leopard
STT-examples
leopard | STT-examples | |
---|---|---|
15 | 5 | |
408 | 111 | |
1.2% | 0.0% | |
8.6 | 0.0 | |
2 days ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | Mozilla Public License 2.0 |
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leopard
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Automatic Speech Recognition with AWS Lambda and Leopard
Take a look at Leopard GitHub Repository or Leopard Docs Page to learn more about Leopard.
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Day 19: Local Transcription w .NET
Looking for more: Open-source demo code Leopard GitHub repository Speech-to-text Benchmark
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Day 13: Voice Recognition with Ubuntu
Voila! Reach out to Picovoice team on GitHub if you have any questions
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Day 8: Making Cool Raspberry Pi Projects even Cooler with Voice AI (3/4)
This tutorial is intended for Raspberry Pi 4. If you're looking for Raspberry Pi 3 or Raspberry Pi 400 or Raspberry Pi 4 (64-bit) check out Leopard C Demos on GitHub
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Day5: Building a local audio transcription engine running on your web browser with JavaScript
2. Serving the Model Leopard is an on-device speech-to-text solution. So we need to transfer the model (deep neural network) to the client to enable voice processing within the browser.
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Making a Podcast Transcription Server with Express.js and Picovoice Leopard
How does Picovoice Leopard compare to other speech-to-text options?
https://github.com/Picovoice/leopard
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Making a Podcast Transcription Server with Express.js (source code in comments)
Check out the source code here
- On-device speech-to-text engine powered by deep learning
- [P] On-device speech-to-text engine powered by deep learning
- picovoice/leopard - DeepSpeech 60x Smaller, 9x faster, and 2x accuracy
STT-examples
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Web Speech API is not available in the Quest browser
You're welcome! Your post actually got me a little interested in seeing what's new with Coqui STT (where all the old Mozilla STT folks moved to) and it seems someone was working on a WebAssembly binding for it, so one could probably finagle something themselves for testing purposes (the bandwidth of loading the model for every user on every page load is unfeasible from a production cost standpoint though)
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DeepSpeech 60x Smaller, 9x faster, and 2x accuracy
I will add https://github.com/coqui-ai/STT, which is a continuation of DeepSpeech. Also, I've been messing around with https://github.com/ideasman42/nerd-dictation, which works on a VOSK backend - accuracy is decent, especially with the bigger model.
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Any privacy friendly automated transcript app?
I don't know of a complete app, but https://github.com/coqui-ai/STT, which grew out of the now-unmaintained Mozilla Deepspeech project, works well and is easy to use. It could be a good starting point if you're comfortable writing a little code.
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[N] 🐸Coqui and OVHCloud are organizing an open-source Speech Recognition Hackaton
👉CoquiSTT - https://github.com/coqui-ai/STT
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Coqui, a startup providing open speech tech for everyone
https://github.com/coqui-ai/STT-examples
If you have any more specific requirements then we can point you in the right direction. Or just join us on Matrix: https://app.element.io/#/room/#coqui-ai_STT:gitter.im :)
What are some alternatives?
speech-to-text-benchmark - speech to text benchmark framework
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
nerd-dictation - Simple, hackable offline speech to text - using the VOSK-API.
LocalSTT - Android Speech Recognition Service using Vosk/Kaldi and Mozilla DeepSpeech
STT - 🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
cheetah - On-device streaming speech-to-text engine powered by deep learning
werpy - 🐍📦 Rapidly calculate and analyze the Word Error Rate (WER) with this powerful yet lightweight Python package.
serverless-leopard
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production