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kesha-voice-kit

Local multilingual voice toolkit — speech-to-text (STT), text-to-speech (TTS), and language detection. Runs entirely offline on Apple Silicon, Linux, and Windows. No API keys, no cloud. NVIDIA Parakeet TDT for STT across 25 European languages, Kokoro-82M + Piper VITS for TTS, plus macOS AVSpeechSynthesizer for ~180 system voices with zero install.

install
source · Clone the upstream repo
git clone https://github.com/drakulavich/kesha-voice-kit
Claude Code · Install into ~/.claude/skills/
git clone --depth=1 https://github.com/drakulavich/kesha-voice-kit ~/.claude/skills/drakulavich-kesha-voice-kit-kesha-voice-kit
manifest: SKILL.md
source content

kesha-voice-kit

Local voice toolkit: transcribe voice messages to text, synthesize speech, detect language of audio or text. Fully offline after 

kesha install
. No API keys, no per-minute billing.

Trigger keywords for when to use this skill: voice message, voice memo, .ogg, .wav, .mp3, audio file, transcribe, transcription, speech-to-text, STT, text-to-speech, TTS, synthesize speech, say, multilingual voice, multilingual ASR, language detection, offline voice, privacy, Apple Silicon, CoreML.

When to use

  • Voice memo arrived (Telegram, WhatsApp, Slack, Signal .ogg/.opus/.m4a): transcribe with 
    kesha --json <path>
    and branch on the detected language.
  • Need to reply with audio: synthesize with 
    kesha say "<text>" > reply.wav
    . Auto-routes by detected language (Kokoro-82M for English, Piper for Russian). For other languages and ~180 more voices use 
    --voice macos-*
    on macOS (zero model download).
  • Need to detect what language a file is in before choosing a pipeline: 
    kesha --json audio.ogg
    returns both audio-based and text-based language detection with confidence scores.

STT: transcribe audio

# JSON output with language detection (recommended for automation)
kesha --json voice.ogg

[{
  "file": "voice.ogg",
  "text": "Привет, как дела?",
  "lang": "ru",
  "audioLanguage": { "code": "ru", "confidence": 0.98 },
  "textLanguage": { "code": "ru", "confidence": 0.99 }
}]

Use 

lang
(or the more detailed 
audioLanguage
/
textLanguage
) to decide how to respond.

Formats: .ogg, .opus, .mp3, .m4a, .wav, .flac, .webm — decoded via symphonia, no ffmpeg required.

Other output modes:

  • kesha audio.ogg
    — plain transcript on stdout
  • kesha --format transcript audio.ogg
    — transcript + 
    [lang: ru, confidence: 0.99]
    footer
  • kesha --verbose audio.ogg
    — human-readable with language info
  • kesha --lang en audio.ogg
    — warn if detected language differs (useful sanity check)

TTS: synthesize speech

kesha say "Hello, world" > hello.wav               # auto-routes en → Kokoro-82M
kesha say "Привет, мир" > privet.wav              # auto-routes ru → Piper
kesha say --voice macos-de-DE "Guten Tag" > de.wav # any macOS system voice — German, French, Italian, ...
kesha say --list-voices                            # Kokoro + Piper + ~180 macos-* voices

Output: WAV mono float32. 

--out <path>
writes to a file instead of stdout.

Language detection standalone

kesha --json audio.ogg
includes both audio-based (
audioLanguage
) and text-based (
textLanguage
) detection. Use audio detection to identify the language before running language-specific logic.

Install

bun add --global @drakulavich/kesha-voice-kit    # or: npm i -g @drakulavich/kesha-voice-kit
kesha install                                    # downloads engine (~350 MB)
kesha install --tts                              # adds Kokoro + Piper RU (~390 MB more, for TTS)

One-time runtime prereq for TTS on each platform:

  • macOS: 
    brew install espeak-ng
  • Linux: 
    sudo apt install espeak-ng
  • Windows: 
    choco install espeak-ng

macos-*
voices need no install — they use voices already on the Mac.

Supported languages

Speech-to-text (25): Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish, Ukrainian.

Text-to-speech: English (Kokoro-82M, ~70 voices), Russian (Piper 

ru-denis
), plus any macOS system voice via 
--voice macos-*
.

Performance

  • ASR: ~19× faster than OpenAI Whisper on Apple Silicon (CoreML via FluidAudio), ~2.5× on CPU (ONNX via 
    ort
    ).
  • TTS: sub-second latency for short utterances on Apple Silicon.

Why local

No API keys to manage. No per-minute billing. Voice data never leaves the machine — important for regulated industries, personal messaging, and anything that shouldn't be in a third-party log.

Links