k2
| k2 | TerpreT | |
|---|---|---|
| 2 | 1 | |
| 1,046 | 42 | |
| 1.5% | - | |
| 7.1 | 10.0 | |
| 3 days ago | over 6 years ago | |
| Cuda | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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k2
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Differentiable Finite State Machines
This uses dense (soft/weighted) transitions from any state to any state, and then some regularization to guide it to more sparse solutions.
In practice, the number of states can be huge (thousands, maybe millions), so representing this as a dense matrix (a 1Mx1M matrix is way too big) is not going to work. It must be sparse, and in practice (all FST you usually deal with) it is. So it's very much a waste to represent it as a dense matrix.
That's why there are many specialized libraries to deal with FSTs. Also in combination with deep learning tools. See e.g. K2 (https://github.com/k2-fsa/k2).
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What are some good speech recognition papers I can implement?
k2
TerpreT
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Differentiable Finite State Machines
If you're interested in these kinds of things, many years ago we created TerpreT (https://arxiv.org/pdf/1608.04428.pdf and https://github.com/51alg/TerpreT) to look into generic program synthesis problems, using a set of very different techniques (gradient descent, ILP, SMT) on different problem settings (turing machines, boolean circuits, LLVM IR-style basic blocks, and straight assembly).
What are some alternatives?
espnet - End-to-End Speech Processing Toolkit
gtn - Automatic differentiation with weighted finite-state transducers.
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
gtn_applications - Applications using the GTN library and code to reproduce experiments in "Differentiable Weighted Finite-State Transducers"