"""Python wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. """ import collections from tensorflow.python import pywrap_tfe as pywrap_tfe from tensorflow.python.eager import context as _context from tensorflow.python.eager import core as _core from tensorflow.python.eager import execute as _execute from tensorflow.python.framework import dtypes as _dtypes from tensorflow.security.fuzzing.py import annotation_types as _atypes from tensorflow.python.framework import op_def_registry as _op_def_registry from tensorflow.python.framework import ops as _ops from tensorflow.python.framework import op_def_library as _op_def_library from tensorflow.python.util.deprecation import deprecated_endpoints from tensorflow.python.util import dispatch as _dispatch from tensorflow.python.util.tf_export import tf_export from typing import TypeVar, List, Any from typing_extensions import Annotated TV_AudioMicrofrontend_out_type = TypeVar("TV_AudioMicrofrontend_out_type", "_atypes.Float32", "_atypes.UInt16") @_dispatch.add_fallback_dispatch_list @_dispatch.add_type_based_api_dispatcher @tf_export('audio_microfrontend') def audio_microfrontend(audio: Annotated[Any, _atypes.Int16], sample_rate:int=16000, window_size:int=25, window_step:int=10, num_channels:int=32, upper_band_limit:float=7500, lower_band_limit:float=125, smoothing_bits:int=10, even_smoothing:float=0.025, odd_smoothing:float=0.06, min_signal_remaining:float=0.05, enable_pcan:bool=False, pcan_strength:float=0.95, pcan_offset:float=80, gain_bits:int=21, enable_log:bool=True, scale_shift:int=6, left_context:int=0, right_context:int=0, frame_stride:int=1, zero_padding:bool=False, out_scale:int=1, out_type:TV_AudioMicrofrontend_out_type=_dtypes.uint16, name=None) -> Annotated[Any, TV_AudioMicrofrontend_out_type]: r"""Audio Microfrontend Op. This Op converts a sequence of audio data into one or more feature vectors containing filterbanks of the input. The conversion process uses a lightweight library to perform: 1. A slicing window function 2. Short-time FFTs 3. Filterbank calculations 4. Noise reduction 5. PCAN Auto Gain Control 6. Logarithmic scaling Arguments audio: 1D Tensor, int16 audio data in temporal ordering. sample_rate: Integer, the sample rate of the audio in Hz. window_size: Integer, length of desired time frames in ms. window_step: Integer, length of step size for the next frame in ms. num_channels: Integer, the number of filterbank channels to use. upper_band_limit: Float, the highest frequency included in the filterbanks. lower_band_limit: Float, the lowest frequency included in the filterbanks. smoothing_bits: Int, scale up signal by 2^(smoothing_bits) before reduction. even_smoothing: Float, smoothing coefficient for even-numbered channels. odd_smoothing: Float, smoothing coefficient for odd-numbered channels. min_signal_remaining: Float, fraction of signal to preserve in smoothing. enable_pcan: Bool, enable PCAN auto gain control. pcan_strength: Float, gain normalization exponent. pcan_offset: Float, positive value added in the normalization denominator. gain_bits: Int, number of fractional bits in the gain. enable_log: Bool, enable logarithmic scaling of filterbanks. scale_shift: Integer, scale filterbanks by 2^(scale_shift). left_context: Integer, number of preceding frames to attach to each frame. right_context: Integer, number of preceding frames to attach to each frame. frame_stride: Integer, M frames to skip over, where output[n] = frame[n*M]. zero_padding: Bool, if left/right context is out-of-bounds, attach frame of zeroes. Otherwise, frame[0] or frame[size-1] will be copied. out_scale: Integer, divide all filterbanks by this number. out_type: DType, type of the output Tensor, defaults to UINT16. Returns filterbanks: 2D Tensor, each row is a time frame, each column is a channel. Args: audio: A `Tensor` of type `int16`. sample_rate: An optional `int`. Defaults to `16000`. window_size: An optional `int`. Defaults to `25`. window_step: An optional `int`. Defaults to `10`. num_channels: An optional `int`. Defaults to `32`. upper_band_limit: An optional `float`. Defaults to `7500`. lower_band_limit: An optional `float`. Defaults to `125`. smoothing_bits: An optional `int`. Defaults to `10`. even_smoothing: An optional `float`. Defaults to `0.025`. odd_smoothing: An optional `float`. Defaults to `0.06`. min_signal_remaining: An optional `float`. Defaults to `0.05`. enable_pcan: An optional `bool`. Defaults to `False`. pcan_strength: An optional `float`. Defaults to `0.95`. pcan_offset: An optional `float`. Defaults to `80`. gain_bits: An optional `int`. Defaults to `21`. enable_log: An optional `bool`. Defaults to `True`. scale_shift: An optional `int`. Defaults to `6`. left_context: An optional `int`. Defaults to `0`. right_context: An optional `int`. Defaults to `0`. frame_stride: An optional `int`. Defaults to `1`. zero_padding: An optional `bool`. Defaults to `False`. out_scale: An optional `int`. Defaults to `1`. out_type: An optional `tf.DType` from: `tf.uint16, tf.float32`. Defaults to `tf.uint16`. name: A name for the operation (optional). Returns: A `Tensor` of type `out_type`. """ _ctx = _context._context or _context.context() tld = _ctx._thread_local_data if tld.is_eager: try: _result = pywrap_tfe.TFE_Py_FastPathExecute( _ctx, "AudioMicrofrontend", name, audio, "sample_rate", sample_rate, "window_size", window_size, "window_step", window_step, "num_channels", num_channels, "upper_band_limit", upper_band_limit, "lower_band_limit", lower_band_limit, "smoothing_bits", smoothing_bits, "even_smoothing", even_smoothing, "odd_smoothing", odd_smoothing, "min_signal_remaining", min_signal_remaining, "enable_pcan", enable_pcan, "pcan_strength", pcan_strength, "pcan_offset", pcan_offset, "gain_bits", gain_bits, "enable_log", enable_log, "scale_shift", scale_shift, "left_context", left_context, "right_context", right_context, "frame_stride", frame_stride, "zero_padding", zero_padding, "out_scale", out_scale, "out_type", out_type) return _result except _core._NotOkStatusException as e: _ops.raise_from_not_ok_status(e, name) except _core._FallbackException: pass try: _result = _dispatcher_for_audio_microfrontend( (audio, sample_rate, window_size, window_step, num_channels, upper_band_limit, lower_band_limit, smoothing_bits, even_smoothing, odd_smoothing, min_signal_remaining, enable_pcan, pcan_strength, pcan_offset, gain_bits, enable_log, scale_shift, left_context, right_context, frame_stride, zero_padding, out_scale, out_type, name,), None) if _result is not NotImplemented: return _result return audio_microfrontend_eager_fallback( audio, sample_rate=sample_rate, window_size=window_size, window_step=window_step, num_channels=num_channels, upper_band_limit=upper_band_limit, lower_band_limit=lower_band_limit, smoothing_bits=smoothing_bits, even_smoothing=even_smoothing, odd_smoothing=odd_smoothing, min_signal_remaining=min_signal_remaining, enable_pcan=enable_pcan, pcan_strength=pcan_strength, pcan_offset=pcan_offset, gain_bits=gain_bits, enable_log=enable_log, scale_shift=scale_shift, left_context=left_context, right_context=right_context, frame_stride=frame_stride, zero_padding=zero_padding, out_scale=out_scale, out_type=out_type, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. except (TypeError, ValueError): _result = _dispatch.dispatch( audio_microfrontend, (), dict(audio=audio, sample_rate=sample_rate, window_size=window_size, window_step=window_step, num_channels=num_channels, upper_band_limit=upper_band_limit, lower_band_limit=lower_band_limit, smoothing_bits=smoothing_bits, even_smoothing=even_smoothing, odd_smoothing=odd_smoothing, min_signal_remaining=min_signal_remaining, enable_pcan=enable_pcan, pcan_strength=pcan_strength, pcan_offset=pcan_offset, gain_bits=gain_bits, enable_log=enable_log, scale_shift=scale_shift, left_context=left_context, right_context=right_context, frame_stride=frame_stride, zero_padding=zero_padding, out_scale=out_scale, out_type=out_type, name=name) ) if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED: return _result raise else: _result = _dispatcher_for_audio_microfrontend( (audio, sample_rate, window_size, window_step, num_channels, upper_band_limit, lower_band_limit, smoothing_bits, even_smoothing, odd_smoothing, min_signal_remaining, enable_pcan, pcan_strength, pcan_offset, gain_bits, enable_log, scale_shift, left_context, right_context, frame_stride, zero_padding, out_scale, out_type, name,), None) if _result is not NotImplemented: return _result # Add nodes to the TensorFlow graph. if sample_rate is None: sample_rate = 16000 sample_rate = _execute.make_int(sample_rate, "sample_rate") if window_size is None: window_size = 25 window_size = _execute.make_int(window_size, "window_size") if window_step is None: window_step = 10 window_step = _execute.make_int(window_step, "window_step") if num_channels is None: num_channels = 32 num_channels = _execute.make_int(num_channels, "num_channels") if upper_band_limit is None: upper_band_limit = 7500 upper_band_limit = _execute.make_float(upper_band_limit, "upper_band_limit") if lower_band_limit is None: lower_band_limit = 125 lower_band_limit = _execute.make_float(lower_band_limit, "lower_band_limit") if smoothing_bits is None: smoothing_bits = 10 smoothing_bits = _execute.make_int(smoothing_bits, "smoothing_bits") if even_smoothing is None: even_smoothing = 0.025 even_smoothing = _execute.make_float(even_smoothing, "even_smoothing") if odd_smoothing is None: odd_smoothing = 0.06 odd_smoothing = _execute.make_float(odd_smoothing, "odd_smoothing") if min_signal_remaining is None: min_signal_remaining = 0.05 min_signal_remaining = _execute.make_float(min_signal_remaining, "min_signal_remaining") if enable_pcan is None: enable_pcan = False enable_pcan = _execute.make_bool(enable_pcan, "enable_pcan") if pcan_strength is None: pcan_strength = 0.95 pcan_strength = _execute.make_float(pcan_strength, "pcan_strength") if pcan_offset is None: pcan_offset = 80 pcan_offset = _execute.make_float(pcan_offset, "pcan_offset") if gain_bits is None: gain_bits = 21 gain_bits = _execute.make_int(gain_bits, "gain_bits") if enable_log is None: enable_log = True enable_log = _execute.make_bool(enable_log, "enable_log") if scale_shift is None: scale_shift = 6 scale_shift = _execute.make_int(scale_shift, "scale_shift") if left_context is None: left_context = 0 left_context = _execute.make_int(left_context, "left_context") if right_context is None: right_context = 0 right_context = _execute.make_int(right_context, "right_context") if frame_stride is None: frame_stride = 1 frame_stride = _execute.make_int(frame_stride, "frame_stride") if zero_padding is None: zero_padding = False zero_padding = _execute.make_bool(zero_padding, "zero_padding") if out_scale is None: out_scale = 1 out_scale = _execute.make_int(out_scale, "out_scale") if out_type is None: out_type = _dtypes.uint16 out_type = _execute.make_type(out_type, "out_type") try: _, _, _op, _outputs = _op_def_library._apply_op_helper( "AudioMicrofrontend", audio=audio, sample_rate=sample_rate, window_size=window_size, window_step=window_step, num_channels=num_channels, upper_band_limit=upper_band_limit, lower_band_limit=lower_band_limit, smoothing_bits=smoothing_bits, even_smoothing=even_smoothing, odd_smoothing=odd_smoothing, min_signal_remaining=min_signal_remaining, enable_pcan=enable_pcan, pcan_strength=pcan_strength, pcan_offset=pcan_offset, gain_bits=gain_bits, enable_log=enable_log, scale_shift=scale_shift, left_context=left_context, right_context=right_context, frame_stride=frame_stride, zero_padding=zero_padding, out_scale=out_scale, out_type=out_type, name=name) except (TypeError, ValueError): _result = _dispatch.dispatch( audio_microfrontend, (), dict(audio=audio, sample_rate=sample_rate, window_size=window_size, window_step=window_step, num_channels=num_channels, upper_band_limit=upper_band_limit, lower_band_limit=lower_band_limit, smoothing_bits=smoothing_bits, even_smoothing=even_smoothing, odd_smoothing=odd_smoothing, min_signal_remaining=min_signal_remaining, enable_pcan=enable_pcan, pcan_strength=pcan_strength, pcan_offset=pcan_offset, gain_bits=gain_bits, enable_log=enable_log, scale_shift=scale_shift, left_context=left_context, right_context=right_context, frame_stride=frame_stride, zero_padding=zero_padding, out_scale=out_scale, out_type=out_type, name=name) ) if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED: return _result raise _result = _outputs[:] if _execute.must_record_gradient(): _attrs = ("sample_rate", _op._get_attr_int("sample_rate"), "window_size", _op._get_attr_int("window_size"), "window_step", _op._get_attr_int("window_step"), "num_channels", _op._get_attr_int("num_channels"), "upper_band_limit", _op.get_attr("upper_band_limit"), "lower_band_limit", _op.get_attr("lower_band_limit"), "smoothing_bits", _op._get_attr_int("smoothing_bits"), "even_smoothing", _op.get_attr("even_smoothing"), "odd_smoothing", _op.get_attr("odd_smoothing"), "min_signal_remaining", _op.get_attr("min_signal_remaining"), "enable_pcan", _op._get_attr_bool("enable_pcan"), "pcan_strength", _op.get_attr("pcan_strength"), "pcan_offset", _op.get_attr("pcan_offset"), "gain_bits", _op._get_attr_int("gain_bits"), "enable_log", _op._get_attr_bool("enable_log"), "scale_shift", _op._get_attr_int("scale_shift"), "left_context", _op._get_attr_int("left_context"), "right_context", _op._get_attr_int("right_context"), "frame_stride", _op._get_attr_int("frame_stride"), "zero_padding", _op._get_attr_bool("zero_padding"), "out_scale", _op._get_attr_int("out_scale"), "out_type", _op._get_attr_type("out_type")) _inputs_flat = _op.inputs _execute.record_gradient( "AudioMicrofrontend", _inputs_flat, _attrs, _result) _result, = _result return _result AudioMicrofrontend = tf_export("raw_ops.AudioMicrofrontend")(_ops.to_raw_op(audio_microfrontend)) _dispatcher_for_audio_microfrontend = audio_microfrontend._tf_type_based_dispatcher.Dispatch def audio_microfrontend_eager_fallback(audio: Annotated[Any, _atypes.Int16], sample_rate: int, window_size: int, window_step: int, num_channels: int, upper_band_limit: float, lower_band_limit: float, smoothing_bits: int, even_smoothing: float, odd_smoothing: float, min_signal_remaining: float, enable_pcan: bool, pcan_strength: float, pcan_offset: float, gain_bits: int, enable_log: bool, scale_shift: int, left_context: int, right_context: int, frame_stride: int, zero_padding: bool, out_scale: int, out_type: TV_AudioMicrofrontend_out_type, name, ctx) -> Annotated[Any, TV_AudioMicrofrontend_out_type]: if sample_rate is None: sample_rate = 16000 sample_rate = _execute.make_int(sample_rate, "sample_rate") if window_size is None: window_size = 25 window_size = _execute.make_int(window_size, "window_size") if window_step is None: window_step = 10 window_step = _execute.make_int(window_step, "window_step") if num_channels is None: num_channels = 32 num_channels = _execute.make_int(num_channels, "num_channels") if upper_band_limit is None: upper_band_limit = 7500 upper_band_limit = _execute.make_float(upper_band_limit, "upper_band_limit") if lower_band_limit is None: lower_band_limit = 125 lower_band_limit = _execute.make_float(lower_band_limit, "lower_band_limit") if smoothing_bits is None: smoothing_bits = 10 smoothing_bits = _execute.make_int(smoothing_bits, "smoothing_bits") if even_smoothing is None: even_smoothing = 0.025 even_smoothing = _execute.make_float(even_smoothing, "even_smoothing") if odd_smoothing is None: odd_smoothing = 0.06 odd_smoothing = _execute.make_float(odd_smoothing, "odd_smoothing") if min_signal_remaining is None: min_signal_remaining = 0.05 min_signal_remaining = _execute.make_float(min_signal_remaining, "min_signal_remaining") if enable_pcan is None: enable_pcan = False enable_pcan = _execute.make_bool(enable_pcan, "enable_pcan") if pcan_strength is None: pcan_strength = 0.95 pcan_strength = _execute.make_float(pcan_strength, "pcan_strength") if pcan_offset is None: pcan_offset = 80 pcan_offset = _execute.make_float(pcan_offset, "pcan_offset") if gain_bits is None: gain_bits = 21 gain_bits = _execute.make_int(gain_bits, "gain_bits") if enable_log is None: enable_log = True enable_log = _execute.make_bool(enable_log, "enable_log") if scale_shift is None: scale_shift = 6 scale_shift = _execute.make_int(scale_shift, "scale_shift") if left_context is None: left_context = 0 left_context = _execute.make_int(left_context, "left_context") if right_context is None: right_context = 0 right_context = _execute.make_int(right_context, "right_context") if frame_stride is None: frame_stride = 1 frame_stride = _execute.make_int(frame_stride, "frame_stride") if zero_padding is None: zero_padding = False zero_padding = _execute.make_bool(zero_padding, "zero_padding") if out_scale is None: out_scale = 1 out_scale = _execute.make_int(out_scale, "out_scale") if out_type is None: out_type = _dtypes.uint16 out_type = _execute.make_type(out_type, "out_type") audio = _ops.convert_to_tensor(audio, _dtypes.int16) _inputs_flat = [audio] _attrs = ("sample_rate", sample_rate, "window_size", window_size, "window_step", window_step, "num_channels", num_channels, "upper_band_limit", upper_band_limit, "lower_band_limit", lower_band_limit, "smoothing_bits", smoothing_bits, "even_smoothing", even_smoothing, "odd_smoothing", odd_smoothing, "min_signal_remaining", min_signal_remaining, "enable_pcan", enable_pcan, "pcan_strength", pcan_strength, "pcan_offset", pcan_offset, "gain_bits", gain_bits, "enable_log", enable_log, "scale_shift", scale_shift, "left_context", left_context, "right_context", right_context, "frame_stride", frame_stride, "zero_padding", zero_padding, "out_scale", out_scale, "out_type", out_type) _result = _execute.execute(b"AudioMicrofrontend", 1, inputs=_inputs_flat, attrs=_attrs, ctx=ctx, name=name) if _execute.must_record_gradient(): _execute.record_gradient( "AudioMicrofrontend", _inputs_flat, _attrs, _result) _result, = _result return _result