Open-source alternatives to Vibe

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

WhisperX is an automated speech recognition toolkit designed to convert spoken audio into text while maintaining precise synchronization with the original media. It functions as an integrated pipeline that combines transcription, phoneme-based alignment, and speaker diarization to produce structured, attributed transcripts. The project distinguishes itself through its use of forced alignment, which matches existing text to audio signals at the phoneme level to generate accurate word-level timestamps. It also incorporates speaker diarization to identify and label unique voices within a recordi

RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes transformer-based recognition and omnilingual pipelines to convert live audio streams into text, providing a WebSocket-based streaming API for raw PCM audio transmission. The project is distinguished by a dual-backend transcription pipeline that uses a lightweight engine for immediate partial suggestions and a heavier model for final high-accuracy results. It includes a wake word detection system to trigger recording and employs a shared-resource inference model to distribute heavy spee

whisper.cpp is a C++ implementation of the Whisper speech-to-text model, serving as a lightweight machine learning inference engine and quantized runtime. It provides high-performance automatic speech recognition and real-time audio transcription without requiring a Python environment. The project utilizes model quantization to reduce memory usage and increase inference speed on local hardware. It incorporates hardware acceleration to optimize processing speed across different processors. The system covers audio processing capabilities including voice activity detection, speaker diarization,

This project provides a self-hosted server for automatic speech recognition, functioning as a containerized inference engine for the Whisper model. It exposes core transcription and translation capabilities through a standardized web interface, allowing for the integration of speech-to-text services into external applications. The service distinguishes itself by incorporating advanced audio analysis tools, including speaker diarization to attribute text to specific individuals and voice activity detection to filter non-speech segments. It supports automated language detection and provides out

Faster-Whisper is a high-performance implementation of the Whisper speech-to-text model designed for efficient audio transcription. It provides an end-to-end processing pipeline that converts spoken audio into written text while maintaining lower memory consumption and faster execution speeds than standard implementations. The project achieves its performance through a specialized inference engine that utilizes optimized kernels and weight quantization to reduce computational complexity. It supports large-scale operations by grouping audio segments into dynamic batches and filtering out non-s

Summarize is a command line tool and multimodal content extractor designed to generate concise summaries from web pages, documents, and media files. It functions as an orchestrator that connects developer tools to various language model providers to process and condense information. The system provides specialized capabilities for audio and video processing, including transcription with speaker identification and the extraction of timestamped visual markers from video slides. It also includes a translation utility to convert generated summaries and extracted text into different target languag

Cheetah is an LLM technical interview assistant composed of a native macOS application and a browser extension. It provides real-time coding and answering suggestions during technical interviews by combining live audio transcription with web-based context extraction. The system functions as a real-time interview coach that converts spoken questions into text using on-device speech-to-text processing. It uses a browser-integrated DOM scraper to extract live code and console logs, allowing the AI to analyze the current coding state and generate technical solutions based on the specific environm

Omi is an open-source wearable AI platform that captures audio and screen data to provide real-time conversational assistance and memory. It integrates a wearable hardware development kit with a vector memory database and large language model capabilities to create a persistent digital record of user interactions. The platform is distinguished by its BLE audio streaming pipeline, which transmits raw audio from wearable hardware for real-time transcription and speaker identification. It utilizes a plugin-based agent tool framework that allows AI assistants to autonomously invoke custom functio

Ecoute is a live transcription tool that provides real-time transcripts for both the user's microphone input (You) and the user's speakers output (Speaker) in a textbox.

Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag

Buzz is a desktop application that provides a local speech-to-text engine for transcribing and translating audio and video files. By leveraging local machine inference, the software ensures data privacy and offline performance, removing the need for cloud connectivity during media processing. The application distinguishes itself through a modular plugin architecture that allows for the integration of custom functionality, such as content summarization and automated text formatting, without modifying the core codebase. It also features a speaker diarization pipeline that identifies and labels

Whisper Real-Time is a speech-to-text engine designed to convert continuous microphone input into written transcripts. It functions as a real-time audio processor that leverages the OpenAI Whisper model to generate immediate textual output from live spoken language. The system utilizes a transformer-based architecture to map audio sequences to text tokens. It manages incoming data through a sliding-window buffering mechanism and a circular buffer, which ensures a steady stream of audio for the inference engine. To maintain accuracy during continuous processing, the software employs a stateful

This project is a multimodal translation framework and large language model capable of speech-to-speech, speech-to-text, and text-to-text translation across nearly 100 languages. It provides a real-time speech translation engine and a comprehensive toolkit for converting spoken audio between languages. The system is distinguished by its ability to preserve the original speaker's tone, pace, and prosody during translation. It utilizes a specialized on-device inference toolkit that converts model checkpoints into C-based libraries, enabling low-latency execution on mobile and edge hardware with

This project is a high-throughput transcription engine and PyTorch inference wrapper designed to convert spoken audio files into text using the OpenAI Whisper model. It functions as a hardware-accelerated speech-to-text transcriber that runs locally on a user's machine. The system focuses on AI model performance tuning to maximize hardware throughput. It utilizes GPU acceleration, half-precision floating point tensors, and Flash-Attention to reduce processing time and memory overhead during transcription. The implementation covers large-scale transcription workflows and local speech-to-text

Chenyme-AAVT is an AI-powered video transcription tool and translation platform. It converts speech from media files into editable text transcripts using speech recognition models and voice activity detection to ensure accurate phrasing and timing. The system functions as a content generator that transforms video transcripts into structured blog posts and marketing graphics using large language models. It also includes a subtitle formatting editor that allows for the modification of subtitle styles with a real-time video preview. The platform provides multilingual translation capabilities th

Autosub is a command-line media processor and automatic subtitle generator that converts audio streams from video and audio files into timed text overlays. It functions as an AI speech-to-text converter that uses OpenAI Whisper to generate synchronized subtitles. The tool includes a language translation pipeline to convert transcribed speech into target languages, enabling multilingual video captioning. It manages the process from audio-stream extraction to the serialization of final subtitle files for local storage. The system covers audio-to-text transcription, time-stamped text mapping, a

This project is a self-hosted meeting transcription and summarization tool that converts audio recordings into text transcripts and structured notes using large language models. It functions as an enterprise meeting documentation manager, allowing for the organization and editing of timestamped records. The system prioritizes data privacy through local-first processing and the ability to deploy on private infrastructure. It supports a provider-agnostic architecture, enabling users to connect to local AI engines, self-hosted servers, or cloud-based API endpoints for both transcription and summ

Whishper is a graphical user interface for transcribing audio and video files into text using the Whisper model. It serves as a speech-to-text tool and subtitle file generator that converts spoken content into editable text and timed subtitle formats. The project features an integrated transcription and translation interface, allowing users to refine automated results and convert transcribed text into different languages. It includes a visual editor for correcting speech recognition errors, adjusting segment timecodes, and performing bilingual translation reviews. The system handles the full

Voicebox is a local speech processing system that provides text-to-speech generation, speech-to-text transcription, and voice cloning. It utilizes local machine learning inference and GPU acceleration to process audio and text data without relying on external API calls. The project features a voice cloning toolkit for creating synthetic profiles from audio samples and a timeline-based voice editor for composing multi-character conversations. It also includes an AI voice management API that allows external applications and AI agents to programmatically manage voice profiles and generate speech

This project is an AI meeting assistant and interview copilot that monitors system audio and screen content to generate real-time responses during video calls. It functions as a system audio transcription tool and a context-aware prompt manager, injecting user documents and behavioral profiles into large language model prompts to tailor AI outputs. The system features a stealth screen overlay, utilizing a transparent window that displays information on top of other applications while remaining invisible to screen-sharing software and proctoring tools. It employs a process-hiding mechanism to

This project is a suite of tools centered around an AI-powered interview assistant, a professional resume builder, and an engineering salary database. The core application provides real-time audio transcription and generates code and system design solutions during technical interviews. The software is designed for stealth and detection avoidance. It utilizes an invisible screen overlay that bypasses screen-capture and screen-sharing software, allowing the user to view information without it appearing on shared displays. To further avoid detection, the system implements keyboard-only operation

Whisper-diarization is a system for identifying and separating different speakers in audio recordings by combining OpenAI Whisper for transcription with automated speaker attribution. It functions as a pipeline that isolates vocal tracks from background noise and assigns transcribed segments to specific individuals. The project uses forced alignment to synchronize transcribed text timestamps with audio signals, improving the accuracy of speaker attribution. It employs voice activity detection to separate speech from silence and noise, ensuring precise boundaries for identification. The syste

This project is a hardware-accelerated transcription server and offline subtitle generator. It functions as a speech-to-text tool that converts audio and video files into plain text, JSON, and SRT subtitle formats using the Whisper model. The system operates as an OpenAI Audio API emulator, providing a local server that mimics a specific audio interface. This allows it to serve transcriptions to existing client configurations without requiring changes to the client software. The service utilizes GPU acceleration to increase voice recognition speed and includes utilities for hardware detectio

all-in-one is a containerized deployment system designed to install and manage a complete suite of productivity and collaboration services. It functions as a cloud suite deployer that orchestrates the installation of a self-hosted content platform, incorporating necessary dependencies via Docker or Kubernetes. The project distinguishes itself by providing a web-based dashboard for orchestrating, updating, and monitoring the lifecycle of service containers. It also serves as a local AI inference server, enabling the execution of generative text models, image diffusion, and speech processing on