Running Kimi-Audio on Windows: An Installation Guide

Kimi-Audio is an open-source audio foundation model capable of speech recognition, audio generation, and conversational AI tasks. While primarily designed for Linux environments, this guide provides detailed instructions for Windows users to leverage its capabilities through multiple methods.

I. System Requirements

1. Hardware Specifications

• GPU: NVIDIA GPU with ≥24GB VRAM (RTX 4090/3090 recommended)
• RAM: 32GB DDR4 minimum
• Storage: 50GB free SSD space
• OS: Windows 10/11 64-bit

2. Software Dependencies

• Python 3.10+
• CUDA 12.1+
• PyTorch 2.1+ with CUDA support
• FFmpeg
• Git for Windows

II. Installation Methods

Method 1: Native Windows Installation

Step 1: Set Up Development Environment

powershell# Install Chocolatey package manager
Set-ExecutionPolicy Bypass -Scope Process -Force
[System.Net.ServicePointManager]::SecurityProtocol = [System.Net.SecurityProtocolType]::Tls12
iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))

# Install essential packages
choco install git python310 cuda ffmpeg -y

Step 2: Configure Python Environment

powershellpython -m venv kimi-env
.\kimi-env\Scripts\activate
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Step 3: Install Kimi-Audio

powershellgit clone https://github.com/MoonshotAI/Kimi-Audio
cd Kimi-Audio
pip install -r requirements.txt

Method 2: Docker Container Approach

Step 1: Install Docker Desktop

• Enable WSL2 backend
• Allocate 8GB+ RAM to Docker

Step 2: Build Kimi-Audio Image

powershelldocker build -t kimi-audio:v0.1 .

Step 3: Run Container with GPU Passthrough

powershelldocker run --gpus all -it kimi-audio:v0.

III. Model Configuration

1. Download Pre-trained Models

powershellhuggingface-cli login
huggingface-cli download moonshotai/Kimi-Audio-7B-Instruct --local-dir ./models

2. Environment Variables

Create .env file:

textMODEL_PATH=./models/Kimi-Audio-7B-Instruct
DEVICE=cuda
PRECISION=bf16

IV. Practical Implementation Scenarios

1. Speech Recognition (ASR)

pythonfrom kimia_infer.api.kimia import KimiAudio

model = KimiAudio(model_path="models/Kimi-Audio-7B-Instruct")
messages = [
{"role": "user", "message_type": "text", "content": "Transcribe this audio:"},
{"role": "user", "message_type": "audio", "content": "input.wav"}
]
_, transcript = model.generate(messages)
print(transcript)

2. Audio Generation

pythonmessages = [
{"role": "user", "message_type": "text", "content": "Generate happy birthday music"}
]
audio, _ = model.generate(messages, output_type="both")

3. Conversational AI

pythonmessages = [
{"role": "user", "message_type": "audio", "content": "user_query.wav"},
{"role": "assistant", "message_type": "text", "content": "How can I help you?"}
]
response_audio, response_text = model.generate(messages)

Live Examples

Example 1: Audio-to-Text (ASR)

1. Prepare the Audio File:
   • Ensure you have an example audio file, e.g., asr_example.wav.

Run the ASR Example:PythonCopy

import soundfile as sf
from kimia_infer.api.kimia import KimiAudio
import torch

# Load the model
model_id = "moonshotai/Kimi-Audio-7B-Instruct"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = KimiAudio(model_path=model_id, load_detokenizer=True)
model.to(device)

# Define sampling parameters
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,
}

# Example 1: Audio-to-Text (ASR)
asr_audio_path = "asr_example.wav"
messages_asr = [
    {"role": "user", "message_type": "text", "content": "Please transcribe the following audio:"},
    {"role": "user", "message_type": "audio", "content": asr_audio_path}
]

# Generate only text output
_, text_output = model.generate(messages_asr, **sampling_params, output_type="text")
print(">>> ASR Output Text: ", text_output)

Example 2: Audio-to-Audio/Text Conversation

1. Prepare the Audio File:
   • Ensure you have an example audio file, e.g., qa_example.wav.

Run the Conversation Example:PythonCopy

# Example 2: Audio-to-Audio/Text Conversation
qa_audio_path = "qa_example.wav"
messages_conversation = [
    {"role": "user", "message_type": "audio", "content": qa_audio_path}
]

# Generate both audio and text output
wav_output, text_output = model.generate(messages_conversation, **sampling_params, output_type="both")

# Save the generated audio
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)

V. Performance Optimization

1. Quantization Techniques

pythonfrom torch.quantization import quantize_dynamic
model = quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)

2. Memory Management

python# Enable gradient checkpointing
model.gradient_checkpointing_enable()

# Use mixed precision
from torch.cuda.amp import autocast
with autocast(dtype=torch.bfloat16):
outputs = model.generate(...)

VI. Troubleshooting Guide

VII. Advanced Features

1. Custom Training

pythonfrom kimia_train.core import AudioDataset
dataset = AudioDataset(
audio_dir="custom_audio",
sampling_rate=24000,
max_duration=30
)

trainer = KimiTrainer(
model=model,
train_dataset=dataset,
batch_size=2,
learning_rate=5e-5
)
trainer.train()

2. API Server Deployment

pythonfrom fastapi import FastAPI
app = FastAPI()
@app.post("/transcribe")
async def transcribe(file: UploadFile):
audio = await file.read()
_, text = model.generate([{"role": "user", "content": audio}])
return {"text": text}

VIII. Comparative Analysis

IX. Use Case Implementations

1. Podcast Production Workflow

1. Raw audio ingestion
2. Automatic chapterization
3. Noise reduction
4. Multilingual dubbing
5. Social media clip generation

2. Accessibility Solutions

• Real-time audio descriptions
• Sign language video generation
• Cognitive load reduction tools
• Multisensory feedback systems

Conclusion

Kimi-Audio is a powerful open-source model for audio tasks, including automatic speech recognition and audio-to-text conversation. By following the steps outlined above, you can successfully run Kimi-Audio on Windows using Docker.

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
4. Running Kimi-Audio on Mac: A Complete Guide