Berry: add support for Tensorflow Lite for microcontrollers (including speech input)

[Staars]

Description:

Add module TFL to Berry in order to run generic TensorFlow Lite models.

Uses optimizations for ESP32 platform (kernels, ESP-DSP, RTOS) where possible.

Avoids boiler plate code as much as possible with a very short API (might be extended in the future if needed).

Can run specialized sessions to fulfill realtime requirements - first example is speech recognition, that can run on a simple ESP32 (no PSRAM mandatory, although it would be utilized). Will play nicely with Edgeimpulse, that provides an easy way to train a model (with compatible feature extraction!) from scratch.
IMPORTANT NOTE: Speech recognition on an ESP32 will never be as robust as on much more powerful smartphone, smart speakers and similar equipment!! But it can do some things surprisingly well.

Initial documentation: https://staars.github.io/docs/TFL/

Checklist:

• The pull request is done against the latest development branch
• Only relevant files were touched
• Only one feature/fix was added per PR and the code change compiles without warnings
• The code change is tested and works with Tasmota core ESP8266 V.2.7.4.9
• The code change is tested and works with Tasmota core ESP32 V.2.0.7
• I accept the CLA.

NOTE: The code change must pass CI tests. Your PR cannot be merged unless tests pass

[Jason2866]

Wow! Amazing work

[s-hadinger]

Awesome!

[TheChatty]

Amazing... can TFL also be used in to recognize water meter digits like in AI-on-the-edge?

[Staars]

Not yet, but this is on the to-do-list.
In fact my initial plan was to start with image recognition and especially digits recognition, but after playing with the speech recognition training pipeline of EdgeImpulse I was too curious to see, if this can be done for Tasmota.
For the next time I would like to consolidate the speech recognition part and provide simpler Berry templates.
After that I will try to add the webcam/image part, but I can not provide an exact time line.