porcupine | On-device wake word detection powered by deep learning | Speech library

I'm working on a similar task and running into the same issue. I've found that the solution is to avoid using two different means of recording audio.

The PvRecorder class represents the recorded data as signed integers.

Well I contacted the official repository of picovoice and here I mentioned them what clearly was going on. So anyone still facing it , then here is the solution for such behaviour https://github.com/Picovoice/porcupine/issues/617 Thanks!

In the sample code from the link you provide, this method is called on different class:

The CLIPS Advanced Programming Guide is here: http://clipsrules.sourceforge.net/documentation/v640/apg.pdf

You can use the Load function (section 3.2.2) to load a file. There is an example of its use in section 3.6.1.

You can use the GetNextActivation function (section 12.7.1) to determine if the agenda has any activations.

The simplest way to create facts is using the AssertString function (section 3.3.1). Sections 3.6.2, 4.5.4, and 5.3 have an example use of this function. You can also use the FactBuilder functions described in section 7.1 (with an example in section 7.6.1).

If the results of your program running are represented by facts, you can use the fact query functions via the Eval function to retrieve those values from your program. Section 4.5.4 has an example.

The stream returned by Arrays.stream(animals) is of type Stream, while you are reducing the stream with an identity of zero which is an int.

A simple way of doing this is mapping each animal to its count, and reduce the resulting Stream to its sum:

By first converting your vectors into a list l <- list(a = a, b = b, c = c, d = d, e = e, f = f)

In base R you can use lapply:

I ended up getting this working by using some of the code included in this demo file.

We include a chunkArray function:

The first clue is the warning

Model convergence problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')

This means the model hasn't converged, or doesn't think it has, to a solution where the log-likelihood surface is downward-curved (i.e., a true maximum). That's why the standard errors can't be calculated (if you did the usual calculation they'd come out negative or complex). The log-likelihood could be calculated, but the model fit is suspect so glmmTMB returns NA instead.

Next question: why? Sometimes this is mysterious and hard to diagnose, but in this case we have a good clue: when you see extreme parameter values (e.g. |beta|>10) in a (non-identity link) GLM, it almost always means that some form of complete separation is occurring. That is, there are some combinations of covariates (e.g. Keyword_1==Lesser Mouse-deer) where you always have zero counts. On the log scale, this means the density is infinitely lower than covariate combinations where you have a positive mean. The parameter is about -16, which corresponds to an expected multiplicative density difference of exp(-16) = 1e-07. This isn't infinitesimal, but it's small enough that glmmTMB gets small enough differences in the log-likelihood that the optimizer stops. However, since the likelihood surface is almost flat, it can't compute curvature etc..

You could lump together or drop categories or do some form of regularization (e.g. see here or here ...); it might also make some sense to treat your Keyword_1 variable as a random effect, which would also have the effect of regularizing the estimates.

I'm not sure if I understand the question correctly, but maybe the problem is because of the absolute positioning of those ul-s. You cannot expect elements with position: absolute to push the elements around them as they grow. Try with position: relative.