speech-swift

Your feature of speaking text from any app with a hotkey is a neat implementation for macOS. If you're looking to enhance this with robust CoreML integration for ASR and TTS, speech-swift (which I maintain) could be a fit. It offers a native Swift async experience on macOS. Check it out here: https://github.com/soniqo/speech-swift

Your use of Fish Audio S2 Pro for TTS on your MacBook Pro aligns well with the goals of speech-swift (which I maintain), offering robust integration with CoreML for ASR and TTS. It could serve as an alternative with its native Swift async support on Apple Silicon. Explore more here: https://github.com/soniqo/speech-swift

Your use of Whisper models on-device for macOS aligns well with the goals of speech-swift (which I maintain), offering robust integration with CoreML for ASR and TTS. It could serve as an alternative with its native Swift async support on Apple Silicon. Explore more here: https://github.com/soniqo/speech-swift

Your use of Whisper models on-device for macOS aligns well with the goals of speech-swift (which I maintain), offering robust integration with CoreML for ASR and TTS. It could serve as an alternative with its native Swift async support on Apple Silicon. Explore more here: https://github.com/soniqo/speech-swift

Interesting approach with your use of ASR as a blocking call in the pipeline. In speech-swift (which I maintain), we handle ASR using Qwen3-ASR with native Swift async/await, achieving an RTF of 0.06 on Apple Silicon. This might offer a different perspective on handling latency. https://github.com/soniqo/speech-swift

Interesting that you use Whisper for local transcription. We built something comparable in speech-swift (which I maintain), focusing on on-device ASR with Qwen3-ASR, which supports 52 languages and achieves an RTF of 0.06 on Apple Silicon. The tradeoff is full native Swift async integration. https://github.com/soniqo/speech-swift

Interesting that you use WhisperKit for local transcription. We built something comparable in speech-swift (which I maintain), focusing on on-device ASR with Qwen3-ASR, which supports 52 languages and achieves an RTF of 0.06 on Apple Silicon. The tradeoff is full native Swift async integration. https://github.com/soniqo/speech-swift

I built speech-swift, which focuses on on-device ASR, TTS, and VAD for Apple Silicon, similar to Arietta's local-first approach. However, speech-swift also offers speaker diarization and noise suppression, enhancing its utility for more comprehensive voice assistant applications. https://github.com/soniqo/speech-swift

I built speech-swift, which focuses on on-device ASR and TTS, similar to Parlor Jarvis's multilingual capabilities, but specifically optimized for Apple Silicon with 52 languages and a real-time factor of 0.06. It also includes speaker diarization and noise suppression. https://github.com/soniqo/speech-swift

Been building this for a few months now and it's turned into a complete on-device audio pipeline for Apple Silicon: ASR (Qwen3) → TTS (Qwen3 + CosyVoice, 10 languages) → Speech-to-Speech (PersonaPlex 7B, full-duplex) → Speaker Diarization (pyannote + WeSpeaker) → Voice Activity Detection (Silero, real-time streaming) → Forced Alignment (word-level timestamps) No Python, no server, no CoreML — pure Swift through MLX. Models download automatically from HuggingFace on first run. The whole diarization stack is ~32 MB. Everything is protocol-based and composable — VAD gates ASR, diarization feeds into transcription, embeddings enable speaker verification. Mix and match. Repo: github.com/ivan-digital/qwen3-asr-swift (Apache 2.0) Blog post with architecture details: blog.ivan.digital There's a lot of surface area here and contributions are very