kesha-voice-kit

Kesha Voice Kit

Give your local tools and LLM agents a voice.Fast speech-to-text, text-to-speech, voice-activity detection, and language detection in one local-first CLI — CoreML on Apple Silicon, ONNX on Linux and Windows.

• Transcribe locally — 25 languages, up to ~19x faster than Whisper on Apple Silicon, ~2.5x on CPU
• Speak back — text-to-speech in 9 languages
• Plug into agents — ship voice workflows as CLI commands, an MCP server, an skill, or a agent
• Small Rust engine — single ~65MB binary, no ffmpeg, no Python, no native Node addons

Quick Start

Runtime: **Bun** >= 1.3.0.

# 1. Install Bun (skip if you have it)
curl -fsSL https://bun.sh/install | bash        # macOS/Linux — or: brew install oven-sh/bun/bun
powershell -c "irm bun.sh/install.ps1 | iex"    # Windows

# 2. Install Kesha
bun add -g @drakulavich/kesha-voice-kit
kesha --version                                 # confirms `kesha` resolved on PATH

# 3. Download the engine and models — pick one path
kesha init                                      # guided: TTS languages and optional VAD / diarization
kesha install --plan && kesha install           # manual: preview the sizes, then download

# 4. Transcribe
kesha audio.ogg                                 # transcript to stdout

`kesha install` pulls ~2.5 GB on Linux/Windows and ~0.6 GB on Apple Silicon, whose CoreML engine reads a smaller model set. It is always explicit — nothing downloads behind your back — and reports download progress on stderr. If `bun --version` fails right after step 1, reload your PATH: `exec $SHELL -l`.

Prefer Homebrew or Docker? See Other install methods.

Air-gapped or behind a corporate mirror? See docs/model-mirror.md.

Platform support

All three targets transcribe, detect the spoken language, run VAD, and speak. The macOS-only rows need Apple frameworks — they are not a missing port. Windows is a tested path rather than a published binary nobody ran: CI does a cold `kesha install` on `windows-latest`, transcribes a fixture, and round-trips a synthesis (#216, #667).

Intel Macs get no published engine binary. Full matrix with maturity labels: docs/product-positioning.md.

Speech-to-text

kesha audio.ogg                            # transcribe (plain text)
kesha --format transcript audio.ogg        # text + language/confidence
kesha --format json audio.ogg              # full JSON with lang fields
kesha --json --timestamps audio.ogg        # JSON with timestamped segments
kesha --itn audio.ogg                      # spelled-out numbers -> digits
kesha --toon audio.ogg                     # compact LLM-friendly TOON
kesha status                               # show installed backend info
kesha status --disk                        # + recursive cache disk usage
kesha status --json                        # machine-readable, for scripts

Multiple files get `head`-style headers; stdout is the transcript, stderr is errors — pipe-friendly:

$ kesha freedom.ogg tahiti.ogg
=== freedom.ogg ===
Свободу попугаям! Свободу!

=== tahiti.ogg ===
Таити, Таити! Не были мы ни в какой Таити! Нас и тут неплохо кормят.

• Record from the mic (macOS): `kesha record --out hello.wav` writes microphone audio to a WAV file (`kesha hello.wav` transcribes it). macOS prompts for microphone access on first use — grant it under System Settings → Privacy & Security → Microphone if it was denied. On Linux/Windows or headless boxes, pass any existing audio file straight to `kesha` instead.
• Dictate straight to text (darwin-arm64): `kesha record --live` transcribes the mic as it captures and prints the transcript to stdout — no WAV in between, so it pipes (`kesha record --live | pbcopy`). To end after trailing silence, explicitly install VAD then opt in: `kesha install --vad && kesha record --live --auto-stop`. The defaults are 1,000 ms of silence after 250 ms of speech; tune them with `--auto-stop-silence-ms`, `--auto-stop-min-speech-ms`, and `--auto-stop-threshold`. Progress goes to stderr. Linux and Windows do not capture the microphone; pass an existing audio file to `kesha` to transcribe it. An interruption is recoverable: Ctrl-C (or SIGTERM) stops the session, still prints what you dictated, and exits 130/143, and the audio is spilled to a recovery WAV under `~/.cache/kesha/recordings/` — named on stderr when the session starts, deleted once the transcript has actually been delivered, kept if anything — a signal, a crash, a closed terminal, a dead pipe — got in the way first (#962).
• Long / silence-heavy audio: install VAD (`kesha install --vad`); Kesha auto-uses it past 120 s. Without VAD, long audio falls back to fixed ASR chunks. See docs/vad.md.
• Speaker diarization (darwin-arm64): `kesha install --diarize` (which installs VAD too), then `kesha --json --speakers meeting.m4a` stamps each segment with a `speaker` id. `--speakers` engages VAD windowing itself at any duration, so it cannot be combined with `--no-vad`. Linux/Windows return a clear "darwin-arm64 only" error (#199).
• Word-level timestamps (every platform): `kesha --json --timestamps audio.ogg` adds a `words` array to each segment — `{ "word": "email", "start": 0.72, "end": 1.12 }` — on the same file-relative clock as the segment, so a word always lies inside the segment carrying it. Read them off the decoder's own frame grid, so: times are quantised to 0.08 s, consecutive spans may overlap (each `end` is a per-word duration prediction, not the next word's `start`), `end >= start` rather than strictly greater, and punctuation stays attached to its word. The key is simply absent where a segment has none — any segment `--itn` rewrote, for one — so check the `transcribe.words` capability rather than expecting an empty array (#720).
• Text-language detection: JSON and TOON results include `textLanguage` with a language code, confidence, and its `source`. On macOS Kesha uses Apple `NLLanguageRecognizer`; elsewhere it uses the bundled `tinyld` fallback, whose confidence scale is different. This is separate from `audioLanguage`, which identifies the spoken audio when available.
• Written-form numbers: `--itn` rewrites what the model spells out — `"two hundred thirty two"` → `"232"`, `"five dollars and fifty cents"` → `"$5.50"`. Opt-in, every platform, timestamps untouched. English-only in practice; Russian and the rest pass through unchanged. Spoken punctuation names stay words (`"dot"`, `"comma"`, `"the period of growth"`) because Kesha transcribes speech rather than dictation — so `"example dot com"` keeps its words too (#822). A sentence `"and"` survives the number that follows it (`"cats and three dogs"` → `"cats and 3 dogs"`), while an `"and"` the number owns still joins it (`"three hundred and five"` → `"305"`) (#1000) — and no longer splits the number around it (`"two hundred and thirty two"` → `"232"`, not `"230 2"`) (#1006). A hyphenated number reads the same as the spaced form (`"twenty-five apples"` → `"25 apples"`), while a hyphen between ordinary words is left alone (`"well-known"`, `"state-of-the-art"`, `"twenty-something"`) (#1004).

Text-to-speech

Kesha speaks back in 9 languages. Kokoro runs natively through FluidAudio CoreML/ANE on Apple Silicon and through ONNX on Linux and Windows; Russian uses Vosk-TTS, while `macos-*` system voices need no model download. On macOS Kesha picks the voice from the text's own language; on Linux and Windows, state the language with `--lang ` (or the voice with `--voice `) — otherwise the engine default speaks.

kesha install --tts                              # English voices; sizes differ per platform — preview: kesha install --plan
kesha install --tts en ru                        # + Russian (+~890 MB, Vosk)
kesha say "Hello, world" > hello.wav
kesha say "Привет, мир" > privet.wav             # auto-routes by language (macOS)
kesha say --lang ru "Привет, мир" > privet.wav   # explicit — the Linux/Windows path
kesha say --voice ru-vosk-m02 "Голос в текст." > ru.wav

Output formats (`--format`, or inferred from the `--out` extension):

kesha say "Hello" --out hi.wav                    # WAV (default, uncompressed)
kesha say "Hello" --format ogg-opus --out hi.ogg  # OGG/Opus — messenger voice notes
kesha say "Hello" --format flac --out hi.flac     # FLAC — lossless, plays in every browser incl. Safari/iOS

`kesha say --list-voices` lists what's installed. Voices, the full catalogue, macOS system voices, SSML, speaking rate (`--rate`, ``), Russian word stress, and Russian/English abbreviation handling are all in **docs/tts.md**.

Languages

Speech-to-text spans 25 languages and text-to-speech 9 — full tables with codes, flags, and per-platform availability in **docs/languages.md**. Audio language detection identifies 107 languages.

Performance

> Up to ~19x faster than Whisper on Apple Silicon (M2), ~2.5x faster on CPU

Compared against Whisper `large-v3-turbo`, all engines auto-detecting language:

Full per-file breakdown (Russian + English): BENCHMARK.md. The CPU figure is the ONNX engine on an M2's CPU cores; no x86 numbers are published yet.

Other install methods

All of these install the Bun CLI wrapper; engine + models still download explicitly via `kesha install`. (Nix is the exception — it currently builds only the engine from source; see below.)

• Homebrew — `brew install drakulavich/tap/kesha-voice-kit` · docs/homebrew.md
• Linux packages (`.deb`/`.rpm`, x64) — published on CLI releases, see docs/linux-packages.md
• Docker (GHCR image) — docs/docker.md
• Nix (`aarch64-darwin` / `x86_64-linux`) — builds the engine from source (`nix build github:drakulavich/kesha-voice-kit#kesha-engine`). The full `kesha` CLI via `nix run` / `nix profile install` is not yet available — it needs a maintainer with Nix to populate a build hash (#946). · docs/nix-install.md
• Shell completions + manpage — `kesha completions bash|zsh|fish` and `kesha manpage` print the packaged files to install wherever your shell expects them.

Integrations

• MCP server — `kesha mcp` exposes transcribe/synthesize/list tools to any MCP client (Claude, Cursor, Codex, Gemini). Setup: docs/mcp.md.
• OpenClaw — give your LLM agent ears. Install & config: docs/openclaw.md.
• Hermes Agent — local STT/TTS through Hermes command providers. Setup: docs/hermes.md.
• Raycast (macOS) — offline microphone dictation from the launcher: *Dictate to Clipboard* records with a live signal meter, auto-stops on silence, transcribes locally, and copies the text. Install from the Raycast Store · source: `raycast/`.
• Programmatic API — `@drakulavich/kesha-voice-kit/core` for use inside a Bun program. See docs/api.md.

More

• Architecture — runtime data flow, the models that ship, the CLI ↔ Rust engine boundary, model pinning, and where tests live.
• Use cases — copy-paste recipes (transcribe a meeting, speak from OpenClaw, run offline, move the cache).
• Product positioning — supported workflows, non-goals, maturity labels, platform matrix.
• Changelog — every release, with the behaviour changes spelled out.
• Diagnostics: `kesha doctor`, `kesha support-bundle` (redacted `.tar.gz` for issues), and `kesha logs` produce local, content-free diagnostics — see docs/diagnostic-logs.md. Every failure prints a stable `error [CODE]: …` line and a documented process exit code.
• Scripting & CI: `--json` (or `--toon`) for machine-readable output, `--include-errors` (with either) to get per-file failures on stdout alongside the results, `--quiet`/`-q` to silence progress, and `--no-color` (or `NO_COLOR=1`) for plain logs. Colors switch off automatically when `CI=true`.
• Privacy / Local Stats: Stats are off by default and fully local. Opt in with `kesha stats enable` to record content-free operational metrics in a local SQLite database — never networked, never storing audio, transcripts, text, or paths. Full commands & lifecycle: docs/local-stats.md.

Contributing

See CONTRIBUTING.md, the Roadmap (Now / Next / Later), and the Decision log (why platform/model choices were made — and reversed). Dev setup: `just dev-setup` (Bun, Rust, nextest, platform libs).

License

Made with 💛🩵 and 🥤 energy under MIT License