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163 lines
4.7 KiB
163 lines
4.7 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/*!
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* \file audio_ops.h
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* \brief
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*/
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#ifndef OPS_BUILT_IN_OP_PROTO_INC_AUDIO_OPS_H_
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#define OPS_BUILT_IN_OP_PROTO_INC_AUDIO_OPS_H_
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief Mel-Frequency Cepstral Coefficient (MFCC) calculation consists of
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taking the DCT-II of a log-magnitude mel-scale spectrogram . \n
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*@par Inputs:
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*Input "spectrogram" is a 3D tensor. Input "sample_rate" is a scalar.
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* @li spectrogram: A 3D float tensor.
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* @li sample_rate: The MFCC sample rate . \n
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*@par Attributes:
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*@li upper_frequency_limit: The highest frequency for calculation.
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*@li lower_frequency_limit: The lowest frequency for calculation.
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*@li filterbank_channel_count: Resolution of the Mel bank.
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*@li dct_coefficient_count: Number of output channels to produce
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per time slice . \n
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*@par Outputs:
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*y: A Tensor of type float32 . \n
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*@attention Constraints:
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*Mfcc runs on the Ascend AI CPU, which delivers poor performance.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator Mfcc . \n
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*@par Restrictions:
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*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
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*/
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REG_OP(Mfcc)
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.INPUT(spectrogram, TensorType({DT_FLOAT}))
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.INPUT(sample_rate, TensorType({DT_INT32}))
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.OUTPUT(y, TensorType({DT_FLOAT}))
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.ATTR(upper_frequency_limit, Float, 4000)
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.ATTR(lower_frequency_limit, Float, 20)
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.ATTR(filterbank_channel_count, Int, 40)
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.ATTR(dct_coefficient_count, Int, 13)
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.OP_END_FACTORY_REG(Mfcc)
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/**
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*@brief Decodes and generates spectrogram using wav float tensor . \n
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*@par Inputs:
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*Input "x" is a 2D matrix.
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* x: A float tensor. Float representation of audio data . \n
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*@par Attributes:
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*@li window_size: Size of the spectrogram window.
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*@li stride: Size of the spectrogram stride.
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*@li magnitude_squared: If true, uses squared magnitude . \n
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*@par Outputs:
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*spectrogram: A 3D float Tensor . \n
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*@attention Constraints:
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*AudioSpectrogram runs on the Ascend AI CPU, which delivers
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poor performance . \n
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator AudioSpectrogram . \n
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*@par Restrictions:
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*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
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*/
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REG_OP(AudioSpectrogram)
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.INPUT(x, TensorType({DT_FLOAT}))
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.OUTPUT(spectrogram, TensorType({DT_FLOAT}))
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.REQUIRED_ATTR(window_size, Int)
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.REQUIRED_ATTR(stride, Int)
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.ATTR(magnitude_squared, Bool, false)
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.OP_END_FACTORY_REG(AudioSpectrogram)
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/**
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*@brief Decodes a 16-bit WAV file into a float tensor . \n
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*@par Inputs:
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*contents: A Tensor of type string. The WAV-encoded audio, usually from a file . \n
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*@par Attributes:
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*@li desired_channels: An optional int. Defaults to "-1".
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Number of sample channels wanted.
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*@li desired_samples: An optional int. Defaults to "-1".
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Length of audio requested . \n
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*@par Outputs:
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*@li *audio: A Tensor of type float32.
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*@li *sample_rate: A Tensor of type int32 . \n
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*@attention Constraints:
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*DecodeWav runs on the Ascend AI CPU, which delivers poor performance.
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*@par Third-party framework compatibility
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*Compatible with the TensorFlow operator DecodeWav . \n
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*@par Restrictions:
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*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
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*/
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REG_OP(DecodeWav)
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.INPUT(contents, TensorType({DT_STRING}))
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.OUTPUT(audio, TensorType({DT_FLOAT}))
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.OUTPUT(sample_rate, TensorType({DT_INT32}))
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.ATTR(desired_channels, Int, -1)
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.ATTR(desired_samples, Int, -1)
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.OP_END_FACTORY_REG(DecodeWav)
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/**
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*@brief Encode audio data using the WAV file format . \n
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*@par Inputs:
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*Including:
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* @li audio: A Tensor of type DT_FLOAT.
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* @li sample_rate: A Tensor of type DT_INT32 . \n
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*@par Outputs:
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*contents: A Tensor of type DT_STRING . \n
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*@attention Constraints:
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*EncodeWav runs on the Ascend AI CPU, which delivers poor performance.
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*@par Third-party framework compatibility
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*Compatible with tensorflow Operator EncodeWav . \n
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*@par Restrictions:
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*Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
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*/
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REG_OP(EncodeWav)
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.INPUT(audio, TensorType({DT_FLOAT}))
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.INPUT(sample_rate, TensorType({DT_INT32}))
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.OUTPUT(contents, TensorType({DT_STRING}))
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.OP_END_FACTORY_REG(EncodeWav)
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} // namespace ge
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#endif // OPS_BUILT_IN_OP_PROTO_INC_AUDIO_OPS_H_
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