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| # Copyright 2019 The TensorFlow Authors All Rights Reserved. | |
| # | |
| # Licensed 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. | |
| # ============================================================================== | |
| """Feature computation for YAMNet.""" | |
| import numpy as np | |
| import tensorflow as tf | |
| def waveform_to_log_mel_spectrogram(waveform, params): | |
| """Compute log mel spectrogram of a 1-D waveform.""" | |
| with tf.name_scope('log_mel_features'): | |
| # waveform has shape [<# samples>] | |
| # Convert waveform into spectrogram using a Short-Time Fourier Transform. | |
| # Note that tf.signal.stft() uses a periodic Hann window by default. | |
| window_length_samples = int( | |
| round(params.SAMPLE_RATE * params.STFT_WINDOW_SECONDS)) | |
| hop_length_samples = int( | |
| round(params.SAMPLE_RATE * params.STFT_HOP_SECONDS)) | |
| fft_length = 2 ** int(np.ceil(np.log(window_length_samples) / np.log(2.0))) | |
| num_spectrogram_bins = fft_length // 2 + 1 | |
| magnitude_spectrogram = tf.abs(tf.signal.stft( | |
| signals=waveform, | |
| frame_length=window_length_samples, | |
| frame_step=hop_length_samples, | |
| fft_length=fft_length)) | |
| # magnitude_spectrogram has shape [<# STFT frames>, num_spectrogram_bins] | |
| # Convert spectrogram into log mel spectrogram. | |
| linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix( | |
| num_mel_bins=params.MEL_BANDS, | |
| num_spectrogram_bins=num_spectrogram_bins, | |
| sample_rate=params.SAMPLE_RATE, | |
| lower_edge_hertz=params.MEL_MIN_HZ, | |
| upper_edge_hertz=params.MEL_MAX_HZ) | |
| mel_spectrogram = tf.matmul( | |
| magnitude_spectrogram, linear_to_mel_weight_matrix) | |
| log_mel_spectrogram = tf.math.log(mel_spectrogram + params.LOG_OFFSET) | |
| # log_mel_spectrogram has shape [<# STFT frames>, MEL_BANDS] | |
| return log_mel_spectrogram | |
| def spectrogram_to_patches(spectrogram, params): | |
| """Break up a spectrogram into a stack of fixed-size patches.""" | |
| with tf.name_scope('feature_patches'): | |
| # Frame spectrogram (shape [<# STFT frames>, MEL_BANDS]) into patches | |
| # (the input examples). | |
| # Only complete frames are emitted, so if there is less than | |
| # PATCH_WINDOW_SECONDS of waveform then nothing is emitted | |
| # (to avoid this, zero-pad before processing). | |
| hop_length_samples = int( | |
| round(params.SAMPLE_RATE * params.STFT_HOP_SECONDS)) | |
| spectrogram_sr = params.SAMPLE_RATE / hop_length_samples | |
| patch_window_length_samples = int( | |
| round(spectrogram_sr * params.PATCH_WINDOW_SECONDS)) | |
| patch_hop_length_samples = int( | |
| round(spectrogram_sr * params.PATCH_HOP_SECONDS)) | |
| features = tf.signal.frame( | |
| signal=spectrogram, | |
| frame_length=patch_window_length_samples, | |
| frame_step=patch_hop_length_samples, | |
| axis=0) | |
| # features has shape [<# patches>, <# STFT frames in an patch>, MEL_BANDS] | |
| return features | |