Phase 6: Automatic Speech Recognition · ~45 minutes · Python
Audio-Language Models — Qwen2.5-Omni, Audio Flamingo, GPT-4o Audio
2026 audio-language models reason over speech + environmental sound + music. Qwen2.5-Omni-7B matches GPT-4o Audio on MMAU-Pro. Audio Flamingo Next beats Gemini 2.5 Pro on LongAudioBench. The gap between open and closed is essentially closed — except on multi-audio tasks, where everyone is near random.
Hiring signal: Understanding of audio-language models — qwen2.5-omni, audio flamingo, gpt-4o audio internals
What you will learn
- Implement audio-language models — qwen2.5-omni, audio flamingo, gpt-4o audio from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
Type: Learn Languages: Python Prerequisites: Phase 6 · 04 (ASR), Phase 12 · 03 (Vision-Language Models), Phase 7 · 10 (Audio Transformers) Time: ~45 minutes
The Problem
You have 5 seconds of audio: dog barks, someone yells "stop!", then silence. Useful questions span multiple axes:
- Transcription. "What was said?" — ASR territory.
- Semantic reasoning. "Is the person in danger?" — requires joint understanding of the bark + yell + silence.
- Music reasoning. "What instruments play the melody?"
- Long-audio retrieval. "Where in this 90-minute lecture did the instructor explain gradient descent?"
A single model that answers all of these with one prompt is an audio-language model (LALM / ALM). Separate from pure ASR: LALMs produce free-form natural-language answers, not just transcripts.
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