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+# Cross-Platform LLM Voice Assistant CLI Demo
+
+A cross-platform voice assistant using Picovoice's Wake Word, STT, TTS and LLM technology with a text-based interface.
+
+## Compatibility
+
+- .NET 8.0
+- Runs on Linux (x86_64), macOS (arm64, x86_64), Windows (x86_64), and Raspberry Pi (5 and 4).
+
+## 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
+
+The demo uses .NET 8.0 to build:
+
+```console
+dotnet build
+```
+
+Run the demo:
+
+```console
+./bin/Debug/net8.0/LLMVoiceAssistant --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
+./bin/Debug/net8.0/LLMVoiceAssistant --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
+
+The demo profiles two metrics: Real-time Factor (RTF) and Token per Second (TPS).
+
+### 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.