Zach Caceres zcaceres

Go: The Weird Parts

Notes on the unique stuff about go, in the context of other languages that I already know.

Project Organization

Root dir go
src and bin inside the project
unique project/package name

The first statement in a go source file must be package packagenamehere

Articles for the New JS Dev

A list of the gists that have proved most useful to new JS devs on GitHub.

Faster, Better Speech Recognition with Wav2Letter's Auto Segmentation Criterion

In 2016, Facebook AI Research (FAIR) broke new ground with Wav2Letter, a fully convolutional speech recognition system.

In Wav2Letter, FAIR showed that systems based on convolutional neural networks (CNNs) could person as well as traditional recurrent neural network-based approaches.

In this article, we'll focus on an understudied module at the core of Wav2Letter: the Auto Segmentation (ASG) Criterion.

Supercharge Your Bash Workflows with GNU `parallel`

GNU parallel is a command line tool for running jobs in parallel.

parallel is awesome and belongs in the toolbox of every programmer. But I found the docs a bit overwhelming at first. Fortunately, you can start being useful with parallel with just a few basic commands.

Why is `parallel` so useful?

Let's compare sequential and parallel execution of the same compute-intensive task.

Imagine you have a folder of .wav audio files to convert to .flac:

	export type MimeTypes =
	\| "audio/aac"
	\| "application/x-abiword"
	\| "application/x-freearc"
	\| "video/x-msvideo"
	\| "application/vnd.amazon.ebook"
	\| "application/octet-stream"
	\| "image/bmp"
	\| "application/x-bzip"
	\| "application/x-bzip2"

	const jsdom = require('jsdom')
	const {
	JSDOM
	} = jsdom

	function toJSDOM(responseBody) {
	return new JSDOM(responseBody)
	}

	/**

	from model_fastai import FastaiImageClassifier

	class PythonServer(object):
	def listen(self):
	print(f'Python Server started listening on {PORT} ...')

	def predict_from_img(self, img_path):
	model = FastaiImageClassifier()
	return model.predict(img_path)

	static async invoke(method, ...args) {
	try {
	const zerorpc = PythonConnector.server();
	return await Utils.promisify(zerorpc.invoke, zerorpc, method, ...args);
	}
	catch (e) {
	return Promise.reject(e)
	}
	}

	...

	// Our prediction endpoint (Receives an image as req.file)
	app.post('/predict', upload.single('img'), async function (req, res) {
	const { path } = req.file
	try {
	const prediction = await PythonConnector.invoke('predict_from_img', path);
	res.json(prediction);
	}
	catch (e) {