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Expands signal's axis dimension into frames of frame_length.

Slides a window of size frame_length over signal's axis dimension with a stride of frame_step, replacing the axis dimension with [frames, frame_length] frames.

If pad_end is True, window positions that are past the end of the axis dimension are padded with pad_value until the window moves fully past the end of the dimension. Otherwise, only window positions that fully overlap the axis dimension are produced.

For example:

# A batch size 3 tensor of 9152 audio samples.
audio = tf.random.normal([3, 9152])

# Compute overlapping frames of length 512 with a step of 180 (frames overlap
# by 332 samples). By default, only 49 frames are generated since a frame
# with start position j*180 for j > 48 would overhang the end.
frames = tf.signal.frame(audio, 512, 180)
frames.shape.assert_is_compatible_with([3, 49, 512])

# When pad_end is enabled, the final two frames are kept (padded with zeros).
frames = tf.signal.frame(audio, 512, 180, pad_end=True)
frames.shape.assert_is_compatible_with([3, 51, 512])

If the dimension along axis is N, and pad_end=False, the number of frames can be computed by:

 num_frames = 1 + (N - frame_size) // frame_step

If pad_end=True, the number of frames can be computed by:

num_frames = -(-N // frame_step) # ceiling division

signal A [..., samples, ...] Tensor. The rank and dimensions may be unknown. Rank must be at least 1.
frame_length The frame length in samples. An integer or scalar Tensor.
frame_step The frame hop size in samples. An integer or scalar Tensor.
pad_end Whether to pad the end of signal with pad_value.
pad_value An optional scalar Tensor to use where the input signal does not exist when pad_end is True.
axis A scalar integer Tensor indicating the axis to frame. Defaults to the last axis. Supports negative values for indexing from the end.
name An optional name for the operation.

A Tensor of frames with shape [..., num_frames, frame_length, ...].

ValueError If frame_length, frame_step, pad_value, or axis are not scalar.