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Const-me avatar

Const-me/Whisper

0
View on GitHub↗
10,489 stars·957 forks·C++·MPL-2.0·36 views

Whisper

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 capture, GPGPU inference optimization, and compute performance profiling to measure the execution time of individual shaders.

Features

  • Automatic Speech Recognition - Implements a high-performance automatic speech recognition system using OpenAI Whisper to transcribe audio in multiple languages.
  • Audio Transcription - Converts recorded audio files into text transcripts using a GPU-accelerated speech recognition model.
  • Real-Time Transcription - Provides instantaneous conversion of live microphone audio streams into text transcripts.
  • GPU-Accelerated Inference - Leverages the parallel processing power of GPUs specifically to accelerate the inference phase of speech recognition.
  • GPGPU Execution Models - Executes neural network computations directly on the graphics processor using compute shaders for high-performance speech recognition.
  • Inference Optimization - Optimizes model execution speed and computational efficiency by running heavy machine learning models on graphics hardware.
  • Whisper-Based Engines - Provides a high-performance inference engine specifically designed to run Whisper-based speech recognition models.
  • Tensor Operations - Implements linear algebra and matrix multiplications within GPU kernels to eliminate CPU-to-GPU data transfer bottlenecks.
  • Real-Time Audio Transcribers - Captures live microphone input and applies voice activity detection for immediate text generation.
  • Voice Activity Detection - Analyzes raw audio streams in real time to identify speech boundaries and trigger the inference engine.
  • Graphics Memory Mapping - Directly maps graphics memory into the application address space to enable high-speed audio data transfer to the GPU.
  • Audio Capture and Playback - Captures audio input from hardware devices for real-time processing and speech activity detection.
  • Speech Recognition - Windows desktop application utilizing GPU acceleration for transcription.

Star history

Star history chart for const-me/whisperStar history chart for const-me/whisper

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Open-source alternatives to Whisper

Similar open-source projects, ranked by how many features they share with Whisper.
  • collabora/whisperlivecollabora avatar

    collabora/WhisperLive

    3,819View on GitHub↗

    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

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  • vaibhavs10/insanely-fast-whisperVaibhavs10 avatar

    Vaibhavs10/insanely-fast-whisper

    12,969View on GitHub↗

    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

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  • ggerganov/whisper.cppggerganov avatar

    ggerganov/whisper.cpp

    50,791View on GitHub↗

    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,

    C++
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  • m-bain/whisperxm-bain avatar

    m-bain/whisperX

    20,228View on GitHub↗

    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

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See all 30 alternatives to Whisper→

Frequently asked questions

What does const-me/whisper do?

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.

What are the main features of const-me/whisper?

The main features of const-me/whisper are: Automatic Speech Recognition, Audio Transcription, Real-Time Transcription, GPU-Accelerated Inference, GPGPU Execution Models, Inference Optimization, Whisper-Based Engines, Tensor Operations.

What are some open-source alternatives to const-me/whisper?

Open-source alternatives to const-me/whisper include: vaibhavs10/insanely-fast-whisper — This project is a high-throughput transcription engine and PyTorch inference wrapper designed to convert spoken audio… collabora/whisperlive — WhisperLive is a real-time speech-to-text server that converts live audio streams into text using Whisper models. It… ggerganov/whisper.cpp — whisper.cpp is a C++ implementation of the Whisper speech-to-text model, serving as a lightweight machine learning… m-bain/whisperx — WhisperX is an automated speech recognition toolkit designed to convert spoken audio into text while maintaining… thewh1teagle/vibe — Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models… koljab/realtimestt — RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes…