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

1. Install Xcode Command Line Tools:bashxcode-select --install
   
2. 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)

1. 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

1. 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

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

Aggregate Devices for Multi-Channel I/O

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

Running Kimi-Audio

Command-Line Execution

bashpython kimi_audio.py --input "What is happiness?" --output_format both

• Flags:
  • --device mps for Metal acceleration on Apple Silicon1.
  • --precision fp16 to reduce memory usage1.

GUI Workflow (Hypothetical)

If a GUI is available:

1. Launch the app and grant microphone access via System Settings > Privacy & Security7.
2. Select input/output devices from dropdown menus.

Performance Optimization

Metal Performance Shaders (MPS)

• Enable MPS Backend:pythonimport torch
  device = torch.device("mps")
• Monitor GPU Usage:
  Use Activity Monitor > GPU History1.

Memory Management

• Reduce Batch Size: Lower --batch_size to 1-2 for long-form audio1.
• Use swapoff: Disable swap to prevent SSD wear (only for 32GB+ RAM systems).

Troubleshooting

Common Issues

• Crackling Audio:
  • Adjust sample rate in Audio MIDI Setup.
  • Disconnect external devices.
• Installation Failures:
  • Reinstall Xcode tools: xcode-select --reset.
  • Use Rosetta 2 terminal for Intel-specific dependencies.

Debugging Tools

• Console Logs: Filter for coreaudiod errors.
• Test MIDI/Audio Routing: Use Audio MIDI Setup > Test MIDI6.

Advanced Use Cases

Fine-Tuning on macOS

1. Convert datasets to Core ML format using coremltools.
2. Use mlcompute framework for on-device training.

API Integration

pythonfrom kimi_audio import KimiModel
model = KimiModel(device="mps")
model.generate("Explain quantum computing in 200 words.")

Alternatives and Complements

• Camel AI: For multi-agent text generation alongside Kimi-Audio1.
• Logic Pro Integration: Route Kimi’s output to DAWs via virtual audio cables.

Future-Proofing for macOS Sequoia

• Check Compatibility: Verify dependencies with Sweetwater’s macOS Sequoia Guide.
• Test Beta Builds: Use Apple’s TestFlight for early M3 optimizations.

Security Considerations

• Sandboxing: Run Kimi in a Docker container via OrbStack (Apple Silicon-native).
• Microphone Permissions: Audit via System Settings > Privacy.

Conclusion

Kimi-Audio provides a powerful and flexible solution for audio-to-text and audio-to-audio/text conversation tasks. By following the steps outlined above, you can easily set up and run Kimi-Audio on your Mac.

The model's capabilities make it suitable for a wide range of applications, from transcription services to interactive voice assistants. For more detailed information and additional examples, refer to the Kimi-Audio GitHub repository.

References

1. Run DeepSeek Janus-Pro 7B on Mac: A Comprehensive Guide Using ComfyUI
2. Run DeepSeek Janus-Pro 7B on Mac: Step-by-Step Guide
3. Run DeepSeek Janus-Pro 7B on Windows: A Complete Installation Guide