Pyannote

Model · Speech recognition

speaker-diarization-3.1

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. This pipeline is the same as pyannote/speaker-diarization-3.0 except it removes the problematic use of onnxruntime. Both speaker segmentation and embedding now run in pure PyTorch. This should ease deployment and possibly speed up inference. It requires pyannote.audio version 3.1 or higher. It ingests mono audio sampled at 16kHz and outputs speaker diarization as an Annotation instance: - stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels. - audio files sampled at a different rate are resampled to 16kHz automatically upon loading. 1.…

Model

wespeaker-voxceleb-resnet34-LM

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. This model requires pyannote.audio version 3.1 or higher. This is a wrapper around WeSpeaker wespeaker-voxceleb-resnet34-LM pretrained speaker embedding model, for use in pyannote.audio. According to this page

Model · Voice activity detection

segmentation-3.0

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…

Model · Speech recognition

speaker-diarization-community-1

Pyannote

This pipeline ingests mono audio sampled at 16kHz and outputs speaker diarization. - stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels. - audio files sampled at a different rate are resampled to 16kHz automatically upon loading. The main improvements brought by Community-1 are: - improved speaker assignment and counting - simpler reconciliation with transcription timestamps with exclusive speaker diarization - easy offline use (i.e. without internet connection) - (optionally) hosted on pyannoteAI cloud 1. pip install pyannote.audio 3. Create access token at hf.co/settings/tokens. Out of the box, Community-1 is much better than…

Model · Voice activity detection

segmentation

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.

Model · Speech recognition

voice-activity-detection

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options.

Model

embedding

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. This model is based on the canonical x-vector TDNN-based architecture, but with filter banks replaced with trainable SincNet features. See XVectorSincNet architecture for implementation details. Using cosine distance directly, this model reaches 2.8% equal error rate (EER) on VoxCeleb 1 test set. This is without voice activity detection (VAD) nor probabilistic linear discriminant analysis (PLDA). Expect even better results when adding one of those.