flashlight
DNS-Challenge
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| flashlight | DNS-Challenge | |
|---|---|---|
| 16 | 2 | |
| 5,145 | 971 | |
| 1.1% | 3.7% | |
| 7.7 | 4.4 | |
| 22 days ago | 27 days ago | |
| C++ | Python | |
| MIT License | Creative Commons Attribution 4.0 |
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flashlight
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
I think a comparison to PyTorch, TensorFlow and/or JAX is more relevant than a comparison to CuPy/NumPy.
And then maybe also a comparison to Flashlight (https://github.com/flashlight/flashlight) or other C/C++ based ML/computing libraries?
Also, there is no mention of it, so I suppose this does not support automatic differentiation?
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Project Resources
This Facebook ai project seems reasonably structured after looking at its CMakeLists.txt. CMake is a build generator for c++, it's how you make binaries to run your project: https://github.com/flashlight/flashlight
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Meta AI Open Sources Flashlight: Fast and Flexible Machine Learning Toolkit in C++
Continue reading | Check out the paper and github link
- Flashlight: A C++ standalone library for machine learning
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[D] Deep Learning Framework for C++.
I built and maintain Flashlight, a C++-first library for ML/DL. We built Flashlight to be:
- [R] C++ for Machine Learning
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What is the most used library for AI in C++ ?
I’ve never used it, but Facebook’s flashlight looks interesting
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Python.
Flashlight bro, not flash. Read again
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Mozilla Common Voice Adds 16 New Languages and 4,600 New Hours of Speech
I've had good results with https://github.com/flashlight/flashlight/blob/master/flashli.... Seems to work well with spoken english in a variety of accents. Biggest limitation is that the architecture they have pretrained models for doesn't really work well with clips longer than ~15 seconds, so you have to segment your input files.
- [D] C++ in Machine Learning.
DNS-Challenge
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Mozilla Common Voice Adds 16 New Languages and 4,600 New Hours of Speech
Is anyone aware of classification (e.g. word prediction) datasets for low-resource and endangered languages?
If so, we would like to use it for the HEAR NeurIPS competition: https://github.com/microsoft/DNS-Challenge/tree/master/datas...
The challenge is restricted only to classification tasks, and sequence modeling like full ASR is unfortunately beyond the scope of the competition.
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How to clone while skipping some of the directories?
Repository
What are some alternatives?
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
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.
STT - 🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
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.
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)
edgedict - Working online speech recognition based on RNN Transducer. ( Trained model release available in release )
oneflow - OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
common-voice-android - Repository of "CV Project" app. It's an unofficial app for Mozilla Common Voice, which permits you to contribute to this project via your device.