whisper.cpp vs Kokoro-FastAPI
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
Kokoro-FastAPI
OpenAI-compatible text-to-speech server for the Kokoro-82M model
Verdict
whisper.cpp ranks higher, 74 to 65. Of the 5 score parts we measure for both, whisper.cpp leads on 1, Kokoro-FastAPI on 1 and 3 are even.
- Adoptionwhisper.cpp leads by 48 points, 71 to 23: a wide gap.
- FreshnessEven at 100.
- MaintenanceEven at 97.
- Easy to runEven at 50.
- Agent-readyKokoro-FastAPI leads by 15 points, 45 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 Kokoro-FastAPI if you are
Self-hosters who need a local OpenAI-style TTS backend
- Drop-in for the OpenAI speech API; works with the stock OpenAI Python client
- Prebuilt CPU and CUDA images for amd64 and arm64 with models baked in
| What we compare | whisper.cpp | Kokoro-FastAPI |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 71, popular | 23, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | 97, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 30, minimal | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 54.4k | 5.5k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | C++ | Python |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
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)
Kokoro-FastAPI
Wraps the Kokoro-82M TTS model in a FastAPI server that implements the OpenAI /v1/audio/speech endpoint, with streaming and output in mp3, wav, opus, flac, aac or pcm. It adds weighted voice mixing, inline multi-speaker tags, pause/rate/pronunciation tokens, per-word timestamps and phoneme endpoints. Prebuilt images cover CPU, NVIDIA CUDA and experimental AMD ROCm; Apple Silicon runs natively via uv.
Who it is for: Self-hosters who need a local OpenAI-style TTS backend
Strengths
- Drop-in for the OpenAI speech API; works with the stock OpenAI Python client
- Prebuilt CPU and CUDA images for amd64 and arm64 with models baked in
- Voice mixing, aliases, inline speaker tags and timestamped captions
- Bundled web UI at /web and Swagger docs at /docs
Weaknesses
- Tied to one model, Kokoro-82M; no other TTS engines
- ROCm support is experimental and amd64 only
- Apple Silicon GPU (MPS) needs a native uv run, not Docker
- Voice tuning from a reference clip is off by default, English only, and approximate
- GPU optional
- Docker + Compose
- Needs Docker or uv, espeak-ng (direct run, fallback)
- Models: Kokoro-82M
- port 8880
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