juanmc2005/diart

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

Voice Activity Detector (VAD) : low-latency, high-performance and lightweight

Research and Production Oriented Speaker Verification, Recognition and Diarization Toolkit

Open Source framework for voice agents, multimodal apps, and realtime AI. Maintained by Daily and the community.

An open-source audio wake word (or phrase) detection framework with a focus on performance and simplicity.

A robust, efficient, low-latency speech-to-text library with advanced voice activity detection, wake word activation and instant transcription.

Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, RK NPU, Axera NPU, Ascend NPU, x86_64 servers, websocket server/client, support 12 programming languages

Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.

End-to-End Speech Processing Toolkit