[Ryan] purchased a large fume extractor designed to sit on the floor below the work area and pull solder fumes down into its filtering elements. The only drawback to this new filter was that its controls were located near his feet. Rather than kicking at his new equipment, he devised a way to automate it.
By adding a Wemos D1 Mini microcontroller running ESPHome, a relay board, and a small AC-to-DC transformer, [Ryan] can now control the single push button used to cycle through speed settings wirelessly. Including the small transformer inside was a clever touch, as it allows the unit to require only a single power cable while keeping all the newfound smarts hidden inside.
The relay controls the button in parallel, so the physical button still works. Now that the extractor is integrated with Home Assistant, he can automate it. The fan can be controlled via his phone, but even better, he automated it to turn on by monitoring the power draw on the smart outlet his soldering iron is plugged into. When he turns on his iron, the fume extractor automatically kicks in.
Check out some other great automations we’ve featured that take over mundane tasks.
The volume slider on our virtual desktops is a skeuomorphic callback to the volume sliders on professional audio equipment on actual, physical desktops. [Maker Vibe] decided that this skeuomorphism was so last century, and made himself aphysical audio control box for his PC.
Since he has three audio outputs he needs to consider, the peripheral he creates could conceivably be called a fader. It certainly has that look, anyway: each output is controlled by a volume slider — connected to a linear potentiometer — and a mute button. Seeing a linear potentiometer used for volume control threw us for a second, until we remembered this was for the computer’s volume control, not an actual volume control circuit. The computer’s volume slider already does the logarithmic conversion. A Seeed Studio Xiao ESP32S3 lives at the heart of this thing, emulating aBluetooth gamepad using a library by LemmingDev.A trio of LEDs round out the electronics to provide an indicator for which audio channels are muted or active.
Those Bluetooth signals are interpreted by a Python script feeding a software called Voicmeeter Banana, because [Maker Vibe] uses Windows, and Redmond’s finest operating system doesn’t expose audio controls in an easily-accessible way. Voicmeeter Banana (and its attendant Python script) takes care of telling Windows what to do.
The whole setup lives on [Maker Vibe]’s desk in a handsome 3D printed box. He used a Circuit vinyl cutter to cut out masks so he could airbrush different colours onto the print after sanding down the layer lines. That’s another one for the archive of how to make front panels.
Before the release of Piper TTS in 2023, existing free-to-use TTS systems such as espeak and Festival sounded robotic and flat. Piper delivered much more natural-sounding output, without requiring massive resources to run. To change the voice style, the Piper AI model can be either retrained from scratch or fine-tuned with less effort. In the latter case, the problem to be solved first was how to generate the necessary volume of training phrases to run the fine-tuning of Piper’s AI model. This was solved using a heavyweight AI model, ChatterBox, which is capable of so-called zero-shot training. Check out the Chatterbox demo here.
As the loss function gets smaller, the model’s accuracy gets better
Training began with a corpus of test phrases in text format to ensure decent coverage of everyday English. [Cal] used ChatterBox to clone audio from a single test phrase generated by a ‘mystery TTS system’ and created 1,300 test phrases from this new voice. This audio set served as training data to fine-tune the Piper AI model on the lashed-up GPU rig.
To verify accuracy, [Cal] used OpenAI’s Whisper software to transcribe the audio back to text, in order to compare with the original text corpus. To overcome issues with punctuation and differences between US and UK English, the text was converted into phonemes using espeak-ng, resulting in a 98% phrase matching accuracy.
After down-sampling the training set using SoX, it was ready for the Piper TTS training system. Despite all the preparation, running the software felt anticlimactic. A few inconsistencies in the dataset necessitated the removal of some data points. After five days of training parked outside in the shade due to concerns about heat, TensorBoard indicated that the model’s loss function was converging. That’s AI-speak for: the model was tuned and ready for action! We think it sounds pretty slick.
We always enjoy [FloatHeadPhysics] explaining any math or physics topic. We don’t know if he’s acting or not, but he seems genuinely excited about every topic he covers, and it is infectious. He also has entertaining imaginary conversations with people like Feynman and Einstein. His recent video on tensors begins by showing the vector form of Ohm’s law, making it even more interesting. Check out the video below.
If you ever thought you could use fewer numbers for many tensor calculations, [FloatHeadPhysics] had the same idea. Luckily, imaginary Feynman explains why this isn’t right, and the answer shows the basic nature of why people use tensors.
This week Jonathan chats with Joseph P. De Veaugh-Geiss about KDE’s eco initiative and the End of 10 campaign! Is Open Source really a win for environmentalism? How does the End of 10 campaign tie in? And what does Pewdiepie have to do with it? Watch to find out!
It should probably come as no surprise to anyone that the images which we look at every day – whether printed or on a display – are simply illusions. That cat picture isn’t actually a cat, but rather a collection of dots that when looked at from far enough away tricks our brain into thinking that we are indeed looking at a two-dimensional cat and happily fills in the blanks. These dots can use the full CMYK color model for prints, RGB(A) for digital images or a limited color space including greyscale.
Perhaps more interesting is the use of dithering to further trick the mind into seeing things that aren’t truly there by adding noise. Simply put, dithering is the process of adding noise to reduce quantization error, which in images shows up as artefacts like color banding. Within the field of digital audio dithering is also used, for similar reasons. Part of the process of going from an analog signal to a digital one involves throwing away data that falls outside the sampling rate and quantization depth.
By adding dithering noise these quantization errors are smoothed out, with the final effect depending on the dithering algorithm used.
If you are a certain age, you probably remember the ads and publicity around Chisanbop — the supposed ancient art of Korean finger math. Was it Korean? Sort of. Was it faster than a calculator? Sort of. [Chris Staecker] offers a great look at Chisanbop, not just how to do it, but also how it became such a significant cultural phenomenon. Take a look at the video below. Long, but worth it.
Technically, the idea is fairly simple. Your right-hand thumb is worth 5, and each finger is worth 1. So to identify 8, you hold down your thumb and the first three digits. The left hand has the same arrangement, but everything is worth ten times the right hand, so the thumb is 50, and each digit is worth 10.
With a little work, it is easy to count and add using this method. Subtraction is just the reverse. As you might expect, multiplication is just repeated addition. But the real story here isn’t how to do Chisanbop. It is more the story of how a Korean immigrant’s system went viral decades before the advent of social media.
You can argue that this is a shortcut that hurts math understanding. Or, you could argue the reverse. However, the truth is that this was around the time the calculator became widely available. Math education would shift from focusing on getting the right answer to understanding the underlying concepts. In a world where adding ten 6-digit numbers is easy with a $5 device, being able to do it with your fingers isn’t necessarily a valuable skill.
