Speech & Audio Model Codebases

Canonical open speech-to-text model codebase with widespread ecosystem support and many downstream implementations.

Speech recognition toolkit with pretrained models, streaming support, diarization, VAD, and production-oriented examples.

Scalable framework and model codebase for speech, language, and multimodal AI with recipes and deployment guidance.

Complete speech toolkit with ASR, TTS, diarization, source separation, and VAD across embedded and edge environments via ONNX Runtime.

Open speech and sound generation family focused on expressive, long-form text-to-speech with streaming and multi-speaker support.

Open-sourced tokenizer-free multilingual speech synthesis model with high-quality TTS and style transfer workflows.

Open Frontier Voice AI toolkit spanning speech understanding, generation, and multilingual TTS workflows, with active research and deployment tooling.

PyTorch speech toolkit with recipes for ASR, TTS, speaker recognition, and speech enhancement.

Lightweight text-to-speech engine optimized for CPU inference with low latency and streaming support. MIT licensed.

C/C++ speech-to-text inference library running 16+ model families on the ggml runtime with GPU acceleration.

Open-source on-device voice AI toolkit for low-latency speech-to-text, intent recognition, and text-to-speech.