# Basic usage
python supertonic_inference.py --text "Hello world" --voice M1 -o output.wav

# With specific seed for reproducibility
python supertonic_inference.py \
  --text "Your text here" \
  --voice F1 \
  --seed 42 \
  --output output.wav

Features

✅ High Quality: Matches official Supertonic library behavior
✅ Reproducible: Use --seed for consistent outputs
✅ Multilingual: Supports 5 languages (EN, KO, ES, PT, FR)
✅ Multiple Voices: 10 voice styles (5 female, 5 male)
✅ Customizable: Adjust quality, speed, and more

QCS6490 Deployment

This repository includes complete resources for porting to Qualcomm QCS6490 (Hexagon HTP V68):

QCS6490 Porting Guide - Step-by-step deployment guide
Architecture Deep Dive - Complete model architecture documentation
Calibration Data Guide - Quantization and validation workflow

Generate Calibration Data

bash

# Generate 10 calibration samples for quantization and accuracy validation
python generate_calibration_data.py

# Validate QNN model accuracy against ONNX reference
python validate_qnn_accuracy.py \
  --calibration-dir calibration_data \
  --qnn-dir qnn_outputs \
  --report accuracy_report.json

Expected Performance on QCS6490:

100-150× real-time inference speed
~75ms latency for 2-second audio (5 diffusion steps)
~70-100 MB total model size (after INT8/FP16 quantization)

Model Files

Required directory structure:

model/
├── onnx/
│   ├── text_encoder.onnx
│   ├── duration_predictor.onnx
│   ├── vector_estimator.onnx
│   ├── vocoder.onnx
│   ├── tts.json
│   └── unicode_indexer.json
└── voice_styles/
    ├── F1.json ... F5.json (Female voices)
    └── M1.json ... M5.json (Male voices)

Command Line Options

Option	Description	Default
`--text`, `-t`	Text to synthesize	Required
`--voice`, `-v`	Voice style (F1-F5, M1-M5)	M1
`--lang`, `-l`	Language (en, ko, es, pt, fr)	en
`--output`, `-o`	Output WAV file path	output/output.wav
`--steps`	Diffusion steps (more = better)	10
`--speed`	Speech speed multiplier	1.0
`--seed`	Random seed (for reproducibility)	None
`--quiet`, `-q`	Suppress progress messages	False

Examples

Basic Synthesis

bash

python supertonic_inference.py \
  --text "The weather is nice today." \
  --voice F1 \
  --output weather.wav

High Quality with Reproducible Output

bash

python supertonic_inference.py \
  --text "Important announcement." \
  --voice M1 \
  --steps 20 \
  --seed 42 \
  --output announcement.wav

Faster Speech

bash

python supertonic_inference.py \
  --text "Quick update message." \
  --voice F2 \
  --speed 1.2 \
  --output quick.wav

Spanish Language

bash

python supertonic_inference.py \
  --text "Hola mundo" \
  --lang es \
  --voice M3 \
  --output spanish.wav

Voice Styles

Code	Type	Description
F1-F5	Female	5 distinct female voices
M1-M5	Male	5 distinct male voices

Supported Languages

en - English
ko - Korean
es - Spanish
pt - Portuguese
fr - French

Python API

python

from supertonic_inference import SupertonicTTS, save_wav

# Initialize
tts = SupertonicTTS(model_dir="model/onnx")

# Synthesize
waveform, duration = tts.synthesize(
    text="Hello world",
    voice_name="M1",
    lang="en",
    diffusion_steps=10,
    speed=1.0,
    seed=42
)

# Save
save_wav("output.wav", waveform, tts.sample_rate)

Performance

Speed: ~25× faster than real-time on CPU
Quality: Matches official Supertonic library
Sample Rate: 44.1 kHz
Variance: ~5% duration variance due to diffusion randomness

Parameters Guide

Diffusion Steps

5-10 steps: Fast, good quality (default: 10)
15-20 steps: Higher quality, slower
Trade-off: Each step improves quality but increases compute time

Speed

0.8-1.0: Slower, more natural
1.0: Normal speed (default)
1.1-1.5: Faster, still intelligible

Seed

None: Different output each run (random)
Integer: Reproducible output with same seed
Use case: Testing, demos, consistent results

Project Structure

supertonic2-qualcomm/
├── supertonic_inference.py    # Main inference script
├── README.md                   # This file
├── model/
│   ├── onnx/                   # ONNX models
│   └── voice_styles/           # Voice embeddings
├── inputs/                     # Test inputs
└── output/                     # Generated audio

Notes

Text is automatically preprocessed (normalization, punctuation)
Periods are added automatically if missing
Unicode characters are handled transparently
Emoji are automatically removed

License

This implementation uses models from Supertone/supertonic-2.

Reference

Official repository: https://github.com/supertone-inc/supertonic

dev-ansh-r/supertonic2-qualcomm-quantized

Note: This repository is a work in progress. The README will be updated with more details on the QCS6490 porting process, model architecture, and calibration data generation workflow in the coming weeks. Please check back for updates!

Supertonic-2 QNN Inference

Quick Start

Features

QCS6490 Deployment

Generate Calibration Data

Model Files

Command Line Options

Examples

Basic Synthesis

High Quality with Reproducible Output

Faster Speech

Spanish Language

Voice Styles

Supported Languages

Python API

Performance

Parameters Guide

Diffusion Steps

Speed

Seed

Project Structure

Notes

License

Reference

dev-ansh-r/
supertonic2-qualcomm-quantized