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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
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