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collabora avatar

collabora/WhisperLive

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WhisperLive

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.

Additional capabilities include high-throughput audio processing via batch inference and TensorRT acceleration, as well as audio signal normalization and recording state control. The service supports live audio captioning through segment-based incremental rendering.

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Features

  • Real-Time Transcription - Converts live audio streams into text in real time using Whisper models for immediate accessibility.
  • Audio Transcription - Provides a backend service that streams microphone input and delivers incremental text transcriptions.
  • GPU-Accelerated Inference - Employs a GPU-accelerated inference engine to optimize throughput for multilingual speech recognition.
  • Real-Time Speech-to-Text Servers - Functions as a real-time audio transcription server using Whisper models and WebSocket streaming.
  • Speaker Diarization - Clusters audio feature vectors to distinguish and segment different speakers within a single audio stream.
  • Whisper-Based Engines - Utilizes a Faster-Whisper engine with CTranslate2 backend to optimize transcription speed and memory usage.
  • Audio Transcription WebSockets - Uses WebSockets to stream raw PCM audio from the client to the server for real-time processing.
  • High-Throughput Transcription - Processes multiple simultaneous audio streams via GPU batching to achieve high transcription throughput.
  • Word-Level Timestamps - Produces precise start and end timestamps and confidence scores for every individual word transcribed.
  • Inference Acceleration Engines - Implements high-performance inference using TensorRT to accelerate speech-to-text processing speeds.
  • Incremental Transcription Previews - Incrementally renders transcribed text segments on the screen as they are emitted by the backend.
  • Technical Jargon Optimizations - Improves transcription accuracy for domain-specific technical terms using keyword boosting.
  • Inference Batching - Groups multiple concurrent user audio segments into single GPU calls to maximize system throughput.
  • Transcription Term Boosts - Provides a mechanism to boost specific technical terms and jargon during the transcription decoding process.
  • Live Captioning Integrations - Displays incrementally processed speech as text on screen for real-time live captioning.
  • Incremental Text Rendering - Updates the user interface incrementally by appending transcribed text chunks as they are emitted.
3,819 stars·526 forks·Python·mit·116 views

Star history

Star history chart for collabora/whisperliveStar history chart for collabora/whisperlive

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does collabora/whisperlive do?

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.

What are the main features of collabora/whisperlive?

The main features of collabora/whisperlive are: Real-Time Transcription, Audio Transcription, GPU-Accelerated Inference, Real-Time Speech-to-Text Servers, Speaker Diarization, Whisper-Based Engines, Audio Transcription WebSockets, High-Throughput Transcription.

Which projects share features with collabora/whisperlive?

Projects with overlapping indexed features include: koljab/realtimestt — RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes… thewh1teagle/vibe — Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models… argmaxinc/whisperkit. const-me/whisper — Whisper is a high-performance speech-to-text inference engine that uses graphics hardware shaders to accelerate the… ahmetoner/whisper-asr-webservice — This project provides a self-hosted server for automatic speech recognition, functioning as a containerized inference… vaibhavs10/insanely-fast-whisper — This project is a high-throughput transcription engine and PyTorch inference wrapper designed to convert spoken audio…

Projects sharing features with WhisperLive

These projects share indexed features with WhisperLive. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • koljab/realtimesttKoljaB avatar

    KoljaB/RealtimeSTT

    9,477View on GitHub↗

    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

    Pythonpythonrealtimespeech-to-text
    View on GitHub↗9,477
  • thewh1teagle/vibethewh1teagle avatar

    thewh1teagle/vibe

    5,298View on GitHub↗

    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

    TypeScriptaicross-platformdesktop
    View on GitHub↗5,298
  • argmaxinc/whisperkitargmaxinc avatar

    argmaxinc/WhisperKit

    5,639View on GitHub↗
    Swiftinferenceiosmacos
    View on GitHub↗5,639
  • const-me/whisperConst-me avatar

    Const-me/Whisper

    10,489View on GitHub↗

    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

    C++
    View on GitHub↗10,489
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