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Official Python Package for Supertonic

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GitHub GitHub Demo Models Colab

Quick Start

pip install supertonic

CLI

# Note: First run will download the model (~260MB) from HuggingFace
supertonic tts 'Supertonic is a lightning fast, on-device TTS system.' -o output.wav

Python

from supertonic import TTS

# Note: First run downloads model automatically (~260MB)
tts = TTS(auto_download=True)

# Get a voice style
style = tts.get_voice_style(voice_name="M1")

# Generate speech
text = "The train delay was announced at 4:45 PM on Wed, Apr 3, 2024 due to track maintenance."
wav, duration = tts.synthesize(text, voice_style=style)

# Save to file
tts.save_audio(wav, "output.wav")

Get Started with the Full Guide

Explore installation options, voice customization, and advanced configuration.

Requirements

Supertonic has minimal dependencies - just 4 core libraries:

  • onnxruntime - Fast ONNX model inference
  • numpy - Numerical operations
  • soundfile - Audio file I/O
  • huggingface-hub - Model downloads

Key Features

⚡ Blazingly Fast: Generates speech up to 167× faster than real-time on consumer hardware (M4 Pro)

🪶 Ultra Lightweight: Only 66M parameters, optimized for efficient on-device performance

📱 On-Device Capable: Complete privacy and zero latency

🎨 Natural Text Handling: Seamlessly processes complex expressions without G2P module

⚙️ Highly Configurable: Adjust inference steps, speech speed, and other parameters

🧩 Flexible Deployment: Deploy across servers, browsers, and edge devices

Performance Benchmarks

Metrics:
  • Characters per Second: Measures throughput by dividing the number of input characters by the time required to generate audio. Higher is better.
  • Real-time Factor (RTF): Measures the time taken to synthesize audio relative to its duration. Lower is better (e.g., RTF of 0.1 means it takes 0.1 seconds to generate one second of audio).

Characters per Second

System Short (59 chars) Mid (152 chars) Long (266 chars)
Supertonic (M4 pro - CPU) 912 1048 1263
Supertonic (M4 pro - WebGPU) 996 1801 2509
Supertonic (RTX4090) 2615 6548 12164
API ElevenLabs Flash v2.5 144 209 287
API OpenAI TTS-1 37 55 82
API Gemini 2.5 Flash TTS 12 18 24
API Supertone Sona speech 1 38 64 92
Open Kokoro 104 107 117
Open NeuTTS Air 37 42 47

Notes: API = Cloud-based API services (measured from Seoul) Open = Open-source models Supertonic (M4 pro - CPU) and (M4 pro - WebGPU): Tested with ONNX Supertonic (RTX4090): Tested with PyTorch model Kokoro: Tested on M4 Pro CPU with ONNX NeuTTS Air: Tested on M4 Pro CPU with Q8-GGUF

Real-time Factor

System Short (59 chars) Mid (152 chars) Long (266 chars)
Supertonic (M4 pro - CPU) 0.015 0.013 0.012
Supertonic (M4 pro - WebGPU) 0.014 0.007 0.006
Supertonic (RTX4090) 0.005 0.002 0.001
API ElevenLabs Flash v2.5 0.133 0.077 0.057
API OpenAI TTS-1 0.471 0.302 0.201
API Gemini 2.5 Flash TTS 1.060 0.673 0.541
API Supertone Sona speech 1 0.372 0.206 0.163
Open Kokoro 0.144 0.124 0.126
Open NeuTTS Air 0.390 0.338 0.343
Additional Performance Data (5-step inference)

Characters per Second (5-step)

System Short (59 chars) Mid (152 chars) Long (266 chars)
Supertonic (M4 pro - CPU) 596 691 850
Supertonic (M4 pro - WebGPU) 570 1118 1546
Supertonic (RTX4090) 1286 3757 6242

Real-time Factor (5-step)

System Short (59 chars) Mid (152 chars) Long (266 chars)
Supertonic (M4 pro - CPU) 0.023 0.019 0.018
Supertonic (M4 pro - WebGPU) 0.024 0.012 0.010
Supertonic (RTX4090) 0.011 0.004 0.002

Natural Text Handling

Supertonic is designed to handle complex, real-world text inputs that contain numbers, currency symbols, abbreviations, dates, and proper nouns.

🎧 View audio samples more easily: Check out our Interactive Demo for a better viewing experience of all audio examples

Overview of Test Cases:

Category Key Challenges Supertonic ElevenLabs OpenAI Gemini Microsoft
Financial Expression Decimal currency, abbreviated magnitudes (M, K), currency symbols, currency codes ✅ ❌ ❌ ❌ ❌
Time and Date Time notation, abbreviated weekdays/months, date formats ✅ ❌ ❌ ❌ ❌
Phone Number Area codes, hyphens, extensions (ext.) ✅ ❌ ❌ ❌ ❌
Technical Unit Decimal numbers with units, abbreviated technical notations ✅ ❌ ❌ ❌ ❌
Example 1: Financial Expression

"The startup secured $5.2M in venture capital, a huge leap from their initial $450K seed round."

Challenges:

  • Decimal point in currency ($5.2M should be read as "five point two million")
  • Abbreviated magnitude units (M for million, K for thousand)
  • Currency symbol ($) that needs to be properly pronounced as "dollars"

Audio Samples:

System Result Audio
Supertonic ✅
ElevenLabs Flash v2.5 ❌
OpenAI TTS-1 ❌
Gemini 2.5 Flash TTS ❌
VibeVoice Realtime 0.5B ❌
Example 2: Time and Date

"The train delay was announced at 4:45 PM on Wed, Apr 3, 2024 due to track maintenance."

Challenges:

  • Time expression with PM notation (4:45 PM)
  • Abbreviated weekday (Wed)
  • Abbreviated month (Apr)
  • Full date format (Apr 3, 2024)

Audio Samples:

System Result Audio
Supertonic ✅
ElevenLabs Flash v2.5 ❌
OpenAI TTS-1 ❌
Gemini 2.5 Flash TTS ❌
VibeVoice Realtime 0.5B ❌
Example 3: Phone Number

"You can reach the hotel front desk at (212) 555-0142 ext. 402 anytime."

Challenges:

  • Area code in parentheses that should be read as separate digits
  • Phone number with hyphen separator (555-0142)
  • Abbreviated extension notation (ext.)
  • Extension number (402)

Audio Samples:

System Result Audio
Supertonic ✅
ElevenLabs Flash v2.5 ❌
OpenAI TTS-1 ❌
Gemini 2.5 Flash TTS ❌
VibeVoice Realtime 0.5B ❌
Example 4: Technical Unit

"Our drone battery lasts 2.3h when flying at 30kph with full camera payload."

Challenges:

  • Decimal time duration with abbreviation (2.3h = two point three hours)
  • Speed unit with abbreviation (30kph = thirty kilometers per hour)
  • Technical abbreviations (h for hours, kph for kilometers per hour)
  • Technical/engineering context requiring proper pronunciation

Audio Samples:

System Result Audio
Supertonic ✅
ElevenLabs Flash v2.5 ❌
OpenAI TTS-1 ❌
Gemini 2.5 Flash TTS ❌
VibeVoice Realtime 0.5B ❌

Note: These samples demonstrate how each system handles text normalization and pronunciation of complex expressions without requiring pre-processing or phonetic annotations.

Citation

The following papers describe the core technologies used in Supertonic. If you use this system in your research or find these techniques useful, please consider citing the relevant papers:

SupertonicTTS: Main Architecture

This paper introduces the overall architecture of SupertonicTTS, including the speech autoencoder, flow-matching based text-to-latent module, and efficient design choices.

@article{kim2025supertonic,
  title={SupertonicTTS: Towards Highly Efficient and Streamlined Text-to-Speech System},
  author={Kim, Hyeongju and Yang, Jinhyeok and Yu, Yechan and Ji, Seunghun and Morton, Jacob and Bous, Frederik and Byun, Joon and Lee, Juheon},
  journal={arXiv preprint arXiv:2503.23108},
  year={2025},
  url={https://arxiv.org/abs/2503.23108}
}

Length-Aware RoPE: Text-Speech Alignment

This paper presents Length-Aware Rotary Position Embedding (LARoPE), which improves text-speech alignment in cross-attention mechanisms.

@article{kim2025larope,
  title={Length-Aware Rotary Position Embedding for Text-Speech Alignment},
  author={Kim, Hyeongju and Lee, Juheon and Yang, Jinhyeok and Morton, Jacob},
  journal={arXiv preprint arXiv:2509.11084},
  year={2025},
  url={https://arxiv.org/abs/2509.11084}
}

Self-Purifying Flow Matching: Training with Noisy Labels

This paper describes the self-purification technique for training flow matching models robustly with noisy or unreliable labels.

@article{kim2025spfm,
  title={Training Flow Matching Models with Reliable Labels via Self-Purification},
  author={Kim, Hyeongju and Yu, Yechan and Yi, June Young and Lee, Juheon},
  journal={arXiv preprint arXiv:2509.19091},
  year={2025},
  url={https://arxiv.org/abs/2509.19091}
}

🏠 Main Repository: github.com/supertone-inc/supertonic

🎧 Try it live: Hugging Face Spaces

🤗 Model Repository: Hugging Face Models

License

Code: MIT License

Model: OpenRAIL-M License

Copyright © 2025 Supertone Inc.