VoxFlash-TTS ⚡

The Fastest Voice Cloning System for Real-Time Inference
Zero-shot · Chinese & English · Edge-Deployable · Consumer GPU

🌐 Live Demo · 🚀 Quick Start · 🏗 Architecture · 📊 Evaluation · 🤗 Model Card

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English · 中文

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What is VoxFlash-TTS?

VoxFlash-TTS is the fastest voice cloning system in the industry, built around a radically compressed latent diffusion architecture. It supports zero-shot voice cloning in Chinese and English, runs on consumer-grade GPUs, and is designed from the ground up for edge deployment.

The key insight: most TTS inference bottlenecks are a sequence length problem. By compressing 24kHz audio into a 9 Hz latent representation — roughly 8× more compressed than EnCodec — VoxFlash reduces end-to-end compute by orders of magnitude without sacrificing acceptable audio quality.

Generating 10 seconds of audio requires processing just 90 latent vectors, compared to 750+ in conventional systems.

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Highlights

• ⚡ Millisecond-level inference on consumer GPUs
• 🎙️ Zero-shot voice cloning — no fine-tuning required
• 🌏 Chinese & English — same-language and cross-lingual cloning
• 💻 Edge-friendly — low VRAM footprint, low-end GPU compatible
• 🐳 One-command Docker deployment — up and running in minutes
• 📦 ~600 MB ONNX — full pipeline in a single deployable artifact

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Architecture

VoxFlash-TTS is built on an ultra-compressed latent diffusion pipeline:

Text Input
    │
    ▼
┌─────────────────────┐
│   Phoneme Encoder   │  ConvNeXtV2 — lightweight, hardware-friendly
└─────────────────────┘
    │
    ▼
┌─────────────────────┐
│  Coarse Alignment   │  Explicit alignment — lower complexity than Cross-Attention
└─────────────────────┘
    │
    ▼
┌─────────────────────┐
│   Diffusion Model   │  Multi-step denoising in latent space (NFE=16)
└─────────────────────┘
    │                     ▲
    │              ┌──────┴──────┐
    │              │Speaker Enc. │  Reference audio → speaker embedding
    │              └─────────────┘
    ▼
┌─────────────────────┐
│    VAE Decoder      │  9 Hz latent → 24kHz high-fidelity waveform
└─────────────────────┘
    │
    ▼
Audio Output

Why 9 Hz?

Transformer self-attention scales at O(n²) with sequence length. Cutting the sequence by 8× reduces attention compute by ~64×. This is why VoxFlash can deliver millisecond inference where others cannot.

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Changelog

• 2026-09-08: Weights updated. See huggingface.co/VoxFlashTTS/VoxFlashTTS

• 2026-07-14: Weights updated. See huggingface.co/VoxFlashTTS/VoxFlashTTS

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Quick Start

Requirements

• NVIDIA GPU with CUDA ≥ 12.3.2
• Docker

Installation

# Pull the image
docker pull berlinisaiah/ttsv2:v8

Run

# Background mode (production)
docker container run -d --gpus all \
  --mount type=bind,source=$(pwd)/resources,target=/app/resources \
  -p 8000:8000 berlinisaiah/ttsv2:v8

# Foreground mode (debug)
docker container run -it --gpus all \
  --mount type=bind,source=$(pwd)/resources,target=/app/resources \
  -p 8000:8000 berlinisaiah/ttsv2:v8

Access WebUI

Open your browser and navigate to:

http://127.0.0.1:8000/demo.html

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Capabilities

Supported Languages

Zero-Shot Cloning

No fine-tuning needed. Provide any reference audio clip and VoxFlash extracts a speaker embedding, injects it into the diffusion process, and outputs speech matching the target voice.

Cross-lingual cloning (e.g. Chinese reference → English output) is supported, demonstrating effective disentanglement of voice timbre from language identity.

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Evaluation

Audio samples are drawn from the Seed-TTS evaluation set for direct comparison with leading systems.

👉 Listen to audio samples at voxflash.github.io

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Use Cases

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Model Size

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Limitations

• Audio quality under extreme compression may fall short of quality-focused systems such as Seed-TTS
• Currently optimized for Chinese and English; other languages have not been systematically evaluated
• Accent naturalness in cross-lingual cloning has room for improvement
• Reference audio shorter than 3 seconds may reduce speaker similarity

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Citation

If VoxFlash-TTS has been useful in your research or engineering work, please cite:

@misc{voxflash2026,
  title     = {VoxFlash-TTS: Ultra-Compressed Latent Diffusion for Real-Time Voice Cloning},
  author    = {VoxFlash},
  year      = {2026},
  url       = {https://github.com/VoxFlash/VoxFlashTTS},
  note      = {GitHub repository}
}

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Contributing

Contributions, issues, and feature requests are welcome. Please open an issue first to discuss what you would like to change.

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License

This project is licensed under the Apache 2.0 License.

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Contact

• 📧 Email: zhangtaiyan072@gmail.com
• 🌐 Demo: voxflash.github.io
• 🤗 Hugging Face: huggingface.co/VoxFlashTTS/VoxFlashTTS

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WeChat Community

Scan the QR code below to join the VoxFlash-TTS Voice Cloning Group on WeChat:

QR code valid for 7 days. Please re-enter the group if it expires.

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Not the most expressive TTS — the fastest, lightest, and easiest-to-deploy voice cloning system.