_hparams.py, while also offering a tuned hparams.py with extra improvements that often yield better audio quality in practice. The repository is structured as a full training pipeline: dataset preparation, preprocessing into spectrograms, Tacotron training, WaveNet (or Griffin-Lim) vocoder training, and final waveform synthesis. ...