whisper.cpp vs buzz
Two of the top projects in Voice, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
whisper.cpp
C/C++ port of OpenAI Whisper for local speech-to-text transcription
buzz
Offline desktop app for transcribing and translating audio with Whisper
Verdict
whisper.cpp ranks higher, 75 to 68. Of the 3 score parts we measure for both, whisper.cpp leads on 1, buzz on 1 and 1 is even.
- Adoptionwhisper.cpp leads by 42 points, 78 to 36: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score buzz on this; whisper.cpp has 97.
- Easy to runWe do not score buzz on this; whisper.cpp has 50.
- Agent-readybuzz leads by 40 points, 70 to 30: a wide gap.
Pick whisper.cpp if you are
Developers embedding offline speech-to-text in apps, servers or devices
- No dependencies in core; zero memory allocations at runtime
- Wide backend coverage: Metal, Core ML, CUDA, Vulkan, ROCm, OpenVINO, CANN, MUSA, NPU
Pick buzz if you are
People transcribing or subtitling audio and video locally on their own computer
- Runs offline; transcription and translation stay on the local machine
- Several Whisper backends: Whisper.cpp with Vulkan, CUDA, Apple Silicon, Hugging Face models
| What we compare | whisper.cpp | buzz |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 78, popular | 36, known |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | Not scored for this kind |
| Easy to run | 50, easy | Not scored for this kind |
| Agent-ready | 30, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 54.4k | 22k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Aug 2026 |
| Language | C++ | Python |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
whisper.cpp
whisper.cpp runs OpenAI's Whisper speech recognition models in plain C/C++ on top of the ggml library, with a C-style API in whisper.h and a whisper-cli example for transcribing 16-bit WAV files. It runs on CPU or can offload to Metal, Core ML, CUDA, Vulkan, ROCm, OpenVINO, Ascend CANN, Moore Threads MUSA and AMD Ryzen AI NPUs. It supports integer quantization and voice activity detection, and builds for desktop, mobile, WebAssembly and Docker.
Who it is for: Developers embedding offline speech-to-text in apps, servers or devices
Strengths
- No dependencies in core; zero memory allocations at runtime
- Wide backend coverage: Metal, Core ML, CUDA, Vulkan, ROCm, OpenVINO, CANN, MUSA, NPU
- Runs on iOS, Android, WebAssembly, Raspberry Pi, and Java bindings exist
- Q5_0-style integer quantization shrinks models; base model needs about 388 MB RAM
Weaknesses
- whisper-cli accepts only 16-bit WAV unless built with optional FFmpeg support
- Core ML, OpenVINO and VitisAI encoders need a separate Python conversion step
- Models must be in ggml format; downloaded via script, not bundled
- Library and CLI only; no built-in web UI or multi-user features
- GPU optional
- Docker
- Needs CMake, ffmpeg (optional), CUDA / ROCm / Vulkan / OpenVINO (optional backends)
- Models: Whisper ggml (tiny, base, small, medium, large-v1/v2/v3, large-v3-turbo), Quantized ggml (e.g. q5_0)
buzz
Buzz is a desktop app that transcribes and translates audio and video files, YouTube links, or live microphone input with OpenAI's Whisper, on the local machine. It supports several Whisper backends (Whisper.cpp with Vulkan, CUDA, Apple Silicon, Hugging Face models) and exports to TXT, SRT and VTT. It also has a CLI, a watch folder and a plugin system.
Who it is for: People transcribing or subtitling audio and video locally on their own computer
Strengths
- Runs offline; transcription and translation stay on the local machine
- Several Whisper backends: Whisper.cpp with Vulkan, CUDA, Apple Silicon, Hugging Face models
- Speaker identification and speech separation for noisy audio
- CLI and watch folder allow scripted or automatic transcription
Weaknesses
- Current releases require Apple silicon; last Intel Mac version is 1.4.5
- Windows installer is unsigned and triggers a security warning
- PyPI install needs ffmpeg, Python 3.12 and manual torch CUDA setup for GPU
- Export formats listed are only TXT, SRT and VTT
- GPU optional
- Needs ffmpeg (PyPI install), Python 3.12 (PyPI install)
- Models: Whisper, Whisper.cpp, Hugging Face Whisper-type models
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