RootDeveloperDS/Visar_OpenWakeWord_Model

Visar (वी-सार) — Custom openWakeWord Edge Model

A lightweight, high-accuracy wake-word detection model custom-trained for low-power edge SBCs, PCs, and offline home automation systems.

Model Highlights

• Target Phrase: "Visar" / "वी-सार" / "Vee-saar"
• Acoustic Tuning: Native Hindi cadence and Indian English phonetic variations (hi-IN-Madhur, hi-IN-Swara, en-IN-Prabhat, en-IN-Neerja).
• Ground Truth: Real-world microphone samples convolved with MIT environmental impulse responses (RIRs).
• Hard Negatives: Penalized against acoustically close Indian words (vichar, vishal, vikas, bazaar).
• Target Hardware: Raspberry Pi, Linux Edge SBCs, Intel x86, Android, ARM64 microcontrollers.

Audio Input Requirements

• Sample Rate: 16,000 Hz
• Channels: 1 (Mono)
• Format: 16-bit Signed Linear PCM

Repository Contents

File / Directory	Description
visar_edge.onnx	Production ONNX inference graph
visar_edge.onnx.data	External weight buffer for ONNX runtime
visar_edge.tflite	Quantized FlatBuffer model for mobile/embedded devices
training_config.yaml	Exact hyperparameter configuration used during training
training_state/	Step checkpoints and optimizer states for fine-tuning
user_calibration_audio/	Microphone ground-truth calibration recordings

Quickstart (Python Inference)

import numpy as np
import openwakeword
from openwakeword.model import Model

# Initialize wake word engine with custom Visar model
oww_model = Model(wakeword_models=["visar_edge.onnx"])

# Pass raw 16kHz 16-bit PCM audio frames (1280 samples / 80ms chunk)
# audio_frame = np.frombuffer(mic_stream.read(1280), dtype=np.int16)
# prediction = oww_model.predict(audio_frame)
# if prediction["visar_edge"] > 0.5:
#     print("Wake-word detected: Visar!")