Introducing AnySpeechRecognizer and AnyDecisionModel: Open Models for Speech and Decisions on Apple Platforms

Language models can do a lot. They write, summarize, translate, and call tools. But they're only a small part of what machine learning can do.

Say a customer calls your support line. First, you want to know what they said (speech recognition). Then, you want to decide where to send them (decision). You could hand both to a language model, but there are models built for each that are better, faster, and less expensive.

In our AnyLanguageModel 1.0 recap , we looked at a year of local and hosted models in Swift. With AnyLanguageModel and swift-huggingface now on their 1.x releases, we're extending the same approach to speech and decisions: familiar Swift APIs with a choice of model.

What's new is the name, a lot of hype, and a crop of models trained to make decisions. We think that's where things are going, so we wanted a Swift API that's ready for them.

Apps often ask language models to make decisions. Is this message urgent? Which team should handle this ticket? How frustrated is this customer? The usual approach is to prompt a language model and parse the text that it generates. That gives you one answer, but doesn't show the probabilities it assigned to the alternatives.

The diarizer tracks up to four speakers, even when they talk over each other. A SpeakerTimeline collects its results and attributes each timed word to a speaker, so you can print a transcript by speaker turn. Like image support in AnyLanguageModel, this goes beyond what Apple's API offers today.

Both packages can download open models from the Hugging Face Hub and run them on device.

Computers have been transcribing speech for decades, and in the past few years, they've gotten very good at it. Apple platforms already have an API for it. macOS 26 and iOS 26 introduced SpeechAnalyzer , Apple's new speech-to-text API. You add modules such as SpeechTranscriber and SpeechDetector to an analyzer, give it a file or a stream of audio buffers, and read results from async sequences as they arrive. AssetInventory installs the models, so your app decides when downloads happen.

It's a well-designed API, but the only engine behind it is Apple's. AnySpeechRecognizer keeps the API and lets you choose the engine: