Overview¶

This section provides an overview of how pydrobert-speech is organized so that you can get your feature representation just right.

The input to pydrobert-speech are (acoustic) signals. The output are features. We call the operator that transforms the signal to some feature representation a computer. Operators that act on the signal and produce a signal, like random dithering, are preprocessors, and operators that act on features and produce features, like unit normalization, are postprocessors. The latter two operators exist in the submodules pydrobert.speech.pre and pydrobert.speech.post and follow the usage pattern

>>> y = Op().apply(x)


A computer, which can be found in pydrobert.speech.compute, will require more complicated initialization. The standard feature representation, which is a 2D time-log-frequency matrix of energy, derives from compute.LinearFilterBankFrameComputer. It calculates coefficients over uniform time slices (frames) using a bank of filters. Children of LinearFilterBankFrameComputer all have similar representations and all use linear banks of filters, but can be computed in different ways. The classic method of computation is the compute.ShortTimeFourierTransformFrameComputer.

Banks of filters are derived from pydrobert.speech.filters.LinearFilterBank. Children of the parent class, such as filters.ComplexGammatoneFilterBank, will decide on the shape of the filters.

LinearFilterBankFrameComputer instances compute coefficients at uniform intervals in time. However, the distribution over frequencies is decided by the distribution of filter frequency responses from the filter bank, which, in turn, depends on a scaling function. Scaling functions can be found in pydrobert.speech.scales, such as scales.MelScaling. Scaling functions transform the frequency domain into some other real domain. In that domain, filter frequency bandwidths are distributed uniformly which, when translated back to the frequency domain, could be quite non-uniform.

In sum, you build a computer by first choosing a scale from pydrobert.speech.scales. You then pass that as an argument to a filter bank that you’ve chosen from pydrobert.speech.filters. Finally, you past that as an argument to your computer of choice. For example:

>>> from pydrobert.speech import *
>>> scale = scales.MelScaling()
>>> bank = filters.ComplexGammatoneFilterBank(scale)
>>> computer = compute.ShortTimeFourierTransformFrameComputer(bank)
>>> # preprocess the signal
>>> feats = computer.compute_full(signal)
>>> # postprocess the signal


This is a bit different from the syntax described in the README. There, we use aliases. Aliases are a simple mechanism for unpacking hierarchies of parameters, such as the hierarchy between these computers, filter banks, and scales. We can streamline the above initialization as

>>> computer = compute.ShortTimeFourierTransformFrameComputer(
...     {"name": "tonebank", "scaling_function": "mel"})


or even

>>> computer = compute.FrameComputer.from_alias("stft",
...     {"name": "tonebank", "scaling_function": "mel"})


The dictionaries are merely keyword argument dictionaries with the special key "name" or "alias" referring to an alias of the subclass you wish to initialize (unless you just pass a string, at which point it’s considered the alias with no arguments). Aliases are listed in each subclass’ alias class member. Besides for brevity, aliases provide a principled way of storing hierarchies on disk via JSON. Thus, it’s possible to access most of pydrobert-speech’s flexibility from the provided command-line hooks.

Finally, there are some visualization functions in the pydrobert.speech.vis module (requires matplotlib), some extensions to pydrobert-kaldi data iterators in pydrobert.speech.corpus, and command line implementations in pydrobert.speech.command_line.