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Luiz GUStavo 💉💉🎉 Profile picture
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5 Mar, 7 tweets, 3 min read
What is pitch?

One "team" activity that is very popular is Karaoke

You go there, sing your best, get your applause and the machine gives you a bad score and people laugh

Well, that's my experience with it so I imagine it's the same for everyone…

How can ML help here?

1/7🧵
When you sing, one important metric is Pitch

It's an attribute of musical tones, just as duration, intensity and timbre

Pitch is a quantified frequency that describes notes as high or low

I described frequency in much more detail here:


2/7🧵
What if we could measure the pitch with a ML model?

The challenge here that makes this problem much harder is how to separate your voice from the background noise and instruments?

That's where the SPICE: Self-Supervised Pitch Estimation can help
ai.googleblog.com/2019/11/spice-…

3/7🧵
SPICE is a pretrained model that can recognize the fundamental pitch from mixed audio recordings, including noise and backing instruments.

The model is available on #TFHub
-> tfhub.dev/google/spice/2 and it can be used from servers to mobile devices and in the browser

4/7🧵
There you can try the model right in the browser! 🤯 without any extra work.

Or you can follow the colab and see all the details to use the model!

5/7🧵 Image
The cool part is that you can convert this pitch back to musical notes!!!

It's not trivial but you can find more information on this blog post I co-wrote:
blog.tensorflow.org/2020/06/estima…

6/7🧵
I'm pretty sure that if the karaoke machine implemented the SPICE model, my scores wouldn't be that bad…

🎵🎶➡️🤖➡️👍

ML for music is a huge area with lot's cool possibilities!

Would you like a thread about that?

RT, like and leave your questions in the comments!
7/7🧵

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More from @gusthema

Luiz GUStavo 💉💉🎉 Profile picture
24 Jun
One term that I learned when I started studying ML is Hyperparameter.

What is it?
When should I worry about it?

Let me try to clarify it...

[3 min]

1/9🧵
First, what are the parameters of a ML model?

Those are typically the weights that you end up with after training your model. Example:

If you are creating a model to solve
AX + B = Y

A and B are the parameters you'll find. They are also know as the weights of the model

2/9🧵
Hyperparameters are the values that control the learning process.
Let's suppose we have the code in the image.

Hyperparameters could be: unit (from the Dense layer), learning_rate from the optimizer or even the batch_size

3/9🧵 Image
Read 9 tweets
Luiz GUStavo 💉💉🎉 Profile picture
18 Jun
This week I've been posting about the itertools Python🐍 module.

If you want to improve your coding skills, one way is adding new tools to your toolbox

Itertools enables you to solve problems that would otherwise be absurdly hard to solve.

[2min]

1/7🧵
After you've learned the basic of Python, I'd suggest you go deeper in the collections manipulation:

• Slicing
• Comprehension
• Generators
• Iterators
• Itertools
• map/filter/zip

I've posted about all this content in the past, I can revisit if you'd like

2/7🧵
This week I've explained all functions on the itertools module

Starting by the basic ones:

3/7🧵
Read 7 tweets
Luiz GUStavo 💉💉🎉 Profile picture
17 Jun
Generating combinations and permutations is usually a tricky task in programming.

In Python 🐍, using the itertools library this becomes much easier, less memory intensive and faster!

Let me tell you what I learned with them

[4.45 min]

1/13🧵
Let's suppose you want to create all possible cards of a regular deck. What you have to do is to join:

• All cards ranks: A, 2 to 10, J, Q and K
• The 4 suits: ♥️♣️♦️♠️

How to generate all cards?

2/13🧵
The operation that can solve this is a cartesian product:

ranks = ['A'] + list(range(2, 11)) + ['J', 'Q', 'K']
suits = ['♥️', '♣️', '♦️', '♠️']

all_cards = it.product(suits, ranks)
>>> ('A', '♥️'),('A', '♣️'),('A', '♦️'),('A', '♠️'),...
len(all_cards)
>>> 52

3/13🧵
Read 13 tweets
Luiz GUStavo 💉💉🎉 Profile picture
15 Jun
Following up from my previous thread, let's continue taking a look at some additional itertools methods

Some of them, as you will see, have very similar built-in versions but the key here is: itertools works on iterables and generators that are lazy evaluated collections

1/14🧵
One example is the method islice. It does slicing but for iterables (potentially endless collections). The main difference is that it doesn't accept negative indexes like regular slicing.

numbers = range(10)
items = it.islice(numbers, 2, 4)
>>> [2, 3]

2/14🧵
zip_longest vs zip
both do the same thing: aggregates elements from multiple iterators.

The difference is that zip_longest aggregates until the longest iterator ends while zip stops on the shortest one.

It will fill the missing values with any value you want

3/14🧵
Read 14 tweets
Luiz GUStavo 💉💉🎉 Profile picture
9 Jun
Quick⚡️ Python🐍 trick:
How do you merge two dictionaries?

[55 sec]🤯

1/6🧵
For Python 3.5 and up you can do:

d1 = {"a":1, "b":2}
d2 = {"c":3, "d":4}
d3 = {**d1, **d2}
d3 == {"a": 1, "b": 2, "c":3, "d":4}

Why?

2/6🧵
The ** operator expands the collection, so, you can think it as this:

d1 = {"a":1, "b":2}
d2 = {"c":3, "d":4}
d3 = {**d1, **d2} -> {"a":1, "b":2, "c":3, "d":4}
d3 == {"a": 1, "b": 2, "c":3, "d":4}

3/6🧵
Read 6 tweets
Luiz GUStavo 💉💉🎉 Profile picture
8 Jun
I was telling a friend that one cool feature from Python is list slice notation

So instead of just posting the link I decided to do a brief explanation.

[5 min]

Python's regular array indexing as in multiple languages is: a[index]

a = [0, 1, 2, 3, 4]
a[0] == 0

1/12🧵
Python has negative indexing too:

a = [0, 1, 2, 3, 4]
a[-1] == 4
a[-2] == 3

2/12🧵
Python also enables creating sub-lists from a list, or a slice:

-> a[start_index:last_index]

a = [0, 1, 2, 3, 4]
a[1:3] == [1, 2]

start_index is inclusive
last_index is exclusive

3/12🧵
Read 12 tweets

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