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gradio.Audio(···)
Creates an audio component that can be used to upload/record audio (as an input) or display audio (as an output).
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
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. |
sources list[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 |
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 |
every float | None default: None | If |
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 |
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. |
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 |
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. |
Class | Interface String Shortcut | Initialization |
---|---|---|
| "audio" | Uses default values |
| "microphone" | Uses sources=["microphone"] |
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()
Event listeners allow you to capture and 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.
The Audio component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Arguments table below.
Listener | Description |
---|---|
| This listener is triggered when the user streams the Audio. |
| 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 |
| This listener is triggered when the user clears the Audio using the X button for the component. |
| This listener is triggered when the user plays the media in the Audio. |
| This listener is triggered when the media in the Audio stops for any reason. |
| This listener is triggered when the user reaches the end of the media playing in the Audio. |
| This listener is triggered when the media in the Audio stops for any reason. |
| This listener is triggered when the user starts recording with the Audio. |
| This listener is triggered when the user pauses recording with the Audio. |
| This listener is triggered when the user stops recording with the Audio. |
| This listener is triggered when the user uploads a file into the Audio. |
Parameter | Description |
---|---|
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 | 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 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 |
scroll_to_output bool default: False | If True, will scroll to output component on completion |
show_progress Literal[('full', 'minimal', 'hidden')] default: "hidden" | 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 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 |
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. |
trigger_mode Literal[('once', 'multiple', 'always_last')] | None default: None | If "once" (default for all events except |
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 |
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 to use this event. If fn is None, show_api will automatically be set to False. |