tf.strings.upper

Write an audio summary.

tf.summary.audio(
    name, data, sample_rate, step=None, max_outputs=3, encoding=None,
    description=None
)

Arguments
`name`	A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes.
`data`	A `Tensor` representing audio data with shape `[k, t, c]`, where `k` is the number of audio clips, `t` is the number of frames, and `c` is the number of channels. Elements should be floating-point values in `[-1.0, 1.0]`. Any of the dimensions may be statically unknown (i.e., `None`).
`sample_rate`	An `int` or rank-0 `int32` `Tensor` that represents the sample rate, in Hz. Must be positive.
`step`	Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be None.
`max_outputs`	Optional `int` or rank-0 integer `Tensor`. At most this many audio clips will be emitted at each step. When more than `max_outputs` many clips are provided, the first `max_outputs` many clips will be used and the rest silently discarded.
`encoding`	Optional constant `str` for the desired encoding. Only "wav" is currently supported, but this is not guaranteed to remain the default, so if you want "wav" in particular, set this explicitly.
`description`	Optional long-form description for this summary, as a constant `str`. Markdown is supported. Defaults to empty.

Returns
True on success, or false if no summary was emitted because no default summary writer was available.

Raises
`ValueError`	if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is None.

TensorFlow

tf.strings / upper

Arguments

Returns

Raises

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.strings / upper

Arguments

Returns

Raises