Gradio Audio Docs

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Audio

gradio.Audio(···)

Description

Creates an audio component that can be used to upload/record audio (as an input) or display audio (as an output).

Behavior

As input: passes the uploaded audio as a Tuple(int, numpy.array) corresponding to (sample rate in Hz, audio data as a 16-bit int array whose values range from -32768 to 32767), or as a str filepath, depending on `type`.

As output: expects a Tuple(int, numpy.array) corresponding to (sample rate in Hz, audio data as a float or int numpy array) or as a str or pathlib.Path filepath or URL to an audio file, which gets displayed

Initialization

Parameter	Description
`value` str \| Path \| tuple[int, np.ndarray] \| Callable \| None default: None	A path, URL, or [sample_rate, numpy array] tuple (sample rate in Hz, audio data as a float or int numpy array) for the default value that Audio component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.
`source` Literal['upload', 'microphone'] default: "upload"	Source of audio. "upload" creates a box where user can drop an audio file, "microphone" creates a microphone input.
`type` Literal['numpy', 'filepath'] default: "numpy"	The format the audio file is converted to before being passed into the prediction function. "numpy" converts the audio to a tuple consisting of: (int sample rate, numpy.array for the data), "filepath" passes a str path to a temporary file containing the audio.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`container` bool default: True	If True, will place the component in a container - providing some extra padding around the border.
`scale` int \| None default: None	relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
`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.
`interactive` bool \| None default: None	if True, will allow users to upload and edit a audio file; if False, can only be used to play audio. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`streaming` bool default: False	If set to True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'microphone'.
`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.
`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.
`format` Literal['wav', 'mp3'] default: "wav"	The file format to save audio files. Either 'wav' or 'mp3'. wav files are lossless but will tend to be larger files. mp3 files tend to be smaller. Default is wav. Applies both when this component is used as an input (when `type` is "format") and when this component is used as an output.
`autoplay` bool default: False	Whether to automatically play the audio when the component is used as an output. Note: browsers will not autoplay audio files if the user has not interacted with the page yet.
`show_share_button` bool \| None default: None	If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.Audio`	"audio"	Uses default values
`gradio.Microphone`	"microphone"	Uses source="microphone"

Demos

from math import log2, pow
import os

import numpy as np
from scipy.fftpack import fft

import gradio as gr

A4 = 440
C0 = A4 * pow(2, -4.75)
name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]


def get_pitch(freq):
    h = round(12 * log2(freq / C0))
    n = h % 12
    return name[n]


def main_note(audio):
    rate, y = audio
    if len(y.shape) == 2:
        y = y.T[0]
    N = len(y)
    T = 1.0 / rate
    yf = fft(y)
    yf2 = 2.0 / N * np.abs(yf[0 : N // 2])
    xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)

    volume_per_pitch = {}
    total_volume = np.sum(yf2)
    for freq, volume in zip(xf, yf2):
        if freq == 0:
            continue
        pitch = get_pitch(freq)
        if pitch not in volume_per_pitch:
            volume_per_pitch[pitch] = 0
        volume_per_pitch[pitch] += 1.0 * volume / total_volume
    volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}
    return volume_per_pitch


demo = gr.Interface(
    main_note,
    gr.Audio(source="microphone"),
    gr.Label(num_top_classes=4),
    examples=[
        [os.path.join(os.path.dirname(__file__),"audio/recording1.wav")],
        [os.path.join(os.path.dirname(__file__),"audio/cantina.wav")],
    ],
    interpretation="default",
)

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

import numpy as np
import gradio as gr

notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]

def generate_tone(note, octave, duration):
    sr = 48000
    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
    frequency = a4_freq * 2 ** (tones_from_a4 / 12)
    duration = int(duration)
    audio = np.linspace(0, duration, duration * sr)
    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
    return sr, audio

demo = gr.Interface(
    generate_tone,
    [
        gr.Dropdown(notes, type="index"),
        gr.Slider(4, 6, step=1),
        gr.Textbox(value=1, label="Duration in seconds"),
    ],
    "audio",
)
if __name__ == "__main__":
    demo.launch()

import os

import numpy as np

import gradio as gr


def reverse_audio(audio):
    sr, data = audio
    return (sr, np.flipud(data))


demo = gr.Interface(fn=reverse_audio, 
                    inputs="microphone", 
                    outputs="audio", 
                    examples=[
                    "https://samplelib.com/lib/preview/mp3/sample-3s.mp3",
                    os.path.join(os.path.dirname(__file__), "audio/recording1.wav")
        ], cache_examples=True)

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

Methods

change

gradio.Audio.change(fn, ···)

Description

This listener is triggered when the component's value 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. This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.Audio.clear(fn, ···)

Description

This listener is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

play

gradio.Audio.play(fn, ···)

Description

This listener is triggered when the user plays the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

pause

gradio.Audio.pause(fn, ···)

Description

This listener is triggered when the media stops playing for any reason (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

stop

gradio.Audio.stop(fn, ···)

Description

This listener is triggered when the user reaches the end of the media track (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

end

gradio.Audio.end(fn, ···)

Description

This listener is triggered when the user reaches the end of the media track (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

stream

gradio.Audio.stream(fn, ···)

Description

This listener is triggered when the user streams the component (e.g. a live webcam component). This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

start_recording

gradio.Audio.start_recording(fn, ···)

Description

This listener is triggered when the user starts recording with the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

stop_recording

gradio.Audio.stop_recording(fn, ···)

Description

This listener is triggered when the user stops recording with the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

upload

gradio.Audio.upload(fn, ···)

Description

This listener is triggered when the user uploads a file into the component (e.g. when the user uploads a video into a video component). This method can be used when this component is in a Gradio Blocks.

Agruments

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. 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.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
`status_tracker` None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` Literal['full', 'minimal', 'hidden'] default: "full"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`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.
`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)
`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).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Guides

Real Time Speech Recognition

Audio

Description
Behavior
Initialization
Shortcuts
Demos
Methods
change
clear
play
pause
stop
end
stream
start_recording
stop_recording
upload
Guides

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BarPlot →