# User Guide
To get started with WebRTC streams, all that's needed is to import the `WebRTC` component from this package and implement its `stream` event.
This page will show how to do so with simple code examples.
For complete implementations of common tasks, see the [cookbook](/cookbook).
## Audio Streaming
### Reply on Pause
Typically, you want to run an AI model that generates audio when the user has stopped speaking. This can be done by wrapping a python generator with the `ReplyOnPause` class
and passing it to the `stream` event of the `WebRTC` component.
=== "Code"
``` py title="ReplyonPause"
import gradio as gr
from gradio_webrtc import WebRTC, ReplyOnPause
def response(audio: tuple[int, np.ndarray]): # (1)
"""This function must yield audio frames"""
...
for numpy_array in generated_audio:
yield (sampling_rate, numpy_array, "mono") # (2)
with gr.Blocks() as demo:
gr.HTML(
"""
Chat (Powered by WebRTC ⚡️)
"""
)
with gr.Column():
with gr.Group():
audio = WebRTC(
mode="send-receive", # (3)
modality="audio",
)
audio.stream(fn=ReplyOnPause(response),
inputs=[audio], outputs=[audio], # (4)
time_limit=60) # (5)
demo.launch()
```
1. The python generator will receive the **entire** audio up until the user stopped. It will be a tuple of the form (sampling_rate, numpy array of audio). The array will have a shape of (1, num_samples). You can also pass in additional input components.
2. The generator must yield audio chunks as a tuple of (sampling_rate, numpy audio array). Each numpy audio array must have a shape of (1, num_samples).
3. The `mode` and `modality` arguments must be set to `"send-receive"` and `"audio"`.
4. The `WebRTC` component must be the first input and output component.
5. Set a `time_limit` to control how long a conversation will last. If the `concurrency_count` is 1 (default), only one conversation will be handled at a time.
=== "Notes"
1. The python generator will receive the **entire** audio up until the user stopped. It will be a tuple of the form (sampling_rate, numpy array of audio). The array will have a shape of (1, num_samples). You can also pass in additional input components.
2. The generator must yield audio chunks as a tuple of (sampling_rate, numpy audio arrays). Each numpy audio array must have a shape of (1, num_samples).
3. The `mode` and `modality` arguments must be set to `"send-receive"` and `"audio"`.
4. The `WebRTC` component must be the first input and output component.
5. Set a `time_limit` to control how long a conversation will last. If the `concurrency_count` is 1 (default), only one conversation will be handled at a time.
### Reply On Stopwords
You can configure your AI model to run whenever a set of "stop words" are detected, like "Hey Siri" or "computer", with the `ReplyOnStopWords` class.
The API is similar to `ReplyOnPause` with the addition of a `stop_words` parameter.
=== "Code"
``` py title="ReplyonPause"
import gradio as gr
from gradio_webrtc import WebRTC, ReplyOnPause
def response(audio: tuple[int, np.ndarray]):
"""This function must yield audio frames"""
...
for numpy_array in generated_audio:
yield (sampling_rate, numpy_array, "mono")
with gr.Blocks() as demo:
gr.HTML(
"""
Chat (Powered by WebRTC ⚡️)
"""
)
with gr.Column():
with gr.Group():
audio = WebRTC(
mode="send",
modality="audio",
)
webrtc.stream(ReplyOnStopWords(generate,
input_sample_rate=16000,
stop_words=["computer"]), # (1)
inputs=[webrtc, history, code],
outputs=[webrtc], time_limit=90,
concurrency_limit=10)
demo.launch()
```
1. The `stop_words` can be single words or pairs of words. Be sure to include common misspellings of your word for more robust detection, e.g. "llama", "lamma". In my experience, it's best to use two very distinct words like "ok computer" or "hello iris".
=== "Notes"
1. The `stop_words` can be single words or pairs of words. Be sure to include common misspellings of your word for more robust detection, e.g. "llama", "lamma". In my experience, it's best to use two very distinct words like "ok computer" or "hello iris".
### Stream Handler
`ReplyOnPause` is an implementation of a `StreamHandler`. The `StreamHandler` is a low-level
abstraction that gives you arbitrary control over how the input audio stream and output audio stream are created. The following example echos back the user audio.
=== "Code"
``` py title="Stream Handler"
import gradio as gr
from gradio_webrtc import WebRTC, StreamHandler
from queue import Queue
class EchoHandler(StreamHandler):
def __init__(self) -> None:
super().__init__()
self.queue = Queue()
def receive(self, frame: tuple[int, np.ndarray]) -> None: # (1)
self.queue.put(frame)
def emit(self) -> None: # (2)
return self.queue.get()
def copy(self) -> StreamHandler:
return EchoHandler()
with gr.Blocks() as demo:
with gr.Column():
with gr.Group():
audio = WebRTC(
mode="send-receive",
modality="audio",
)
audio.stream(fn=EchoHandler(),
inputs=[audio], outputs=[audio],
time_limit=15)
demo.launch()
```
1. The `StreamHandler` class implements three methods: `receive`, `emit` and `copy`. The `receive` method is called when a new frame is received from the client, and the `emit` method returns the next frame to send to the client. The `copy` method is called at the beginning of the stream to ensure each user has a unique stream handler.
2. The `emit` method SHOULD NOT block. If a frame is not ready to be sent, the method should return `None`.
=== "Notes"
1. The `StreamHandler` class implements three methods: `receive`, `emit` and `copy`. The `receive` method is called when a new frame is received from the client, and the `emit` method returns the next frame to send to the client. The `copy` method is called at the beginning of the stream to ensure each user has a unique stream handler.
2. The `emit` method SHOULD NOT block. If a frame is not ready to be sent, the method should return `None`.
### Server-To-Client Only
To stream only from the server to the client, implement a python generator and pass it to the component's `stream` event. The stream event must also specify a `trigger` corresponding to a UI interaction that starts the stream. In this case, it's a button click.
=== "Code"
``` py title="Server-To-CLient"
import gradio as gr
from gradio_webrtc import WebRTC
from pydub import AudioSegment
def generation(num_steps):
for _ in range(num_steps):
segment = AudioSegment.from_file("audio_file.wav")
array = np.array(segment.get_array_of_samples()).reshape(1, -1)
yield (segment.frame_rate, array)
with gr.Blocks() as demo:
audio = WebRTC(label="Stream", mode="receive", # (1)
modality="audio")
num_steps = gr.Slider(label="Number of Steps", minimum=1,
maximum=10, step=1, value=5)
button = gr.Button("Generate")
audio.stream(
fn=generation, inputs=[num_steps], outputs=[audio],
trigger=button.click # (2)
)
```
1. Set `mode="receive"` to only receive audio from the server.
2. The `stream` event must take a `trigger` that corresponds to the gradio event that starts the stream. In this case, it's the button click.
=== "Notes"
1. Set `mode="receive"` to only receive audio from the server.
2. The `stream` event must take a `trigger` that corresponds to the gradio event that starts the stream. In this case, it's the button click.
## Video Streaming
### Input/Output Streaming
Set up a video Input/Output stream to continuosly receive webcam frames from the user and run an arbitrary python function to return a modified frame.
=== "Code"
``` py title="Input/Output Streaming"
import gradio as gr
from gradio_webrtc import WebRTC
def detection(image, conf_threshold=0.3): # (1)
... your detection code here ...
return modified_frame # (2)
with gr.Blocks() as demo:
image = WebRTC(label="Stream", mode="send-receive", modality="video") # (3)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
image.stream(
fn=detection,
inputs=[image, conf_threshold], # (4)
outputs=[image], time_limit=10
)
if __name__ == "__main__":
demo.launch()
```
1. The webcam frame will be represented as a numpy array of shape (height, width, RGB).
2. The function must return a numpy array. It can take arbitrary values from other components.
3. Set the `modality="video"` and `mode="send-receive"`
4. The `inputs` parameter should be a list where the first element is the WebRTC component. The only output allowed is the WebRTC component.
=== "Notes"
1. The webcam frame will be represented as a numpy array of shape (height, width, RGB).
2. The function must return a numpy array. It can take arbitrary values from other components.
3. Set the `modality="video"` and `mode="send-receive"`
4. The `inputs` parameter should be a list where the first element is the WebRTC component. The only output allowed is the WebRTC component.
### Server-to-Client Only
Set up a server-to-client stream to stream video from an arbitrary user interaction.
=== "Code"
``` py title="Server-To-Client"
import gradio as gr
from gradio_webrtc import WebRTC
import cv2
def generation():
url = "https://download.tsi.telecom-paristech.fr/gpac/dataset/dash/uhd/mux_sources/hevcds_720p30_2M.mp4"
cap = cv2.VideoCapture(url)
iterating = True
while iterating:
iterating, frame = cap.read()
yield frame # (1)
with gr.Blocks() as demo:
output_video = WebRTC(label="Video Stream", mode="receive", # (2)
modality="video")
button = gr.Button("Start", variant="primary")
output_video.stream(
fn=generation, inputs=None, outputs=[output_video],
trigger=button.click # (3)
)
demo.launch()
```
1. The `stream` event's `fn` parameter is a generator function that yields the next frame from the video as a **numpy array**.
2. Set `mode="receive"` to only receive audio from the server.
3. The `trigger` parameter the gradio event that will trigger the stream. In this case, the button click event.
=== "Notes"
1. The `stream` event's `fn` parameter is a generator function that yields the next frame from the video as a **numpy array**.
2. Set `mode="receive"` to only receive audio from the server.
3. The `trigger` parameter the gradio event that will trigger the stream. In this case, the button click event.
## Additional Outputs
In order to modify other components from within the WebRTC stream, you must yield an instance of `AdditionalOutputs` and add an `on_additional_outputs` event to the `WebRTC` component.
This is common for displaying a multimodal text/audio conversation in a Chatbot UI.
=== "Code"
``` py title="Additional Outputs"
from gradio_webrtc import AdditionalOutputs, WebRTC
def transcribe(audio: tuple[int, np.ndarray],
transformers_convo: list[dict],
gradio_convo: list[dict]):
response = model.generate(**inputs, max_length=256)
transformers_convo.append({"role": "assistant", "content": response})
gradio_convo.append({"role": "assistant", "content": response})
yield AdditionalOutputs(transformers_convo, gradio_convo) # (1)
with gr.Blocks() as demo:
gr.HTML(
"""
Talk to Qwen2Audio (Powered by WebRTC ⚡️)
"""
)
transformers_convo = gr.State(value=[])
with gr.Row():
with gr.Column():
audio = WebRTC(
label="Stream",
mode="send", # (2)
modality="audio",
)
with gr.Column():
transcript = gr.Chatbot(label="transcript", type="messages")
audio.stream(ReplyOnPause(transcribe),
inputs=[audio, transformers_convo, transcript],
outputs=[audio], time_limit=90)
audio.on_additional_outputs(lambda s,a: (s,a), # (3)
outputs=[transformers_convo, transcript],
queue=False, show_progress="hidden")
demo.launch()
```
1. Pass your data to `AdditionalOutputs` and yield it.
2. In this case, no audio is being returned, so we set `mode="send"`. However, if we set `mode="send-receive"`, we could also yield generated audio and `AdditionalOutputs`.
3. The `on_additional_outputs` event does not take `inputs`. It's common practice to not run this event on the queue since it is just a quick UI update.
=== "Notes"
1. Pass your data to `AdditionalOutputs` and yield it.
2. In this case, no audio is being returned, so we set `mode="send"`. However, if we set `mode="send-receive"`, we could also yield generated audio and `AdditionalOutputs`.
3. The `on_additional_outputs` event does not take `inputs`. It's common practice to not run this event on the queue since it is just a quick UI update.