Projects sharing features with Waveglow

Mycroft Core is an open-source voice assistant platform that processes spoken commands and runs modular skills for tasks like home automation and information retrieval. It is built around a cloud-paired device framework, where a voice assistant device links to a cloud account via a spoken pairing code to synchronize API keys and skills. The platform operates through an intent-parsing pipeline that processes speech recognition, intent extraction, and skill matching, all coordinated by a message bus architecture that decouples speech processing, skill execution, and audio output. A wake word en

OpenNMT-py is a PyTorch neural machine translation framework used for training and deploying neural machine translation and large language models. It functions as a distributed model training system, an inference engine, and a toolkit for fine-tuning large language models. The framework distinguishes itself with a dedicated toolkit for adapting large language models through low-rank adaptation, quantization, and instruction tuning. It also includes a neural machine translation server that allows trained models to be hosted and exposed via REST API endpoints. The project covers a broad range

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

GAN-based Mel-Spectrogram Inversion Network for Text-to-Speech Synthesis

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

Speech Recognition using DeepSpeech2.

This project is a neural text-to-speech framework and PyTorch model designed to synthesize human speech. It converts written text into synthetic audio by predicting mel spectrograms, which serve as an intermediate representation for voice generation. The system includes a conditioning model for WaveNet to ensure natural-sounding audio output. It provides a distributed training framework that utilizes multi-GPU processing and automatic mixed precision to optimize training speed and reduce memory usage. The project covers the full pipeline of neural speech synthesis, from model training using