ai

About a week ago, my home Internet (AT&T Fiber) went from the ~1 Gbps I pay for down to about 100 Mbps (see how I monitor my home Internet with a Pi). It wasn't too inconvenient, and I considered waiting it out to see if the speed recovered at some point, because latency was fine.

But as you can see around 7/7 on that graph, the 100 Mbps went down to about eight, and that's the point where my wife starts noticing how slow the Internet is. Action level.

So I fired up AT&T's support chat. I'm a programmer, I can usually find ways around the wily ways of chatbots.

Except AT&T's AI-powered chatbot seems to have a fiendish tendency to equate 'WiFi' with 'Internet', no doubt due to so many people thinking they are one and the same.

I'm in full-on procrastination mode with Open Sauce coming up in 10 days and a project I haven't started on for it, so I decided to try building the stable AI PC with all the AI accelerator chips I own:

• Hailo-8 (26 TOPS)
• Hailo-8L (13 TOPS)
• 2x Coral Dual Edge TPU (8+8 = 16 TOPS)
• 2x Coral Edge TPU (4+4 = 8 TOPS)

After my first faltering attempt in my testing of Raspberry Pi's new AI Kit, I decided to try building it again, but with a more 'proper' PCIe setup, with external 12V power to the PCIe devices, courtesy of an uPCIty Lite PCIe HAT for the Pi 5.

I'm... not sure it's that much less janky, but at least I had one board with a bunch of M.2 cards instead of many precariously stacked on top of each other!

Raspberry Pi today launched the AI Kit, a $70 addon which straps a Hailo-8L on top of a Raspberry Pi 5, using the recently-launched M.2 HAT (the Hailo-8L is of the M.2 M-key variety, and comes preinstalled).

The Hailo-8L's claim to fame is 3-4 TOPS/W efficiency, which, along with the Pi's 3-4W idle power consumption, puts it alongside Nvidia's edge devices like the Jetson Orin in terms of TOPS/$ and TOPS/W for price and efficiency.

Google's Coral TPU has been a popular choice for a machine learning/AI accelerator for the Pi for years now, but Google seems to have left the project on life support, after the Coral hardware was scalped for a couple years about as badly as the Raspberry Pi itself!

Besides the Pi 5 being approximately 2.5x faster for general compute, the addition of other blocks of the Arm architecture in the Pi 5's upgrade to A76 cores promises to speed up other tasks, too.

On the Pi 4, popular image processing models for object detection, pose detection, etc. would top out at 2-5 fps using the built-in CPU. Accessories like the Google Coral TPU speed things up considerably (and are eminently useful in builds like my Frigate NVR), but a Coral adds on $60 to the cost of your Pi project.

With the Pi 5, if I can double or triple inference speed—even at the expense of maxing out CPU usage—it could be worth it for some things.

Coral.ai TPUs are AI accelerators used for tasks like machine vision and audio processing. Raspberry Pis are often integrated into small robotics and IoT products—or used to analyze live video feeds with Frigate.

Until today, nobody I know of has been able to get a PCI Express Coral TPU working on the Raspberry Pi. The Compute Module 4, unfortunately, had some quirks in its PCIe implementation, preventing the use of the Coral over PCIe.

The Raspberry Pi 5 has a much improved PCIe bus—capable of reaching Gen 3 speeds even!—and I've already tested the first PCIe NVMe HATs for Pi 5.

So can the Pi 5 handle the Coral TPU natively over PCIe?

Yes. Though currently, you need to tweak a few things to get it working.

I recently tried setting up an M.2 Coral TPU on a machine running Debian 12 'Bookworm', which ships with Python 3.11, making the installation of the pyCoral library very difficult (maybe impossible for now?).

Some of the devs responded 'just install an older Ubuntu or Debian release' in the GitHub issues, as that would give me a compatible Python version (3.9 or earlier)... but in this case I didn't want to do that.

Late last year, OpenAI announced Whisper, a new speech-to-text language model that is extremely accurate in translating many spoken languages into text. The whisper repository contains instructions for installation and use.

tl;dr:

# Install whisper and its dependencies.
pip3 install git+https://github.com/openai/whisper.git 

# (When needed) Update whisper.
pip3 install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git

# Make sure ffmpeg is installed.
brew install ffmpeg

# Translate speech into text.
whisper my_audio_file.mp3 --language English

One thing I do quite regularly for my YouTube channel is extract the audio track, convert it to text using an online tool (I used to use Welder until they were bought out by Veed), and then hand-edit the file to fix references to product names, people, etc.

According to AI, this is me.

Or at least, what I look like if I deliver the prompt Jeff Geerling, realistic, photograph, sharp focus to Stable Diffusion, a machine learning, text-to-image model. But if you take that same prompt and paste it into the Stable Diffusion Demo, you'll get a different result.