vandanavk commented on a change in pull request #13325: [MXNET-1210 ] Gluon Audio - Example URL: https://github.com/apache/incubator-mxnet/pull/13325#discussion_r236772198
########## File path: example/gluon/urban_sounds/transforms.py ########## @@ -0,0 +1,207 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# coding: utf-8 +# pylint: disable= arguments-differ +"Audio transforms." + +import warnings +import numpy as np +try: + import librosa +except ImportError as e: + warnings.warn("librosa dependency could not be resolved or \ + imported, could not provide some/all transform.") + +from mxnet import ndarray as nd +from mxnet.gluon.block import Block + +class MFCC(Block): + """Extracts Mel frequency cepstrum coefficients from the audio data file + More details : https://librosa.github.io/librosa/generated/librosa.feature.mfcc.html + + Attributes + ---------- + sampling_rate: int, default 22050 + sampling rate of the input audio signal + num_mfcc: int, default 20 + number of mfccs to return + + + Inputs: + - **x**: input tensor (samples, ) shape. + + Outputs: + - **out**: output array is a scaled NDArray with (samples, ) shape. + + """ + + def __init__(self, sampling_rate=22050, num_mfcc=20): + self._sampling_rate = sampling_rate + self._num_fcc = num_mfcc + super(MFCC, self).__init__() + + def forward(self, x): + if isinstance(x, np.ndarray): + y = x + elif isinstance(x, nd.NDArray): + y = x.asnumpy() + else: + warnings.warn("MFCC - allowed datatypes mx.nd.NDArray and numpy.ndarray") + return x + + audio_tmp = np.mean(librosa.feature.mfcc(y=y, sr=self._sampling_rate, n_mfcc=self._num_fcc).T, axis=0) + return nd.array(audio_tmp) + + +class Scale(Block): + """Scale audio numpy.ndarray from a 16-bit integer to a floating point number between + -1.0 and 1.0. The 16-bit integer is the sample resolution or bit depth. + + Attributes + ---------- + scale_factor : float + The factor to scale the input tensor by. + + + Inputs: + - **x**: input tensor (samples, ) shape. + + Outputs: + - **out**: output array is a scaled NDArray with (samples, ) shape. + + Examples + -------- + >>> scale = audio.transforms.Scale(scale_factor=2) + >>> audio_samples = mx.nd.array([2,3,4]) + >>> scale(audio_samples) + [1. 1.5 2. ] + <NDArray 3 @cpu(0)> + + """ + + def __init__(self, scale_factor=2**31): + self.scale_factor = scale_factor + super(Scale, self).__init__() + + def forward(self, x): + if self.scale_factor == 0: + warnings.warn("Scale factor cannot be 0.") + return x + if isinstance(x, np.ndarray): + return nd.array(x/self.scale_factor) + return x / self.scale_factor + + +class PadTrim(Block): + """Pad/Trim a 1d-NDArray of NPArray (Signal or Labels) + + Attributes + ---------- + max_len : int + Length to which the array will be padded or trimmed to. + fill_value: int or float + If there is a need of padding, what value to pad at the end of the input array. + + + Inputs: + - **x**: input tensor (samples, ) shape. + + Outputs: + - **out**: output array is a scaled NDArray with (max_len, ) shape. + + Examples + -------- + >>> padtrim = audio.transforms.PadTrim(max_len=9, fill_value=0) + >>> audio_samples = mx.nd.array([1,2,3,4,5]) + >>> padtrim(audio_samples) + [1. 2. 3. 4. 5. 0. 0. 0. 0.] + <NDArray 9 @cpu(0)> + + """ + + def __init__(self, max_len, fill_value=0): + self._max_len = max_len + self._fill_value = fill_value + super(PadTrim, self).__init__() + + def forward(self, x): + if isinstance(x, np.ndarray): + x = nd.array(x) + if self._max_len > x.size: + pad = nd.ones((self._max_len - x.size,)) * self._fill_value + x = nd.concat(x, pad, dim=0) + elif self._max_len < x.size: + x = x[:self._max_len] + return x + + +class MEL(Block): + """Create MEL Spectrograms from a raw audio signal. Relatively pretty slow. + + Attributes + ---------- + sampling_rate: int, default 22050 + sampling rate of the input audio signal + num_fft: int, default 2048 + length of the Fast Fourier transform window + num_mels: int, default 20 + number of mel bands to generate + hop_length: int, default 512 + total samples between successive frames + + + Inputs: + - **x**: input tensor (samples, ) shape. + + Outputs: + - **out**: output array which consists of mel spectograms, shape = (n_mels, 1) + + Usage (see librosa.feature.melspectrogram docs): + MEL(sr=16000, n_fft=1600, hop_length=800, n_mels=64) + + Examples + -------- + >>> mel = audio.transforms.MEL() + >>> audio_samples = mx.nd.array([1,2,3,4,5]) + >>> mel(audio_samples) + [[3.81801406e+04] + [9.86858240e-29] + [1.87405472e-29] + [2.38637225e-29] + [3.94043010e-29] + [3.67071565e-29] + [7.29390295e-29] + [8.84324438e-30]... + <NDArray 128x1 @cpu(0)> + + """ + + def __init__(self, sampling_rate=22050, num_fft=2048, num_mels=20, hop_length=512): + self._sampling_rate = sampling_rate + self._num_fft = num_fft + self._num_mels = num_mels + self._hop_length = hop_length + super(MEL, self).__init__() + + def forward(self, x): + if isinstance(x, nd.NDArray): + x = x.asnumpy() + specs = librosa.feature.melspectrogram(x, sr=self._sampling_rate,\ + n_fft=self._num_fft, n_mels=self._num_mels, hop_length=self._hop_length) + return nd.array(specs) + Review comment: new line at the end ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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