Components
Audio

New to Gradio? Start here: Getting Started

AnnotatedImage

BarPlot

Audio

gradio.Audio(···)

import gradio as gr with gr.Blocks() as demo: gr.Audio("https://cdn.pixabay.com/download/audio/2022/03/09/audio_7e096b862f.mp3") demo.launch()

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 component: passes audio as one of these formats (depending on `type`): a `str` filepath, or `tuple` of (sample rate in Hz, audio data as numpy array). If the latter, the audio data is a 16-bit `int` array whose values range from -32768 to 32767 and shape of the audio data array is (samples,) for mono audio or (samples, channels) for multi-channel audio.

Your function should accept one of these types:

def predict(
	value: str | tuple[int, np.ndarray] | None
)
	...

As output component: expects audio data in any of these formats: a `str` or `pathlib.Path` filepath or URL to an audio file, or a `bytes` object (recommended for streaming), or a `tuple` of (sample rate in Hz, audio data as numpy array). Note: if audio is supplied as a numpy array, the audio will be normalized by its peak value to avoid distortion or clipping in the resulting audio.

Your function should return one of these types:

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

Initialization

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.

sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None

default = None

A list of sources permitted for audio. "upload" creates a box where user can drop an audio file, "microphone" creates a microphone input. The first element in the list will be used as the default source. If None, defaults to ["upload", "microphone"], or ["microphone"] if `streaming` is True.

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

the label for this component. Appears above the component 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 is assigned to.

every: Timer | float | None

default = None

Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.

inputs: Component | list[Component] | set[Component] | None

default = None

Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.

show_label: bool | None

default = None

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 an 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 as an input, will automatically stream webcam feed. When used set as an output, takes audio chunks yield from the backend and combines them into one streaming audio output.

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.

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.

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.

format: Literal['wav', 'mp3'] | None

default = None

the file extension with which 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. This parameter applies both when this component is used as an input (and `type` is "filepath") to determine which file format to convert user-provided audio to, and when this component is used as an output to determine the format of audio returned to the user. If None, no file format conversion is done and the audio is kept as is. In the case where output audio is returned from the prediction function as numpy array and no `format` is provided, it will be returned as a "wav" file.

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_download_button: bool | None

default = None

If True, will show a download button in the corner of the component for saving audio. If False, icon does not appear. By default, it will be True for output components and False for input components.

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.

editable: bool

default = True

If True, allows users to manipulate the audio file if the component is interactive. Defaults to True.

min_length: int | None

default = None

The minimum length of audio (in seconds) that the user can pass into the prediction function. If None, there is no minimum length.

max_length: int | None

default = None

The maximum length of audio (in seconds) that the user can pass into the prediction function. If None, there is no maximum length.

waveform_options: WaveformOptions | dict | None

default = None

A dictionary of options for the waveform display. Options include: waveform_color (str), waveform_progress_color (str), show_controls (bool), skip_length (int), trim_region_color (str). Default is None, which uses the default values for these options. [See `gr.WaveformOptions` docs](#waveform-options).

loop: bool

default = False

If True, the audio will loop when it reaches the end and continue playing from the beginning.

recording: bool

default = False

If True, the audio component will be set to record audio from the microphone if the source is set to "microphone". Defaults to False.

Shortcuts

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

Demos

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 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 numpy as np import gradio as gr def reverse_audio(audio): sr, data = audio return (sr, np.flipud(data)) input_audio = gr.Audio( sources=["microphone"], waveform_options=gr.WaveformOptions( waveform_color="#01C6FF", waveform_progress_color="#0066B4", skip_length=2, show_controls=False, ), ) demo = gr.Interface( fn=reverse_audio, inputs=input_audio, outputs="audio" ) if __name__ == "__main__": demo.launch()


import numpy as np

import gradio as gr

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

input_audio = gr.Audio(
    sources=["microphone"],
    waveform_options=gr.WaveformOptions(
        waveform_color="#01C6FF",
        waveform_progress_color="#0066B4",
        skip_length=2,
        show_controls=False,
    ),
)
demo = gr.Interface(
    fn=reverse_audio,
    inputs=input_audio,
    outputs="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 Audio component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.

Listener	Description
`Audio.stream(fn, ···)`	This listener is triggered when the user streams the Audio.
`Audio.change(fn, ···)`	Triggered when the value of the Audio 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.
`Audio.clear(fn, ···)`	This listener is triggered when the user clears the Audio using the clear button for the component.
`Audio.play(fn, ···)`	This listener is triggered when the user plays the media in the Audio.
`Audio.pause(fn, ···)`	This listener is triggered when the media in the Audio stops for any reason.
`Audio.stop(fn, ···)`	This listener is triggered when the user reaches the end of the media playing in the Audio.
`Audio.pause(fn, ···)`	This listener is triggered when the media in the Audio stops for any reason.
`Audio.start_recording(fn, ···)`	This listener is triggered when the user starts recording with the Audio.
`Audio.pause_recording(fn, ···)`	This listener is triggered when the user pauses recording with the Audio.
`Audio.stop_recording(fn, ···)`	This listener is triggered when the user stops recording with the Audio.
`Audio.upload(fn, ···)`	This listener is triggered when the user uploads a file into the Audio.
`Audio.input(fn, ···)`	This listener is triggered when the user changes the value of the Audio.

Event Parameters

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.

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.

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.

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.

scroll_to_output: bool

default = False

If True, will scroll to output component on completion

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

default = "minimal"

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

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.

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.

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.

js: str | 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.

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).

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.

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.

time_limit: int | None

default = None

stream_every: float

default = 0.5

like_user_message: bool

default = False

Helper Classes

WaveformOptions

gradio.WaveformOptions(···)

Description

A dataclass for specifying options for the waveform display in the Audio component. An instance of this class can be passed into the `waveform_options` parameter of `gr.Audio`.

Initialization

waveform_color: str | None

default = None

The color (as a hex string or valid CSS color) of the full waveform representing the amplitude of the audio. Defaults to a light gray color.

waveform_progress_color: str | None

default = None

The color (as a hex string or valid CSS color) that the waveform fills with to as the audio plays. Defaults to the accent color.

trim_region_color: str | None

default = None

The color (as a hex string or valid CSS color) of the trim region. Defaults to the accent color.

show_recording_waveform: bool

default = True

Whether to show the waveform when recording audio. Defaults to True.

show_controls: bool

default = False

Whether to show the standard HTML audio player below the waveform when recording audio or playing recorded audio. Defaults to False.

skip_length: int | float

default = 5

The percentage (between 0 and 100) of the audio to skip when clicking on the skip forward / skip backward buttons. Defaults to 5.

sample_rate: int

default = 44100

The output sample rate (in Hz) of the audio after editing. Defaults to 44100.

Guides

Real Time Speech Recognition

←

AnnotatedImage

BarPlot

→

Audio

Description

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

Behavior

Your function should accept one of these types:

Your function should return one of these types:

Initialization

Shortcuts

Demos

Event Listeners

Description

Supported Event Listeners

Event Parameters

Helper Classes

WaveformOptions

Description

A dataclass for specifying options for the waveform display in the Audio component. An instance of this class can be passed into the waveform_options parameter of gr.Audio.

Initialization

Guides

A dataclass for specifying options for the waveform display in the Audio component. An instance of this class can be passed into the `waveform_options` parameter of `gr.Audio`.