From 16e51a97352166f702f856ef8e657cf0f1205696 Mon Sep 17 00:00:00 2001
From: Albert Ho <82767499+albho@users.noreply.github.com>
Date: Tue, 3 Sep 2024 16:29:39 -0700
Subject: [PATCH] add Node.js README (#14)

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+## See It In Action!
+
+[![LLM VA in Action](https://img.youtube.com/vi/NLDylYXuXCs/0.jpg)](https://www.youtube.com/watch?v=NLDylYXuXCs)
+
+## Compatibility
+
+- Node.js 16+
+- Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64), and Raspberry Pi (4, 5).
+
+## AccessKey
+
+AccessKey is your authentication and authorization token for deploying Picovoice SDKs, including picoLLM. Anyone who is
+using Picovoice needs to have a valid AccessKey. You must keep your AccessKey secret. You would need internet
+connectivity to validate your AccessKey with Picovoice license servers even though the LLM inference is running 100%
+offline and completely free for open-weight models. Everyone who signs up for
+[Picovoice Console](https://console.picovoice.ai/) receives a unique AccessKey.
+
+## picoLLM Model
+
+picoLLM Inference Engine supports many open-weight models. The models are on
+[Picovoice Console](https://console.picovoice.ai/).
+
+## Usage
+
+Install the required packages:
+
+```console
+npm install
+```
+
+Run the demo:
+
+```console
+npm run start -- --access_key ${ACCESS_KEY} --picollm_model_path ${PICOLLM_MODEL_PATH} 
+```
+
+Replace `${ACCESS_KEY}` with yours obtained from Picovoice Console and `${PICOLLM_MODEL_PATH}` with the path to the
+model downloaded from Picovoice Console.
+
+To see all available options, type the following:
+
+```console
+npm run start -- --help
+```
+
+## Custom Wake Word
+
+The demo's default wake phrase is `Picovoice`. You can generate your custom (branded) wake word using Picovoice Console
+by following [Porcupine Wake Word documentation (https://picovoice.ai/docs/porcupine/). Once you have the model trained,
+simply pass it to the demo
+application using `--keyword_model_path` argument.
+
+## Profiling
+
+To see the runtime profiling metrics, run the demo with the `--profile` argument:
+
+```console
+npm run start -- --access_key ${ACCESS_KEY} --picollm_model_path ${PICOLLM_MODEL_PATH}  --profile 
+```
+
+Replace `${ACCESS_KEY}` with yours obtained from Picovoice Console and `${PICOLLM_MODEL_PATH}` with the path to the
+model downloaded from Picovoice Console.
+
+The demo profiles three metrics: Real-time Factor (RTF), Token per Second (TPS), and Latency.
+
+### Real-time Factor (RTF)
+
+RTF is a standard metric for measuring the speed of speech processing (e.g., wake word, speech-to-text, and
+text-to-speech). RTF is the CPU time divided by the processed (recognized or synthesized) audio length. Hence, a lower
+RTF means a more efficient engine.
+
+### Token per Second (TPS)
+
+Token per second is the standard metric for measuring the speed of LLM inference engines. TPS is the number of
+generated tokens divided by the compute time used to create them. A higher TPS is better.
+
+### Latency
+
+We measure the latency as the delay between the end of the user's utterance (i.e., the time when the user finishes
+talking) and the
+time that the voice assistant generates the first chunk of the audio response (i.e., when the user starts hearing the
+response).