Project Title

Whisper — High-Performance GPGPU Inference for OpenAI's Whisper ASR Model

Overview

Whisper is a high-performance GPGPU inference implementation of OpenAI's Whisper automatic speech recognition (ASR) model, designed for Windows. It offers a vendor-agnostic approach based on DirectCompute, providing a faster and more efficient alternative to OpenAI's original implementation. This C++ project stands out for its plain C++ implementation with no runtime dependencies, except for essential OS components, and its ability to leverage Direct3D 11.0 capable GPUs for enhanced performance.

Key Features

Vendor-agnostic GPGPU based on DirectCompute
Plain C++ implementation with no runtime dependencies
Faster performance compared to OpenAI's implementation
Mixed F16/F32 precision support
Built-in performance profiler
Low memory usage
Media Foundation for audio handling, supporting most audio and video formats
Voice activity detection for audio capture
Easy to use COM-style API with C# wrapper available
Pre-built binaries for 64-bit Windows

Use Cases

Researchers and developers working on speech recognition projects can use Whisper for faster and more efficient transcription of audio files.
Enterprises requiring real-time transcription and translation services can leverage Whisper's capabilities to improve their applications.
Educational institutions can utilize Whisper for developing speech recognition applications in their curriculum or research.

Advantages

Significantly faster transcription times compared to PyTorch and CUDA implementations
Reduced runtime dependencies, leading to a lighter footprint
Supports a wide range of audio and video formats, making it versatile for various media types
Built-in performance profiler for monitoring and optimizing execution times
Pre-built binaries simplify the setup process for Windows users

Limitations / Considerations

Only supports 64-bit Windows platforms, limiting its use on other operating systems
Requires a Direct3D 11.0 capable GPU, which may not be available on older hardware
The library requires AVX1 and F16C support on the CPU side, which may not be present on all systems

Mozilla DeepSpeech: An open-source speech-to-text engine that uses machine learning techniques, differing in its approach and underlying technology.
Kaldi: A toolkit for speech recognition research, offering a comprehensive set of tools but with a steeper learning curve compared to Whisper.
ESPnet: An end-to-end speech processing toolkit, which includes speech recognition but focuses more on a modular design and extensibility.

Basic Information

GitHub: https://github.com/Const-me/Whisper
Stars: 9,745
License: Unknown
Last Commit: 2025-09-25

📊 Project Information

Project Name: Whisper
GitHub URL: https://github.com/Const-me/Whisper
Programming Language: C++
⭐ Stars: 9,745
🍴 Forks: 870
📅 Created: 2023-01-07
🔄 Last Updated: 2025-09-25

🏷️ Project Topics

Topics: [, ]

📚 Documentation

[1080Ti](https://en.wikipedia.org/wiki/GeForce_10_series#GeForce_10_(10xx)
3:24 min speech
ASIO
Sandy Bridge
AVX1
F16C
RenderDoc

This article is automatically generated by AI based on GitHub project information and README content analysis

Whisper

Project Description