On-device macOS dictation that transcribes speech locally and offers an optional local AI enhancement (Fluid Intelligence) for smart formatting and post-processing. Key features: low-latency model choices, live transcription overlay, per-app prompts and privacy-by-default; best on Apple Silicon.
Runs text-to-speech with instant voice cloning fully on-device, from phones to GPUs. Built on small LLM backbones (120M-360M params) plus a 50Hz neural codec; clones a voice from ~3 seconds of audio across English, Spanish, German, and French.
Delivers multilingual, on-device text-to-speech via ONNX Runtime with prebuilt ONNX assets and cross-platform SDKs (Python, Node, mobile); targets low-latency, privacy-preserving TTS with ready demos and 31-language support in v3.
Real‑time full‑duplex speech‑to‑speech system that controls conversational role via text prompts and voice timbre via audio-conditioned embeddings. Built on Moshi; optimized for low-latency, persona-consistent spoken interactions.
Generates low-latency, streaming text-to-speech entirely on CPUs (no GPU or cloud API required), using an ~100M-parameter model with voice cloning and multilingual support. Optimized for low resource use (2 CPU cores, ~200ms to first audio chunk) — suited for local, privacy-sensitive, or embedded TTS.
Orchestrates low-latency, multi-stage pipelines for omni and multimodal models by running each stage with its own scheduler and using zero-copy shared memory for tensor transfer. Emphasizes per-stage bottleneck tuning and OpenAI-compatible streaming endpoints, suitable for TTS and multimodal serving.
Local-first voice cloning studio that runs on your machine to clone voices, generate speech in 23 languages, apply audio effects, and compose multi-voice projects. Includes five switchable TTS engines, a REST API, and native GPU/MLX support for privacy-sensitive offline workflows.
Provides a Spotify-like local UI for running ACE-Step 1.5 to generate full songs (including vocals), batch variations, and manage a local music library, with reference-audio styling and built-in editing/stem tools for users running the model locally.
Generates high‑fidelity, expressive speech and environmental sounds from text. The MOSS‑TTS Family provides specialized models for long‑form TTS, multi‑speaker dialogue, voice design and realtime streaming, plus torch‑free inference paths (llama.cpp / ONNX) and Hugging Face releases.
Converts text to natural-sounding speech across 600+ languages in a zero-shot way, with short-reference voice cloning and fine-grained voice-design controls; uses a diffusion language-model-style architecture to balance quality and very low inference latency.
Desktop app for local voice cloning, real-time dictation, and end-to-end video dubbing using zero-shot TTS across 600+ languages; features multi-engine TTS/ASR, speaker diarization, vocal isolation, batch pipelines, and invisible audio watermarking — all run fully offline.