Tutorial ini adalah bagian dari: Makerfabs MaTouch AI ESP32S3 2.8" Camera
The latest MaTouch AI board integrate I2S voice input/I2S speaker/ 3 million camera OV3660/ 320*240 resolution display, with ESP32S3 strong processor& Wifi ability, to make this board a good tool/platform for AI development with ESP32.
Makerfabs MaTouch ESP32-S3 2.8" kamera Bangun Asisten Suara AI di ESP32-S3 (Azure + DeepSeek)
Tahan sebuah tombol, ajukan pertanyaan, dan papan menjawab dengan suara lantang
Asisten suara lengkap dalam satu papan kecil. Tahan tombol SPEAK dan ajukan pertanyaan. Papan merekam Anda dengan kedua mikrofon, mengirim audio ke Microsoft Azure untuk diubah menjadi teks, mengirim teks tersebut ke DeepSeek untuk dipikirkan, mengirim jawaban kembali ke Azure untuk diubah menjadi ucapan, dan memutarnya melalui speaker internalnya. Seluruh percakapan muncul di layar sebagai gelembung obrolan.
Asisten AI Suara yang Berbicara berjalan di papan MaTouch AI ESP32-S3
Apa yang terjadi sejak Anda menekan tombol
Inilah seluruh perjalanan satu pertanyaan, langkah demi langkah. Layak dibaca sekali, karena semua yang Anda lihat di layar dan di LED berhubungan dengan salah satu tahap ini.
Anda menekan dan TAHAN tombol SPEAK. Ini adalah tahan-untuk-berbicara, bukan ketuk-untuk-berbicara: perekaman berjalan tepat selama jari Anda menahan, hingga enam detik. LED status berubah menjadi biru dan tombol menampilkan LISTENING.
Kedua mikrofon merekam Anda. Papan mengambil sampel 16.000 kali per detik dari pasangan stereo, merata-ratakan dua saluran menjadi satu, menerapkan sedikit penguatan, dan menyimpan hasilnya di PSRAM. Bilah kemajuan merayap melintasi tombol saat Anda berbicara. Dua detik ucapan sekitar 64 KB.
Anda melepaskan tombol. Perekaman berhenti. Papan menulis header WAV 44-byte di depan audio - label kecil itulah yang mengubah sampel mentah menjadi file yang akan diterima Azure.
Audio dikirim ke Azure Speech-to-Text. Audio diunggah dalam potongan 4 KB melalui koneksi aman, dan kembali sebagai satu baris teks. Suara 64 KB Anda telah menjadi sekitar 25 byte tulisan. LED berubah menjadi kuning.
Pertanyaan Anda muncul di layar sebagai gelembung obrolan biru, sehingga Anda dapat melihat dengan tepat apa yang didengarnya - yang berguna, karena kata-kata yang salah dengar menjelaskan sebagian besar jawaban aneh.
Teks dikirim ke DeepSeek. Papan mengirim pertanyaan Anda plus instruksi tetap untuk menjaga jawaban dalam dua kalimat pendek. Model berpikir - benar-benar berpikir, ini adalah model penalaran - dan mengembalikan jawaban.
Jawaban muncul di layar sebagai gelembung abu-abu. Anda dapat membacanya sebelum mendengarnya.
Jawaban dikirim kembali ke Azure untuk diucapkan. Papan meminta audio PCM mentah 16 kHz, yang persis format yang diinginkan amplifier-nya, sehingga tidak ada decoder MP3 di mana pun dalam proyek ini. Tombol sekarang menampilkan GETTING VOICE dan LED tetap kuning, karena belum ada yang terdengar.
Seluruh klip diunduh ke PSRAM sebelum satu sampel pun diputar. Ini penting - lihat catatan di bawah.
Pemutaran. Saat audio diserahkan ke speaker, LED berubah menjadi hijau dan tombol menampilkan SPEAKING. Anda mendengar jawabannya.
Mengapa audio diunduh terlebih dahulu alih-alih diputar saat tiba. Streaming langsung dari jaringan ke speaker terdengar seperti ketukan. Buffer speaker hanya menampung sekitar sepersepuluh detik, dan setiap jeda dalam transfer WiFi yang lebih lama dari itu mengosongkannya, menghasilkan ketukan yang terdengar. Mengunduh seluruh jawaban ke PSRAM terlebih dahulu memakan sekitar satu detik waktu tunggu ekstra dan menghilangkan setiap celah. Itulah juga mengapa layar menampilkan GETTING VOICE sebelum menampilkan SPEAKING - keduanya memang tahap yang berbeda.
Berapa lama setiap tahap berlangsung
Diukur pada perangkat keras nyata, untuk pertanyaan sederhana:
Tahap | Waktu tipikal |
|---|---|
Perekaman | selama Anda menahan tombol |
Ucapan-ke-teks (Azure) | sekitar 1,8 detik |
Berpikir (DeepSeek) | sekitar 1,8 detik untuk pertanyaan sederhana, jauh lebih lama untuk yang membutuhkan perhitungan nyata |
Mengambil suara (Azure) | sekitar 7 detik - bagian terbesar |
Total, dari pelepasan hingga suara pertama | kira-kira 11 detik |
Setiap pertukaran mencetak waktu sendiri ke monitor serial, sehingga Anda dapat mengukur sendiri daripada mempercayai ini. Jika ingin lebih cepat, perubahan paling efektif adalah meminta jawaban yang lebih pendek di SYSTEM_PROMPT - lebih sedikit teks untuk diucapkan berarti lebih sedikit audio yang perlu disintesis dan diunduh.
Papan itu sendiri tidak pernah memahami apa pun. Ia adalah utusan dengan telinga yang baik dan suara yang baik - kecerdasannya disewa per detik.
Mengapa tiga layanan ini
Azure menangani ucapan masuk dan keluar. Text-to-speech-nya dapat mengembalikan PCM mentah 16 kHz, yang persis yang diinginkan chip speaker, sehingga tidak ada decoder MP3 di mana pun dalam proyek ini. Speech-to-text-nya menerima WAV biasa dalam POST biasa.
DeepSeek adalah otak percakapan. Ia cepat dan biayanya kurang dari satu sen per jawaban.
OpenAI tidak digunakan di sini - lihat proyek 05, di mana ia melakukan pekerjaan visi.
Nama model DeepSeek berubah. Nama lama deepseek-chat dan deepseek-reasoner dihentikan pada Juli 2026. Sebagian besar tutorial online masih menggunakannya dan akan mengembalikan kesalahan. Nama saat ini adalah deepseek-v4-flash dan deepseek-v4-pro. Proyek ini menggunakan v4-flash.
Jebakan model penalaran
DeepSeek v4-flash berpikir sebelum menjawab, dan pemikiran itu diperhitungkan dalam batas token Anda. AturLLM_MAX_TOKENS terlalu rendah dan seluruh anggaran habis untuk penalaran, jawaban kembali kosong, dan papan tidak mengatakan apa-apa. Di sini diatur ke 400 karena alasan itu. Pertanyaan sulit juga memakan waktu lebih lama - jawaban fakta sederhana sekitar dua detik, pertanyaan yang membutuhkan pengerjaan nyata bisa memakan waktu jauh lebih lama.Membaca lampu status
Warna | Arti |
|---|---|
Biru | mendengarkan Anda |
Kuning | cloud sedang berpikir, atau suara sedang diambil |
Hijau | berbicara - berubah hijau tepat saat suara mulai |
Merah | sesuatu gagal - periksa monitor serial |
Kontrol di layar
Obrolan bergulir seperti percakapan telepon, pesan tertua bergerak ke atas dan menghilang. CLEAR menghapusnya. Meter sinyal WiFi dengan pembacaan dBm aktual berada di sudut, yang berguna ketika Anda bertanya-tanya apakah balasan lambat disebabkan jaringan atau layanan.
Tentang papan MaTouch AI ESP32-S3 2.8"
Setiap proyek di halaman ini berjalan di MaTouch AI ESP32-S3 2.8" TFT ST7789V dari Makerfabs. Ini adalah papan serba bisa: layar sentuh berwarna, kamera 3 megapiksel, dua mikrofon, dan amplifier speaker sungguhan, semuanya digerakkan oleh ESP32-S3 dengan 8 MB PSRAM. Kombinasi itulah yang memungkinkan proyek AI ini berjalan di satu papan tanpa perangkat tambahan lain.
8 MB PSRAM lebih penting daripada angka lain di sini. Itulah yang memungkinkan papan menyimpan bingkai kamera, beberapa detik audio rekaman, atau foto berenkode base64 dalam memori secara bersamaan - tidak ada yang muat di RAM normal ESP32.
Dokumentasi pabrikan: Halaman wiki Makerfabs.
Spesifikasi utama
Prosesor: ESP32-S3, dual core 240 MHz, WiFi 2.4 GHz + Bluetooth 5.0
Memori: 16 MB flash, 8 MB PSRAM (diperlukan oleh hampir semua proyek di sini)
Layar: 2.8" IPS, 320×240, driver ST7789V, SPI
Sentuh: GT911 kapasitif, melacak 5 jari sekaligus
Kamera: OV3660, 3 megapiksel, hingga 2048×1536
Mikrofon: dua INMP441 I2S digital (pasangan stereo asli)
Speaker: amplifier kelas-D MAX98357A, 3.2 W ke 4 Ω
Penyimpanan: slot kartu microSD (mode SPI)
Daya: USB-C, konektor baterai JST, pengisi daya TP4056, sakelar daya
Juga di papan: LED RGB WS2812B, jam waktu nyata bertenaga baterai PCF8563T, dan pengukur baterai MAX17048 yang tidak tercantum dalam spesifikasi resmi
Dua port USB-C tidak sama. Speaker papan berbagi pin sinyalnya (IO19 dan IO20) dengan port USB native, karena pin tersebut adalah jalur data USB bawaan ESP32-S3. Selalu unggah dan beri daya melalui port USB-C CH340K (yang di samping tombol RESET), dan atur USB CDC On Boot ke Disabled. Gunakan port yang salah dan audio akan bermasalah atau unggahan gagal.
Pengaturan Arduino IDE
Pengaturan ini penting. Sebagian besar masalah yang dilaporkan orang dengan papan ini adalah salah satu dari ini yang salah, dan pengaturan tersebut kembali ke default saat Anda mengubah versi inti, jadi periksa lagi setelah perubahan apa pun.
Pengaturan | Nilai |
|---|---|
Board | ESP32S3 Dev Module |
Versi inti ESP32 | 2.0.17 |
PSRAM | OPI PSRAM |
Ukuran Flash | 16MB (128Mb) |
Skema Partisi | 16M Flash (3MB APP/9.9MB FATFS) |
USB CDC On Boot | Disabled |
Kecepatan Unggah | 921600 |
Hapus Semua Flash Sebelum Unggah | Disabled |
Port | port USB-C CH340K |
Gunakan inti ESP32 2.0.17, bukan 3.x. Espressif menghapus model deteksi wajah di perangkat pada inti 3, sehingga proyek wajah tidak akan bisa dikompilasi di sana. Mengunci 2.0.17 membuat setiap proyek di halaman ini berfungsi dengan satu konfigurasi. Di Boards Manager, menu dropdown versi memungkinkan Anda berpindah bolak-balik kapan saja.
Gunakan GFX Library for Arduino versi 1.5.6, bukan 1.6.x. Rilis 1.6 dibuat untuk inti ESP32 3 dan bisa macet saat start-up pada inti 2.0.17. Jika layar Anda tetap hitam setelah mengunggah, ini hal pertama yang perlu diperiksa.
Pustaka yang diperlukan
Instal ini melalui Tools → Manage Libraries di Arduino IDE. Nomor versi penting - harap gunakan yang tercantum.
Pustaka | Versi | Penulis |
|---|---|---|
GFX Library for Arduino | 1.5.6 | moononournation |
bb_captouch | 1.3.1 | Larry Bank |
ArduinoJson | 7.x | Benoit Blanchon |
Adafruit NeoPixel | versi terbaru apa pun | Adafruit |
Menyiapkan secrets.h
Detail WiFi Anda dan kunci API apa pun dimasukkan ke dalam secrets.h, yang disertakan dalam unduhan dengan nilai placeholder. Buka tab tersebut di Arduino IDE dan ganti dengan milik Anda sendiri.
WiFi harus 2.4 GHz. ESP32-S3 tidak dapat melihat jaringan 5 GHz sama sekali. Jika router Anda menggabungkan kedua pita dalam satu nama (Asus menyebutnya Smart Connect), matikan fitur tersebut atau beri nama khusus untuk pita 2.4 GHz dan gunakan nama itu di secrets.h.
Mendapatkan kunci API Anda
Proyek ini terhubung ke layanan AI cloud, jadi Anda memerlukan kunci sendiri. Jika Anda belum pernah melakukan ini sebelumnya, jangan khawatir - ini sama seperti kata sandi yang mengidentifikasi akun Anda ke layanan tersebut. Hanya butuh beberapa menit, sekali saja.
Kunci bukanlah langganan ke situs web. Membayar ChatGPT Plus, misalnya, tidak memberi Anda kunci API - keduanya adalah produk terpisah dengan penagihan terpisah. Anda perlu akun di platform pengembang, seperti yang dijelaskan di bawah.
Microsoft Azure Speech - untuk mendengarkan dan berbicara
Azure mengubah ucapan Anda menjadi teks dan mengubah jawaban kembali menjadi suara. Tingkat gratisnya cukup besar untuk semua yang ada di halaman ini.
Buka portal.azure.com dan masuk dengan akun Microsoft (akun gratis sudah cukup).
Jika Anda belum pernah menggunakan Azure sebelumnya, Anda akan melihat layar Welcome to Azure yang menawarkan tiga pilihan. Pilih Start with an Azure free trial - Anda perlu langganan sebelum Azure mengizinkan Anda membuat apa pun. (Pelajar sebaiknya memilih Azure for Students: hasilnya sama, tanpa perlu kartu.) Abaikan Manage Microsoft Entra ID, yang merupakan hal yang sama sekali berbeda.
Klik Create a resource, cari Speech, dan pilih Speech service yang diterbitkan oleh Microsoft.
Isi formulir: grup sumber daya apa pun, nama apa pun, dan pilih Region yang dekat dengan Anda - catat wilayah tersebut persis seperti yang muncul, misalnya
eastus.Untuk Pricing tier pilih F0 (Free). Itu memungkinkan sekitar lima jam pengenalan ucapan dan setengah juta karakter sintesis ucapan setiap bulan.
Klik Review + create, lalu Create. Tunggu sekitar satu menit, lalu klik Go to resource.
Di menu sebelah kiri, buka Keys and Endpoint. Salin KEY 1 dan Location/Region.
Masukkan itu ke dalam secrets.h sebagai AZURE_SPEECH_KEY dan AZURE_REGION. Untuk AZURE_STT_HOST, gunakan <region>.stt.speech.microsoft.com - jadi dengan wilayah eastus itu menjadi eastus.stt.speech.microsoft.com.
Tentang kartu kredit. Uji coba gratis Azure meminta kartu untuk memverifikasi identitas Anda. Itu tidak mengenakan biaya. Anda mendapatkan kredit $200 selama 30 hari, dan setelah itu akun beralih ke Pay-As-You-Go - tetapi tingkat F0 Speech tetap gratis, bulan demi bulan, dan semua yang ada di proyek ini muat dengan nyaman di dalamnya. Jika Anda lebih suka tidak memberikan kartu sama sekali dan Anda seorang pelajar, opsi Azure for Students memberi Anda kredit tanpa kartu.
Itu harus berupa sumber daya "Speech service". Kunci dari sumber daya Translator, Language, atau Cognitive Services umum terlihat identik dan sepenuhnya valid - tetapi setiap permintaan ucapan mengembalikan kesalahan 401. Ini menimpa kami selama pengujian dan menghabiskan satu jam. Jika ucapan gagal dengan 401 sementara kunci terlihat benar, periksa jenis sumber daya apa yang Anda buat.
DeepSeek - bagian berpikir
DeepSeek adalah model bahasa yang sebenarnya menjawab pertanyaan Anda. Harganya murah - kredit beberapa dolar mencakup ribuan balasan.
Buka platform.deepseek.com dan buat akun.
Buka API keys di menu dan klik Create new API key.
Salin segera. Kunci hanya ditampilkan sekali dan tidak akan pernah lagi - jika hilang, hapus kunci itu dan buat yang baru.
Tambahkan sejumlah kecil kredit di bawah Top up. Tidak ada tingkat gratis, tetapi pengisian terkecil bertahan sangat lama dengan penggunaan ini.
Masukkan kunci ke dalam secrets.h sebagai DEEPSEEK_KEY. Kunci dimulai dengan sk-.
Nama model berubah pada Juli 2026. deepseek-chat dan deepseek-reasoner lama telah dihentikan, jadi sebagian besar tutorial yang Anda temukan online akan gagal dengan kesalahan 400. Gunakan deepseek-v4-flash, yang sudah diatur oleh proyek-proyek ini.
Berapa biaya untuk menjalankannya
Sangat sedikit, tetapi tidak gratis, dan Anda harus tahu kira-kira berapa yang Anda keluarkan sebelum meninggalkan proyek berjalan.
Layanan | Perkiraan biaya |
|---|---|
Azure Speech | tingkat gratis mencakup sekitar 5 jam mendengarkan dan 0,5 Juta karakter berbicara per bulan |
DeepSeek | sebagian kecil sen per jawaban - ribuan balasan untuk beberapa dolar |
OpenAI vision | kira-kira satu atau dua sen per gambar, tergantung modelnya |
Harga berubah, jadi anggap ini sebagai panduan, bukan penawaran. Setiap layanan ini memiliki halaman penggunaan tempat Anda dapat memantau pengeluaran, dan semuanya memungkinkan Anda menetapkan batas pengeluaran - yang layak dilakukan sejak hari pertama.
Jaga kunci Anda tetap privat. Siapa pun yang memilikinya dapat menghabiskan uang Anda. Jangan menaruhnya di video, tangkapan layar, postingan forum, atau repositori kode publik. Jika sebuah kunci pernah terekspos, hapus di situs web penyedia dan buat yang baru - hanya butuh beberapa detik, dan itu satu-satunya perbaikan yang nyata.
Pemecahan Masalah
Gejala | Penyebab dan perbaikan |
|---|---|
Layar tetap hitam | Versi pustaka GFX salah (gunakan 1.5.6) atau pengaturan papan salah. |
|
|
Tidak ada yang terunggah / tidak ada port COM | Port USB-C salah, atau driver CH340 tidak terpasang. |
Kamera gagal dan tidak pernah pulih | Jalur reset kamera terhubung ke tombol RESET papan, sehingga perangkat lunak tidak dapat memulai ulang. Tekan RESET. Jika masih gagal, pasang kembali kabel pita kamera. |
Unduh kodenya
Sketsa Arduino lengkap untuk proyek ini, bersama dengan pins.h dan semua yang dibutuhkannya, dapat diunduh secara gratis.
Ekstrak, buka file .ino di Arduino IDE, periksa pengaturan di atas, dan unggah melalui port USB-C CH340K.
Tutorial ini adalah bagian dari: Makerfabs MaTouch AI ESP32S3 2.8" Camera
/*
* ===========================================================================
* 04_Voice_Assistant — MaTouch AI ESP32-S3 2.8" TFT ST7789V
* ===========================================================================
*
----------
* ROBOJAX.COM - MaTouch AI ESP32-S3 2.8" project series
*
* WATCH THE VIDEO
* https://youtu.be/6AL3g3tC_Hk
*
* WRITTEN TUTORIALS - every project, with photos and full explanation
* Camera and touchscreen.... https://robojax.com/RTJ849
* Offline face recognition.. https://robojax.com/RTJ850
* AI voice assistant........ https://robojax.com/RTJ851
* AI vision................. https://robojax.com/RTJ852
*
* GET THE BOARD - SAVE $5 with coupon code: Robojax_Makerfab
* https://www.makerfabs.com/matouch-ai-esp32s3-2-8-tft-st7789v.html
* (enter the code at checkout)
*
* All of this code is free. If it helped you, a subscribe on YouTube is
* the best way to support more of it.
*
* ---------------------------------------------------------------------------
*
* A complete voice assistant on a $40 board:
*
* hold SPEAK -> both INMP441 microphones record you
* -> Azure Speech turns the audio into text
* -> DeepSeek v4-flash thinks of an answer
* -> Azure Speech turns the answer into audio
* -> the MAX98357 speaker says it out loud
*
* and the whole conversation is drawn as chat bubbles on the touchscreen.
*
* WHY THIS COMBINATION OF SERVICES (each is used where it is best):
* - Azure STT accepts a plain WAV in a plain POST with one header. OpenAI's
* transcription endpoint wants multipart/form-data - miserable on an MCU.
* - Azure TTS can return RAW 16 kHz PCM ("riff-16khz-16bit-mono-pcm"),
* which streams straight into the I2S speaker with NO MP3 decoder at all.
* - DeepSeek v4-flash is fast and nearly free per reply. NOTE: the old
* model names deepseek-chat / deepseek-reasoner were RETIRED in July 2026.
* Most tutorials online still use them and are broken. See secrets.h.
*
* A detail the vendor examples get wrong: this board has TWO microphones on
* one I2S bus (left + right), but every Makerfabs demo records left-only and
* throws one away. This sketch records both and averages them.
*
* ---------------------------------------------------------------------------
* *** WHICH USB PORT - THIS MATTERS ***
* The speaker shares IO19/IO20 with the NATIVE USB port. Upload and power
* through the CH340K UART USB-C port, and set USB CDC On Boot = Disabled.
* If you use the wrong port the audio will be garbage or uploads will fail.
* ---------------------------------------------------------------------------
*
* FILL IN secrets.h BEFORE FLASHING (WiFi + all three API keys).
*
* BOARD SETTINGS (Tools menu - EVERY line matters, wrong = black screen
* or compile errors. These reset when you switch cores - recheck them!)
*
* Board : ESP32S3 Dev Module
* ESP32 core : 2.0.17
* PSRAM : OPI PSRAM <-- required, audio buffer lives there
* Flash Size : 16MB (128Mb)
* Partition Scheme : 16M Flash (3MB APP/9.9MB FATFS)
* USB CDC On Boot : Disabled <-- required, see USB note above
* Upload Speed : 921600
* Port : the CH340K USB-C port (the one near RESET)
*
* LIBRARIES
* GFX Library for Arduino v1.5.6 (NOT 1.6.x - that pairs with core 3)
* bb_captouch v1.3.1
* ArduinoJson v7.x
* Adafruit NeoPixel any recent
*
* ---------------------------------------------------------------------------
* FUNCTIONS IN THIS SKETCH
* led(r,g,b) + LED_* macros RGB status colours (blue/amber/green/red)
* getTouch(&x,&y) read the touch panel, mapped to screen coordinates
* speakButtonHeld() true while a finger is on the SPEAK button
* bubbleLines(t) how many lines a message wraps to
* drawOneBubble(m,y) draw a single chat bubble
* redrawChat() rebuild the chat area from history, newest at bottom
* clearChat() wipe the chat history (CLEAR button)
* chatBubble(t,user) add a message to history and redraw
* drawWifi() WiFi signal bars + dBm readout
* drawBar(label,col) bottom bar: SPEAK button + CLEAR + WiFi meter
* micInit() I2S input - BOTH INMP441 mics, stereo
* spkInit() I2S output - MAX98357 speaker
* recordWhileHeld() record while SPEAK held, downmix stereo->mono
* writeWavHeader(...) prepend the 44-byte RIFF/WAVE header
* dumpWavToSD(...) save the exact upload to SD (/stt_debug.wav)
* readHttpResponse() read an HTTPS reply, de-chunking it properly
* azureSTT(...) chunked upload of the WAV -> recognised text
* deepseekChat(...) question -> deepseek-v4-flash -> answer text
* azureTTSSpeak(text) answer -> Azure voice -> PSRAM -> speaker
* setup() / loop() boot + WiFi / one conversation turn per press
*
* Robojax.com
* ===========================================================================
*/
#include <Arduino_GFX_Library.h>
#include <bb_captouch.h>
#include <Adafruit_NeoPixel.h>
#include <ArduinoJson.h>
#include <WiFi.h>
#include <WiFiClientSecure.h>
#include <HTTPClient.h>
#include <SPI.h>
#include <SD.h>
#include "driver/i2s.h"
#include "pins.h"
#include "secrets.h"
/* --- audio geometry ------------------------------------------------------- */
#define SAMPLE_RATE 16000
#define RECORD_MAX_S 6 // hard cap on one question
#define WAV_HEADER_LEN 44
#define REC_BUF_BYTES (SAMPLE_RATE * RECORD_MAX_S * 2) // 16-bit mono
/* Software gain applied to the recording. The INMP441 capture is quiet at
* 16-bit depth; if the serial monitor reports "mic peak" under ~10% while you
* speak normally, raise this (6 -> 10 -> 16). If it reports clipping (100%),
* lower it. */
#define MIC_GAIN 6
/* 1 = read BOTH microphones (stereo bus) and average them - better SNR.
* 0 = vendor-style single left mic. Use 0 as a fallback if recordings come
* back silent or garbled in stereo mode. */
#define USE_BOTH_MICS 1
/* Status LED brightness, 0-255. The WS2812 runs from the power rail and is
* uncomfortably bright at full power - 25 is plenty visible on camera. */
#define LED_BRIGHTNESS 25
/* HWSPI (not ESP32SPI): the debug WAV dump writes to the SD card, which
* shares these pins - both must go through the same SPI driver. */
Arduino_HWSPI *bus = new Arduino_HWSPI(
TFT_DC, TFT_CS, TFT_SCLK, TFT_MOSI, TFT_MISO, &SPI, true);
Arduino_GFX *gfx = new Arduino_ST7789(bus, TFT_RES, 1, true);
BBCapTouch bbct;
Adafruit_NeoPixel rgb(RGB_LED_NUM, RGB_LED_PIN, NEO_GRB + NEO_KHZ800);
/* --- big buffers live in PSRAM, allocated once at boot -------------------- */
uint8_t *wav_buf = nullptr; // WAV_HEADER_LEN + up to REC_BUF_BYTES
/* --- state ---------------------------------------------------------------- */
enum State { ST_IDLE, ST_RECORDING, ST_STT, ST_LLM, ST_TTS, ST_ERROR };
State state = ST_IDLE;
/* --- diagnostics: shown ON SCREEN so the serial monitor is optional -------- */
bool ok_sd = false;
float g_mic_peak_pct = 0; // last recording's raw peak, % of full scale
char g_stt_err[64] = ""; // last STT failure cause, verbatim
char g_llm_err[64] = ""; // last DeepSeek failure cause, verbatim
/* --- layout --------------------------------------------------------------- */
#define CHAT_H 200 // chat area: y 0..199
#define BAR_Y 202 // button bar below it
#define BTN_SPEAK_X 4
#define BTN_SPEAK_W 160
#define BTN_CLEAR_X 170
#define BTN_CLEAR_W 58
#define WIFI_X 236 // signal indicator, right end of the bar
#define BTN_H 36
/* --- chat history: last 8 messages, redrawn newest-at-bottom like a phone.
* This is what prevents new text printing over old - the whole area is
* rebuilt from history on every message, older lines scroll up and out. */
#define CHAT_HISTORY 8
struct ChatMsg { char text[160]; bool from_user; };
ChatMsg chat_hist[CHAT_HISTORY];
int chat_count = 0;
/* --- LED status colours: visible from across the room --------------------- */
void led(uint8_t r, uint8_t g, uint8_t b) {
rgb.setPixelColor(0, rgb.Color(r, g, b));
rgb.show();
}
#define LED_IDLE() led(0, 0, 0)
#define LED_LISTEN() led(0, 60, 255) // blue - recording
#define LED_THINK() led(255, 120, 0) // amber - waiting on the cloud
#define LED_SPEAK() led(0, 255, 40) // green - talking
#define LED_ERROR() led(255, 0, 0) // red
/* ===========================================================================
* Touch
* =========================================================================== */
bool getTouch(uint16_t *x, uint16_t *y) {
TOUCHINFO ti;
if (!bbct.getSamples(&ti)) return false;
if (ti.count < 1) return false;
*x = ti.y[0];
*y = (ti.x[0] > 240) ? 0 : (240 - ti.x[0]);
return true;
}
bool speakButtonHeld() {
uint16_t x, y;
if (!getTouch(&x, &y)) return false;
return (x >= BTN_SPEAK_X && x < BTN_SPEAK_X + BTN_SPEAK_W && y >= BAR_Y);
}
/* ===========================================================================
* Chat UI — word-wrapped bubbles, user right/blue, assistant left/grey
* =========================================================================== */
#define CHAT_CHARS 42 // chars per line at textsize 1
static int bubbleLines(const char *t) {
int l = ((int)strlen(t) + CHAT_CHARS - 1) / CHAT_CHARS;
return l < 1 ? 1 : l;
}
void drawOneBubble(const ChatMsg &m, int y) {
int len = strlen(m.text);
int lines = bubbleLines(m.text);
int h = lines * 10 + 8;
uint16_t bg = m.from_user ? gfx->color565(0, 70, 140) : gfx->color565(50, 50, 55);
int w = (len > CHAT_CHARS ? CHAT_CHARS : len) * 6 + 10;
if (w < 30) w = 30;
int x = m.from_user ? (316 - w) : 4;
gfx->fillRoundRect(x, y, w, h, 5, bg);
gfx->setTextSize(1);
gfx->setTextColor(WHITE);
for (int i = 0; i < lines; i++) {
char line[CHAT_CHARS + 1] = {0};
strncpy(line, m.text + i * CHAT_CHARS, CHAT_CHARS);
gfx->setCursor(x + 5, y + 5 + i * 10);
gfx->print(line);
}
}
/* Rebuild the whole chat area from history: newest message anchored at the
* bottom, older ones stacked upward until the area is full. */
void redrawChat() {
gfx->fillRect(0, 0, 320, CHAT_H, BLACK);
int shown = min(chat_count, CHAT_HISTORY);
int y = CHAT_H - 2;
for (int i = 0; i < shown; i++) {
ChatMsg &m = chat_hist[(chat_count - 1 - i) % CHAT_HISTORY];
int h = bubbleLines(m.text) * 10 + 8;
y -= h;
if (y < 0) break; // area full - older ones drop off
drawOneBubble(m, y);
y -= 4;
}
}
void clearChat() {
chat_count = 0;
redrawChat();
}
void chatBubble(const char *text, bool from_user) {
ChatMsg &m = chat_hist[chat_count % CHAT_HISTORY];
strncpy(m.text, text, sizeof(m.text) - 1);
m.text[sizeof(m.text) - 1] = 0;
m.from_user = from_user;
chat_count++;
redrawChat();
}
/* WiFi bars + dBm, right end of the button bar. Refreshed from the loop. */
void drawWifi() {
gfx->fillRect(WIFI_X, BAR_Y, 320 - WIFI_X, BTN_H, BLACK);
bool up = (WiFi.status() == WL_CONNECTED);
long rssi = up ? WiFi.RSSI() : -100;
// -55 dBm or better = full bars; each 10 dB drops one
int bars = rssi > -55 ? 4 : rssi > -65 ? 3 : rssi > -75 ? 2 : rssi > -85 ? 1 : 0;
for (int b = 0; b < 4; b++) {
int bh = 6 + b * 6; // heights 6,12,18,24
uint16_t col = (b < bars) ? GREEN : gfx->color565(60, 60, 60);
gfx->fillRect(WIFI_X + 2 + b * 8, BAR_Y + 28 - bh, 6, bh, col);
}
gfx->setTextSize(1);
gfx->setCursor(WIFI_X + 38, BAR_Y + 6);
if (up) {
gfx->setTextColor(CYAN);
gfx->printf("%lddBm", rssi);
} else {
gfx->setTextColor(RED);
gfx->print("DOWN");
}
gfx->setCursor(WIFI_X + 38, BAR_Y + 18);
gfx->setTextColor(gfx->color565(120, 120, 120));
gfx->print("WiFi");
}
void drawBar(const char *label, uint16_t colour) {
gfx->fillRect(0, BAR_Y, 320, 240 - BAR_Y, BLACK);
gfx->fillRoundRect(BTN_SPEAK_X, BAR_Y, BTN_SPEAK_W, BTN_H, 6, colour);
gfx->drawRoundRect(BTN_SPEAK_X, BAR_Y, BTN_SPEAK_W, BTN_H, 6, WHITE);
gfx->setTextSize(2);
gfx->setTextColor(WHITE);
gfx->setCursor(BTN_SPEAK_X + 10, BAR_Y + 10);
gfx->print(label);
// CLEAR wipes the chat history
gfx->fillRoundRect(BTN_CLEAR_X, BAR_Y, BTN_CLEAR_W, BTN_H, 6, gfx->color565(110, 35, 35));
gfx->drawRoundRect(BTN_CLEAR_X, BAR_Y, BTN_CLEAR_W, BTN_H, 6, WHITE);
gfx->setTextSize(1);
gfx->setTextColor(WHITE);
gfx->setCursor(BTN_CLEAR_X + 14, BAR_Y + 15);
gfx->print("CLEAR");
drawWifi();
}
/* ===========================================================================
* I2S — microphones on port 0, speaker on port 1. Separate hardware
* ports, so recording and playback can never fight over a bus.
* =========================================================================== */
void micInit() {
i2s_config_t cfg = {
.mode = (i2s_mode_t)(I2S_MODE_MASTER | I2S_MODE_RX),
.sample_rate = SAMPLE_RATE,
.bits_per_sample = I2S_BITS_PER_SAMPLE_16BIT,
/* BOTH channels - this is the two-microphone fix. The vendor examples
* use ONLY_LEFT here and waste the second microphone. USE_BOTH_MICS 0
* falls back to the vendor-proven single-mic configuration. */
#if USE_BOTH_MICS
.channel_format = I2S_CHANNEL_FMT_RIGHT_LEFT,
#else
.channel_format = I2S_CHANNEL_FMT_ONLY_LEFT,
#endif
.communication_format = I2S_COMM_FORMAT_STAND_I2S,
.intr_alloc_flags = ESP_INTR_FLAG_LEVEL1,
.dma_buf_count = 8,
.dma_buf_len = 256,
.use_apll = false,
.tx_desc_auto_clear = false,
.fixed_mclk = 0
};
i2s_pin_config_t pins = {
.mck_io_num = I2S_PIN_NO_CHANGE,
.bck_io_num = I2S_MIC_SCK,
.ws_io_num = I2S_MIC_WS,
.data_out_num = I2S_PIN_NO_CHANGE,
.data_in_num = I2S_MIC_SD
};
i2s_driver_install(I2S_MIC_PORT, &cfg, 0, NULL);
i2s_set_pin(I2S_MIC_PORT, &pins);
}
void spkInit() {
i2s_config_t cfg = {
.mode = (i2s_mode_t)(I2S_MODE_MASTER | I2S_MODE_TX),
.sample_rate = SAMPLE_RATE,
.bits_per_sample = I2S_BITS_PER_SAMPLE_16BIT,
.channel_format = I2S_CHANNEL_FMT_ONLY_LEFT, // mono - the amp downmixes anyway
.communication_format = I2S_COMM_FORMAT_STAND_I2S,
.intr_alloc_flags = ESP_INTR_FLAG_LEVEL1,
.dma_buf_count = 8,
.dma_buf_len = 256,
.use_apll = false,
.tx_desc_auto_clear = true,
.fixed_mclk = 0
};
i2s_pin_config_t pins = {
.mck_io_num = I2S_PIN_NO_CHANGE,
.bck_io_num = I2S_SPK_BCLK,
.ws_io_num = I2S_SPK_LRC,
.data_out_num = I2S_SPK_DOUT,
.data_in_num = I2S_PIN_NO_CHANGE
};
i2s_driver_install(I2S_SPK_PORT, &cfg, 0, NULL);
i2s_set_pin(I2S_SPK_PORT, &pins);
i2s_zero_dma_buffer(I2S_SPK_PORT);
}
/* ===========================================================================
* Recording — runs while the SPEAK button is held (up to RECORD_MAX_S).
* Reads stereo pairs, averages L+R into one mono stream, applies a little
* software gain, and fills wav_buf after the 44-byte header slot.
* Returns the number of audio bytes recorded.
* =========================================================================== */
size_t recordWhileHeld() {
int16_t *mono = (int16_t *)(wav_buf + WAV_HEADER_LEN);
size_t mono_samples = 0;
const size_t max_samples = SAMPLE_RATE * RECORD_MAX_S;
int16_t chunk[512];
uint32_t last_touch_ok = millis();
int32_t peak = 0; // loudest raw sample - mic health check
i2s_zero_dma_buffer(I2S_MIC_PORT);
while (mono_samples < max_samples) {
size_t got = 0;
i2s_read(I2S_MIC_PORT, chunk, sizeof(chunk), &got, 80 / portTICK_PERIOD_MS);
#if USE_BOTH_MICS
size_t n = got / 4; // 4 bytes = one L+R pair
for (size_t i = 0; i < n && mono_samples < max_samples; i++) {
int32_t raw = ((int32_t)chunk[i * 2] + (int32_t)chunk[i * 2 + 1]) / 2;
#else
size_t n = got / 2; // 2 bytes = one mono sample
for (size_t i = 0; i < n && mono_samples < max_samples; i++) {
int32_t raw = chunk[i];
#endif
if (abs(raw) > peak) peak = abs(raw);
int32_t mixed = raw * MIC_GAIN;
if (mixed > 32767) mixed = 32767;
if (mixed < -32768) mixed = -32768;
mono[mono_samples++] = (int16_t)mixed;
}
/* The GT911 is polled between I2S reads. A 250 ms grace period stops a
* momentary missed touch sample from cutting the recording short. */
if (speakButtonHeld()) last_touch_ok = millis();
else if (millis() - last_touch_ok > 250) break;
// live progress on the button
static uint32_t last_draw = 0;
if (millis() - last_draw > 200) {
last_draw = millis();
gfx->fillRect(BTN_SPEAK_X + 2, BAR_Y + BTN_H - 6,
(int)((BTN_SPEAK_W - 4) * mono_samples / max_samples), 4, WHITE);
}
}
/* Mic health line: peak as % of full scale BEFORE gain.
* 0% = the mic is not being read at all (config/pin problem)
* under 3% = too quiet - speak closer or raise MIC_GAIN
* 3-40% = healthy speech level
*/
g_mic_peak_pct = peak * 100.0 / 32768.0;
Serial.printf("mic peak: %.1f%% of full scale (gain x%d applied%s)\n",
g_mic_peak_pct, MIC_GAIN,
peak == 0 ? " - MIC IS SILENT, check USE_BOTH_MICS" : "");
return mono_samples * 2;
}
/* Dump the exact WAV we are about to POST onto the SD card, so it can be
* played on a PC - you hear exactly what Azure hears. Overwritten each time. */
void dumpWavToSD(size_t audio_bytes) {
if (!ok_sd) return;
SD.remove("/stt_debug.wav");
File f = SD.open("/stt_debug.wav", FILE_WRITE);
if (!f) return;
f.write(wav_buf, WAV_HEADER_LEN + audio_bytes);
f.close();
Serial.println("debug copy saved to SD as /stt_debug.wav");
}
/* Standard 44-byte RIFF/WAVE header for 16 kHz 16-bit mono PCM. */
void writeWavHeader(uint8_t *h, uint32_t data_bytes) {
uint32_t file_len = data_bytes + 36;
uint32_t byte_rate = SAMPLE_RATE * 2;
memcpy(h, "RIFF", 4); memcpy(h + 4, &file_len, 4);
memcpy(h + 8, "WAVEfmt ", 8);
uint32_t fmt_len = 16; memcpy(h + 16, &fmt_len, 4);
uint16_t fmt = 1, ch = 1; memcpy(h + 20, &fmt, 2); memcpy(h + 22, &ch, 2);
uint32_t rate = SAMPLE_RATE; memcpy(h + 24, &rate, 4); memcpy(h + 28, &byte_rate, 4);
uint16_t align = 2, bits = 16; memcpy(h + 32, &align, 2); memcpy(h + 34, &bits, 2);
memcpy(h + 36, "data", 4); memcpy(h + 40, &data_bytes, 4);
}
/* ===========================================================================
* HTTP response reader — shared by all three cloud calls.
* Returns the status code and fills body_out. Handles chunked transfer
* encoding PROPERLY: the chunk-size markers must be stripped, or they end
* up embedded inside the JSON body and the parse fails on long replies.
* =========================================================================== */
/* Block until the connection has data (or the budget runs out). Returns false
* on timeout / closed-and-empty. Every read below goes through this, because
* a reasoning model can think for many seconds between the response headers
* and the first byte of the body - and a bare read() would just time out. */
static bool waitData(WiFiClientSecure &c, uint32_t ms) {
uint32_t t0 = millis();
while (!c.available()) {
if (!c.connected()) return false;
if (millis() - t0 > ms) return false;
delay(10);
}
return true;
}
static int readHttpResponse(WiFiClientSecure &client, String &body_out, uint32_t idle_ms) {
body_out = "";
if (!waitData(client, idle_ms)) { Serial.println("HTTP: no response at all"); return 0; }
String status_line = client.readStringUntil('\n');
int code = 0;
sscanf(status_line.c_str(), "HTTP/%*s %d", &code);
bool chunked = false;
while (waitData(client, idle_ms)) {
String h = client.readStringUntil('\n');
if (h == "\r" || h.length() <= 1) break; // blank line = end of headers
h.toLowerCase();
if (h.startsWith("transfer-encoding:") && h.indexOf("chunked") >= 0) chunked = true;
}
if (chunked) {
int blanks = 0;
while (true) {
/* The chunk-size line may not arrive for a long time while the model
* reasons. Waiting here - instead of letting read() time out - is the
* whole fix: a timed-out read looks exactly like "0" (final chunk),
* which silently truncated the body to nothing. */
if (!waitData(client, idle_ms)) {
Serial.println("HTTP: timed out waiting for the next chunk");
break;
}
String szline = client.readStringUntil('\n');
szline.trim();
if (szline.length() == 0) { // stray blank line
if (++blanks > 4) break;
continue;
}
blanks = 0;
long sz = strtol(szline.c_str(), NULL, 16);
if (sz <= 0) break; // genuine final chunk
long got = 0;
while (got < sz) {
if (!waitData(client, idle_ms)) break;
while (client.available() && got < sz) { body_out += (char)client.read(); got++; }
}
if (waitData(client, 3000)) client.readStringUntil('\n'); // CRLF after chunk
if (got < sz) { Serial.println("HTTP: short chunk"); break; }
}
} else {
while (waitData(client, idle_ms))
while (client.available()) body_out += (char)client.read();
}
return code;
}
/* ===========================================================================
* CLOUD CALL 1 — Azure speech-to-text
* One POST, one header, plain WAV body. This is why Azure does the ears.
* =========================================================================== */
bool azureSTT(size_t audio_bytes, String &text_out) {
/* HTTPClient's one-shot POST fails on bodies this large (it attempts one
* giant TLS write and dies with error -3 SEND_PAYLOAD_FAILED). So this
* function speaks HTTP directly and streams the WAV up in 4 KB chunks -
* reliable, and if it ever stalls we know the exact byte it stopped at. */
WiFiClientSecure client;
client.setInsecure(); // no cert bundle on-device; see notes
client.setTimeout(15); // seconds, for reads
writeWavHeader(wav_buf, audio_bytes);
dumpWavToSD(audio_bytes); // PC-playable copy of what we send
size_t total = WAV_HEADER_LEN + audio_bytes;
if (!client.connect(AZURE_STT_HOST, 443)) {
snprintf(g_stt_err, sizeof(g_stt_err), "TLS connect failed");
Serial.println("STT: TLS connect failed");
return false;
}
/* Two valid host forms use DIFFERENT URL paths - detect which one is in
* secrets.h: <resource>.cognitiveservices.azure.com -> /stt/speech/...
* <region>.stt.speech.microsoft.com -> /speech/... */
bool custom_subdomain = (strstr(AZURE_STT_HOST, ".cognitiveservices.azure.com") != NULL);
String req = String("POST ") + (custom_subdomain ? "/stt" : "") +
"/speech/recognition/conversation/cognitiveservices/v1"
"?language=" AZURE_STT_LANG "&format=simple HTTP/1.1\r\n"
"Host: " AZURE_STT_HOST "\r\n"
"Ocp-Apim-Subscription-Key: " AZURE_SPEECH_KEY "\r\n"
"Content-Type: audio/wav; codecs=audio/pcm; samplerate=16000\r\n"
"Accept: application/json\r\n"
"Connection: close\r\n"
"Content-Length: " + String(total) + "\r\n\r\n";
client.print(req);
/* body, 4 KB at a time */
size_t sent = 0;
while (sent < total) {
size_t n = min((size_t)4096, total - sent);
size_t w = client.write(wav_buf + sent, n);
if (w == 0) {
delay(50); // brief stall - retry once
w = client.write(wav_buf + sent, n);
if (w == 0) {
snprintf(g_stt_err, sizeof(g_stt_err), "upload stalled at %uKB",
(unsigned)(sent / 1024));
Serial.printf("STT: upload stalled at %u/%u bytes\n",
(unsigned)sent, (unsigned)total);
client.stop();
return false;
}
}
sent += w;
yield();
}
Serial.printf("STT: uploaded %u bytes\n", (unsigned)sent);
/* read the reply with proper de-chunking */
String resp;
int code = readHttpResponse(client, resp, 10000);
client.stop();
if (code != 200) {
snprintf(g_stt_err, sizeof(g_stt_err), "HTTP %d", code);
Serial.printf("STT HTTP %d: %s\n", code, resp.c_str());
return false;
}
JsonDocument doc;
DeserializationError err = deserializeJson(doc, resp);
if (err) {
snprintf(g_stt_err, sizeof(g_stt_err), "bad JSON reply");
Serial.printf("STT parse error, raw response: %s\n", resp.c_str());
return false;
}
const char *status = doc["RecognitionStatus"];
if (!status || strcmp(status, "Success") != 0) {
/* The status names the exact failure:
* InitialSilenceTimeout = Azure heard silence (mic level too low)
* NoMatch = heard sound but no recognisable words
* BabbleTimeout = heard only noise */
snprintf(g_stt_err, sizeof(g_stt_err), "%s", status ? status : "no status");
Serial.printf("STT status: %s\nraw: %s\n", status ? status : "null", resp.c_str());
return false;
}
text_out = doc["DisplayText"].as<String>();
if (text_out.length() == 0) snprintf(g_stt_err, sizeof(g_stt_err), "empty text");
return text_out.length() > 0;
}
/* ===========================================================================
* CLOUD CALL 2 — DeepSeek chat completion
* OpenAI-compatible format. Model name is deepseek-v4-flash - the old
* deepseek-chat name is dead, see secrets.h.
* =========================================================================== */
bool deepseekChat(const String &question, String &answer_out) {
/* Manual HTTP, same as azureSTT: HTTPClient truncates larger TLS response
* bodies (long answers + the model's hidden reasoning), which shows up as
* "bad JSON reply". Reading until the server closes the connection is
* reliable regardless of reply length. */
JsonDocument req;
req["model"] = DEEPSEEK_MODEL;
req["max_tokens"] = LLM_MAX_TOKENS;
JsonArray msgs = req["messages"].to<JsonArray>();
JsonObject sys = msgs.add<JsonObject>();
sys["role"] = "system"; sys["content"] = SYSTEM_PROMPT;
JsonObject usr = msgs.add<JsonObject>();
usr["role"] = "user"; usr["content"] = question;
String body;
serializeJson(req, body);
WiFiClientSecure client;
client.setInsecure();
/* 60 s: deepseek-v4-flash is a REASONING model. Easy questions answer in
* ~2 s, but anything that needs actual working-out (an Ohm's law problem,
* say) can think for 10-30 s before sending a single byte. */
client.setTimeout(60);
if (!client.connect(DEEPSEEK_HOST, 443)) {
snprintf(g_llm_err, sizeof(g_llm_err), "TLS connect failed");
Serial.println("LLM: TLS connect failed");
return false;
}
client.print(String("POST /chat/completions HTTP/1.1\r\n"
"Host: " DEEPSEEK_HOST "\r\n"
"Authorization: Bearer " DEEPSEEK_KEY "\r\n"
"Content-Type: application/json\r\n"
"Connection: close\r\n"
"Content-Length: ") + String(body.length()) + "\r\n\r\n");
client.print(body);
/* read the reply with proper de-chunking; generous window - long
* questions make the model think for a while before it responds */
String resp;
int code = readHttpResponse(client, resp, 60000);
client.stop();
if (code != 200) {
snprintf(g_llm_err, sizeof(g_llm_err), "HTTP %d", code);
Serial.printf("LLM HTTP %d: %s\n", code, resp.c_str());
return false;
}
JsonDocument doc;
DeserializationError err = deserializeJson(doc, resp);
if (err) {
snprintf(g_llm_err, sizeof(g_llm_err), "bad JSON reply");
Serial.printf("LLM parse error, raw: %s\n", resp.c_str());
return false;
}
const char *content = doc["choices"][0]["message"]["content"];
const char *finish = doc["choices"][0]["finish_reason"];
if (!content || !content[0]) {
/* v4-flash is a reasoning model: if finish_reason is "length", the whole
* token budget went to internal reasoning - raise LLM_MAX_TOKENS. */
if (finish && strcmp(finish, "length") == 0)
snprintf(g_llm_err, sizeof(g_llm_err), "empty - raise LLM_MAX_TOKENS");
else
snprintf(g_llm_err, sizeof(g_llm_err), "empty content");
Serial.printf("LLM empty content, raw: %s\n", resp.c_str());
return false;
}
answer_out = String(content);
answer_out.trim();
if (answer_out.length() == 0) snprintf(g_llm_err, sizeof(g_llm_err), "blank answer");
return answer_out.length() > 0;
}
/* ===========================================================================
* CLOUD CALL 3 — Azure text-to-speech, streamed straight to the speaker
* We ask for riff-16khz-16bit-mono-pcm: a WAV whose payload is exactly what
* the I2S peripheral eats. Skip the 44-byte header, forward the rest.
* No MP3 decoder, no audio library, no buffering the whole reply.
* =========================================================================== */
/* Read exactly n bytes from a client (or until timeout). */
static size_t readExact(WiFiClientSecure &c, uint8_t *dst, size_t n) {
size_t got = 0;
uint32_t t0 = millis();
while (got < n && millis() - t0 < 10000) {
int r = c.read(dst + got, n - got);
if (r > 0) { got += r; t0 = millis(); }
else if (!c.connected() && !c.available()) break;
else delay(2);
}
return got;
}
bool azureTTSSpeak(const String &text) {
// Escape the XML special characters for the SSML body
String safe = text;
safe.replace("&", "&");
safe.replace("<", "<");
safe.replace(">", ">");
String ssml = "<speak version='1.0' xml:lang='" AZURE_TTS_LANG "'>"
"<voice name='" AZURE_TTS_VOICE "'>" + safe + "</voice></speak>";
/* Manual HTTP like the other two cloud calls - and for a hard reason:
* Azure sends this audio with CHUNKED transfer encoding, and HTTPClient's
* raw stream hands over the chunk framing (ASCII "2000\r\n" lines) mixed
* into the PCM. Played as sound, every chunk boundary is an audible KNOCK.
* Here we parse the framing properly and keep only clean audio bytes. */
WiFiClientSecure client;
client.setInsecure();
client.setTimeout(20);
const char *host = AZURE_REGION ".tts.speech.microsoft.com";
if (!client.connect(host, 443)) {
Serial.println("TTS: TLS connect failed");
return false;
}
client.print(String("POST /cognitiveservices/v1 HTTP/1.1\r\n"
"Host: ") + host + "\r\n"
"Ocp-Apim-Subscription-Key: " AZURE_SPEECH_KEY "\r\n"
"Content-Type: application/ssml+xml\r\n"
"X-Microsoft-OutputFormat: riff-16khz-16bit-mono-pcm\r\n"
"User-Agent: MaTouchRobojax\r\n"
"Connection: close\r\n"
"Content-Length: " + String(ssml.length()) + "\r\n\r\n");
client.print(ssml);
/* status + headers; note whether the body is chunked */
String status_line = client.readStringUntil('\n');
int code = 0;
sscanf(status_line.c_str(), "HTTP/%*s %d", &code);
bool chunked = false;
long content_len = -1;
while (client.connected() || client.available()) {
String h = client.readStringUntil('\n');
if (h == "\r" || h.length() <= 1) break;
h.toLowerCase();
if (h.startsWith("transfer-encoding:") && h.indexOf("chunked") >= 0) chunked = true;
if (h.startsWith("content-length:")) content_len = h.substring(15).toInt();
}
if (code != 200) {
Serial.printf("TTS HTTP %d\n", code);
client.stop();
return false;
}
const size_t AUDIO_CAP = 1200 * 1024; // ~37 s of speech
uint8_t *audio = (uint8_t *)ps_malloc(AUDIO_CAP);
if (!audio) { client.stop(); return false; }
size_t alen = 0;
if (chunked) {
/* chunked: <hex size>\r\n <bytes> \r\n ... 0\r\n\r\n */
while (true) {
String szline = client.readStringUntil('\n');
long sz = strtol(szline.c_str(), NULL, 16);
if (sz <= 0) break;
if (alen + sz > AUDIO_CAP) break;
size_t got = readExact(client, audio + alen, sz);
alen += got;
client.readStringUntil('\n'); // trailing CRLF after each chunk
if (got < (size_t)sz) break;
}
} else if (content_len > 0) {
alen = readExact(client, audio, min((size_t)content_len, AUDIO_CAP));
} else {
/* no framing info: read until the server closes */
uint32_t idle = millis();
while ((client.connected() || client.available()) && millis() - idle < 5000) {
int r = client.read(audio + alen, min((size_t)2048, AUDIO_CAP - alen));
if (r > 0) { alen += r; idle = millis(); }
else delay(5);
}
}
client.stop();
Serial.printf("TTS: %u KB clean audio (%s), playing\n",
(unsigned)(alen / 1024), chunked ? "de-chunked" : "plain");
bool ok = (alen > WAV_HEADER_LEN);
if (ok) {
/* NOW the audio actually starts - this is the honest moment to go green */
LED_SPEAK();
drawBar("SPEAKING...", gfx->color565(0, 130, 40));
/* skip the RIFF header; a silence pre-roll softens the amp wake-up pop */
static const uint8_t lead_in[640] = {0}; // 20 ms of silence
size_t w = 0;
i2s_write(I2S_SPK_PORT, lead_in, sizeof(lead_in), &w, portMAX_DELAY);
i2s_write(I2S_SPK_PORT, audio + WAV_HEADER_LEN, alen - WAV_HEADER_LEN, &w, portMAX_DELAY);
i2s_write(I2S_SPK_PORT, lead_in, sizeof(lead_in), &w, portMAX_DELAY);
}
free(audio);
// let the DMA buffers drain so the last word is not cut off
delay(150);
i2s_zero_dma_buffer(I2S_SPK_PORT);
return ok;
}
/* ===========================================================================
* SETUP
* =========================================================================== */
void setup() {
Serial.begin(115200);
delay(400);
Serial.println("\n=== 04 Voice Assistant | Robojax.com ===");
Serial.println("Mics -> Azure STT -> DeepSeek -> Azure TTS -> speaker");
pinMode(TFT_BLK, OUTPUT);
digitalWrite(TFT_BLK, LOW);
pinMode(SD_CS, OUTPUT);
digitalWrite(SD_CS, HIGH);
// one shared SPI bus for TFT + SD (started before either device)
SPI.begin(TFT_SCLK, TFT_MISO, TFT_MOSI);
gfx->begin();
gfx->fillScreen(BLACK);
digitalWrite(TFT_BLK, HIGH);
// SD is optional here - it only stores the /stt_debug.wav diagnostic copy
ok_sd = SD.begin(SD_CS, SPI, 20000000);
digitalWrite(SD_CS, HIGH);
Serial.println(ok_sd ? "SD ok - will save /stt_debug.wav after each recording"
: "SD not found - debug WAV dump disabled (not fatal)");
bbct.init(TOUCH_SDA, TOUCH_SCL, TOUCH_RST, TOUCH_INT);
delay(50);
rgb.begin();
rgb.setBrightness(LED_BRIGHTNESS);
LED_IDLE();
/* One recording buffer for the whole session, in PSRAM. This is the 8 MB
* that makes the board worth buying. */
wav_buf = (uint8_t *)ps_malloc(WAV_HEADER_LEN + REC_BUF_BYTES);
if (!wav_buf) {
gfx->setTextColor(RED);
gfx->setTextSize(2);
gfx->setCursor(10, 100);
gfx->print("PSRAM alloc failed!");
gfx->setTextSize(1);
gfx->setCursor(10, 130);
gfx->print("Tools > PSRAM > OPI PSRAM must be set.");
while (1) delay(1000);
}
micInit();
spkInit();
gfx->setTextSize(1);
gfx->setTextColor(YELLOW);
gfx->setCursor(4, 4);
gfx->printf("Connecting to %s ...", WIFI_SSID);
Serial.printf("Connecting to %s ", WIFI_SSID);
WiFi.mode(WIFI_STA);
WiFi.begin(WIFI_SSID, WIFI_PASS);
uint32_t t0 = millis();
while (WiFi.status() != WL_CONNECTED && millis() - t0 < 20000) {
delay(300);
Serial.print(".");
}
Serial.println();
clearChat();
if (WiFi.status() == WL_CONNECTED) {
Serial.printf("Connected, IP %s\n", WiFi.localIP().toString().c_str());
chatBubble("Hold SPEAK and ask me anything.", false);
} else {
chatBubble("WiFi failed. Remember: the ESP32 is 2.4GHz only. Check secrets.h, then press RESET.", false);
LED_ERROR();
}
drawBar("HOLD+TALK", gfx->color565(0, 90, 160));
}
/* ===========================================================================
* LOOP — one full conversation turn per button press
* =========================================================================== */
void loop() {
/* CLEAR button: edge-detected so one tap wipes once. Reading the panel
* twice per loop (here and in speakButtonHeld) is fine - the GT911 just
* reports its current state. */
static bool tap_latch = false;
static uint8_t tap_release = 0;
if (state == ST_IDLE) {
uint16_t cx, cy;
if (getTouch(&cx, &cy)) {
tap_release = 0;
if (!tap_latch) {
tap_latch = true;
if (cx >= BTN_CLEAR_X && cx < BTN_CLEAR_X + BTN_CLEAR_W && cy >= BAR_Y) {
clearChat();
chatBubble("Hold SPEAK and ask me anything.", false);
}
}
} else if (tap_latch && ++tap_release >= 4) {
tap_latch = false;
tap_release = 0;
}
/* live WiFi signal indicator, refreshed every 2 s while idle */
static uint32_t last_wifi = 0;
if (millis() - last_wifi > 2000) {
last_wifi = millis();
drawWifi();
}
}
if (state == ST_IDLE && speakButtonHeld()) {
/* ---- record ---- */
state = ST_RECORDING;
LED_LISTEN();
drawBar("LISTENING...", gfx->color565(0, 60, 200));
uint32_t t_rec = millis();
size_t audio_bytes = recordWhileHeld();
t_rec = millis() - t_rec;
Serial.printf("Recorded %u bytes (%.1f s)\n", (unsigned)audio_bytes, audio_bytes / 32000.0);
if (audio_bytes < SAMPLE_RATE / 2) { // under a quarter second - a tap
drawBar("HOLD+TALK", gfx->color565(0, 90, 160));
LED_IDLE();
state = ST_IDLE;
return;
}
/* ---- speech to text ---- */
state = ST_STT;
LED_THINK();
drawBar("HEARD YOU...", gfx->color565(150, 90, 0));
uint32_t t_stt = millis();
String question;
if (!azureSTT(audio_bytes, question)) {
/* Show the REAL cause on screen - no serial monitor needed. */
char diag[96];
snprintf(diag, sizeof(diag), "STT failed: %s | mic peak %.1f%%%s",
g_stt_err, g_mic_peak_pct,
ok_sd ? " | saved /stt_debug.wav" : "");
chatBubble(diag, false);
drawBar("HOLD+TALK", gfx->color565(0, 90, 160));
LED_IDLE();
state = ST_IDLE;
return;
}
t_stt = millis() - t_stt;
chatBubble(question.c_str(), true);
Serial.printf("STT (%lu ms): %s\n", (unsigned long)t_stt, question.c_str());
/* ---- think ---- */
state = ST_LLM;
drawBar("THINKING...", gfx->color565(150, 90, 0));
uint32_t t_llm = millis();
String answer;
if (!deepseekChat(question, answer)) {
char diag[96];
snprintf(diag, sizeof(diag), "DeepSeek failed: %s", g_llm_err);
chatBubble(diag, false);
drawBar("HOLD+TALK", gfx->color565(0, 90, 160));
LED_IDLE();
state = ST_IDLE;
return;
}
t_llm = millis() - t_llm;
chatBubble(answer.c_str(), false);
Serial.printf("LLM (%lu ms): %s\n", (unsigned long)t_llm, answer.c_str());
/* ---- speak ----
* Still amber here: the voice has to be synthesised and downloaded first
* (a few seconds). azureTTSSpeak() itself flips the bar and LED to green
* at the exact moment audio starts coming out of the speaker. */
state = ST_TTS;
LED_THINK();
drawBar("GETTING VOICE", gfx->color565(150, 90, 0));
uint32_t t_tts = millis();
bool spoke = azureTTSSpeak(answer);
t_tts = millis() - t_tts;
/* Timing summary on serial - this feeds the "honest numbers" segment. */
Serial.printf("TIMINGS rec %.1fs | stt %lums | llm %lums | tts %lums%s\n",
audio_bytes / 32000.0, (unsigned long)t_stt,
(unsigned long)t_llm, (unsigned long)t_tts,
spoke ? "" : " (TTS FAILED)");
drawBar("HOLD+TALK", gfx->color565(0, 90, 160));
LED_IDLE();
state = ST_IDLE;
}
delay(20);
}
Hal-hal yang mungkin Anda butuhkan
-
LainnyaProduct page for MaTouch AI ESP32S3 2.8" TFT ST7789Vmakerfabs.com
Sumber daya & referensi
-
DokumentasiMakerfabs MaTouch ESP32-S3 2.8" Camera and Touchscreen: user's manualwiki.makerfabs.com
-
Dokumentasi
-
DokumentasiProduct page for MaTouch AI ESP32S3 2.8" TFT ST7789Vmakerfabs.com
-
UnduhArduino GFX Library on Githubgithub.com
Berkas📁
File yang Diperlukan (.h)
File Lainnya
Skema
-
MaTouch_AI 2.8“ MaTouch AI ESP32S3 2.8" TFT ST7789V schematicThe latest MaTouch AI board integrate I2S voice input/I2S speaker/ 3 million camera OV3660/ 320*240 resolution display, with ESP32S3 strong processor& Wifi ability, to make this board a good tool/platform for AI development with ESP32.
MaTouch_AI 2.8“ SPI TFT ST7789V V1.1.PDF0.15 MB