Master Audio Understanding & Generation with Kimi-Audio: Your Guide to Open-Source Audio Foundation Models

Want to dive into advanced audio processing? Kimi-Audio is an open-source audio foundation model that’s changing the game. This article explores its features, architecture, and potential, and shows you how to get started.

What is Kimi-Audio and Why Should You Care?

Kimi-Audio excels in audio understanding, generation, and even conversational AI. It’s not just another audio tool; it's designed as a universal model. This means one framework can handle a wide array of tasks, simplifying your audio processing workflows.

Key Benefits of Kimi-Audio:

• Universal Capabilities: From speech recognition to audio question answering and even speech emotion recognition, Kimi-Audio does it all.
• Top-Tier Performance: Kimi-Audio achieves state-of-the-art results on many audio benchmarks.
• Extensive Training: Pre-trained on over 13 million hours of diverse audio and text data for robust performance.
• Efficient Design: The chunk-wise streaming detokenizer allows for low-latency audio generation.
• Open Source: The code, model checkpoints, and evaluation toolkit are available for everyone.

Breaking Down the Kimi-Audio Architecture

Kimi-Audio's architecture is a unique blend of components that work together:

Three Core Components:

1. Audio Tokenizer: This converts audio input into discrete semantic tokens and continuous acoustic features. This dual representation allows Kimi-Audio to capture both the content and nuances of the audio.
2. Audio LLM: A transformer-based model processes multimodal inputs, generating text and audio tokens. The pre-trained text LLM core ensures strong language understanding capabilities.
3. Audio Detokenizer: Converts predicted audio tokens back into waveforms, using a flow-matching model and vocoder for high-fidelity audio.

Quick Start: Generate Audio and Text

Let's see Kimi-Audio in action. This example demonstrates how to generate text from audio using speech recognition, and how to create both text and speech in a conversational turn.

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

# 1. Load Model
model_path = "moonshotai/Kimi-Audio-7B-Instruct"
model = KimiAudio(model_path=model_path, load_detokenizer=True)

# 2. 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,
}

# 3. Example 1: Audio-to-Text (ASR)
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)

# 4. Example 2: Audio-to-Audio/Text Conversation
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)
print ( f">>> Conversational Output Audio saved to: {output_audio_path} ")
print ( ">>> Conversational Output Text: ", text_output)
print ( "Kimi-Audio inference examples complete.")

This code snippet shows how to load the model, define sampling parameters, and perform audio-to-text transcription and audio-to-audio/text conversation.

Evaluating Kimi-Audio: Benchmarks and the Eval Toolkit

Kimi-Audio's performance is state-of-the-art across various benchmarks. Standardized evaluation is crucial, so the developers created the Kimi-Audio Evaluation Toolkit.

Evaluation Toolkit Features:

• Integrates Kimi-Audio and other current audio LLMs.
• Standardized metric calculation, including utilizing LLMs for intelligent judging.
• Unified platform for side-by-side comparisons with reproducible inference 'recipes'.
• Benchmark for evaluating speech conversation skills, especially control, empathy, and style.

You can find the Kimi-Audio-Evalkit and start running your own evaluations to reproduce the results.

Generation Testset for Conversation Evaluation

The team provides a Kimi-Audio-Generation-Testset, perfect for benchmarking the conversational capabilities of audio-based dialogue models. The test set assesses the models' abilities to generate relevant and appropriately styled audio responses.

Leveraging Kimi-Audio for Your Projects

Kimi-Audio opens opportunities for projects involving:

• Advanced speech recognition systems.
• Context-aware audio analysis and question answering.
• Realistic and engaging speech-based conversational AI.

By understanding Kimi-Audio's flexible architecture and leveraging its evaluation toolkit, developers and researchers can push the boundaries of audio processing.

Dive Deeper: Contributing and Staying Updated

Kimi-Audio is a community-driven project. Contribute to the project on GitHub by reporting issues, suggesting enhancements, or even submitting code. Stay updated with the latest developments to harness the full potential of this amazing open-source audio foundation model.