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 + ONNX G2P (~490 MB more, for TTS)

No system deps — G2P runs as ONNX alongside Kokoro/Piper. macos-* voices need no install either — 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

• Source: https://github.com/drakulavich/kesha-voice-kit
• npm: https://www.npmjs.com/package/@drakulavich/kesha-voice-kit
• Releases: https://github.com/drakulavich/kesha-voice-kit/releases