PocketSphinx is an offline speech recognition engine that converts raw audio from files or live microphone streams into written text without requiring a network connection. It functions as a speech-to-text library, a real-time transcription engine, and a voice command processor, capable of detecting and transcribing spoken commands from continuous audio streams with configurable acoustic and language models. The engine uses weighted finite-state transducers to represent acoustic, phonetic, and language models as a single search graph for efficient decoding. It employs fixed-point acoustic mod
faster-whisper is an automatic speech recognition framework and an optimized implementation of the Whisper speech-to-text engine. It functions as a CTranslate2 inference engine designed to convert spoken audio into written text. The project serves as a model quantization tool that transforms large audio model weights into lower precision formats. This process reduces memory usage and increases execution speed on hardware by utilizing integer quantized weights. The framework covers a broad range of capabilities including batch audio transcription for parallel processing and voice activity det
This project is a deep learning framework designed for end-to-end speech-to-text transcription. It utilizes the WaveNet neural network architecture to process spoken audio input and generate written text transcripts, leveraging connectionist temporal classification to map variable-length audio sequences to character-level outputs. The system distinguishes itself through a comprehensive training pipeline that supports distributed execution across multiple graphics processing units. It includes specialized utilities for audio data augmentation and the transformation of raw audio files into opti
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. Beyond basic transcription, Vibe identifies and labels different speakers through speaker diarization, and offers a choice of Command-Line Interface or HTTP API for automated and remote workflows. It also includes plugins to export transcripts to c
Whisper Real-Time est un moteur de synthèse parole-texte conçu pour convertir l'entrée microphone continue en transcriptions écrites. Il fonctionne comme un processeur audio en temps réel qui tire parti du modèle OpenAI Whisper pour générer une sortie textuelle immédiate à partir de la langue parlée en direct.
Les fonctionnalités principales de davabase/whisper_real_time sont : Whisper-Based Engines, Speech Recognition Engines, Real-Time Audio Transcribers, Speech-to-Text Engines, Speech Transcription Engines, Rolling Context Windows, Asynchronous Inference, Meeting Automation & Transcription.
Les alternatives open-source à davabase/whisper_real_time incluent : cmusphinx/pocketsphinx — PocketSphinx is an offline speech recognition engine that converts raw audio from files or live microphone streams… guillaumekln/faster-whisper — faster-whisper is an automatic speech recognition framework and an optimized implementation of the Whisper… thewh1teagle/vibe — Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models… buriburisuri/speech-to-text-wavenet — This project is a deep learning framework designed for end-to-end speech-to-text transcription. It utilizes the… ufal/whisper_streaming — Whisper streaming is an automated speech recognition engine designed to convert live audio into text. It functions as… const-me/whisper — Whisper is a high-performance speech-to-text inference engine that uses graphics hardware shaders to accelerate the…