Interfaze Ships diffusion-gemma-asr-small, an Open-Source Diffusion ASR Model Transcribing Six Languages via DiffusionGemma’s Parallel Denoising Decoder
Interfaze, a young YC’s startup, has open-sourced a new speech recognition model. It is called diffusion-gemma-asr-small. The model transcribes audio through a diffusion decoder, not an autoregressive one. It is described as the first multilingual audio diffusion ASR model. One adapter handles six languages. The research team trained only about 42M parameters on top of a frozen 26B backbone. That is roughly 0.16% of the model’s weights. Here two terms matter up front. Autoregress
An open-source speech-to-text model called diffusion-gemma-asr-small converts audio to text using a diffusion decoder rather than the more common autoregressive approach, meaning it refines all output tokens in parallel instead of generating them one at a time. The model handles six languages through a single adapter of about 42 million parameters attached to a much larger frozen backbone, making it efficient for multilingual transcription without loading separate models per language. Unlike autoregressive models that generate output length-dependent costs, this diffusion approach scales cost by denoising steps instead, making it well-suited for batch transcription where longer and shorter clips require similar processing time. While it performs competitively against other diffusion-based speech recognition systems, it trails the autoregressive Whisper model on benchmarks, a gap the research team attributes to training data rather than architectural differences.
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