RPI4B Dual SPH0645 RevA

Hardware-ASR HID Prototype

Raspberry Pi 4 project: microphone input, local transcription tests, and keyboard-style output.

Purpose

Original point of the project was to design a headset which can have more clarity on microphone input and restructure the output using a local speech recognition LLM called faster whisper.

The proof of concept combines embedded Linux, I²S audio hardware, local speech recognition, and Bluetooth HID keyboard output. Two MEMS microphones provide digital audio input and a way to compare microphone signals for cleaner offline transcription.

I would have chosen Optical MEMS over MEMS because its more precise using lasers over capacitate but only company which I looked into has it behind closed doors and not consumer available.

Hardware setup

• Raspberry Pi 4B with 2 GB RAM
• Two SPH0645-style I²S MEMS microphone breakouts
• MAX98357A I²S amplifier and small speaker for prototype audio/output testing
• Breadboard and jumper-wire prototype
• KiCad schematic, PCB layout, and Raspberry Pi wiring

Software pipeline

1. Record or load microphone audio from a WAV file.
2. Decode, combine the channels, and resample for a 16 kHz recognizer input.
3. Use push-to-record to capture a complete phrase.
4. Run local speech-to-text with faster-whisper on the Raspberry Pi.
5. Clean up punctuation and casing in the recognized text.
6. Send the final text to a phone or computer as Bluetooth keyboard input.

Backend code

The hardware-facing audio path is written in Rust rather than Python. It keeps capture and signal handling in compiled code while still producing standard WAV files for the recognizer.

• Capture — reads a fixed amount of stereo audio from the default input device.
• Split audio — returns overlapping borrowed slices without copying each chunk.
• Decode and resample — reads WAV input, combines channels, and writes 16 kHz mono WAV output.
• Compare channels — applies a Hann window, calculates the Fourier transforms, compares the spectra, and estimates a bounded sample delay. The FFT theory maps the equations to the Rust functions.

cd backend
cargo test
cargo run -- prepare input.wav ready.wav
cargo run -- capture captured.wav 3000

The automated tests use generated WAV data. The default-device capture path is implemented, but Raspberry Pi ALSA operation and capture latency have not been measured in this repository.

Hardware design

The Model 1 KiCad carrier brings the two microphone breakouts, Raspberry Pi harness, channel-select jumpers, clock-damping positions, and signal test points into one editable design. Both microphones share the I²S clock and data lines while opposite SELECT states place them in separate left and right slots.

• KiCad 10 ERC: 0 errors / 0 warnings
• KiCad 10 DRC: 0 violations / 0 unconnected pads

3d view	Two-layer PCB routing

Prototype

Raspberry Pi and microphone wiring	Prototype testing setup

What I learned

• The ASR model is only one part of the problem. Bad audio makes the rest of the pipeline look worse than it really is.
• A project like this needs measurable tests: microphone distance, background noise, latency, and transcription error rate.
• Voice Activity Detection can negatively affect transcription from microphone input; push-to-record improved accuracy during the proof of concept.
• Writing down the wiring and sample layout made the project easier to explain and debug.

Project files

• Model 1 KiCad project
• Rust audio backend
• FFT theory and original spectral model
• Project specifications workbook
• Original project overview
• Original audio pipeline

What did I make?

I built a Raspberry Pi automatic speech to text recognition proof-of-concept model with the intention of proving signal processing strategies using Fourier Transform. By using Rust for low latency response transcriptions from input, the MEMS microphones and a complimentary PCB design I incorporated several aspects of embedded, software and electrical engineering towards this project.

Primary references

• Knowles SPH0645LM4H-B datasheet
• Adafruit I²S MEMS Microphone Breakout guide
• Raspberry Pi 4 Model B product brief
• faster-whisper