NobodyWho

NobodyWho

NobodyWho is an open-source, on-device inference library and SDK that runs text, vision, and speech models (LLM, STT, TTS) locally across platforms, emphasizing performance, privacy, and offline use.

NobodyWho is ai tools software teams evaluate for voice & speech. Use this page to review pricing, integration signals, and the best alternatives before you commit.

Free API 70/100
#76 in Voice & Speech (76 tools)
Just launched
Data reviewed Aug 20, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: Voice & Speech

What it does

AI Tools software for decision-makers comparing workflow fit and alternatives.

Best fit

Voice & Speech

Pricing snapshot

Free from $0

Next step

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NobodyWho

NobodyWho is an open-source on-device inference library and SDK designed to run text, vision, and speech models locally without servers. It provides SDKs and examples (Chat, STT, TTS) across multiple languages and platforms so developers can integrate AI with minimal code. The project emphasizes fast, optimized inference (Metal, CUDA, Vulkan), strict local privacy (no cloud logging, offline operation), and broad model compatibility (GGUF format and Hugging Face model URIs). The library is licensed under EUPL 1.2 and is free for individuals and companies.

Run AI models on any device Discussion | Link

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Key Features

On-device LLM, STT, and TTS

Provides SDKs and runtime support for chat (LLM), speech-to-text (STT), and text-to-speech (TTS) with code examples in multiple languages.

Multi-platform SDKs

Platform bindings and examples for React Native, Flutter, Kotlin, Swift, Python, and Godot, plus explicit platform support for Apple, Android, Windows, and Linux.

High-performance inference

Optimized kernels for Metal, CUDA, and Vulkan to deliver fast inference even on integrated graphics and mobile chips.

Privacy & offline-first

Designed to keep data local: no cloud logging, prompts remain in local GPU memory, and models can run offline in secure environments.

Model compatibility

Direct support for GGUF format and the ability to pick models from Hugging Face; examples reference HF URIs (e.g., hf://onnx-community/whisper-base).

Open-source and permissive for companies

The library is open source under EUPL 1.2 and stated to be free for both individuals and companies.

Pricing

Free Tier Available

Completely free, no API keys, or usage fees.

Free

$0
  • Open-source library under EUPL 1.2
  • No API keys or usage fees

Use Cases

On-device chat assistants

Embed chat/LLM functionality directly in apps using Chat.fromPath with local GGUF models for low-latency, private responses.

Speech transcription

Use STT.transcribeFile with locally hosted models (e.g., onnx-community/whisper) to transcribe audio without sending data to the cloud.

Text-to-speech for apps

Synthesize speech locally with Tts.load and save WAV output for offline or private TTS in applications.

Secure and offline deployments

Run models on-device for scenarios requiring no internet connectivity or strict data locality, such as in-flight or secure environments.

Integrations

Hugging Face

Pick and run models hosted on Hugging Face; site advises selecting models from Hugging Face and supports HF URIs.

onnx-community (Whisper)

STT examples reference hf://onnx-community/whisper-base for speech transcription.

Third-party model authors (Mistral, Qwen, Gemma, Liquid, OpenAI listed)

The site lists model names (Mistral, Open AI, Liquid, Qwen, Deepseek, Gemma) as supported model sources or examples.

Benefits

Local-first privacy: prompts and data remain on-device with no cloud logging.
Offline operation enables use in secure or disconnected environments.
High performance with optimized kernels for Metal, CUDA, and Vulkan.
Broad model compatibility (GGUF / Hugging Face) and language/platform SDK support.
Open-source under EUPL 1.2 and free for individual and commercial use.

Limitations

No verified limitations are available.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Explore the Docs and pick a model from Hugging Face or a GGUF model.
  2. 2 Install or import the NobodyWho SDK for your platform (examples shown for React Native, Flutter, Kotlin, Swift, Python, Godot).
  3. 3 Load a model path (e.g., Chat.fromPath({ modelPath: "./model.gguf" })) and call the provided APIs (ask, transcribeFile, synthesize) to run inference locally.

Support

docs

Documentation referenced on the site (Docs link in navigation).

github

Project and source code available via GitHub (link in site navigation).

community

Community channels listed on the site (Discord and social links).

email

Email contact is listed in the site footer/navigation (explicit address not shown).

API

Available: Yes

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