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[mai mult...]An offline Text-to-Speech (TTS) system allows an ESP32-based device to convert text into spoken audio without relying on cloud services. Offline TTS is essential for privacy-sensitive applications, deterministic latency, industrial systems, and deployments without internet connectivity.
Unlike voice recognition, TTS is a speech synthesis problem and is computationally intensive. This guide explains what is realistically achievable on ESP32 hardware and how to design a robust offline TTS system.
1. ESP32 Hardware Constraints
These constraints make modern neural TTS models infeasible. ESP32 systems must rely on rule-based or concatenative synthesis approaches.
2. Offline TTS Approaches on ESP32
Phrase-Based (Pre-Recorded Audio)
This approach provides excellent audio quality with minimal CPU usage but limited flexibility.
Phoneme-Based Concatenative TTS
This method allows dynamic speech generation at the cost of voice naturalness and complexity.
Formant / Rule-Based Synthesis
Speech is generated mathematically using vocal tract models. This requires very little memory but produces highly robotic speech.
3. Recommended System Architecture
The most practical ESP32 TTS systems use a hybrid architecture combining phrase playback for common prompts and phoneme synthesis for dynamic data such as numbers.
4. Audio Output Options
ESP32 Internal DAC
I2S Audio Output (Recommended)
5. Text Processing Pipeline
Text Normalization
Text normalization converts raw text into speakable words. This includes expanding numbers, abbreviations, and symbols.
Tokenization
Text is split into words or phrases that can be mapped to audio assets or phonemes.
Phoneme Conversion
Words are mapped to phonemes using lookup tables or simplified grapheme-to-phoneme rules.
6. Audio Asset Design
| Asset Type | Typical Size |
|---|---|
| Single phoneme | 1–4 KB |
| 40 phonemes | 80–120 KB |
| Phrase set | 100 KB–2 MB |
7. Timing and Prosody Control
Basic prosody improvements include inserting silence, adjusting phoneme duration, and optional pitch shifting.
8. Firmware Architecture
Use DMA buffering for I2S and avoid dynamic memory allocation during playback.
9. Existing ESP32 Offline TTS Libraries
10. Power Optimization
11. Debugging and Testing
12. Security and Privacy
Offline TTS ensures that no text or audio data leaves the device, making it suitable for privacy-critical applications.
[mai mult...]Offline voice recognition on the ESP32 enables devices to understand spoken commands without an internet connection. This is critical for low-latency response, privacy-sensitive applications, and battery-powered or remote systems.
Typical use cases include smart switches, robotics, industrial controls, toys, and assistive devices. This guide focuses on keyword spotting (KWS) and command recognition, which are the only practical forms of offline voice recognition on ESP32-class microcontrollers.
1. Understanding ESP32 Constraints
Hardware Limitations
These constraints mean full speech-to-text is not feasible. ESP32-based systems are limited to small vocabularies (usually 5–50 commands) using highly optimized models.
2. Voice Recognition Approaches
Keyword Spotting (KWS)
Keyword spotting detects predefined words or phrases such as “Hey Device” or “Turn on light”.
Command Classification
Command classification selects one command from a known set (e.g., start, stop, left, right). It is often triggered after a wake word.
3. Audio Capture Fundamentals
Microphone Selection
I2S MEMS microphones are strongly recommended for ESP32 voice projects.
Analog microphones are discouraged unless paired with high-quality external ADC and filtering.
Audio Configuration
4. Audio Preprocessing Pipeline
Accurate voice recognition depends heavily on audio preprocessing.
MFCC Features
ESP32 implementations typically use fixed-point MFCCs for performance.
5. Machine Learning Models
| Model | Accuracy | Speed | Memory |
|---|---|---|---|
| DNN | Medium | Fast | Low |
| CNN | High | Medium | Medium |
| DS-CNN | Very High | Fast | Low |
Depthwise Separable CNNs (DS-CNN) are the industry standard for embedded keyword spotting.
6. ESP32 Voice Recognition Frameworks
ESP-SR (Espressif)
Memory usage typically ranges from 300–600 KB RAM and 1–2 MB flash.
TensorFlow Lite for Microcontrollers
7. Training a Custom Model
Target model size should remain under 250 KB, with inference RAM usage below 100 KB.
8. Firmware Architecture
Pin inference to a single core and avoid dynamic memory allocation for real-time stability.
9. Wake Word + Command Flow
10. Power Optimization
11. Debugging and Testing
12. Security and Privacy
Offline voice recognition ensures no audio data is transmitted or stored externally, improving privacy and predictability.
[mai mult...]