Model · Voice activity detection
Pyannote
Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. This model ingests 10 seconds of mono audio sampled at 16kHz and outputs speaker diarization as a (numframes, numclasses) matrix where the 7 classes are non-speech, speaker #1, speaker #2, speaker #3, speakers #1 and #2, speakers #1 and #3, and speakers #2 and #3. The various concepts behind this model are described in details in this paper. It has been trained by Séverin Baroudi with pyannote.audio 3.0.0 using the combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse. This companion repository by Alexis Plaquet also…
Access requested at publisher
mit
pyannote-audio
Model · Voice activity detection
Pyannote
Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. In order to reproduce the results of the paper "End-to-end speaker segmentation for overlap-aware resegmentation ", use pyannote/segmentation@Interspeech2021 with the following hyper-parameters: Expected outputs (and VBx baseline) are also provided in the /reproducibleresearch sub-directories.
Access requested at publisher
mit
pyannote-audio
Model · Voice activity detection
NVIDIA
For the detailed information, see the Overview subcard.
Access requested at publisher
other
nemo
Core AI (.aimodel) conversion of NVIDIA's streaming speaker-diarization model (up to eight speakers, 10 ms output frames) for iOS 27+ / macOS 27+. Converted from checkpoint revision (Nemotron-3-Diarization.nemo, SHA-256 867c53f552998f772e5b5e5c082962ae85ee7ca5669c2bc17d7f615133d4e96d) with coreai-torch 0.4.1 (standard graphs) and 0.4.2 (Neural Engine encoder), coreai-core 1.0.0b2. No weights were retrained, quantized or pruned. The model is split into two graphs that a host streaming loop calls once per encoder/head (packed speaker cache + FIFO + chunk embeddings → speaker probabilities). The Arrival-Order Speaker Cache, FIFO and chunking logic of Streaming Sortformer run on the host; see…
Open weights
openmdw-1.1
GGUF conversions of NVIDIA's Nemotron-3 Diarization (Streaming Sortformer v3) for transcribe.cpp, the runtime behind speaker labels in Glimpse. The model answers "who spoke when" for up to 8 speakers at 10 ms resolution. It produces no text. AMI meeting corpus, IHM test set: 16 meetings, 9.06 hours. Scored against NVIDIA's forced-alignment RTTMs with a 0 s collar, overlapped speech included, and a plain 0.5 threshold at 10 ms with no post-processing. Every row went through the same pipeline. Compared with Sortformer v2.1, errors drop by 42% and speaker confusion by 76%, and the 1-second streaming mode beats v2.1's offline mode. Throughput on an Apple M2 Pro (offline mode, times faster than…
Open weights
other
Glade is a native Swift library for on-device speech inference on Apple silicon. This repository supplies converted model assets and configuration for Glade's model-specific host runtime. Glade's runtime source is currently private. The official Silero 16 kHz VAD converted for Glade using Apple Core AI APIs on macOS 27 and iOS 27. The bundle batches eight consecutive 32 ms frames (256 ms), while preserving the original 128-dimensional LSTM state and 64-sample audio context across calls. Use CPU-only specialization for this small recurrent model. Each input audio tensor has shape [8, 1, 1, 576], containing the preceding 64 samples and current 512 samples for each frame. h and c are [1, 128…
Open weights
mit
coreai
