cppflow
DeepSpeech
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cppflow | DeepSpeech | |
---|---|---|
9 | 67 | |
761 | 24,278 | |
- | 1.4% | |
0.0 | 0.0 | |
11 months ago | 2 months ago | |
C++ | C++ | |
MIT License | Mozilla Public License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cppflow
- Easily run TensorFlow models from C++
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[P] libtensorflow_cc: Pre-built TensorFlow C++ API
It’s been awhile since I’ve looked at it, so not sure how hard it would be to get to work. I only commented since you mentioned that you would support other operating systems. For others interested in cross platform support there is also cppflow.
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Deep learning classification with C++
what about start with keras and convert model to c++ ? https://github.com/pplonski/keras2cpp https://github.com/serizba/cppflow
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Using embedding model in C++ app
My solution so far: I am using a compiled Tensorflow C DLL in combination with cppflow (https://github.com/serizba/cppflow). However, I get problems when I take models which use operations from the tensorflow_text python module since I don’t know how to get their C++ API.
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What is the most used library for AI in C++ ?
I use cppflow to run compiled tensorflow models natively in C++. It works like a charm :)
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[Python] Importing a TensorFlow AI?
I toyed around with this idea a while back but I never got around to finishing the implementation. If all you need is inference with no training and you are relatively familiar with c++ you could look into creating a module for Godot that interfaces with the Tensorflow C API. Something like cppflow would provide an even easier API to work with. Looking into that project could also explain how they interface with the Tensorflow C API if you'd rather cut out the middle man. A module like this would let you train your model in Python and then load it and perform inference in Godot natively.
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Simplest way to deploy Keras NN model into C++?
If your re using keras with TensorFlow you can save it as a saved model format and then you can easily use cppflow to perform inference with it.
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I trained a Neural Network to understand my commands when playing my game
The whole game is written in C++ using SFML for the graphics, entt as Entity-Component-System and tensorflow for the Neural Network. Tensorflow itself is written in C, so I use cppflow to integrate it into my C++ framework.
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TF-agent with C/C++ environment
Found this which seems more recent (uses TF 2, updated 4 days ago): https://github.com/serizba/cppflow
DeepSpeech
- Common Voice
- Ask HN: Speech to text models, are they usable yet?
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Looking to recreate a cool AI assistant project with free tools
- [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest way to implement it using free, open-source software. Here's what he used originally, followed by some open source candidates I'm considering but would love feedback and advice before starting: Original Tools: - YoloV8 does the heavy lifting with the object detection - OpenAI Whisper handles voice - GPT-4 handles the “AI” - Google Custom Search Engine handles web browsing - MacOS/iOS handles streaming the video from my iPhone to my Mac - Python for the rest Open Source Alternatives: - [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection - Replacing GPT-4 is still a challenge as I know there are some good open-source LLms like Llama 2, but I don't know how to apply this in the code perhaps in the form of api - [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text - [Coqui TTS](https://github.com/coqui-ai/TTS) instead of Whisper for text-to-speech - Browser automation with [Selenium](https://www.selenium.dev/) instead of Google Custom Search - Stream video from phone via RTSP instead of iOS integration - Python for rest of code I'm new to working with tools like OpenCV, DeepSpeech, etc so would love any advice on the best way to replicate the original project in an open source way before I dive in. Are there any good guides or better resources out there? What are some pitfalls to avoid? Any help is much appreciated!
- Speech-to-Text in Real Time
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Linux Mint XFCE
algo assim? https://github.com/mozilla/DeepSpeech
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Are there any secure and free auto transcription software ?
If you're not afraid to get a little technical, you could take a look at mozilla/DeepSpeech (installation & usage docs here).
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Web Speech API is (still) broken on Linux circa 2023
There is a lot of TTS and SST development going on (https://github.com/mozilla/TTS; https://github.com/mozilla/DeepSpeech; https://github.com/common-voice/common-voice). That is the only way they work: Contributions from the wild.
- Deepspeech /common voice.
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Mozilla Launches Responsible AI Challenge
Mozilla did release DeepSpeech[0] and Firefox Translation[1] (the latter of which they included in Firefox, to offer client-side webpage translations.)
They definitely have fewer resources than OpenAI, and they do not produce SOTA research (their publications have plummeted to 1/year anyway[2]). So the only way for them to make progress is to seek government grants or make challenges like these.
This challenge is unlikely to be profitable for the winning team: the expected value of winnings are likely around $1K when taking into account the probability that another team gets a better rank, but ML research projects are often more expensive (recently, Alpaca spent upwards of $600 on computation alone; and of course pretraining large models is much more expensive). So the main gain will be publicity.
[0]: https://github.com/mozilla/deepspeech
[1]: https://github.com/mozilla/firefox-translations/
[2]: https://research.mozilla.org/
What are some alternatives?
examples - TensorFlow examples
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
qt-tf-lite-example - Qt TensorFlow Lite example
picovoice - On-device voice assistant platform powered by deep learning
keras2cpp - This is a bunch of code to port Keras neural network model into pure C++.
STT - 🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
ssd_keras - A Keras port of Single Shot MultiBox Detector
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
emlearn - Machine Learning inference engine for Microcontrollers and Embedded devices
PaddleSpeech - Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.