473 lines
20 KiB
Python
473 lines
20 KiB
Python
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"""Python wrappers around TensorFlow ops.
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This file is MACHINE GENERATED! Do not edit.
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"""
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import collections
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from tensorflow.python import pywrap_tfe as pywrap_tfe
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from tensorflow.python.eager import context as _context
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from tensorflow.python.eager import core as _core
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from tensorflow.python.eager import execute as _execute
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from tensorflow.python.framework import dtypes as _dtypes
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from tensorflow.security.fuzzing.py import annotation_types as _atypes
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from tensorflow.python.framework import op_def_registry as _op_def_registry
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.framework import op_def_library as _op_def_library
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from tensorflow.python.util.deprecation import deprecated_endpoints
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from tensorflow.python.util import dispatch as _dispatch
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from tensorflow.python.util.tf_export import tf_export
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from typing import TypeVar, List, Any
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from typing_extensions import Annotated
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def audio_spectrogram(input: Annotated[Any, _atypes.Float32], window_size: int, stride: int, magnitude_squared:bool=False, name=None) -> Annotated[Any, _atypes.Float32]:
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r"""Produces a visualization of audio data over time.
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Spectrograms are a standard way of representing audio information as a series of
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slices of frequency information, one slice for each window of time. By joining
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these together into a sequence, they form a distinctive fingerprint of the sound
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over time.
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This op expects to receive audio data as an input, stored as floats in the range
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-1 to 1, together with a window width in samples, and a stride specifying how
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far to move the window between slices. From this it generates a three
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dimensional output. The first dimension is for the channels in the input, so a
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stereo audio input would have two here for example. The second dimension is time,
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with successive frequency slices. The third dimension has an amplitude value for
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each frequency during that time slice.
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This means the layout when converted and saved as an image is rotated 90 degrees
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clockwise from a typical spectrogram. Time is descending down the Y axis, and
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the frequency decreases from left to right.
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Each value in the result represents the square root of the sum of the real and
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imaginary parts of an FFT on the current window of samples. In this way, the
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lowest dimension represents the power of each frequency in the current window,
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and adjacent windows are concatenated in the next dimension.
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To get a more intuitive and visual look at what this operation does, you can run
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tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the
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resulting spectrogram as a PNG image.
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Args:
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input: A `Tensor` of type `float32`. Float representation of audio data.
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window_size: An `int`.
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How wide the input window is in samples. For the highest efficiency
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this should be a power of two, but other values are accepted.
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stride: An `int`.
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How widely apart the center of adjacent sample windows should be.
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magnitude_squared: An optional `bool`. Defaults to `False`.
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Whether to return the squared magnitude or just the
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magnitude. Using squared magnitude can avoid extra calculations.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `float32`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "AudioSpectrogram", name, input, "window_size", window_size,
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"stride", stride, "magnitude_squared", magnitude_squared)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return audio_spectrogram_eager_fallback(
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input, window_size=window_size, stride=stride,
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magnitude_squared=magnitude_squared, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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window_size = _execute.make_int(window_size, "window_size")
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stride = _execute.make_int(stride, "stride")
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if magnitude_squared is None:
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magnitude_squared = False
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magnitude_squared = _execute.make_bool(magnitude_squared, "magnitude_squared")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"AudioSpectrogram", input=input, window_size=window_size,
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stride=stride,
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magnitude_squared=magnitude_squared, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("window_size", _op._get_attr_int("window_size"), "stride",
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_op._get_attr_int("stride"), "magnitude_squared",
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_op._get_attr_bool("magnitude_squared"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"AudioSpectrogram", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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AudioSpectrogram = tf_export("raw_ops.AudioSpectrogram")(_ops.to_raw_op(audio_spectrogram))
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def audio_spectrogram_eager_fallback(input: Annotated[Any, _atypes.Float32], window_size: int, stride: int, magnitude_squared: bool, name, ctx) -> Annotated[Any, _atypes.Float32]:
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window_size = _execute.make_int(window_size, "window_size")
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stride = _execute.make_int(stride, "stride")
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if magnitude_squared is None:
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magnitude_squared = False
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magnitude_squared = _execute.make_bool(magnitude_squared, "magnitude_squared")
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input = _ops.convert_to_tensor(input, _dtypes.float32)
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_inputs_flat = [input]
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_attrs = ("window_size", window_size, "stride", stride, "magnitude_squared",
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magnitude_squared)
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_result = _execute.execute(b"AudioSpectrogram", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"AudioSpectrogram", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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_DecodeWavOutput = collections.namedtuple(
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"DecodeWav",
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["audio", "sample_rate"])
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@_dispatch.add_fallback_dispatch_list
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@_dispatch.add_type_based_api_dispatcher
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@tf_export('audio.decode_wav')
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def decode_wav(contents: Annotated[Any, _atypes.String], desired_channels:int=-1, desired_samples:int=-1, name=None):
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r"""Decode a 16-bit PCM WAV file to a float tensor.
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The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float.
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When desired_channels is set, if the input contains fewer channels than this
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then the last channel will be duplicated to give the requested number, else if
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the input has more channels than requested then the additional channels will be
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ignored.
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If desired_samples is set, then the audio will be cropped or padded with zeroes
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to the requested length.
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The first output contains a Tensor with the content of the audio samples. The
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lowest dimension will be the number of channels, and the second will be the
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number of samples. For example, a ten-sample-long stereo WAV file should give an
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output shape of [10, 2].
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Args:
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contents: A `Tensor` of type `string`.
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The WAV-encoded audio, usually from a file.
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desired_channels: An optional `int`. Defaults to `-1`.
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Number of sample channels wanted.
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desired_samples: An optional `int`. Defaults to `-1`.
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Length of audio requested.
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name: A name for the operation (optional).
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Returns:
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A tuple of `Tensor` objects (audio, sample_rate).
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audio: A `Tensor` of type `float32`.
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sample_rate: A `Tensor` of type `int32`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "DecodeWav", name, contents, "desired_channels",
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desired_channels, "desired_samples", desired_samples)
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_result = _DecodeWavOutput._make(_result)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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_result = _dispatcher_for_decode_wav(
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(contents, desired_channels, desired_samples, name,), None)
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if _result is not NotImplemented:
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return _result
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return decode_wav_eager_fallback(
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contents, desired_channels=desired_channels,
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desired_samples=desired_samples, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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except (TypeError, ValueError):
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_result = _dispatch.dispatch(
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decode_wav, (), dict(contents=contents,
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desired_channels=desired_channels,
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desired_samples=desired_samples, name=name)
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)
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if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
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return _result
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raise
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else:
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_result = _dispatcher_for_decode_wav(
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(contents, desired_channels, desired_samples, name,), None)
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if _result is not NotImplemented:
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return _result
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# Add nodes to the TensorFlow graph.
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if desired_channels is None:
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desired_channels = -1
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desired_channels = _execute.make_int(desired_channels, "desired_channels")
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if desired_samples is None:
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desired_samples = -1
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desired_samples = _execute.make_int(desired_samples, "desired_samples")
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try:
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"DecodeWav", contents=contents, desired_channels=desired_channels,
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desired_samples=desired_samples, name=name)
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except (TypeError, ValueError):
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_result = _dispatch.dispatch(
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decode_wav, (), dict(contents=contents,
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desired_channels=desired_channels,
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desired_samples=desired_samples, name=name)
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)
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if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
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return _result
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raise
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("desired_channels", _op._get_attr_int("desired_channels"),
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"desired_samples", _op._get_attr_int("desired_samples"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"DecodeWav", _inputs_flat, _attrs, _result)
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_result = _DecodeWavOutput._make(_result)
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return _result
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DecodeWav = tf_export("raw_ops.DecodeWav")(_ops.to_raw_op(decode_wav))
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_dispatcher_for_decode_wav = decode_wav._tf_type_based_dispatcher.Dispatch
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def decode_wav_eager_fallback(contents: Annotated[Any, _atypes.String], desired_channels: int, desired_samples: int, name, ctx):
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if desired_channels is None:
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desired_channels = -1
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desired_channels = _execute.make_int(desired_channels, "desired_channels")
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if desired_samples is None:
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desired_samples = -1
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desired_samples = _execute.make_int(desired_samples, "desired_samples")
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contents = _ops.convert_to_tensor(contents, _dtypes.string)
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_inputs_flat = [contents]
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_attrs = ("desired_channels", desired_channels, "desired_samples",
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desired_samples)
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_result = _execute.execute(b"DecodeWav", 2, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"DecodeWav", _inputs_flat, _attrs, _result)
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_result = _DecodeWavOutput._make(_result)
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return _result
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@_dispatch.add_fallback_dispatch_list
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@_dispatch.add_type_based_api_dispatcher
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@tf_export('audio.encode_wav')
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def encode_wav(audio: Annotated[Any, _atypes.Float32], sample_rate: Annotated[Any, _atypes.Int32], name=None) -> Annotated[Any, _atypes.String]:
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r"""Encode audio data using the WAV file format.
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This operation will generate a string suitable to be saved out to create a .wav
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audio file. It will be encoded in the 16-bit PCM format. It takes in float
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values in the range -1.0f to 1.0f, and any outside that value will be clamped to
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that range.
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`audio` is a 2-D float Tensor of shape `[length, channels]`.
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`sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100).
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Args:
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audio: A `Tensor` of type `float32`. 2-D with shape `[length, channels]`.
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sample_rate: A `Tensor` of type `int32`.
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Scalar containing the sample frequency.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `string`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "EncodeWav", name, audio, sample_rate)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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_result = _dispatcher_for_encode_wav(
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(audio, sample_rate, name,), None)
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if _result is not NotImplemented:
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return _result
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return encode_wav_eager_fallback(
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audio, sample_rate, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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except (TypeError, ValueError):
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_result = _dispatch.dispatch(
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encode_wav, (), dict(audio=audio, sample_rate=sample_rate,
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name=name)
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)
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if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
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return _result
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raise
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else:
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_result = _dispatcher_for_encode_wav(
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(audio, sample_rate, name,), None)
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if _result is not NotImplemented:
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return _result
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# Add nodes to the TensorFlow graph.
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try:
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"EncodeWav", audio=audio, sample_rate=sample_rate, name=name)
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except (TypeError, ValueError):
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_result = _dispatch.dispatch(
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encode_wav, (), dict(audio=audio, sample_rate=sample_rate,
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name=name)
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)
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if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
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return _result
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raise
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ()
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_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"EncodeWav", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
EncodeWav = tf_export("raw_ops.EncodeWav")(_ops.to_raw_op(encode_wav))
|
||
|
|
_dispatcher_for_encode_wav = encode_wav._tf_type_based_dispatcher.Dispatch
|
||
|
|
|
||
|
|
|
||
|
|
def encode_wav_eager_fallback(audio: Annotated[Any, _atypes.Float32], sample_rate: Annotated[Any, _atypes.Int32], name, ctx) -> Annotated[Any, _atypes.String]:
|
||
|
|
audio = _ops.convert_to_tensor(audio, _dtypes.float32)
|
||
|
|
sample_rate = _ops.convert_to_tensor(sample_rate, _dtypes.int32)
|
||
|
|
_inputs_flat = [audio, sample_rate]
|
||
|
|
_attrs = None
|
||
|
|
_result = _execute.execute(b"EncodeWav", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"EncodeWav", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def mfcc(spectrogram: Annotated[Any, _atypes.Float32], sample_rate: Annotated[Any, _atypes.Int32], upper_frequency_limit:float=4000, lower_frequency_limit:float=20, filterbank_channel_count:int=40, dct_coefficient_count:int=13, name=None) -> Annotated[Any, _atypes.Float32]:
|
||
|
|
r"""Transforms a spectrogram into a form that's useful for speech recognition.
|
||
|
|
|
||
|
|
Mel Frequency Cepstral Coefficients are a way of representing audio data that's
|
||
|
|
been effective as an input feature for machine learning. They are created by
|
||
|
|
taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the
|
||
|
|
higher frequencies that are less significant to the human ear. They have a long
|
||
|
|
history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
|
||
|
|
is a good resource to learn more.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
spectrogram: A `Tensor` of type `float32`.
|
||
|
|
Typically produced by the Spectrogram op, with magnitude_squared
|
||
|
|
set to true.
|
||
|
|
sample_rate: A `Tensor` of type `int32`.
|
||
|
|
How many samples per second the source audio used.
|
||
|
|
upper_frequency_limit: An optional `float`. Defaults to `4000`.
|
||
|
|
The highest frequency to use when calculating the
|
||
|
|
ceptstrum.
|
||
|
|
lower_frequency_limit: An optional `float`. Defaults to `20`.
|
||
|
|
The lowest frequency to use when calculating the
|
||
|
|
ceptstrum.
|
||
|
|
filterbank_channel_count: An optional `int`. Defaults to `40`.
|
||
|
|
Resolution of the Mel bank used internally.
|
||
|
|
dct_coefficient_count: An optional `int`. Defaults to `13`.
|
||
|
|
How many output channels to produce per time slice.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `float32`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "Mfcc", name, spectrogram, sample_rate, "upper_frequency_limit",
|
||
|
|
upper_frequency_limit, "lower_frequency_limit", lower_frequency_limit,
|
||
|
|
"filterbank_channel_count", filterbank_channel_count,
|
||
|
|
"dct_coefficient_count", dct_coefficient_count)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return mfcc_eager_fallback(
|
||
|
|
spectrogram, sample_rate,
|
||
|
|
upper_frequency_limit=upper_frequency_limit,
|
||
|
|
lower_frequency_limit=lower_frequency_limit,
|
||
|
|
filterbank_channel_count=filterbank_channel_count,
|
||
|
|
dct_coefficient_count=dct_coefficient_count, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if upper_frequency_limit is None:
|
||
|
|
upper_frequency_limit = 4000
|
||
|
|
upper_frequency_limit = _execute.make_float(upper_frequency_limit, "upper_frequency_limit")
|
||
|
|
if lower_frequency_limit is None:
|
||
|
|
lower_frequency_limit = 20
|
||
|
|
lower_frequency_limit = _execute.make_float(lower_frequency_limit, "lower_frequency_limit")
|
||
|
|
if filterbank_channel_count is None:
|
||
|
|
filterbank_channel_count = 40
|
||
|
|
filterbank_channel_count = _execute.make_int(filterbank_channel_count, "filterbank_channel_count")
|
||
|
|
if dct_coefficient_count is None:
|
||
|
|
dct_coefficient_count = 13
|
||
|
|
dct_coefficient_count = _execute.make_int(dct_coefficient_count, "dct_coefficient_count")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"Mfcc", spectrogram=spectrogram, sample_rate=sample_rate,
|
||
|
|
upper_frequency_limit=upper_frequency_limit,
|
||
|
|
lower_frequency_limit=lower_frequency_limit,
|
||
|
|
filterbank_channel_count=filterbank_channel_count,
|
||
|
|
dct_coefficient_count=dct_coefficient_count, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("upper_frequency_limit", _op.get_attr("upper_frequency_limit"),
|
||
|
|
"lower_frequency_limit", _op.get_attr("lower_frequency_limit"),
|
||
|
|
"filterbank_channel_count",
|
||
|
|
_op._get_attr_int("filterbank_channel_count"),
|
||
|
|
"dct_coefficient_count",
|
||
|
|
_op._get_attr_int("dct_coefficient_count"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"Mfcc", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
Mfcc = tf_export("raw_ops.Mfcc")(_ops.to_raw_op(mfcc))
|
||
|
|
|
||
|
|
|
||
|
|
def mfcc_eager_fallback(spectrogram: Annotated[Any, _atypes.Float32], sample_rate: Annotated[Any, _atypes.Int32], upper_frequency_limit: float, lower_frequency_limit: float, filterbank_channel_count: int, dct_coefficient_count: int, name, ctx) -> Annotated[Any, _atypes.Float32]:
|
||
|
|
if upper_frequency_limit is None:
|
||
|
|
upper_frequency_limit = 4000
|
||
|
|
upper_frequency_limit = _execute.make_float(upper_frequency_limit, "upper_frequency_limit")
|
||
|
|
if lower_frequency_limit is None:
|
||
|
|
lower_frequency_limit = 20
|
||
|
|
lower_frequency_limit = _execute.make_float(lower_frequency_limit, "lower_frequency_limit")
|
||
|
|
if filterbank_channel_count is None:
|
||
|
|
filterbank_channel_count = 40
|
||
|
|
filterbank_channel_count = _execute.make_int(filterbank_channel_count, "filterbank_channel_count")
|
||
|
|
if dct_coefficient_count is None:
|
||
|
|
dct_coefficient_count = 13
|
||
|
|
dct_coefficient_count = _execute.make_int(dct_coefficient_count, "dct_coefficient_count")
|
||
|
|
spectrogram = _ops.convert_to_tensor(spectrogram, _dtypes.float32)
|
||
|
|
sample_rate = _ops.convert_to_tensor(sample_rate, _dtypes.int32)
|
||
|
|
_inputs_flat = [spectrogram, sample_rate]
|
||
|
|
_attrs = ("upper_frequency_limit", upper_frequency_limit,
|
||
|
|
"lower_frequency_limit", lower_frequency_limit, "filterbank_channel_count",
|
||
|
|
filterbank_channel_count, "dct_coefficient_count", dct_coefficient_count)
|
||
|
|
_result = _execute.execute(b"Mfcc", 1, inputs=_inputs_flat, attrs=_attrs,
|
||
|
|
ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"Mfcc", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|