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| # Copyright 2022 The MT3 Authors. | |
| # | |
| # 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. | |
| """Audio spectrogram functions.""" | |
| import dataclasses | |
| from ddsp import spectral_ops | |
| import tensorflow as tf | |
| # defaults for spectrogram config | |
| DEFAULT_SAMPLE_RATE = 16000 | |
| DEFAULT_HOP_WIDTH = 128 | |
| DEFAULT_NUM_MEL_BINS = 512 | |
| # fixed constants; add these to SpectrogramConfig before changing | |
| FFT_SIZE = 2048 | |
| MEL_LO_HZ = 20.0 | |
| class SpectrogramConfig: | |
| """Spectrogram configuration parameters.""" | |
| sample_rate: int = DEFAULT_SAMPLE_RATE | |
| hop_width: int = DEFAULT_HOP_WIDTH | |
| num_mel_bins: int = DEFAULT_NUM_MEL_BINS | |
| def abbrev_str(self): | |
| s = '' | |
| if self.sample_rate != DEFAULT_SAMPLE_RATE: | |
| s += 'sr%d' % self.sample_rate | |
| if self.hop_width != DEFAULT_HOP_WIDTH: | |
| s += 'hw%d' % self.hop_width | |
| if self.num_mel_bins != DEFAULT_NUM_MEL_BINS: | |
| s += 'mb%d' % self.num_mel_bins | |
| return s | |
| def frames_per_second(self): | |
| return self.sample_rate / self.hop_width | |
| def split_audio(samples, spectrogram_config): | |
| """Split audio into frames.""" | |
| return tf.signal.frame( | |
| samples, | |
| frame_length=spectrogram_config.hop_width, | |
| frame_step=spectrogram_config.hop_width, | |
| pad_end=True) | |
| def compute_spectrogram(samples, spectrogram_config): | |
| """Compute a mel spectrogram.""" | |
| overlap = 1 - (spectrogram_config.hop_width / FFT_SIZE) | |
| return spectral_ops.compute_logmel( | |
| samples, | |
| bins=spectrogram_config.num_mel_bins, | |
| lo_hz=MEL_LO_HZ, | |
| overlap=overlap, | |
| fft_size=FFT_SIZE, | |
| sample_rate=spectrogram_config.sample_rate) | |
| def flatten_frames(frames): | |
| """Convert frames back into a flat array of samples.""" | |
| return tf.reshape(frames, [-1]) | |
| def input_depth(spectrogram_config): | |
| return spectrogram_config.num_mel_bins | |