whisper.cpp vs Speech-to-Speech
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
Speech-to-Speech
Modular voice-agent pipeline exposed through the OpenAI Realtime API
Verdict
whisper.cpp ranks higher, 74 to 66. Of the 5 score parts we measure for both, whisper.cpp leads on 2, Speech-to-Speech on none and 3 are even.
- Adoptionwhisper.cpp leads by 33 points, 71 to 38: a wide gap.
- FreshnessEven at 100.
- Maintenancewhisper.cpp leads by 3 points, 97 to 94: close to even.
- Easy to runEven at 50.
- Agent-readyEven at 30.
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 Speech-to-Speech if you are
Developers building self-hosted voice agents or Realtime API backends
- Implements core OpenAI Realtime events over WebSocket and WebRTC, so client swaps are easy
- Fully local on Apple Silicon (MLX) or NVIDIA CUDA, with no API key needed
| What we compare | whisper.cpp | Speech-to-Speech |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 71, popular | 38, known |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | 94, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 30, minimal | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 54.4k | 13.4k |
| 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)
Speech-to-Speech
Runs a VAD, STT, LLM and TTS cascade, with each stage in its own thread and every backend swappable by CLI flag. It serves the core OpenAI Realtime event set over WebSocket and WebRTC, so existing Realtime clients can point at it. Defaults are Parakeet TDT for speech recognition and Qwen3-TTS for speech output, with the LLM running locally or through any OpenAI-compatible endpoint.
Who it is for: Developers building self-hosted voice agents or Realtime API backends
Strengths
- Implements core OpenAI Realtime events over WebSocket and WebRTC, so client swaps are easy
- Fully local on Apple Silicon (MLX) or NVIDIA CUDA, with no API key needed
- Many interchangeable STT and TTS backends, including Whisper, Kokoro, Pocket TTS and OmniVoice
- Apache-2.0, installable from PyPI, with a packaged microphone client
Weaknesses
- Qwen3-TTS GGML wheel targets CUDA 12.8 and glibc 2.39 by default
- Fully local NVIDIA setup budgets about 24 GB VRAM; the README calls this an estimate
- Only the core Realtime event set is implemented, not the full API
- Some extras conflict, e.g. DeepFilterNet needs numpy<2 while Pocket TTS needs numpy>=2
- RAM ≥ 16 GB
- GPU optional
- Docker + Compose
- Needs OpenAI-compatible LLM server (optional), PortAudio and libsndfile on Ubuntu
- Models: Parakeet TDT, Qwen3-TTS, Whisper, Kokoro-82M, Transformers LLMs
- port 8765
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