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. 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. Audio-language model: audio encoder + projector + LLM decoder Every 2026 LALM has the same skeleton: Audio encoder. Whisper encoder · BEATs · CLAP · WavLM · or a custom encoder per model. Projector. Linear or MLP bridging audio-encoder features into the LLM's token embedding space. LLM. Llama / Qwen / Gemma-based decoder. Takes interleaved text + audio tokens; generates text. Training: Stage 1. Freeze encoder + LLM; train projector only on ASR / captioning data. Stage 2. Full / LoRA fine-tune…
Audio-Language Models — Qwen2.5-Omni, Audio Flamingo, GPT-4o Audio: 2026 audio-language models reason over speech + environmental sound + music.…
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