🧃 Speaker Diarization CoreML Speaker diarization based on pyannote models optimized for Apple Neural Engine. Models are trained on acoustic signatures so it supports any lanugage. Usage See the SDK for more details https://github.com/FluidInference/FluidAudio Please note that the SDK itself is Apache 2.0, but the parent model from Pyannote is cc by 4.0 Technical Specifications Input : 16kHz mono audio Output : Speaker segments with timestamps and IDs Framework : CoreML (converted from PyTorch) Optimization : Apple Neural Engine (ANE) optimized operations Precision : FP32 on CPU/GPU, FP16 on ANE Performance See the origianl model for detailed DER benchmark, for the purpose of our conversion, we tried to match the original model as much as possible: The models on CoreML exhibit a ~10x Speedup on CPU and ~20x speed up on GPU. Due to different precisions, there are minor differences in the values generated but the differences are mostly negilible, though it does account for some errors that needs to be adjusted during clustering: We see this when running the end to end pipeline with the Pytorch model versus the Core ML model (patched the Pyannote pipeline to run the Core ML model inst…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy