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9513380c76
To avoid the following error: `Cannot convert a symbolic Tensor (transpose_1:0) to a numpy array`
129 lines
4.9 KiB
Python
129 lines
4.9 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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""" Spectrogram specific data augmentation """
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# pylint: disable=import-error
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import numpy as np
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import tensorflow as tf
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from tensorflow.contrib.signal import stft, hann_window
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# pylint: enable=import-error
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__email__ = 'research@deezer.com'
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__author__ = 'Deezer Research'
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__license__ = 'MIT License'
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def compute_spectrogram_tf(
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waveform,
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frame_length=2048, frame_step=512,
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spec_exponent=1., window_exponent=1.):
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""" Compute magnitude / power spectrogram from waveform as
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a n_samples x n_channels tensor.
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:param waveform: Input waveform as (times x number of channels)
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tensor.
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:param frame_length: Length of a STFT frame to use.
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:param frame_step: HOP between successive frames.
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:param spec_exponent: Exponent of the spectrogram (usually 1 for
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magnitude spectrogram, or 2 for power spectrogram).
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:param window_exponent: Exponent applied to the Hann windowing function
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(may be useful for making perfect STFT/iSTFT
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reconstruction).
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:returns: Computed magnitude / power spectrogram as a
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(T x F x n_channels) tensor.
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"""
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stft_tensor = tf.transpose(
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stft(
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tf.transpose(waveform),
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frame_length,
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frame_step,
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window_fn=lambda f, dtype: hann_window(
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f,
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periodic=True,
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dtype=waveform.dtype) ** window_exponent),
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perm=[1, 2, 0])
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return tf.abs(stft_tensor) ** spec_exponent
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def time_stretch(
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spectrogram,
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factor=1.0,
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method=tf.image.ResizeMethod.BILINEAR):
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""" Time stretch a spectrogram preserving shape in tensorflow. Note that
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this is an approximation in the frequency domain.
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:param spectrogram: Input spectrogram to be time stretched as tensor.
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:param factor: (Optional) Time stretch factor, must be >0, default to 1.
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:param mehtod: (Optional) Interpolation method, default to BILINEAR.
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:returns: Time stretched spectrogram as tensor with same shape.
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"""
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T = tf.shape(spectrogram)[0]
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T_ts = tf.cast(tf.cast(T, tf.float32) * factor, tf.int32)[0]
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F = tf.shape(spectrogram)[1]
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ts_spec = tf.image.resize_images(
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spectrogram,
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[T_ts, F],
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method=method,
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align_corners=True)
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return tf.image.resize_image_with_crop_or_pad(ts_spec, T, F)
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def random_time_stretch(spectrogram, factor_min=0.9, factor_max=1.1, **kwargs):
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""" Time stretch a spectrogram preserving shape with random ratio in
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tensorflow. Applies time_stretch to spectrogram with a random ratio drawn
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uniformly in [factor_min, factor_max].
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:param spectrogram: Input spectrogram to be time stretched as tensor.
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:param factor_min: (Optional) Min time stretch factor, default to 0.9.
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:param factor_max: (Optional) Max time stretch factor, default to 1.1.
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:returns: Randomly time stretched spectrogram as tensor with same shape.
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"""
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factor = tf.random_uniform(
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shape=(1,),
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seed=0) * (factor_max - factor_min) + factor_min
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return time_stretch(spectrogram, factor=factor, **kwargs)
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def pitch_shift(
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spectrogram,
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semitone_shift=0.0,
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method=tf.image.ResizeMethod.BILINEAR):
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""" Pitch shift a spectrogram preserving shape in tensorflow. Note that
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this is an approximation in the frequency domain.
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:param spectrogram: Input spectrogram to be pitch shifted as tensor.
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:param semitone_shift: (Optional) Pitch shift in semitone, default to 0.0.
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:param mehtod: (Optional) Interpolation method, default to BILINEAR.
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:returns: Pitch shifted spectrogram (same shape as spectrogram).
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"""
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factor = 2 ** (semitone_shift / 12.)
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T = tf.shape(spectrogram)[0]
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F = tf.shape(spectrogram)[1]
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F_ps = tf.cast(tf.cast(F, tf.float32) * factor, tf.int32)[0]
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ps_spec = tf.image.resize_images(
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spectrogram,
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[T, F_ps],
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method=method,
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align_corners=True)
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paddings = [[0, 0], [0, tf.maximum(0, F - F_ps)], [0, 0]]
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return tf.pad(ps_spec[:, :F, :], paddings, 'CONSTANT')
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def random_pitch_shift(spectrogram, shift_min=-1., shift_max=1., **kwargs):
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""" Pitch shift a spectrogram preserving shape with random ratio in
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tensorflow. Applies pitch_shift to spectrogram with a random shift
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amount (expressed in semitones) drawn uniformly in [shift_min, shift_max].
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:param spectrogram: Input spectrogram to be pitch shifted as tensor.
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:param shift_min: (Optional) Min pitch shift in semitone, default to -1.
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:param shift_max: (Optional) Max pitch shift in semitone, default to 1.
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:returns: Randomly pitch shifted spectrogram (same shape as spectrogram).
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"""
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semitone_shift = tf.random_uniform(
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shape=(1,),
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seed=0) * (shift_max - shift_min) + shift_min
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return pitch_shift(spectrogram, semitone_shift=semitone_shift, **kwargs)
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