# Rhino Speech-to-Intent Engine
Made in Vancouver, Canada by [Picovoice](https://picovoice.ai)
Rhino is Picovoice's Speech-to-Intent engine. It directly infers intent from spoken commands within a given context of
interest, in real-time. For example, given a spoken command:
> Can I have a small double-shot espresso?
Rhino infers that the user would like to order a drink and emits the following inference result:
```json
{
"isUnderstood": "true",
"intent": "orderBeverage",
"slots": {
"beverage": "espresso",
"size": "small",
"numberOfShots": "2"
}
}
```
Rhino is:
* using deep neural networks trained in real-world environments.
* compact and computationally-efficient, making it perfect for IoT.
* self-service. Developers and designers can train custom models using [Picovoice Console](https://console.picovoice.ai/).
## Compatibility
- Python 3.9+
- Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64), and Raspberry Pi (Zero, 3, 4, 5).
## Installation
```console
pip3 install pvrhino
```
## AccessKey
Rhino requires a valid Picovoice `AccessKey` at initialization. `AccessKey` acts as your credentials when using Rhino SDKs.
You can get your `AccessKey` for free. Make sure to keep your `AccessKey` secret.
Signup or Login to [Picovoice Console](https://console.picovoice.ai/) to get your `AccessKey`.
## Usage
Create an instance of the engine:
```python
import pvrhino
access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)
handle = pvrhino.create(access_key=access_key, context_path='/absolute/path/to/context')
```
Where `context_path` is the absolute path to Speech-to-Intent context created either using
[Picovoice Console](https://console.picovoice.ai/) or one of the default contexts available on Rhino's GitHub repository.
The sensitivity of the engine can be tuned using the `sensitivity` parameter. It is a floating-point number within
[0, 1]. A higher sensitivity value results in fewer misses at the cost of (potentially) increasing the erroneous
inference rate.
```python
import pvrhino
access_key = "${ACCESS_KEY}" # AccessKey obtained from Picovoice Console (https://console.picovoice.ai/)
handle = pvrhino.create(access_key=access_key, context_path='/absolute/path/to/context', sensitivity=0.25)
```
When initialized, the valid sample rate is given by `handle.sample_rate`. Expected frame length (number of audio samples
in an input array) is `handle.frame_length`. The engine accepts 16-bit linearly-encoded PCM and operates on
single-channel audio.
```python
def get_next_audio_frame():
pass
while True:
is_finalized = rhino.process(get_next_audio_frame())
if is_finalized:
inference = rhino.get_inference()
if not inference.is_understood:
# add code to handle unsupported commands
pass
else:
intent = inference.intent
slots = inference.slots
# add code to take action based on inferred intent and slot values
```
When done resources have to be released explicitly:
```python
handle.delete()
```
## Non-English Contexts
In order to run inference on non-English contexts you need to use the corresponding model file. The model files for all supported languages are available [here](../../lib/common).
## Demos
[pvrhinodemo](https://pypi.org/project/pvrhinodemo/) provides command-line utilities for processing real-time
audio (i.e. microphone) and files using Rhino.
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