kimi

Running Kimi-Audio on Mac: A Complete Guide

Anas Mohammad

Apr 29, 2025 • 4 min read

Kimi-Audio is a universal audio foundation model capable of audio understanding, generation, and processing. Kimi-Audio is an open-source AI model designed for audio-to-text (ASR) and audio-to-audio/text conversation tasks

This guide adapts the workflow for macOSSystem Requirements and Compatibility

Hardware Requirements

Apple Silicon (M1/M2/M3) or Intel-based Mac:
- M-series chips are preferred for optimized performance.
- Intel Macs require Rosetta 2 for x86 compatibility.
RAM: Minimum 16GB (32GB recommended for large models).
Storage: 20GB free space for models and dependencies.

Software Requirements

macOS 12.0 or later (Ventura, Sonoma, or Sequoia).
Python 3.8+ and pip for dependency management.
CUDA Support: Not natively available on macOS; use Core ML or CPU-based inference.

Installation Steps

Step 1: Set Up Developer Tools

Install Xcode Command Line Tools:bashxcode-select --install
Install Homebrew:bash/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Step 2: Clone Kimi-Audio Repository

bashgit clone https://github.com/[Kimi-Audio-Repo].git # Replace with actual repo URL[^1] cd Kimi-Audio

Step 3: Create a Virtual Environment

bashpython3 -m venv kimi-env
source kimi-env/bin/activate

Step 4: Install Dependencies

bashpip install -r requirements.txt # Adapt requirements for macOS compatibility[^1][4]

Alternate Method

A. You can load the Kimi-Audio model using the KimiAudio class from the kimia_infer.api.kimia module. Ensure you have the model path or the model ID from the Hugging Face Hub.

B. Define the sampling parameters for audio and text generation.

Common Adjustments:
- Replace torch with Apple's torch-mps for Metal Performance Shaders1:bashpip install torch torchaudio
- Use onnxruntime for CPU/GPU inference on Apple Silicon.

Define Sampling Parameters:PythonCopy

sampling_params = {
    "audio_temperature": 0.8,
    "audio_top_k": 10,
    "text_temperature": 0.0,
    "text_top_k": 5,
    "audio_repetition_penalty": 1.0,
    "audio_repetition_window_size": 64,
    "text_repetition_penalty": 1.0,
    "text_repetition_window_size": 16,
}

Load the Model:PythonCopy

from kimia_infer.api.kimia import KimiAudio

model_path = "moonshotai/Kimi-Audio-7B-Instruct"
model = KimiAudio(model_path=model_path, load_detokenizer=True)

Live Examples

Example 1: Audio-to-Text (ASR)

Prepare the Audio File:
- Ensure you have an audio file ready for transcription. For example, asr_example.wav.

Transcribe the Audio:PythonCopy

import soundfile as sf

messages_asr = [
    {"role": "user", "message_type": "text", "content": "Please transcribe the following audio:"},
    {"role": "user", "message_type": "audio", "content": "test_audios/asr_example.wav"}
]

_, text_output = model.generate(messages_asr, **sampling_params, output_type="text")
print(">>> ASR Output Text: ", text_output)  # Expected output: "This is not a farewell, this is the end of one chapter and the beginning of a new one。" [^224^]

Example 2: Audio-to-Audio/Text Conversation

Prepare the Audio File:
- Ensure you have an audio file ready for the conversation. For example, qa_example.wav.

Generate Audio and Text Output:PythonCopy

messages_conversation = [
    {"role": "user", "message_type": "audio", "content": "test_audios/qa_example.wav"}
]

wav_output, text_output = model.generate(messages_conversation, **sampling_params, output_type="both")

output_audio_path = "output_audio.wav"
sf.write(output_audio_path, wav_output.detach().cpu().view(-1).numpy(), 24000)  # Assuming 24kHz output
print(f">>> Conversational Output Audio saved to: {output_audio_path}")
print(">>> Conversational Output Text: ", text_output)  # Expected output: "A." [^224^]

Audio Device Configuration

CoreAudio Setup

Configure Input/Output Devices:
- Navigate to System Settings > Sound to select devices.
- Use Audio MIDI Setup for advanced routing.
Sample Rate Synchronization:
- Match sample rates (e.g., 44.1kHz or 48kHz) between Kimi-Audio and macOS.

Aggregate Devices for Multi-Channel I/O

Open Audio MIDI Setup > Create Aggregate Device.
Combine physical interfaces (e.g., Scarlett 18i20) with built-in microphones.