wav2letter++

It is a state-of-the-art automatic speech recognition toolkit. It is intended for use by speech recognition researchers and professionals.

It is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.

It is a unified, developer-friendly API to the best available Speech-To-Text and Text-To-Speech services.

It can be used to complement any regular touch user interface with a real time voice user interface. It offers real time feedback for faster and more intuitive experience that enables end user to recover from possible errors quickly and with no interruptions.

The purpose of this project is to provide a package for speech processing and feature extraction. This library provides most frequent used speech features including MFCCs and filterbank energies alongside with the log-energy of filterbanks.

It is an On-Premises, Streaming Speech Recognition System built with PyTorch and fastai.

Transcribe and translate audio files using OpenAI's Whisper API. You can upload any audio file, and the application will send it through the OpenAI Whisper API using Laravel's queued jobs. Translation makes use of the new OpenAI Chat API and chunks the generated VTT file into smaller parts to fit them into the prompt context limit.

It builds upon the capabilities of the WhisperLive and WhisperSpeech by integrating Mistral, a Large Language Model (LLM), on top of the real-time speech-to-text pipeline. Both LLM and Whisper are optimized to run efficiently as TensorRT engines, maximizing performance and real-time processing capabilities.