Interfaze Ships diffusion-gemma-asr-small, an Open-Source Diffusion ASR Model Transcribing Six Languages — 2026-07-03

## Short Segments WebBrain introduces a local-first AI browser agent that automates tasks in Chrome and Firefox. This open-source tool, developed by Emre Sokullu, reads pages, extracts data, and automates multi-step tasks directly within your browser. Unlike most browser AI plugins, WebBrain can operate entirely on a local model, ensuring that no page data leaves your machine unless you choose to connect a cloud API for additional capabilities. It integrates seamlessly into your browser's side panel, maintaining your authenticated session without storing data externally or adding telemetry. WebBrain supports multiple languages, auto-detecting your browser's language on first launch. With its dual modes, 'Ask' for read-only and 'Act' for interactive actions, WebBrain offers a versatile tool for users seeking privacy and functionality in browser automation. This development highlights a shift towards more secure and user-controlled browser automation solutions. ## Feature Story Interfaze launches diffusion-gemma-asr-small, a groundbreaking open-source ASR model transcribing six languages using a diffusion decoder. This model, hailed as the first multilingual audio diffusion ASR, marks a significant shift from traditional autoregressive models by refining all tokens in parallel. With a mere 42 million parameters trained on a frozen 26 billion backbone, it represents just 0.16% of the model's weights, yet it delivers impressive performance. Unlike autoregressive models that generate text one token at a time, diffusion models like this one refine all tokens simultaneously, offering a new approach to speech-to-text conversion. The diffusion-gemma-asr-small model uses DiffusionGemma's parallel denoising decoder, which employs uniform, random-token diffusion instead of the absorbing scheme. This method allows transcription costs to scale with denoising steps rather than transcript length, providing a more efficient solution. In terms of performance, the model leads its diffusion peers on the LibriSpeech benchmark with a 6.6% word error rate, outperforming Whisfusion's 8.3%, though it still trails behind the autoregressive Whisper model. The adapter is available under the Apache-2.0 license, while DiffusionGemma and whisper-small are loaded separately under their respective licenses. Diffusion-gemma-asr-small is an audio-native ASR model that converts speech to text using a discrete diffusion decoder, part of Google's 26 billion parameter DiffusionGemma model. This model activates 4 billion parameters, utilizing 128 experts with top-8 routing, and generates text through discrete diffusion rather than autoregression. Google's DiffusionGemma, released as an open-source experimental model, applies diffusion to text generation at production scale, generating a 256-token block in parallel rather than sequentially. This approach allows for faster text generation, up to four times quicker than traditional methods, making it suitable for speed-critical, interactive local workflows. Interfaze's release of diffusion-gemma-asr-small underlines the growing interest in diffusion models as a viable alternative to autoregressive models, particularly for applications requiring high throughput and efficiency. As the first open-source multilingual diffusion ASR model, it sets a precedent for future developments in the field, offering a new tool for developers and researchers exploring innovative speech-to-text solutions. Looking ahead, the diffusion-gemma-asr-small model could pave the way for more efficient and versatile ASR systems, potentially transforming how we approach multilingual audio transcription.