Hugging Face releases AnySpeechRecognizer and AnyDecisionModel: swap in local speech models like Parakeet on Apple devices, with Parakeet Unified at a 1.43% word error rate
Two Swift packages: one lets Apple's SpeechAnalyzer API use open speech models, the other answers yes/no, multiple-choice and rating questions straight from model probabilities.
// Key points
- AnySpeechRecognizer implements Apple's SpeechAnalyzer API, defaulting to Apple Speech but able to swap in NVIDIA Parakeet or Alibaba Qwen3-ASR running locally on Core ML, plus Sortformer diarization for up to 4 speakers.
- On LibriSpeech (M4 Max), Parakeet Unified file transcription hits 1.43% WER on test-clean versus 2.12% for Apple's engine; Parakeet transcribes an hour of audio in about 20 to 40 seconds using about 100 MB of memory.
- Every engine tested had a word error rate above 23% on AMI meeting recordings.
- AnyDecisionModel reads answers from model probabilities instead of parsing generated text, across 6 backends including MLX, Core AI, llama.cpp and Ollama; it needs Swift 6.2 and macOS 26 / iOS 26 or later.
Builder's takeFor on-device transcription products like AI Interview, this is a cheap place to experiment: same API, swap the engine and compare. But take the meeting-audio result seriously, every engine above 23% WER: multi-speaker, far-field interviews are much harder than read speech, so evaluate on your own recordings. Reading answers from probabilities instead of parsing text is also worth borrowing; scoring tasks get much more stable.
// Background · from #Open models
Full timeline →- Oct 30 Mistral opens the weights of Mistral Large 4: an MoE of about 1T parameters, priced at $0.70 input and $2.10 output per million tokens on the API
- Oct 30 IBM ships Granite Switch 4.1.1: only the adapters and speech model were retrained, the base model is unchanged and no migration is needed
- Oct 29 NVIDIA opens Nemotron 3.5 Transcribe: one model for transcription in 40 languages, translation, speaker labels and audio Q&A, first among open-weight models on the Open ASR English leaderboard