Dlib
Kaldi Speech Recognition Toolkit
Dlib | Kaldi Speech Recognition Toolkit | |
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33 | 22 | |
13,031 | 13,735 | |
- | 0.9% | |
8.2 | 6.7 | |
7 days ago | 4 days ago | |
C++ | Shell | |
Boost Software License 1.0 | GNU General Public License v3.0 or later |
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Dlib
- Modern Image Processing Algorithms Implementation in C
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[Cpp] Une assez grande liste de bibliothèques graphiques C ++
dlib
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32 years old. HRT in April or May. Things I can do to maximize results and what to expect.
The apparent gender estimates from photos are using dlib, and I really ought to get what I'm doing cleaned up in such a way that other people can use it easily.
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What are some C++ projects with high quality code that I can read through?
I really like dlib's code https://github.com/davisking/dlib
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C++ for machine learning
Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.
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What programming language should I learn after C++ for Audio DSP?
If you know C++, you don't need anything else. Go and learn APIs for C++ libraries. If you're into DSP, why not study Dlib?.
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Exponential vs linear progress?
The data is mostly in this spreadsheet. The apparently facial gender estimates are made with Dlib. The mental health assessments are from Beck's Depression Inventory and the Snaith-Hamilton Pleasure Scale. The graph is made with gnuplot.
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Flutter OpenCV and dlib for face detector & recognition
The plugin uses dlib library with a very fast HOG detector for both face recognition and detector following the relative examples.
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How long after starting HRT did facial recognition not recognize you?
The dlib facial recognition model thinks that I am now a distance of about 0.3 from where I started, which is far enough to start getting many false positive matches, but still within the design intent that different pictures of the same individual will be within 0.6 of each other.
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Does hrt effect facial recognition software?
Dlib's face recognition module thinks that I am about 0.25 units away from where I started; its design intent is that distinct individuals will be 0.6 or more apart, although in practice other people start showing up around 0.3.
Kaldi Speech Recognition Toolkit
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Amazon plans to charge for Alexa in June–unless internal conflict delays revamp
Yeah, whisper is the closest thing we have, but even it requires more processing power than is present in most of these edge devices in order to feel smooth. I've started a voice interface project on a Raspberry Pi 4, and it takes about 3 seconds to produce a result. That's impressive, but not fast enough for Alexa.
From what I gather a Pi 5 can do it in 1.5 seconds, which is closer, so I suspect it's only a matter of time before we do have fully local STT running directly on speakers.
> Probably anathema to the space, but if the devices leaned into the ~five tasks people use them for (timers, weather, todo list?) could probably tighten up the AI models to be more accurate and/or resource efficient.
Yes, this is the approach taken by a lot of streaming STT systems, like Kaldi [0]. Rather than use a fully capable model, you train a specialized one that knows what kinds of things people are likely to say to it.
[0] http://kaldi-asr.org/
- Unsupervised (Semi-Supervised) ASR/STT training recipes
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Steve's Explanation of the Viterbi Algorithm
You can study CTC in isolation, ignoring all the HMM background. That is how CTC was also originally introduced, by mostly ignoring any of the existing HMM literature. So e.g. look at the original CTC paper. But I think the distill.pub article (https://distill.pub/2017/ctc/) is also good.
For studying HMMs, any speech recognition lecture should cover that. We teach that at RWTH Aachen University but I don't think there are public recordings. But probably you should find some other lectures online somewhere.
You also find a lot of tutorials for Kaldi: https://kaldi-asr.org/
Maybe check this book: https://www.microsoft.com/en-us/research/publication/automat...
The relation of CTC and HMM becomes intuitively clear once you get the concept of HMMs. Often in terms of speech recognition, it is all formulated as finite state automata (FSA) (or finite state transducer (FST), or weighted FST (WFST)), and the CTC FST just looks a bit different (simpler) than the traditional HMMs, but in all cases, you can think about having states with possible transitions.
This is all mostly about the modeling. The training is more different. For CTC, you often calculate the log prob of the full sequence over all possible alignments directly, while for HMMs, people often use a fixed alignment, and calculate framewise cross entropy.
I did some research on the relation of CTC training and HMM training: https://www-i6.informatik.rwth-aachen.de/publications/downlo...
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[D] What's stopping you from working on speech and voice?
- https://github.com/kaldi-asr/kaldi
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C++ for machine learning
Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.
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The Advantages and disadvantages of In-House Speech Acknowledgment
Frameworks as well as toolkits like Kaldi were at first promoted by the research study area, yet nowadays used by both scientists and also market experts, reduced the access obstacle in the advancement of automatic speech recognition systems. Nonetheless, cutting edge methods need big speech data readies to achieve a usable system.
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xbp-src to only cross compile 32-bit
Hello. I'm trying to package the openfst library (here)[https://github.com/void-linux/void-packages/pull/39015] but a developer says 32-bit must be cross compiled from 64-bit. I see xbps-src has a nocross option, but I don't see a way to only cross compile. What do you think I should do? I have currently limited the archs to 64-bit ones. Here's my issue with the developer's response: https://github.com/kaldi-asr/kaldi/issues/4808 Thank you.
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Machine Learning with Unix Pipes
If you interested in unix-like software design and not yet familiar with kaldi toolkit, you definitely need to check it https://kaldi-asr.org
It extended Unix design with archives, control lists and matrices and enabled really flexible unix-like processing. For example, recognition of a dataset looks like this:
extract-wav scp:list.scp ark:- | compute-mfcc-feats ark:- ark:- | lattice-decoder-faster final.mdl HCLG.fst ark:- ark:- | lattice-rescore ark:- ark:'|gzip -c > lat.gzip'
Another example is gstreamer command line.
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Lexicap: Lex Fridman Podcast Whisper Captions by Andrej Karpathy
No, speaker diarization is not part of Whisper. There are open source projects - such as Kaldi [1], but it's hard to get them running if you are not an area expert.
[1] https://kaldi-asr.org/
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Is there a way to integrate a raspberry pi with a keyboard to do speech to text?
State-of-the-art ASR, like what you get on smartphones, has unfortunately high resource requirements. Some recent smartphone models are able to run ASR on-device, but more typically, ASR is done by sending audio to a web service. Check out the (currently experimental) Web SpeechRecognition API in a Chrome browser. Here is a demo of the API in action. For something open source, check out Kaldi ASR.
What are some alternatives?
mlpack - mlpack: a fast, header-only C++ machine learning library
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
DeepSpeech - DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
Boost - Super-project for modularized Boost
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
Face Recognition - The world's simplest facial recognition api for Python and the command line
speech-and-text-unity-ios-android - Speed to text in Unity iOS use Native Speech Recognition
OpenCV - Open Source Computer Vision Library
espnet - End-to-End Speech Processing Toolkit
Caffe - Caffe: a fast open framework for deep learning.
rhasspy - Offline private voice assistant for many human languages