Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models directly on your device, with no cloud dependency. It can transcribe audio from files, microphones, system output, and network streams, and supports both batch processing of multiple files and real-time captioning from continuous input.
Die Hauptfunktionen von thewh1teagle/vibe sind: Speech to Text Transcription, On-Device Transcriptions, Audio Transcription, CLI Transcription Tools, Real-Time Transcription, Local Model Inference Servers, Real-Time Audio Transcribers, Transcription with Speaker Labels.
Open-Source-Alternativen zu thewh1teagle/vibe sind unter anderem: argmaxinc/whisperkit. collabora/whisperlive — WhisperLive is a real-time speech-to-text server that converts live audio streams into text using Whisper models. It… const-me/whisper — Whisper is a high-performance speech-to-text inference engine that uses graphics hardware shaders to accelerate the… k2-fsa/sherpa-onnx — Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device… cmusphinx/pocketsphinx — PocketSphinx is an offline speech recognition engine that converts raw audio from files or live microphone streams… m-bain/whisperx — WhisperX is an automated speech recognition toolkit designed to convert spoken audio into text while maintaining…
WhisperLive is a real-time speech-to-text server that converts live audio streams into text using Whisper models. It functions as a backend service that receives microphone input via WebSockets and provides incremental transcriptions with word-level timestamps. The system utilizes a GPU-accelerated inference engine and a keyword-boosted transcription API to improve the recognition accuracy of domain-specific jargon, acronyms, and product names. It also includes a speaker diarization tool that clusters audio embeddings to identify and label different participants within a recording. Additiona
Whisper is a high-performance speech-to-text inference engine that uses graphics hardware shaders to accelerate the transcription of spoken audio into written text. It implements a GPU-accelerated automatic speech recognition framework specifically designed to run Whisper models. The system focuses on high-speed processing for both recorded audio files and live microphone streams. It utilizes voice activity detection to analyze raw audio in real time, triggering the inference engine only when human speech is detected. The engine covers a broad range of capabilities including real-time audio
Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web