Buzz vs whisper.cpp

Buzz

• People who want to transcribe recordings without uploading them to an online service
• Creators who need SRT or VTT subtitle files or TXT transcripts
• Users who want live transcription from a microphone during events or presentations
• Users with an Nvidia GPU or Apple Silicon Mac who want faster processing

whisper.cpp

• Developers who want to embed offline speech-to-text in a C, C++, or other native app
• People on Apple Silicon who want GPU or Neural Engine accelerated transcription
• Users who need to transcribe audio privately on their own computer
• Projects targeting constrained or embedded hardware such as Raspberry Pi

Buzz

• Runs offline on your own computer, according to the README
• Supports several Whisper backends, including CUDA for Nvidia GPUs, Apple Silicon, and Vulkan
• Offers multiple export formats and a transcript viewer with search and playback speed control

whisper.cpp

• No external dependencies in the core implementation, which eases integration into many platforms
• Supports a wide range of hardware including CPU, Metal, CUDA-based NVIDIA GPUs, Vulkan, and Core ML
• Includes quantization options that reduce model size and memory use

Buzz

• Users on Intel Macs, since the README says the last supported version for them is 1.4.5
• People who need a hosted, no-install web service, because Buzz is installed locally
• Users who expect a fully signed Windows app, since the README says the Windows app is not signed and shows a warning

whisper.cpp

• Non-technical users who want a ready-made app with a graphical interface, since the README centers on command-line usage and building from source
• Anyone who needs audio in formats other than 16-bit WAV without first converting it, because the CLI example only accepts 16-bit WAV
• Teams needing a hosted transcription API, since this is a local inference library rather than a service

Buzz

• Some acceleration options depend on specific hardware and versions; the README says CUDA setup applies to version 1.4.6 or later, which it describes as not yet released at the time of the README
• The Windows installer is unsigned, so installation shows a security warning
• The README is truncated, so detailed setup and model requirements are unclear from this text

whisper.cpp

• Command-line example only accepts 16-bit WAV, so audio often needs conversion first
• Building and choosing models requires comfort with CMake and the terminal
• Core ML and some accelerator paths require extra setup and platform-specific steps