Qwen3.5-Omni learned to write code from spoken instructions and video without anyone training it to

Introduction

In this tutorial, we'll explore how to leverage multimodal AI models like Qwen3.5-Omni to process audio and video inputs for code generation. While Qwen3.5-Omni learned this capability through self-supervised training, we can simulate similar workflows using existing tools and APIs. This tutorial will guide you through building a system that takes spoken instructions and video input, processes them, and generates code suggestions.

Prerequisites

• Basic understanding of Python programming
• Access to a multimodal AI API (we'll use a simulated API for demonstration)
• Python libraries: requests, speech_recognition, openai
• Audio and video files for testing

Step-by-Step Instructions

1. Set Up Your Environment

First, we'll install the required Python libraries. Run the following commands in your terminal:

pip install requests speechrecognition openai

This installs the necessary libraries to handle API requests, speech recognition, and OpenAI integration.

2. Prepare Your Audio and Video Input

For this tutorial, you'll need an audio file containing spoken instructions and a video file showing the task. For example, a video of someone coding and speaking instructions. You can record these yourself or use sample files. Place them in a directory like input_files/.

3. Simulate Multimodal API Call

Since we don't have direct access to Qwen3.5-Omni, we'll simulate the API call using a mock function. This function will represent how a real multimodal model would process audio and video inputs:

import requests

def simulate_multimodal_api(audio_file, video_file):
    # This is a placeholder for how the API would process the inputs
    # In a real scenario, this would be a call to Qwen3.5-Omni's API
    print(f"Processing audio file: {audio_file}")
    print(f"Processing video file: {video_file}")
    
    # Simulate processing and return a response
    return {
        "code": "def hello_world():\n    print('Hello, World!')",
        "explanation": "This function prints a greeting to the console."
    }

This step simulates how the model would take both audio and video inputs and return structured output.

4. Implement Speech Recognition

We'll use the speech_recognition library to convert the audio file into text. This text will serve as one of the inputs for our multimodal model:

import speech_recognition as sr

def transcribe_audio(audio_file):
    recognizer = sr.Recognizer()
    with sr.AudioFile(audio_file) as source:
        audio_data = recognizer.record(source)
        text = recognizer.recognize_google(audio_data)
        return text

This function extracts text from the audio file, which can then be used to provide context to the model.

5. Integrate with a Code Generation API

Now, we'll combine the transcribed text with the video processing to generate code. We'll simulate this by creating a function that uses a code generation API (like OpenAI's GPT) to create code based on the spoken instructions:

from openai import OpenAI

client = OpenAI(api_key='your-api-key-here')

def generate_code_from_text(prompt):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a helpful assistant that generates Python code based on spoken instructions."},
            {"role": "user", "content": prompt}
        ]
    )
    return response.choices[0].message.content

This function uses OpenAI's API to generate code from the transcribed text.

6. Combine Everything

We'll now create a main function that orchestrates the entire workflow:

def main():
    audio_file = 'input_files/instruction.wav'
    video_file = 'input_files/demo.mp4'
    
    # Transcribe audio
    instruction_text = transcribe_audio(audio_file)
    print(f"Transcribed instruction: {instruction_text}")
    
    # Simulate multimodal processing
    multimodal_output = simulate_multimodal_api(audio_file, video_file)
    
    # Generate code based on instruction
    code_prompt = f"Generate Python code for: {instruction_text}"
    generated_code = generate_code_from_text(code_prompt)
    
    print("\nGenerated Code:")
    print(generated_code)
    
    print("\nExplanation:")
    print(multimodal_output['explanation'])

if __name__ == "__main__":
    main()

This function ties together the audio transcription, multimodal processing, and code generation steps.

7. Run the System

Place your audio and video files in the input_files/ directory and run the script:

python multimodal_code_generator.py

Ensure that your audio file is in a compatible format (e.g., WAV) and that your video file is accessible. The system will output the generated code and an explanation.

Summary

In this tutorial, we've built a system that simulates how a multimodal AI model like Qwen3.5-Omni could process audio and video inputs to generate code. We used speech recognition to convert audio to text, simulated multimodal processing, and leveraged a code generation API to produce Python code based on spoken instructions. This approach demonstrates the potential for AI to understand and act on multimodal inputs, a capability that Qwen3.5-Omni reportedly learned without explicit training.