| --- |
| language: |
| - hi |
| - en |
| tags: |
| - openwakeword |
| - wake-word-detection |
| - voice-assistant |
| - edge-ai |
| - onnx |
| - tflite |
| license: apache-2.0 |
| --- |
| |
| # 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) |
| ```python |
| 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!") |
| |
| |
