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Gradio Component

The automatic gradio UI is a great way to test your stream. However, you may want to customize the UI to your liking or simply build a standalone Gradio application.

The WebRTC Component

To build a standalone Gradio application, you can use the WebRTC component and implement the stream event. Similarly to the Stream object, you must set the mode and modality arguments and pass in a handler.

Below are some common examples of how to use the WebRTC component.

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(
    """
    <h1 style='text-align: center'>
    Chat (Powered by WebRTC ⚡️)
    </h1>
    """
    )
    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.

import asyncio
import base64
import logging
import os

import gradio as gr
import numpy as np
from google import genai
from gradio_webrtc import (
    AsyncStreamHandler,
    WebRTC,
    async_aggregate_bytes_to_16bit,
    get_twilio_turn_credentials,
)

class GeminiHandler(AsyncStreamHandler):
    def __init__(
        self, expected_layout="mono", output_sample_rate=24000, output_frame_size=480
    ) -> None:
        super().__init__(
            expected_layout,
            output_sample_rate,
            output_frame_size,
            input_sample_rate=16000,
        )
        self.client: genai.Client | None = None
        self.input_queue = asyncio.Queue()
        self.output_queue = asyncio.Queue()
        self.quit = asyncio.Event()
        self.connected = asyncio.Event()

    def copy(self) -> "GeminiHandler":
        return GeminiHandler(
            expected_layout=self.expected_layout,
            output_sample_rate=self.output_sample_rate,
            output_frame_size=self.output_frame_size,
        )

    async def stream(self):
        while not self.quit.is_set():
            audio = await self.input_queue.get()
            yield audio

    async def connect(self, api_key: str):
        client = genai.Client(api_key=api_key, http_options={"api_version": "v1alpha"})
        config = {"response_modalities": ["AUDIO"]}
        async with client.aio.live.connect(
            model="gemini-2.0-flash-exp", config=config
        ) as session:
            self.connected.set()
            async for audio in session.start_stream(
                stream=self.stream(), mime_type="audio/pcm"
            ):
                if audio.data:
                    yield audio.data

    async def receive(self, frame: tuple[int, np.ndarray]) -> None:
        _, array = frame
        array = array.squeeze()
        audio_message = base64.b64encode(array.tobytes()).decode("UTF-8")
        self.input_queue.put_nowait(audio_message)

    async def generator(self):
        async for audio_response in async_aggregate_bytes_to_16bit(
            self.connect(api_key=self.latest_args[1])
        ):
            self.output_queue.put_nowait(audio_response)

    async def emit(self):
        if not self.args_set.is_set():
            await self.wait_for_args()

        if not self.connected.is_set():
            asyncio.create_task(self.generator())
            await self.connected.wait()

        array = await self.output_queue.get()
        return (self.output_sample_rate, array)

    def shutdown(self) -> None:
        self.quit.set()

with gr.Blocks() as demo:
    gr.HTML(
        """
        <div style='text-align: center'>
            <h1>Gen AI SDK Voice Chat</h1>
            <p>Speak with Gemini using real-time audio streaming</p>
            <p>Get an API Key <a href="https://support.google.com/googleapi/answer/6158862?hl=en">here</a></p>
        </div>
    """
    )
    with gr.Row() as api_key_row:
        api_key = gr.Textbox(
            label="API Key",
            placeholder="Enter your API Key",
            value=os.getenv("GOOGLE_API_KEY", ""),
            type="password",
        )
    with gr.Row(visible=False) as row:
        webrtc = WebRTC(
            label="Audio",
            modality="audio",
            mode="send-receive",
            rtc_configuration=get_twilio_turn_credentials(),
            pulse_color="rgb(35, 157, 225)",
            icon_button_color="rgb(35, 157, 225)",
            icon="https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png",
        )

    webrtc.stream(
        GeminiHandler(),
        inputs=[webrtc, api_key],
        outputs=[webrtc],
        time_limit=90,
        concurrency_limit=2,
    )
    api_key.submit(
        lambda: (gr.update(visible=False), gr.update(visible=True)),
        None,
        [api_key_row, row],
    )    

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.
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.
    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.

Tip

You can configure the time_limit and concurrency_limit parameters of the stream event similar to the Stream object.

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.

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(
    """
    <h1 style='text-align: center'>
    Talk to Qwen2Audio (Powered by WebRTC ⚡️)
    </h1>
    """
    )
    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.
  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.