## How to add your own VAD model
1. Your model can be implemented in **any** framework you want but it must implement the `PauseDetectionModel` protocol.
```python
ModelOptions: TypeAlias = Any
class PauseDetectionModel(Protocol):
def vad(
self,
audio: tuple[int, NDArray[np.int16] | NDArray[np.float32]],
options: ModelOptions,
) -> tuple[float, list[AudioChunk]]: ...
def warmup(
self,
) -> None: ...
```
* The `vad` method should take a numpy array of audio data and return a tuple of the form `(speech_duration, and list[AudioChunk])` where `speech_duration` is the duration of the human speech in the audio chunk and `AudioChunk` is a dictionary with the following fields: `(start, end)` where `start` and `end` are the start and end times of the human speech in the audio array.
* The `audio` tuple should be of the form `(sample_rate, audio_array)` where `sample_rate` is the sample rate of the audio array and `audio_array` is a numpy array of the audio data. It can be of type `np.int16` or `np.float32`.
* The `warmup` method is optional but recommended to warm up the model when the server starts.
2. Once you have your model implemented, you can use it in the `ReplyOnPause` class by passing in the model and any options you need.
```python
from fastrtc import ReplyOnPause, Stream
from your_model import YourModel
def echo(audio):
yield audio
model = YourModel() # implement the PauseDetectionModel protocol
reply_on_pause = ReplyOnPause(
echo,
model=model,
options=YourModelOptions(),
)
stream = Stream(reply_on_pause, mode="send-receive", modality="audio")
stream.ui.launch()
```
3. Open a [PR](https://github.com/freddyaboulton/fastrtc/edit/main/docs/vad_gallery.md) to add your model to the gallery! Ideally you model package should be pip installable so other can try it out easily.