1. Components
  2. Audio

New to Gradio? Start here: Getting Started

See the Release History

←

AnnotatedImage

BarPlot

→

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 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

Parameters
🔗 
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 a function is provided, the function will be called each time the app loads to set the initial value of this 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 | I18nData | 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 | Literal['hidden']
default = True

If False, component will be hidden. If "hidden", component will be visually hidden and not take up space in the layout but still exist in the DOM If "hidden", component will be visually hidden and not take up space in the layout but still exist in the DOM.

🔗 
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 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 | tuple[int | str, ...] | None
default = None

in a gr.render, Components with the same key across re-renders are treated as the same component, not a new component. Properties set in 'preserved_by_key' are not reset across a re-render.

🔗 
preserved_by_key: list[str] | str | None
default = "value"

A list of parameters from this component's constructor. Inside a gr.render() function, if a component is re-rendered with the same key, these (and only these) parameters will be preserved in the UI (if they have been changed by the user or an event listener) instead of re-rendered based on the values provided during constructor.

🔗 
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.

🔗 
editable: bool
default = True

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

🔗 
buttons: list[Literal['download', 'share']] | None
default = None

A list of buttons to show in the top right corner of the component. Valid options are "download" and "share". The "download" button allows the user to save the audio to their device. The "share" button allows the user to share the audio via Hugging Face Spaces Discussions. By default, all buttons are shown.

🔗 
waveform_options: WaveformOptions | dict | None
default = None

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

🔗 
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.

🔗 
subtitles: str | Path | list[dict[str, Any]] | None
default = None

A subtitle file (srt, vtt, or json) for the audio, or a list of subtitle dictionaries in the format [{"text": str, "timestamp": [start, end]}] where timestamps are in seconds. JSON files should contain an array of subtitle objects.

Shortcuts

ClassInterface String ShortcutInitialization

gradio.Audio

"audio"

Uses default values

gradio.Microphone

"microphone"

Uses sources=["microphone"]

Demos

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.

ListenerDescription

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

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
default = None

defines how the endpoint appears in the API docs. Can be a string or None. 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.

🔗 
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.

🔗 
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

🔗 
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.

🔗 
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 | 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.

🔗 
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.

🔗 
api_visibility: Literal['public', 'private', 'undocumented']
default = "public"

controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by clients), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private".

🔗 
time_limit: int | None
default = None
🔗 
stream_every: float
default = 0.5
🔗 
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.

🔗 
validator: Callable | None
default = None

Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function and should return a `gr.validate()` for each input value.

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

Parameters
🔗 
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

If True, shows a waveform when recording audio or playing audio. If False, uses the default browser audio players. For streamed audio, the default browser audio player is always used.

🔗 
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.

🔗 
sample_rate: int
default = 44100

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

is_audio_correct_length

Validates that the audio length is within the specified min and max length (in seconds). You can use this to construct a validator that will check if the user-provided audio is either too short or too long.

import gradio as gr
demo = gr.Interface(
    lambda x: x,
    inputs="audio",
    outputs="audio",
    validator=lambda audio: gr.validators.is_audio_correct_length(audio, min_length=1, max_length=5)
)
demo.launch()

Initialization

Parameters
🔗 
audio: tuple[int, 'np.ndarray']

A tuple of (sample rate in Hz, audio data as numpy array).

🔗 
min_length: float | None

Minimum length of audio in seconds. If None, no minimum length check is performed.

🔗 
max_length: float | None

Maximum length of audio in seconds. If None, no maximum length check is performed.

Guides

Real Time Speech Recognition

←

AnnotatedImage

BarPlot

→