Situatie
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
- Dual-core Xtensa LX6 CPU up to 240 MHz
- ~520 KB shared SRAM
- 4–16 MB external flash (typical)
- Optional PSRAM on WROVER modules
- No dedicated DSP or GPU
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)
- Store WAV/PCM files in flash or SPIFFS
- Playback using DAC or I2S
This approach provides excellent audio quality with minimal CPU usage but limited flexibility.
Phoneme-Based Concatenative TTS
- Text to phoneme conversion
- Phoneme sequencing
- Audio concatenation and playback
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
- 8-bit resolution
- Low audio quality
- External amplifier required
I2S Audio Output (Recommended)
- External DAC or MAX98357A amplifier
- 16-bit PCM audio
- Sample rates: 16 kHz or 22.05 kHz
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
- 16-bit PCM, mono
- Consistent pitch and speed
- Normalized volume
| 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
- Text processing task
- Audio synthesis task
- Audio playback task
Use DMA buffering for I2S and avoid dynamic memory allocation during playback.
9. Existing ESP32 Offline TTS Libraries
- SAM-based ESP32 TTS (very small footprint)
- Flite (requires large flash and PSRAM)
- Custom phrase engines
10. Power Optimization
- Disable Wi-Fi and Bluetooth during playback
- Lower CPU frequency when streaming audio
- Precompute phoneme sequences
11. Debugging and Testing
- Serial logging of phoneme sequences
- Check for audio buffer underflows
- Verify DAC/I2S gain levels
12. Security and Privacy
Offline TTS ensures that no text or audio data leaves the device, making it suitable for privacy-critical applications.
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