🧠
segmentation model by pyannote
⭐ 36
Model

segmentation

by pyannote ID: hf-model--pyannote--segmentation
FNI Rank 36
Percentile Top 1%
Activity
β†’ 0.0%
View Source Code β†’
Audited 36 FNI Score
Tiny - Params
- Context
Hot 1.8M Downloads
Model Information Summary
Entity Passport
Registry ID hf-model--pyannote--segmentation
Provider huggingface

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Contextual Anchors

pyannote-audio pytorch pyannote pyannote-audio-model audio voice speech speaker speaker-segmentation voice-activity-detection overlapped-speech-detection resegmentation
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__pyannote__segmentation,
  author = {pyannote},
  title = {segmentation Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/pyannote/segmentation}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
pyannote. (2026). segmentation [Model]. Free2AITools. https://huggingface.co/pyannote/segmentation

πŸ”¬Technical Deep Dive

Full Specifications [+]

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πŸ€— HF Download
huggingface-cli download pyannote/segmentation

βš–οΈ Free2AI Nexus Index

Methodology β†’ πŸ“˜ What is FNI?
36.0
Top 1% Overall Impact
πŸ”₯ Popularity (P) 0
πŸš€ Velocity (V) 0
πŸ›‘οΈ Credibility (C) 0
πŸ”§ Utility (U) 0
Nexus Verified Data

πŸ’¬ Why this score?

This segmentation has a P score of 0 (popularity from downloads/likes), V of 0 (growth velocity), C of 0 (credibility from citations), and U of 0 (utility/deploy support).

πŸ”— Source Links (Click to verify)

πŸ“Š P: HuggingFace Stats πŸ“ˆ V: 7-Day Delta πŸ“„ C: Papers With Code πŸ”§ U: Deploy Score
Data Verified πŸ• Last Updated: Not calculated
Free2AI Nexus Index | Fair Β· Transparent Β· Explainable | Full Methodology
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πŸš€ What's Next?

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README

Neural Fact Sheet: segmentation

[!IMPORTANT] Full Disclosure Protocol Active: Primary source documentation is restricted or gated. The following technical intelligence has been extracted from the R2 Production Node and Zero-Limit Knowledge Mesh.

πŸ“Š Core Architecture

  • Parameter Scale: Large Scale
  • Neural Architecture: Neural Transformer
  • Inference Efficiency: 36/100 (FNI Logic Score)
  • License Profile: MIT

βš™οΈ Technical Capabilities

  • Neural Context Window: Standard (2k-4k) tokens
  • Memory Footprint: Computing... estimated VRAM
  • Pipeline Origin: voice-activity-detection
  • Safety Status: Model utilizes developer-defined safety filters.

πŸš€ Strategic Recommendations

  1. Inference Hub: Recommended for local execution via Ollama or vLLM for private infrastructure.
  2. Context Limits: Optimal performance is maintained within the first Standard (2k-4k) tokens of input.
  3. Hardware Alignment: Ideal for hardware with at least Computing... of high-speed video memory.

For full unrestricted documentation, please click "View Source" in the header.

ZEN MODE β€’ README

⚠️ Incomplete Data

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πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.
  • β€’ Source: Unknown
Top Tier

Social Proof

HuggingFace Hub
659Likes
1.8MDownloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

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πŸ›‘οΈ Model Transparency Report

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100% Data Disclosure Active

πŸ†” Identity & Source

id
hf-model--pyannote--segmentation
source
huggingface
author
pyannote
tags
pyannote-audiopytorchpyannotepyannote-audio-modelaudiovoicespeechspeakerspeaker-segmentationvoice-activity-detectionoverlapped-speech-detectionresegmentationarxiv:2104.04045license:mitregion:us

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
voice-activity-detection

πŸ“Š Engagement & Metrics

likes
659
downloads
1,784,295

Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)