Components
Dialogue

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Dialogue

gradio.Dialogue(···)

Description

Creates a Dialogue component for displaying or collecting multi-speaker conversations. This component can be used as input to allow users to enter dialogue involving multiple speakers, or as output to display diarized speech, such as the result of a transcription or speaker identification model. Each message can be associated with a specific speaker, making it suitable for use cases like conversations, interviews, or meetings.

Behavior

As input component: Returns the dialogue as a string.

Your function should accept one of these types:

def predict(
	value: tuple[str, list[tuple[str, str]]] | None
)
	...

As output component: Expects a string or a list of dictionaries of dialogue lines, where each dictionary contains 'speaker' and 'text' keys, or a string.

Your function should return one of these types:

def predict(···) -> tuple[np.ndarray | PIL.Image.Image | str, list[tuple[np.ndarray | tuple[int, int, int, int], str]]] | None
	...	
	return value

Initialization

🔗

value: list[dict[str, str]] | Callable | None

default = None

Value of the dialogue. It is a list of dictionaries, each containing a 'speaker' key and a 'text' key. If a function is provided, the function will be called each time the app loads to set the initial value of this component.

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speakers: list[str] | None

default = None

The different speakers allowed in the dialogue. If `None` or an empty list, no speakers will be displayed. Instead, the component will be a standard textarea that optionally supports `tags` autocompletion.

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formatter: Callable | None

default = None

A function that formats the dialogue line dictionary, e.g. {"speaker": "Speaker 1", "text": "Hello, how are you?"} into a string, e.g. "Speaker 1: Hello, how are you?". This function is run on user input and the resulting string is passed into the prediction function.

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tags: list[str] | None

default = None

The different tags allowed in the dialogue. Tags are displayed in an autocomplete menu below the input textbox when the user starts typing `:`. Use the exact tag name expected by the AI model or inference function.

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separator: str

default = " "

The separator between the different dialogue lines used to join the formatted dialogue lines into a single string. For example, a newline character or empty string.

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color_map: dict[str, str] | None

default = None

A dictionary mapping speaker names to colors. The colors may be specified as hex codes or by their names. For example: {"Speaker 1": "red", "Speaker 2": "#FFEE22"}. If not provided, default colors will be assigned to speakers. This is only used if `interactive` is False.

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label: str | None

default = "Dialogue"

the label for this component, displayed above the component if `show_label` is `True` and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component corresponds to.

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info: str | None

default = "Type colon (:) in the dialogue line to see the available tags"

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placeholder: str | None

default = None

placeholder hint to provide behind textarea.

🔗

show_label: bool | None

default = None

if True, will display the label. If False, the copy button is hidden as well as well as the label.

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container: bool

default = True

if True, will place the component in a container - providing some extra padding around the border.

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scale: int | None

default = None

relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.

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min_width: int

default = 160

minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.

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interactive: bool | None

default = None

if True, will be rendered as an editable textbox; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.

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visible: bool

default = True

If False, component will be hidden.

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elem_id: str | None

default = None

An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

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autofocus: bool

default = False

If True, will focus on the textbox when the page loads. Use this carefully, as it can cause usability issues for sighted and non-sighted users.

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autoscroll: bool

default = True

If True, will automatically scroll to the bottom of the textbox when the value changes, unless the user scrolls up. If False, will not scroll to the bottom of the textbox when the value changes.

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elem_classes: list[str] | str | None

default = None

An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

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render: bool

default = True

If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.

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key: int | str | None

default = None

if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.

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max_lines: int | None

default = None

maximum number of lines allowed in the dialogue.

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show_submit_button: bool

default = True

If True, includes a submit button to submit the dialogue.

🔗

show_copy_button: bool

default = True

If True, includes a copy button to copy the text in the textbox. Only applies if show_label is True.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.Dialogue`	"dialogue"	Uses default values

Demos

import gradio as gr import httpx tags = [ "(laughs)", "(clears throat)", "(sighs)", "(gasps)", "(coughs)", "(singing)", "(sings)", "(mumbles)", "(beep)", "(groans)", "(sniffs)", "(claps)", "(screams)", "(inhales)", "(exhales)", "(applause)", "(burps)", "(humming)", "(sneezes)", "(chuckle)", "(whistles)", ] speakers = ["Speaker 1", "Speaker 2"] client = httpx.AsyncClient(timeout=180) API_URL = "https://router.huggingface.co/fal-ai/fal-ai/dia-tts" async def query(dialogue: str, token: gr.OAuthToken | None): if token is None: raise gr.Error( "No token provided. Use Sign in with Hugging Face to get a token." ) headers = { "Authorization": f"Bearer {token.token}", } response = await client.post(API_URL, headers=headers, json={"text": dialogue}) url = response.json()["audio"]["url"] print("URL: ", url) return url def formatter(speaker, text): speaker = speaker.split(" ")[1] return f"[S{speaker}] {text}" with gr.Blocks() as demo: with gr.Sidebar(): login_button = gr.LoginButton() gr.HTML( """ <h1 style='text-align: center; display: flex; align-items: center; justify-content: center;'> <img src="https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/dancing_huggy.gif" alt="Dancing Huggy" style="height: 100px; margin-right: 10px"> Dia Dialogue Generation Model </h1> <h2 style='text-align: center; display: flex; align-items: center; justify-content: center;'>Model by  <a href="https://huggingface.co/nari-labs/Dia-1.6B"> Nari Labs</a>. Powered by HF and   <a href="https://fal.ai/">Fal AI</a>  API.</h2> <h4>Dia is a dialogue generation model that can generate realistic dialogue between two speakers. Use the dialogue component to create a conversation and then hit the submit button in the bottom right corner to see it come to life .</h4> """ ) with gr.Row(): with gr.Column(): dialogue = gr.Dialogue( speakers=speakers, tags=tags, formatter=formatter ) with gr.Column(): with gr.Row(): audio = gr.Audio(label="Audio") with gr.Row(): gr.DeepLinkButton(value="Share Audio via Link") with gr.Row(): gr.Examples( examples=[ [ [ { "speaker": "Speaker 1", "text": "Why did the chicken cross the road?", }, {"speaker": "Speaker 2", "text": "I don't know!"}, { "speaker": "Speaker 1", "text": "to get to the other side! (laughs)", }, ] ], [ [ { "speaker": "Speaker 1", "text": "I am a little tired today (sighs).", }, {"speaker": "Speaker 2", "text": "Hang in there!"}, ] ], ], inputs=[dialogue], cache_examples=False, ) dialogue.submit(query, [dialogue], audio) if __name__ == "__main__": demo.launch()

import gradio as gr
import httpx


tags = [
    "(laughs)",
    "(clears throat)",
    "(sighs)",
    "(gasps)",
    "(coughs)",
    "(singing)",
    "(sings)",
    "(mumbles)",
    "(beep)",
    "(groans)",
    "(sniffs)",
    "(claps)",
    "(screams)",
    "(inhales)",
    "(exhales)",
    "(applause)",
    "(burps)",
    "(humming)",
    "(sneezes)",
    "(chuckle)",
    "(whistles)",
]
speakers = ["Speaker 1", "Speaker 2"]

client = httpx.AsyncClient(timeout=180)
API_URL = "https://router.huggingface.co/fal-ai/fal-ai/dia-tts"


async def query(dialogue: str, token: gr.OAuthToken | None):
    if token is None:
        raise gr.Error(
            "No token provided. Use Sign in with Hugging Face to get a token."
        )
    headers = {
        "Authorization": f"Bearer {token.token}",
    }
    response = await client.post(API_URL, headers=headers, json={"text": dialogue})
    url = response.json()["audio"]["url"]
    print("URL: ", url)
    return url


def formatter(speaker, text):
    speaker = speaker.split(" ")[1]
    return f"[S{speaker}] {text}"


with gr.Blocks() as demo:
    with gr.Sidebar():
        login_button = gr.LoginButton()
    gr.HTML(
        """
        
         Dia Dialogue Generation Model
        
        Model by   Nari Labs. Powered by HF and   Fal AI   API.
        Dia is a dialogue generation model that can generate realistic dialogue between two speakers. Use the dialogue component to create a conversation and then hit the submit button in the bottom right corner to see it come to life .
        """
    )
    with gr.Row():
        with gr.Column():
            dialogue = gr.Dialogue(
                speakers=speakers, tags=tags, formatter=formatter
            )
        with gr.Column():
            with gr.Row():
                audio = gr.Audio(label="Audio")
            with gr.Row():
                gr.DeepLinkButton(value="Share Audio via Link")
    with gr.Row():
        gr.Examples(
            examples=[
                [
                    [
                        {
                            "speaker": "Speaker 1",
                            "text": "Why did the chicken cross the road?",
                        },
                        {"speaker": "Speaker 2", "text": "I don't know!"},
                        {
                            "speaker": "Speaker 1",
                            "text": "to get to the other side! (laughs)",
                        },
                    ]
                ],
                [
                    [
                        {
                            "speaker": "Speaker 1",
                            "text": "I am a little tired today (sighs).",
                        },
                        {"speaker": "Speaker 2", "text": "Hang in there!"},
                    ]
                ],
            ],
            inputs=[dialogue],
            cache_examples=False,
        )

    dialogue.submit(query, [dialogue], audio)

if __name__ == "__main__":
    demo.launch()

Event Listeners

Description

Event listeners allow you to respond to user interactions with the UI components you've defined in a Gradio Blocks app. When a user interacts with an element, such as changing a slider value or uploading an image, a function is called.

Supported Event Listeners

The Dialogue component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.

Listener	Description
`Dialogue.change(fn, ···)`	Triggered when the value of the Dialogue changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
`Dialogue.input(fn, ···)`	This listener is triggered when the user changes the value of the Dialogue.
`Dialogue.submit(fn, ···)`	This listener is triggered when the user presses the Enter key while the Dialogue is focused.

Event Parameters

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fn: Callable | None | Literal['decorator']

default = "decorator"

the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.

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inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None

default = None

List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.

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outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None

default = None

List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.

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api_name: str | None | Literal[False]

default = None

defines how the endpoint appears in the API docs. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. If False, the endpoint will not be exposed in the API docs and downstream apps (including those that `gr.load` this app) will not be able to use this event.

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api_description: str | None | Literal[False]

default = None

Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs.

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scroll_to_output: bool

default = False

If True, will scroll to output component on completion

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show_progress: Literal['full', 'minimal', 'hidden']

default = "full"

how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all

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show_progress_on: Component | list[Component] | None

default = None

Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.

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queue: bool

default = True

If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.

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batch: bool

default = False

If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.

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max_batch_size: int

default = 4

Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)

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preprocess: bool

default = True

If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).

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postprocess: bool

default = True

If False, will not run postprocessing of component data before returning 'fn' output to the browser.

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cancels: dict[str, Any] | list[dict[str, Any]] | None

default = None

A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.

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trigger_mode: Literal['once', 'multiple', 'always_last'] | None

default = None

If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.

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js: str | Literal[True] | None

default = None

Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.

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concurrency_limit: int | None | Literal['default']

default = "default"

If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).

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concurrency_id: str | None

default = None

If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.

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show_api: bool

default = True

whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.

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time_limit: int | None

default = None

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stream_every: float

default = 0.5

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like_user_message: bool

default = False

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key: int | str | tuple[int | str, ...] | None

default = None

A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.

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