Awesome-Diarization

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awesome-diarization is an Open Source research tool that curates resources for speaker diarization. It includes papers, libraries, datasets, and software frameworks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is awesome-diarization?

awesome-diarization is a comprehensive, curated list of resources dedicated to speaker diarization. This open-source repository organizes a wide array of materials, including academic papers covering various topics like LLM-enhanced diarization, supervised and online diarization, and joint diarization with ASR. It also features a collection of software frameworks and libraries, such as FunASR, SpeechBrain, and pyannote-audio, available in multiple programming languages like Python, Java, and C++. Additionally, it provides information on evaluation tools, clustering algorithms, speaker embedding methods, and relevant datasets, making it an invaluable resource for researchers and developers in speech technology.

Best used for

Ideal for professors and content creators who need to access a comprehensive collection of speaker diarization research, discover relevant software frameworks, and find datasets for their projects. Especially valuable for those looking to stay updated on the latest advancements and tools in speech technology and speaker recognition.

Common actions

explore research papers

find software libraries

evaluate diarization systems

understand speaker embedding

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Capabilities

Key features

Curated research papers
Speaker diarization software
Evaluation tools listed
Clustering algorithms
Speaker embedding resources
Datasets for diarization

Target Audience

professorcontent creator

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of resources are included in awesome-diarization?

awesome-diarization includes a wide range of resources such as academic papers on various speaker diarization topics, software frameworks and libraries (e.g., FunASR, SpeechBrain), evaluation tools, clustering algorithms, speaker embedding methods, and datasets for training and testing.

Is awesome-diarization suitable for beginners in speaker diarization?

While awesome-diarization provides a comprehensive list of resources, its content is highly technical, focusing on research papers and advanced software. It is primarily geared towards users with a technical background and existing knowledge in speech technology or machine learning.

How can I contribute to the awesome-diarization list?

You can contribute to awesome-diarization by sending a pull request on GitHub. The repository has a contributing guide that outlines the process for adding new papers, libraries, datasets, or other relevant resources to the curated list.

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Subcategories trending in Research & Education

Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

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This tool also appears in

Content & Design › Audio & Music

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