Whisperkit CLI

whisperkit-cli

On-device Whisper transcription + Qwen3-TTS synthesis
Local file-based audio I/O -- models are downloaded from HuggingFace on first run, then all inference runs on-device with no network required. Perfect for agents that receive voice messages/attachments and reply with text or generated audio files.

The agent saves incoming audio attachments to a temp path, runs the CLI, and either returns the transcribed text in chat or attaches the generated .wav/.m4a reply.

Why agents love this skill

• Runs on ANE -- no GPU contention, low power, always available
• No API keys, no per-request costs, no data leaves the machine after setup
• One-time model download on first run, then fully offline
• Handles audio files from user messages (m4a, wav, mp3, flac)
• Generates reply audio files the agent can attach/send
• 9 built-in voices + 10 languages
• Natural-language style instructions (1.7B model)

Installation

brew install whisperkit-cli

First run automatically downloads models as needed.

Core Commands

Transcribe (Audio File -> Text)

whisperkit-cli transcribe --help

Agent patterns

# Transcribe user-uploaded audio attachment (recommended default)
whisperkit-cli transcribe --audio-path /tmp/user-message.m4a

Important model notes

• By default, whisperkit-cli transcribe automatically selects the highest-quality model that fits on your Apple Silicon device (typically a large-v3 variant on M1+). This is great for accuracy but may be slower for real-time agent workflows.
• --model small is the fastest option and works well across languages. For non-English audio, pass --language with the ISO code (e.g. --language ja for Japanese). Avoid .en model variants for non-English audio.

# Explicit small model (fast + good quality for most cases)
whisperkit-cli transcribe --model small --audio-path /tmp/voice-note.wav

# Non-English audio -- specify the language ISO code
whisperkit-cli transcribe --model small --language ja --audio-path /tmp/japanese-message.m4a

# Higher quality with auto language detection (no --language needed)
# --prompt provides context as if it were the previous transcript segment,
# helping the model spell proper nouns and domain terms correctly
whisperkit-cli transcribe --model large-v3-v20240930_626MB --audio-path /tmp/long-meeting.m4a \
  --word-timestamps --prompt "Argmax, WhisperKit, CoreML"

Output goes to stdout (clean text) -- agent copies it directly into the chat reply.

TTS (Text -> Audio File)

whisperkit-cli tts --help

Agent patterns

# Generate reply audio file (agent will attach it)
whisperkit-cli tts --text "Got it, I'll handle the report by Friday" \
  --output-path /tmp/agent-reply

# With voice + language
whisperkit-cli tts --text "こんにちは、世界" \
  --speaker ono-anna --language japanese \
  --output-path /tmp/japanese-reply.m4a

# 1.7B model with expressive style instruction
whisperkit-cli tts --model 1.7b \
  --text "Once upon a time in a galaxy far, far away..." \
  --instruction "Read dramatically like a movie trailer narrator" \
  --output-path /tmp/story-reply.m4a

# From text file (great for long LLM summaries)
whisperkit-cli tts --text-file /tmp/llm-response.txt \
  --output-path /tmp/voice-reply.m4a

You can include the extension in --output-path (e.g. /tmp/reply.m4a) or omit it and the CLI will append it based on --output-format (default .m4a). Use --output-format wav for .wav. Default voice is aiden if --speaker is omitted.

Voices (TTS)

ryan, aiden, ono-anna, sohee, eric, dylan, serena, vivian, uncle-fu

Languages (TTS)

english, chinese, japanese, korean, german, french, russian, portuguese, spanish, italian

Local OpenAI-Compatible API Server

whisperkit-cli serve --port 50060

Auto-selects the best model for your device. To specify a model explicitly:

whisperkit-cli serve --model small --port 50060

Exposes OpenAI-compatible endpoints at http://127.0.0.1:50060:

• POST /v1/audio/transcriptions -- transcribe audio to text
• POST /v1/audio/translations -- translate audio to English
• GET /health -- health check

Agent Usage Patterns

# Typical voice message flow
# User sends audio -> agent saves to /tmp/user-audio.m4a
whisperkit-cli transcribe --model small --audio-path /tmp/user-audio.m4a

# Agent sends text to LLM, gets response, generates voice reply
whisperkit-cli tts --text "{{llm_response}}" --output-path /tmp/reply --speaker ryan

# Agent attaches /tmp/reply.m4a to the chat message

Full docs & model list

https://github.com/argmaxinc/WhisperKit

Whisper model sizes (speed vs quality trade-off):

• tiny (76MB), base (146MB) -- fastest, lower accuracy
• small (~486MB) -- recommended for most agents, fastest. Works across languages when --language is specified. Avoid .en variants for non-English.
• large-v3-v20240930_626MB (~626MB) -- quantized large model, best balance of accuracy and size. Auto-detects language without needing --language.
• large-v3-v20240930 (~1.6GB) -- auto-selected default on M1+, full-precision large model.

Model names use the short form after the openai_whisper- prefix (e.g. --model small resolves to openai_whisper-small). Append .en for English-only variants.

TTS model sizes:

• 0.6b -- fast, works on all Apple Silicon devices
• 1.7b -- best quality + style instructions, macOS 15+

Run whisperkit-cli transcribe --help or whisperkit-cli tts --help for the latest flags.