🎙️ Vaniq-Edge: Ultra-Low Latency Conversational TTS of 8.5M parameters.

Complete local text-to-waveform speech synthesis at 24kHz output.
Lightweight, edge-optimized English TTS designed for zero-lag conversational AI, microservices, and low-resource devices.

🎧 Audio Samples

Listen to the model's native 24 kHz generation across different speech contexts:

1. Conversational

"Wait, are you actually being for real? I thought the train left twenty minutes ago!"

2. Technical / Academic

"The asynchronous gradient descent algorithm converges rapidly when paired with adaptive learning rates."

3. Narrative / Audiobook

"Night fell over the quiet valley, and as the cold wind whispered through the ancient pines, a distant light flickered in the dark."

Benchmark Metrics

Vaniq-Edge was benchmarked against popular open-source TTS models (standard VITS, Piper, and Supertonic 3) across four key metrics: quality, intelligibility, storage footprint, and computational efficiency. Tests were conducted using standard 50-word English prompts.

🎭 Audio Quality & Intelligibility

This dashboard compares the predicted naturalness (UTMOS22) and pronunciation accuracy (Whisper WER%). Vaniq-Edge achieves top-tier quality while maintaining competitive intelligibility.

⚡ Inference Speed (RTF)

Measured in Real-Time Factor (RTF) — seconds required to generate 1.0s of audio. Lower is faster. Vaniq-Edge runs significantly faster than real-time on a standard CPU and offers lightning-fast performance on GPU.

💾 Model Footprint & Resource Efficiency

Vaniq-Edge is explicitly optimized for constrained environments, offering a tiny disk footprint compared to high-fidelity alternatives, making it ideal for on-device and mobile deployment.

Quickstart

1. Install Dependencies

pip install torch torchaudio phonemizer espeakng-loader==0.2.4 num2words scipy huggingface_hub Unidecode

2. Python API

import sys
import torch
import scipy.io.wavfile as wavfile
from huggingface_hub import snapshot_download

# 1. Download model weights & inference script from Hugging Face
model_path = snapshot_download(repo_id="Abiray/Vaniq-Edge")
if model_path not in sys.path:
    sys.path.insert(0, model_path)

# 2. Import VaniqTTS and set device automatically (GPU or CPU)
from inference import VaniqTTS

device = "cuda" if torch.cuda.is_available() else "cpu"
tts = VaniqTTS(model_path, device=device)

# 3. Generate 24kHz Audio
sample_rate, wav_array = tts.synthesize("Hello world! Vaniq-Edge is running locally.")

# 4. Save output
wavfile.write("output.wav", sample_rate, wav_array)

ONNX Runtime

The official verified dynamic export is published directly in this repository. It supports dynamic sequence lengths, CPU/CUDA provider selection, and runs the complete text-to-waveform path without importing PyTorch:

git clone [https://huggingface.co/Abiray/Vaniq-Edge](https://huggingface.co/Abiray/Vaniq-Edge)
cd Vaniq-Edge
python -m pip install -r onnx/requirements.txt
python onnx/inference_onnx.py \
  --text "The complete model now runs through ONNX Runtime." \
  --output sample-onnx.wav \
  --provider cpu \
  --speed 1.0

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