I built this because I needed speaker diarization for two things: a meeting summarization script (record → diarize → transcribe → feed to Claude for summaries), and a robotics project where I need real-time speaker identification.
I started with pyannote, which is the standard tool for this. It worked, but processing a single call took forever on CPU, and the fans on my MacBook sounded like a jet engine. So I decided to build something faster.
The pipeline: Silero VAD → WeSpeaker ResNet34 embeddings (ONNX Runtime) → GMM+BIC speaker count estimation → spectral clustering. All classical ML after the embedding step — no neural segmentation model like pyannote uses.
Results on VoxConverse (216 files, 1–20 speakers):
DER: ~10.8% (pyannote free models: ~11.2%)
CPU speed: RTF 0.12 vs 0.86 (pyannote community-1) — about 7x faster
10-min recording: ~1.2 min vs ~8.6 min
Speaker count: 87–97% within ±1 for 1–5 speakers
What it doesn't do well: 8+ speakers (count estimation breaks down), overlapping speech (single speaker per frame), and it's only been benchmarked on one dataset so far.
Usage:
pip install diarize
from diarize import diarize
result = diarize("meeting.wav")
No GPU, no API keys, no HuggingFace account. Apache 2.0.
Happy to answer questions about the architecture, benchmarks, or tradeoffs.
I started with pyannote, which is the standard tool for this. It worked, but processing a single call took forever on CPU, and the fans on my MacBook sounded like a jet engine. So I decided to build something faster.
The pipeline: Silero VAD → WeSpeaker ResNet34 embeddings (ONNX Runtime) → GMM+BIC speaker count estimation → spectral clustering. All classical ML after the embedding step — no neural segmentation model like pyannote uses.
Results on VoxConverse (216 files, 1–20 speakers):
DER: ~10.8% (pyannote free models: ~11.2%) CPU speed: RTF 0.12 vs 0.86 (pyannote community-1) — about 7x faster 10-min recording: ~1.2 min vs ~8.6 min Speaker count: 87–97% within ±1 for 1–5 speakers
What it doesn't do well: 8+ speakers (count estimation breaks down), overlapping speech (single speaker per frame), and it's only been benchmarked on one dataset so far.
Usage: pip install diarize
from diarize import diarize result = diarize("meeting.wav")
No GPU, no API keys, no HuggingFace account. Apache 2.0. Happy to answer questions about the architecture, benchmarks, or tradeoffs.