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.
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: Speech Recognition (ASR) — CTC, RNN-T, Attention, From CLIP to BLIP-2 — Q-Former as Modality Bridge (Multimodal AI Systems), Audio Transformers — Whisper Architecture (Transformers & LLMs from Scratch)
- 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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You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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