senko

This repository offers a fast and accurate speaker diarization pipeline.

For the Non-Technical Reader

Imagine you have a recording of a meeting with multiple speakers. Speaker diarization is like having a highly efficient assistant who automatically identifies and labels each person speaking throughout the recording. Instead of manually noting who said what, this tool does it for you, saving time and making it easier to create transcripts, analyze conversations, or simply keep track of who participated. Think of it as 'voice fingerprinting' for your audio files, making them searchable and organized by speaker.

For the Technical Reader

Senko is a speaker diarization pipeline optimized for speed and accuracy, based on the 3D-Speaker project. Key features include:

• VAD: Utilizes Pyannote segmentation-3.0 or Silero VAD instead of FSMN-VAD.
• Feature Extraction: Employs GPU-accelerated Fbank feature extraction using kaldifeat on NVIDIA or CPU-based extraction using all cores.
• Embedding: Batched inference of the CAM++ embedding model.
• Clustering: GPU-accelerated clustering via RAPIDS on NVIDIA GPUs (compute capability 7.0+).
• CoreML: Pyannote segmentation-3.0 and CAM++ run through CoreML on macOS.

Benchmarks include 13.5% DER on VoxConverse, 13.3% on AISHELL-4, and 26.5% on AMI-IHM. The pipeline achieves rapid processing times, with 1 hour of audio processed in approximately 5 seconds on an RTX 4090 + Ryzen 9 7950X and 7.7 seconds on an Apple M3.

Why It Matters

Senko's speed and accuracy make it a valuable tool for various applications. Its open-source nature promotes accessibility and community-driven development. The focus on optimization, including GPU acceleration and CoreML support, highlights a commitment to efficient resource utilization. This can significantly reduce the cost and time associated with speaker diarization tasks, making it more feasible for a wider range of users and organizations. The use of open-source components also provides greater transparency and control over the diarization process.

The "Voice AI Space Lab" Idea

Imagine building a real-time meeting summarizer that not only transcribes what's being said but also identifies each speaker and generates speaker-specific summaries. This could be integrated into existing video conferencing platforms to provide instant meeting recaps, highlighting key points and action items for each participant. Think of it as a super-powered meeting assistant!

The Collaborative CTA

Given Senko's reliance on both PyTorch and CoreML for different platforms, what are the community's experiences with maintaining consistent performance and accuracy across these different backends, and what strategies have proven most effective for cross-platform deployment? GitHub Repository