About LibraTool
Our mission is simple: make great tools free for everyone.
How It Works
Every tool on LibraTool runs entirely in your browser. When you drop a file, it is processed by JavaScript running on your own device — nothing is ever uploaded to a server.
Privacy Policy
Your files are processed in your browser and are not uploaded. Some tools first download an engine or AI model; live speech-to-text uses your browser's own speech service.
- Files and text you use in a tool are processed on your device and are not sent to LibraTool.
- Some tools download an engine or an open AI model to your browser the first time you use them. Nothing of yours is sent back.
- Live speech-to-text is the exception: your browser sends the audio to its maker (Google in Chrome, Microsoft in Edge, Apple in Safari).
- We use PostHog analytics to count visits and tool use. It keeps a random ID in your browser's localStorage and is set to discard IP addresses; it never receives your files or what you type into tools. Cloudflare measures page speed without cookies.
- Our host, Vercel, sees your IP address when a page loads, as every web host does, and keeps it in short-lived server logs.
- No account is needed. Your settings and favourites stay in your own browser.
Why is it free?
AI has made building these tools nearly free. We pass those savings directly to you.
No login. Your files never leave your device, so how big a file you can open depends on your device's memory.
The tools run in your browser, so your files aren't uploaded and there are no servers processing them for us to pay for.
422
free tools, no login required
81
languages supported
Open models we run on your device
Some tools use open machine-learning models. They run in your browser, not on a server — but the model file itself has to be downloaded once, so here is exactly what each one is, who made it and how big it is. Nothing downloads until you ask for it.
- onnx-community/whisper-baseApache-2.0 · 77 MB
- onnx-community/whisper-tinyApache-2.0 · 43 MB
- onnx-community/silero-vadMIT · 2 MB
- opencv/face_detection_yunetMIT · 0 MB
- CoderViking/realesr-general-x4v3-onnxBSD-3-Clause · 5 MB
- nathanfhh/PaddleOCR-ONNXApache-2.0 · 11 MB
- nathanfhh/PaddleOCR-ONNXApache-2.0 · 16 MB
- PaddlePaddle/latin_PP-OCRv5_mobile_rec_onnxApache-2.0 · 8 MB
- PaddlePaddle/cyrillic_PP-OCRv5_mobile_rec_onnxApache-2.0 · 8 MB
- PaddlePaddle/korean_PP-OCRv5_mobile_rec_onnxApache-2.0 · 13 MB
- PaddlePaddle/arabic_PP-OCRv5_mobile_rec_onnxApache-2.0 · 8 MB
- PaddlePaddle/devanagari_PP-OCRv5_mobile_rec_onnxApache-2.0 · 8 MB
- PaddlePaddle/th_PP-OCRv5_mobile_rec_onnxApache-2.0 · 8 MB
- PaddlePaddle/el_PP-OCRv5_mobile_rec_onnxApache-2.0 · 7 MB
- PaddlePaddle/ta_PP-OCRv5_mobile_rec_onnxApache-2.0 · 8 MB
- PaddlePaddle/te_PP-OCRv5_mobile_rec_onnxApache-2.0 · 8 MB
- onnx-community/Kokoro-82M-v1.0-ONNXApache-2.0 · 83 MBJapanese voice "jf_gongitsune" trained on a TNC (テレビ西日本) reading of 「ごんぎつね」 from the koniwa corpus, CC BY 3.0
- rhasspy/piper-voicesCC0-1.0 · 73 MBPiper voice "ukrainian_tts" (lada, mykyta, tetiana) — OHF-Voice voice-datasets, CC0
- rhasspy/piper-voicesCC-BY-4.0 · 61 MBPiper voice "Serena" — trained on "serena-synthetic-it-27h" by committa, CC BY 4.0
- rhasspy/piper-voicesCC-BY-4.0 · 73 MBPiper voice "mls" (German) — trained on Multilingual LibriSpeech (Pratap et al., OpenSLR 94), CC BY 4.0
- rhasspy/piper-voicesCC-BY-4.0 · 73 MBPiper voice "mls" (French) — trained on Multilingual LibriSpeech (Pratap et al., OpenSLR 94), CC BY 4.0
- rhasspy/piper-voicesLicenseRef-public-domain · 27 MBPiper voice "carlfm" — trained on carlfm01/my-speech-datasets (public domain)
- diffusionstudio/piper-wasmGPL-3.0 · 18 MBeSpeak NG (GPL-3.0) + piper-phonemize (MIT), WebAssembly build by diffusionstudio
- danielgatis/rembgApache-2.0 · 44 MBIS-Net "isnet-general-use" by Xuebin Qin et al. (DIS), Apache-2.0; ONNX export by rembg (MIT); int8-quantised by LibraTool
- spotify/basic-pitchApache-2.0 · 0 MBBasic Pitch by Spotify (Bittner et al., ICASSP 2022), Apache-2.0
- Rikorose/DeepFilterNetMIT OR Apache-2.0 · 8 MBDeepFilterNet3 by Hendrik Schröter et al., MIT OR Apache-2.0
- csukuangfj/sherpa-onnx-spleeter-2stemsMIT · 19 MBSpleeter 2-stems by Deezer (Hennequin et al., 2020), MIT; ONNX export by k2-fsa/sherpa-onnx; merged and int8-compacted by LibraTool
- Best-Practice/spleeter-4stems-onnxMIT · 38 MBSpleeter 4-stems by Deezer (Hennequin et al., 2020), MIT; ONNX conversion by Best-Practice (Apache-2.0); merged and int8-compacted by LibraTool
- mediapipe-models/selfie_segmenterApache-2.0 · 0 MBMediaPipe Selfie Segmenter by Google (Tingbo Hou, Siargey Pisarchyk, Karthik Raveendran), Apache-2.0
- mediapipe-models/selfie_multiclass_256x256Apache-2.0 · 16 MBMediaPipe Selfie Multiclass Segmenter by Google (Adel Ahmadyan), Apache-2.0
- musescore/MuseScoreMIT · 23 MBFluidR3Mono GM soundfont: Fluid (R3) © 2000-2002, 2008 Frank Wen; mono conversion © 2014-17 Michael Cowgill; MIT
Weights are served from our own domain rather than a third-party CDN, and each is pinned to an exact published version. Engines are open source too: ONNX Runtime and ffmpeg (both compiled to WebAssembly), pdf.js, and RNNoise.
Open-source software
LibraTool is built on open-source software that runs in your browser, including ffmpeg, eSpeak NG and ONNX Runtime. Each keeps its own licence. The notices list every component, its licence and where to get its source code, including our offer to provide the source of the GPL and LGPL components.
Third-party licencesTHIRD_PARTY_NOTICES.txtContact & Feedback
Have feedback, found a bug, or want to suggest a new tool? We read every message and use your suggestions to decide what to build next.
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