speaker-diarization

Identify speakers in multi-speaker media using pyannote-audio and output per-segment data.

Updated Jan 30, 2026

Search Tags:#transcripts #diarization #video-editing #audio-analysis #pyannote #speaker-identification #multispeaker

Ready to run

npx skills add https://github.com/akrindev/trimer-clip --skill speaker-diarization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: speaker-diarization
Source: https://github.com/akrindev/trimer-clip/tree/main/skills/speaker-diarization
Command: npx skills add https://github.com/akrindev/trimer-clip --skill speaker-diarization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyannote.audio, torch, torchaudio, speechbrain, and includes scripts (resource) components.

What problem does it solve?

Identify who speaks when in multi-speaker media using pyannote-audio to assign speech segments to speakers and produce diarization data.

Core Features & Use Cases

End-to-end speaker diarization for podcasts, interviews, panels, and conversations with overlapping speech.
Output formats include JSON, RTTM, and SRT with per-speaker segmentation and duration data.
Integration-ready for downstream tasks such as speaker-specific clip extraction, transcripts alignment, and video editing workflows.

Quick Start

Run speaker diarization on a video to generate per-speaker segments and a diarization result.

Frequently Asked Questions about speaker-diarization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I identify who speaks when in multi-speaker audio files?▼

To identify who speaks when in multi-speaker audio, this Skill uses pyannote-audio to assign speech segments to specific speakers. It processes podcasts, interviews, and panels, handling overlapping speech to produce accurate per-speaker diarization data.

What output formats are supported for speaker diarization data?▼

Speaker diarization data is exported in JSON, RTTM, and SRT formats. These outputs include per-speaker segmentation and duration data, making the results integration-ready for downstream tasks like transcript alignment and video editing workflows.

Do I need a HuggingFace token to run speaker diarization with pyannote?▼

Yes, you need a HuggingFace token to run speaker diarization using pyannote-audio. The environment also requires the torch, torchaudio, and speechbrain dependencies to process multi-speaker media and generate per-segment diarization outputs.

Can I use speaker diarization for extracting speaker-specific clips from video?▼

Yes, speaker diarization can be used for extracting speaker-specific clips from video. By generating per-speaker segmentation data, the output integrates directly into video editing workflows to isolate and extract individual speaker segments.

Does pyannote handle overlapping speech in podcast and interview diarization?▼

Yes, pyannote handles overlapping speech in podcast and interview diarization. It assigns overlapping speech segments to distinct speakers, ensuring accurate speaker identification and per-segment duration tracking in complex multi-speaker media environments.

What are the limitations of using pyannote for speaker identification?▼

Using pyannote for speaker identification requires pre-configured torch and speechbrain environments along with a valid HuggingFace token. It is designed for multi-speaker media diarization and does not perform speech-to-text transcription or voice biometric identification.