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World desk4 min

Forrest Note: An ESP32 Voice Note Taker That Uses OpenAI Whisper

Forrest Note is a DIY ESP32-S3 voice-note project that sends recordings to OpenAI for Whisper transcription and saves organized Markdown notes to GitHub.
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Yes. Forrest Note is an ESP32-S3 firmware project for recording voice notes and turning them into organized Markdown notes with OpenAI’s API. It is a build project—not a ready-made consumer recorder—and its documented transcription workflow is cloud-based, not offline.

What Forrest Note does

Built on the Pala Note hardware and firmware foundation, Forrest Note is designed for a small e-paper voice-note device. Hold the record button, speak, then release it. The device records the audio and sends it to OpenAI’s Whisper API using the whisper-1 model. The transcript is then sent to gpt-4o-mini, which the project uses to create a one-word topic title, a one-sentence summary, a cleaned-up note body, topic tags, and sometimes calendar-event fields. The resulting note retains the raw transcript alongside the processed text. These are behaviors described in the Forrest Note repository README; they are not independent test results.

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The finished notes are Markdown files pushed to a GitHub repository you configure. That makes them available to a notes workflow such as Obsidian, but it also means the build depends on configuring both OpenAI and GitHub access.

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Is Whisper running on the ESP32?

No—not in the documented Forrest Note setup. The device captures the recording, but the README’s detailed workflow sends audio to OpenAI for transcription and sends the transcript to OpenAI for cleanup. You need an OpenAI API key with billing enabled and an internet connection; the project specifies 2.4 GHz Wi-Fi.

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The README describes the path as having no third-party server between the device and OpenAI or GitHub. That is the project’s description, not an independent security audit. Audio and note content still leave the device for those cloud services.

Which board does the project target?

Forrest Note specifies the Waveshare ESP32-S3 1.54-inch e-Paper AIoT Development Board, black-and-white non-G variant. The README identifies an N8R8 ESP32-S3 module with 8 MB flash and 8 MB OPI PSRAM, a 1.54-inch 200 × 200 e-paper display, onboard audio codec, microphone and speaker, microSD/TF storage, RTC, environmental sensor, LiPo charging, and 2.4 GHz Wi-Fi/BLE. The four-colour “1.54G” version is explicitly not the target. Check the exact variant and board revision against a current seller listing before buying.

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The board is the specified target, not proof that every similarly named ESP32-S3 board will work. Forrest Note credits the upstream Pala Note project for hardware bring-up, e-ink and audio/codec drivers, the recording engine, and the user interface. The case design belongs to Pala Note and is not redistributed in the Forrest Note repository.

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What you need to build it

  • The assembled Pala Note hardware based on the specified Waveshare board.
  • A USB-C cable and a computer for firmware configuration and flashing.
  • An OpenAI API key with billing enabled.
  • A GitHub repository and a fine-grained token with Contents read/write permissions.
  • Access to a 2.4 GHz Wi-Fi network.

The repository documents an Arduino-based build using Arduino ESP32 core 3.2.0, Adafruit GFX Library, ArduinoJson, OPI PSRAM, a custom partition table, and 8 MB flash settings. Follow the README for the firmware configuration and exact flashing instructions rather than assuming defaults for another ESP32 project apply. Its documented flashing procedure includes holding the record/BOOT button while connecting USB and keeping it held through the firmware write.

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Forrest Note says its additions are MIT-licensed and instructs builders to honor the upstream license for inherited portions. Check both repositories’ license terms before redistributing a build.

Whisper model and recording-size limits

whisper-1 is the model Forrest Note documents in its implementation. It should not be confused with OpenAI’s current general-purpose starting recommendation: OpenAI’s speech-to-text guide says to start with gpt-transcribe for new general-purpose transcription. The guide also sets a 25 MB maximum audio-file size for transcription requests and recommends compressing or splitting larger files. It warns against splitting mid-sentence because doing so can remove context and reduce accuracy. See OpenAI’s speech-to-text guide for current API guidance; plan recording length around the upload limit and check current documentation before building a long-recording workflow.

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How it differs from local ESP32 voice recognition

Espressif’s ESP-SR tools are an adjacent option, not a component identified in Forrest Note. Espressif describes an audio front end, wake-word engine, speech-command recognition, and speech synthesis; its cited getting-started overview specifies Chinese for speech synthesis. This kind of command-recognition setup is different from sending free-form dictation to a cloud transcription service. The ESP-SR documentation does not establish that Forrest Note uses ESP-SR.

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Espressif also documents the ESP32-S3-Korvo-1, a separate development board with a microphone array and offline speech-command recognition. That is not the board Forrest Note names, and the cited documentation does not establish firmware compatibility. See Espressif’s ESP32-S3-Korvo-1 guide for that distinct hardware.

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Who this project suits

  • Consider it if you want a DIY voice-note device that turns free-form speech into structured Markdown and can feed a GitHub-backed Obsidian workflow.
  • Look elsewhere if offline transcription is a requirement, you want a plug-and-play recorder, or you do not want audio and transcripts sent to cloud services.
  • Check before committing if you need a particular battery life, recording duration, transcription accuracy, board price, or availability. The project README does not establish those outcomes, and current board stock and pricing are not specified here.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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