Hui Zhang
3 years ago
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5ef4a34e93
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__version__ = "0.2.2"
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"""
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Module containing functions cloned from librosa
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To make sure nnAudio would not become broken when updating librosa
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"""
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import numpy as np
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import warnings
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### ----------------Functions for generating kenral for Mel Spectrogram------------ ###
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# This code is equalvant to from librosa.filters import mel
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# By doing so, we can run nnAudio without installing librosa
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def fft2gammatonemx(sr=20000, n_fft=2048, n_bins=64, width=1.0, fmin=0.0,
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fmax=11025, maxlen=1024):
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"""
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# Ellis' description in MATLAB:
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# [wts,cfreqa] = fft2gammatonemx(nfft, sr, nfilts, width, minfreq, maxfreq, maxlen)
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# Generate a matrix of weights to combine FFT bins into
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# Gammatone bins. nfft defines the source FFT size at
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# sampling rate sr. Optional nfilts specifies the number of
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# output bands required (default 64), and width is the
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# constant width of each band in Bark (default 1).
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# minfreq, maxfreq specify range covered in Hz (100, sr/2).
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# While wts has nfft columns, the second half are all zero.
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# Hence, aud spectrum is
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# fft2gammatonemx(nfft,sr)*abs(fft(xincols,nfft));
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# maxlen truncates the rows to this many bins.
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# cfreqs returns the actual center frequencies of each
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# gammatone band in Hz.
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#
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# 2009/02/22 02:29:25 Dan Ellis dpwe@ee.columbia.edu based on rastamat/audspec.m
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# Sat May 27 15:37:50 2017 Maddie Cusimano, mcusi@mit.edu 27 May 2017: convert to python
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"""
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wts = np.zeros([n_bins, n_fft], dtype=np.float32)
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# after Slaney's MakeERBFilters
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EarQ = 9.26449;
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minBW = 24.7;
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order = 1;
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nFr = np.array(range(n_bins)) + 1
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em = EarQ * minBW
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cfreqs = (fmax + em) * np.exp(nFr * (-np.log(fmax + em) + np.log(fmin + em)) / n_bins) - em
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cfreqs = cfreqs[::-1]
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GTord = 4
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ucircArray = np.array(range(int(n_fft / 2 + 1)))
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ucirc = np.exp(1j * 2 * np.pi * ucircArray / n_fft);
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# justpoles = 0 :taking out the 'if' corresponding to this.
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ERB = width * np.power(np.power(cfreqs / EarQ, order) + np.power(minBW, order), 1 / order);
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B = 1.019 * 2 * np.pi * ERB;
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r = np.exp(-B / sr)
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theta = 2 * np.pi * cfreqs / sr
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pole = r * np.exp(1j * theta)
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T = 1 / sr
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ebt = np.exp(B * T);
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cpt = 2 * cfreqs * np.pi * T;
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ccpt = 2 * T * np.cos(cpt);
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scpt = 2 * T * np.sin(cpt);
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A11 = -np.divide(np.divide(ccpt, ebt) + np.divide(np.sqrt(3 + 2 ** 1.5) * scpt, ebt), 2);
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A12 = -np.divide(np.divide(ccpt, ebt) - np.divide(np.sqrt(3 + 2 ** 1.5) * scpt, ebt), 2);
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A13 = -np.divide(np.divide(ccpt, ebt) + np.divide(np.sqrt(3 - 2 ** 1.5) * scpt, ebt), 2);
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A14 = -np.divide(np.divide(ccpt, ebt) - np.divide(np.sqrt(3 - 2 ** 1.5) * scpt, ebt), 2);
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zros = -np.array([A11, A12, A13, A14]) / T;
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wIdx = range(int(n_fft / 2 + 1))
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gain = np.abs((-2 * np.exp(4 * 1j * cfreqs * np.pi * T) * T + 2 * np.exp(
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-(B * T) + 2 * 1j * cfreqs * np.pi * T) * T * (
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np.cos(2 * cfreqs * np.pi * T) - np.sqrt(3 - 2 ** (3 / 2)) * np.sin(
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2 * cfreqs * np.pi * T))) * (-2 * np.exp(4 * 1j * cfreqs * np.pi * T) * T + 2 * np.exp(
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-(B * T) + 2 * 1j * cfreqs * np.pi * T) * T * (np.cos(2 * cfreqs * np.pi * T) + np.sqrt(
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3 - 2 ** (3 / 2)) * np.sin(2 * cfreqs * np.pi * T))) * (
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-2 * np.exp(4 * 1j * cfreqs * np.pi * T) * T + 2 * np.exp(
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-(B * T) + 2 * 1j * cfreqs * np.pi * T) * T * (
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np.cos(2 * cfreqs * np.pi * T) - np.sqrt(3 + 2 ** (3 / 2)) * np.sin(
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2 * cfreqs * np.pi * T))) * (
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-2 * np.exp(4 * 1j * cfreqs * np.pi * T) * T + 2 * np.exp(
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-(B * T) + 2 * 1j * cfreqs * np.pi * T) * T * (
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np.cos(2 * cfreqs * np.pi * T) + np.sqrt(3 + 2 ** (3 / 2)) * np.sin(
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2 * cfreqs * np.pi * T))) / (
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-2 / np.exp(2 * B * T) - 2 * np.exp(4 * 1j * cfreqs * np.pi * T) + 2 * (
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1 + np.exp(4 * 1j * cfreqs * np.pi * T)) / np.exp(B * T)) ** 4);
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# in MATLAB, there used to be 64 where here it says n_bins:
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wts[:, wIdx] = ((T ** 4) / np.reshape(gain, (n_bins, 1))) * np.abs(
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ucirc - np.reshape(zros[0], (n_bins, 1))) * np.abs(ucirc - np.reshape(zros[1], (n_bins, 1))) * np.abs(
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ucirc - np.reshape(zros[2], (n_bins, 1))) * np.abs(ucirc - np.reshape(zros[3], (n_bins, 1))) * (np.abs(
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np.power(np.multiply(np.reshape(pole, (n_bins, 1)) - ucirc, np.conj(np.reshape(pole, (n_bins, 1))) - ucirc),
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-GTord)));
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wts = wts[:, range(maxlen)];
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return wts, cfreqs
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def gammatone(sr, n_fft, n_bins=64, fmin=20.0, fmax=None, htk=False,
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norm=1, dtype=np.float32):
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"""Create a Filterbank matrix to combine FFT bins into Gammatone bins
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Parameters
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----------
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sr : number > 0 [scalar]
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sampling rate of the incoming signal
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n_fft : int > 0 [scalar]
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number of FFT components
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n_bins : int > 0 [scalar]
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number of Mel bands to generate
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fmin : float >= 0 [scalar]
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lowest frequency (in Hz)
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fmax : float >= 0 [scalar]
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highest frequency (in Hz).
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If `None`, use `fmax = sr / 2.0`
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htk : bool [scalar]
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use HTK formula instead of Slaney
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norm : {None, 1, np.inf} [scalar]
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if 1, divide the triangular mel weights by the width of the mel band
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(area normalization). Otherwise, leave all the triangles aiming for
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a peak value of 1.0
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dtype : np.dtype
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The data type of the output basis.
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By default, uses 32-bit (single-precision) floating point.
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Returns
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-------
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G : np.ndarray [shape=(n_bins, 1 + n_fft/2)]
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Gammatone transform matrix
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"""
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if fmax is None:
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fmax = float(sr) / 2
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n_bins = int(n_bins)
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weights,_ = fft2gammatonemx(sr=sr, n_fft=n_fft, n_bins=n_bins, fmin=fmin, fmax=fmax, maxlen=int(n_fft//2+1))
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return (1/n_fft)*weights
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def mel_to_hz(mels, htk=False):
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"""Convert mel bin numbers to frequencies
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Examples
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--------
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>>> librosa.mel_to_hz(3)
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200.
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>>> librosa.mel_to_hz([1,2,3,4,5])
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array([ 66.667, 133.333, 200. , 266.667, 333.333])
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Parameters
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----------
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mels : np.ndarray [shape=(n,)], float
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mel bins to convert
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htk : bool
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use HTK formula instead of Slaney
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Returns
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-------
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frequencies : np.ndarray [shape=(n,)]
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input mels in Hz
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See Also
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--------
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hz_to_mel
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"""
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mels = np.asanyarray(mels)
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if htk:
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return 700.0 * (10.0**(mels / 2595.0) - 1.0)
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# Fill in the linear scale
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f_min = 0.0
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f_sp = 200.0 / 3
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freqs = f_min + f_sp * mels
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# And now the nonlinear scale
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min_log_hz = 1000.0 # beginning of log region (Hz)
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min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
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logstep = np.log(6.4) / 27.0 # step size for log region
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if mels.ndim:
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# If we have vector data, vectorize
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log_t = (mels >= min_log_mel)
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freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel))
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elif mels >= min_log_mel:
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# If we have scalar data, check directly
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freqs = min_log_hz * np.exp(logstep * (mels - min_log_mel))
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return freqs
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def hz_to_mel(frequencies, htk=False):
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"""Convert Hz to Mels
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Examples
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--------
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>>> librosa.hz_to_mel(60)
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0.9
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>>> librosa.hz_to_mel([110, 220, 440])
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array([ 1.65, 3.3 , 6.6 ])
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Parameters
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----------
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frequencies : number or np.ndarray [shape=(n,)] , float
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scalar or array of frequencies
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htk : bool
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use HTK formula instead of Slaney
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Returns
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-------
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mels : number or np.ndarray [shape=(n,)]
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input frequencies in Mels
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See Also
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--------
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mel_to_hz
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"""
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frequencies = np.asanyarray(frequencies)
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if htk:
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return 2595.0 * np.log10(1.0 + frequencies / 700.0)
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# Fill in the linear part
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f_min = 0.0
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f_sp = 200.0 / 3
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mels = (frequencies - f_min) / f_sp
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# Fill in the log-scale part
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min_log_hz = 1000.0 # beginning of log region (Hz)
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min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
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logstep = np.log(6.4) / 27.0 # step size for log region
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if frequencies.ndim:
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# If we have array data, vectorize
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log_t = (frequencies >= min_log_hz)
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mels[log_t] = min_log_mel + np.log(frequencies[log_t]/min_log_hz) / logstep
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elif frequencies >= min_log_hz:
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# If we have scalar data, heck directly
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mels = min_log_mel + np.log(frequencies / min_log_hz) / logstep
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return mels
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def fft_frequencies(sr=22050, n_fft=2048):
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'''Alternative implementation of `np.fft.fftfreq`
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Parameters
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----------
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sr : number > 0 [scalar]
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Audio sampling rate
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n_fft : int > 0 [scalar]
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FFT window size
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Returns
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-------
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freqs : np.ndarray [shape=(1 + n_fft/2,)]
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Frequencies `(0, sr/n_fft, 2*sr/n_fft, ..., sr/2)`
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Examples
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--------
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>>> librosa.fft_frequencies(sr=22050, n_fft=16)
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array([ 0. , 1378.125, 2756.25 , 4134.375,
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5512.5 , 6890.625, 8268.75 , 9646.875, 11025. ])
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'''
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return np.linspace(0,
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float(sr) / 2,
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int(1 + n_fft//2),
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endpoint=True)
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def mel_frequencies(n_mels=128, fmin=0.0, fmax=11025.0, htk=False):
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"""
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This function is cloned from librosa 0.7.
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Please refer to the original
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`documentation <https://librosa.org/doc/latest/generated/librosa.mel_frequencies.html?highlight=mel_frequencies#librosa.mel_frequencies>`__
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for more info.
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Parameters
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----------
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n_mels : int > 0 [scalar]
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Number of mel bins.
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fmin : float >= 0 [scalar]
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Minimum frequency (Hz).
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fmax : float >= 0 [scalar]
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Maximum frequency (Hz).
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htk : bool
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If True, use HTK formula to convert Hz to mel.
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Otherwise (False), use Slaney's Auditory Toolbox.
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Returns
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-------
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bin_frequencies : ndarray [shape=(n_mels,)]
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Vector of n_mels frequencies in Hz which are uniformly spaced on the Mel
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axis.
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Examples
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--------
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>>> librosa.mel_frequencies(n_mels=40)
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array([ 0. , 85.317, 170.635, 255.952,
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341.269, 426.586, 511.904, 597.221,
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682.538, 767.855, 853.173, 938.49 ,
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1024.856, 1119.114, 1222.042, 1334.436,
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1457.167, 1591.187, 1737.532, 1897.337,
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2071.84 , 2262.393, 2470.47 , 2697.686,
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2945.799, 3216.731, 3512.582, 3835.643,
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4188.417, 4573.636, 4994.285, 5453.621,
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5955.205, 6502.92 , 7101.009, 7754.107,
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8467.272, 9246.028, 10096.408, 11025. ])
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"""
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# 'Center freqs' of mel bands - uniformly spaced between limits
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min_mel = hz_to_mel(fmin, htk=htk)
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max_mel = hz_to_mel(fmax, htk=htk)
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mels = np.linspace(min_mel, max_mel, n_mels)
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return mel_to_hz(mels, htk=htk)
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def mel(sr, n_fft, n_mels=128, fmin=0.0, fmax=None, htk=False,
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norm=1, dtype=np.float32):
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"""
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This function is cloned from librosa 0.7.
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Please refer to the original
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`documentation <https://librosa.org/doc/latest/generated/librosa.filters.mel.html>`__
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for more info.
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Create a Filterbank matrix to combine FFT bins into Mel-frequency bins
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Parameters
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----------
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sr : number > 0 [scalar]
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sampling rate of the incoming signal
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n_fft : int > 0 [scalar]
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number of FFT components
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n_mels : int > 0 [scalar]
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number of Mel bands to generate
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fmin : float >= 0 [scalar]
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lowest frequency (in Hz)
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fmax : float >= 0 [scalar]
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highest frequency (in Hz).
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If `None`, use `fmax = sr / 2.0`
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htk : bool [scalar]
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use HTK formula instead of Slaney
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norm : {None, 1, np.inf} [scalar]
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if 1, divide the triangular mel weights by the width of the mel band
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(area normalization). Otherwise, leave all the triangles aiming for
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a peak value of 1.0
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dtype : np.dtype
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The data type of the output basis.
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By default, uses 32-bit (single-precision) floating point.
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Returns
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-------
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M : np.ndarray [shape=(n_mels, 1 + n_fft/2)]
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Mel transform matrix
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Notes
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-----
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This function caches at level 10.
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Examples
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--------
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>>> melfb = librosa.filters.mel(22050, 2048)
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>>> melfb
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array([[ 0. , 0.016, ..., 0. , 0. ],
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[ 0. , 0. , ..., 0. , 0. ],
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...,
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[ 0. , 0. , ..., 0. , 0. ],
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[ 0. , 0. , ..., 0. , 0. ]])
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Clip the maximum frequency to 8KHz
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>>> librosa.filters.mel(22050, 2048, fmax=8000)
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array([[ 0. , 0.02, ..., 0. , 0. ],
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[ 0. , 0. , ..., 0. , 0. ],
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...,
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[ 0. , 0. , ..., 0. , 0. ],
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[ 0. , 0. , ..., 0. , 0. ]])
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>>> import matplotlib.pyplot as plt
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>>> plt.figure()
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>>> librosa.display.specshow(melfb, x_axis='linear')
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>>> plt.ylabel('Mel filter')
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>>> plt.title('Mel filter bank')
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>>> plt.colorbar()
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>>> plt.tight_layout()
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>>> plt.show()
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"""
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if fmax is None:
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fmax = float(sr) / 2
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if norm is not None and norm != 1 and norm != np.inf:
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raise ParameterError('Unsupported norm: {}'.format(repr(norm)))
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# Initialize the weights
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n_mels = int(n_mels)
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weights = np.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)
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# Center freqs of each FFT bin
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fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft)
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# 'Center freqs' of mel bands - uniformly spaced between limits
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mel_f = mel_frequencies(n_mels + 2, fmin=fmin, fmax=fmax, htk=htk)
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fdiff = np.diff(mel_f)
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ramps = np.subtract.outer(mel_f, fftfreqs)
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for i in range(n_mels):
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# lower and upper slopes for all bins
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lower = -ramps[i] / fdiff[i]
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upper = ramps[i+2] / fdiff[i+1]
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# .. then intersect them with each other and zero
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weights[i] = np.maximum(0, np.minimum(lower, upper))
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if norm == 1:
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# Slaney-style mel is scaled to be approx constant energy per channel
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enorm = 2.0 / (mel_f[2:n_mels+2] - mel_f[:n_mels])
|
||||
weights *= enorm[:, np.newaxis]
|
||||
|
||||
# Only check weights if f_mel[0] is positive
|
||||
if not np.all((mel_f[:-2] == 0) | (weights.max(axis=1) > 0)):
|
||||
# This means we have an empty channel somewhere
|
||||
warnings.warn('Empty filters detected in mel frequency basis. '
|
||||
'Some channels will produce empty responses. '
|
||||
'Try increasing your sampling rate (and fmax) or '
|
||||
'reducing n_mels.')
|
||||
|
||||
return weights
|
||||
### ------------------End of Functions for generating kenral for Mel Spectrogram ----------------###
|
||||
|
||||
|
||||
### ------------------Functions for making STFT same as librosa ---------------------------------###
|
||||
def pad_center(data, size, axis=-1, **kwargs):
|
||||
'''Wrapper for np.pad to automatically center an array prior to padding.
|
||||
This is analogous to `str.center()`
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> # Generate a vector
|
||||
>>> data = np.ones(5)
|
||||
>>> librosa.util.pad_center(data, 10, mode='constant')
|
||||
array([ 0., 0., 1., 1., 1., 1., 1., 0., 0., 0.])
|
||||
|
||||
>>> # Pad a matrix along its first dimension
|
||||
>>> data = np.ones((3, 5))
|
||||
>>> librosa.util.pad_center(data, 7, axis=0)
|
||||
array([[ 0., 0., 0., 0., 0.],
|
||||
[ 0., 0., 0., 0., 0.],
|
||||
[ 1., 1., 1., 1., 1.],
|
||||
[ 1., 1., 1., 1., 1.],
|
||||
[ 1., 1., 1., 1., 1.],
|
||||
[ 0., 0., 0., 0., 0.],
|
||||
[ 0., 0., 0., 0., 0.]])
|
||||
>>> # Or its second dimension
|
||||
>>> librosa.util.pad_center(data, 7, axis=1)
|
||||
array([[ 0., 1., 1., 1., 1., 1., 0.],
|
||||
[ 0., 1., 1., 1., 1., 1., 0.],
|
||||
[ 0., 1., 1., 1., 1., 1., 0.]])
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data : np.ndarray
|
||||
Vector to be padded and centered
|
||||
|
||||
size : int >= len(data) [scalar]
|
||||
Length to pad `data`
|
||||
|
||||
axis : int
|
||||
Axis along which to pad and center the data
|
||||
|
||||
kwargs : additional keyword arguments
|
||||
arguments passed to `np.pad()`
|
||||
|
||||
Returns
|
||||
-------
|
||||
data_padded : np.ndarray
|
||||
`data` centered and padded to length `size` along the
|
||||
specified axis
|
||||
|
||||
Raises
|
||||
------
|
||||
ParameterError
|
||||
If `size < data.shape[axis]`
|
||||
|
||||
See Also
|
||||
--------
|
||||
numpy.pad
|
||||
'''
|
||||
|
||||
kwargs.setdefault('mode', 'constant')
|
||||
|
||||
n = data.shape[axis]
|
||||
|
||||
lpad = int((size - n) // 2)
|
||||
|
||||
lengths = [(0, 0)] * data.ndim
|
||||
lengths[axis] = (lpad, int(size - n - lpad))
|
||||
|
||||
if lpad < 0:
|
||||
raise ParameterError(('Target size ({:d}) must be '
|
||||
'at least input size ({:d})').format(size, n))
|
||||
|
||||
return np.pad(data, lengths, **kwargs)
|
||||
|
||||
### ------------------End of functions for making STFT same as librosa ---------------------------###
|
||||
@ -0,0 +1,535 @@
|
||||
"""
|
||||
Module containing helper functions such as overlap sum and Fourier kernels generators
|
||||
"""
|
||||
|
||||
import torch
|
||||
from torch.nn.functional import conv1d, fold
|
||||
|
||||
import numpy as np
|
||||
from time import time
|
||||
import math
|
||||
from scipy.signal import get_window
|
||||
from scipy import signal
|
||||
from scipy import fft
|
||||
import warnings
|
||||
|
||||
from nnAudio.librosa_functions import *
|
||||
|
||||
## --------------------------- Filter Design ---------------------------##
|
||||
def torch_window_sumsquare(w, n_frames, stride, n_fft, power=2):
|
||||
w_stacks = w.unsqueeze(-1).repeat((1,n_frames)).unsqueeze(0)
|
||||
# Window length + stride*(frames-1)
|
||||
output_len = w_stacks.shape[1] + stride*(w_stacks.shape[2]-1)
|
||||
return fold(w_stacks**power, (1,output_len), kernel_size=(1,n_fft), stride=stride)
|
||||
|
||||
def overlap_add(X, stride):
|
||||
n_fft = X.shape[1]
|
||||
output_len = n_fft + stride*(X.shape[2]-1)
|
||||
|
||||
return fold(X, (1,output_len), kernel_size=(1,n_fft), stride=stride).flatten(1)
|
||||
|
||||
def uniform_distribution(r1,r2, *size, device):
|
||||
return (r1 - r2) * torch.rand(*size, device=device) + r2
|
||||
|
||||
def extend_fbins(X):
|
||||
"""Extending the number of frequency bins from `n_fft//2+1` back to `n_fft` by
|
||||
reversing all bins except DC and Nyquist and append it on top of existing spectrogram"""
|
||||
X_upper = torch.flip(X[:,1:-1],(0,1))
|
||||
X_upper[:,:,:,1] = -X_upper[:,:,:,1] # For the imaganinry part, it is an odd function
|
||||
return torch.cat((X[:, :, :], X_upper), 1)
|
||||
|
||||
|
||||
def downsampling_by_n(x, filterKernel, n):
|
||||
"""A helper function that downsamples the audio by a arbitary factor n.
|
||||
It is used in CQT2010 and CQT2010v2.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : torch.Tensor
|
||||
The input waveform in ``torch.Tensor`` type with shape ``(batch, 1, len_audio)``
|
||||
|
||||
filterKernel : str
|
||||
Filter kernel in ``torch.Tensor`` type with shape ``(1, 1, len_kernel)``
|
||||
|
||||
n : int
|
||||
The downsampling factor
|
||||
|
||||
Returns
|
||||
-------
|
||||
torch.Tensor
|
||||
The downsampled waveform
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> x_down = downsampling_by_n(x, filterKernel)
|
||||
"""
|
||||
|
||||
x = conv1d(x,filterKernel,stride=n, padding=(filterKernel.shape[-1]-1)//2)
|
||||
return x
|
||||
|
||||
|
||||
def downsampling_by_2(x, filterKernel):
|
||||
"""A helper function that downsamples the audio by half. It is used in CQT2010 and CQT2010v2
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : torch.Tensor
|
||||
The input waveform in ``torch.Tensor`` type with shape ``(batch, 1, len_audio)``
|
||||
|
||||
filterKernel : str
|
||||
Filter kernel in ``torch.Tensor`` type with shape ``(1, 1, len_kernel)``
|
||||
|
||||
Returns
|
||||
-------
|
||||
torch.Tensor
|
||||
The downsampled waveform
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> x_down = downsampling_by_2(x, filterKernel)
|
||||
"""
|
||||
|
||||
x = conv1d(x,filterKernel,stride=2, padding=(filterKernel.shape[-1]-1)//2)
|
||||
return x
|
||||
|
||||
|
||||
## Basic tools for computation ##
|
||||
def nextpow2(A):
|
||||
"""A helper function to calculate the next nearest number to the power of 2.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
A : float
|
||||
A float number that is going to be rounded up to the nearest power of 2
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
The nearest power of 2 to the input number ``A``
|
||||
|
||||
Examples
|
||||
--------
|
||||
|
||||
>>> nextpow2(6)
|
||||
3
|
||||
"""
|
||||
|
||||
return int(np.ceil(np.log2(A)))
|
||||
|
||||
## Basic tools for computation ##
|
||||
def prepow2(A):
|
||||
"""A helper function to calculate the next nearest number to the power of 2.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
A : float
|
||||
A float number that is going to be rounded up to the nearest power of 2
|
||||
|
||||
Returns
|
||||
-------
|
||||
int
|
||||
The nearest power of 2 to the input number ``A``
|
||||
|
||||
Examples
|
||||
--------
|
||||
|
||||
>>> nextpow2(6)
|
||||
3
|
||||
"""
|
||||
|
||||
return int(np.floor(np.log2(A)))
|
||||
|
||||
|
||||
def complex_mul(cqt_filter, stft):
|
||||
"""Since PyTorch does not support complex numbers and its operation.
|
||||
We need to write our own complex multiplication function. This one is specially
|
||||
designed for CQT usage.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cqt_filter : tuple of torch.Tensor
|
||||
The tuple is in the format of ``(real_torch_tensor, imag_torch_tensor)``
|
||||
|
||||
Returns
|
||||
-------
|
||||
tuple of torch.Tensor
|
||||
The output is in the format of ``(real_torch_tensor, imag_torch_tensor)``
|
||||
"""
|
||||
|
||||
cqt_filter_real = cqt_filter[0]
|
||||
cqt_filter_imag = cqt_filter[1]
|
||||
fourier_real = stft[0]
|
||||
fourier_imag = stft[1]
|
||||
|
||||
CQT_real = torch.matmul(cqt_filter_real, fourier_real) - torch.matmul(cqt_filter_imag, fourier_imag)
|
||||
CQT_imag = torch.matmul(cqt_filter_real, fourier_imag) + torch.matmul(cqt_filter_imag, fourier_real)
|
||||
|
||||
return CQT_real, CQT_imag
|
||||
|
||||
|
||||
def broadcast_dim(x):
|
||||
"""
|
||||
Auto broadcast input so that it can fits into a Conv1d
|
||||
"""
|
||||
|
||||
if x.dim() == 2:
|
||||
x = x[:, None, :]
|
||||
elif x.dim() == 1:
|
||||
# If nn.DataParallel is used, this broadcast doesn't work
|
||||
x = x[None, None, :]
|
||||
elif x.dim() == 3:
|
||||
pass
|
||||
else:
|
||||
raise ValueError("Only support input with shape = (batch, len) or shape = (len)")
|
||||
return x
|
||||
|
||||
|
||||
def broadcast_dim_conv2d(x):
|
||||
"""
|
||||
Auto broadcast input so that it can fits into a Conv2d
|
||||
"""
|
||||
|
||||
if x.dim() == 3:
|
||||
x = x[:, None, :,:]
|
||||
|
||||
else:
|
||||
raise ValueError("Only support input with shape = (batch, len) or shape = (len)")
|
||||
return x
|
||||
|
||||
|
||||
## Kernal generation functions ##
|
||||
def create_fourier_kernels(n_fft, win_length=None, freq_bins=None, fmin=50,fmax=6000, sr=44100,
|
||||
freq_scale='linear', window='hann', verbose=True):
|
||||
""" This function creates the Fourier Kernel for STFT, Melspectrogram and CQT.
|
||||
Most of the parameters follow librosa conventions. Part of the code comes from
|
||||
pytorch_musicnet. https://github.com/jthickstun/pytorch_musicnet
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n_fft : int
|
||||
The window size
|
||||
|
||||
freq_bins : int
|
||||
Number of frequency bins. Default is ``None``, which means ``n_fft//2+1`` bins
|
||||
|
||||
fmin : int
|
||||
The starting frequency for the lowest frequency bin.
|
||||
If freq_scale is ``no``, this argument does nothing.
|
||||
|
||||
fmax : int
|
||||
The ending frequency for the highest frequency bin.
|
||||
If freq_scale is ``no``, this argument does nothing.
|
||||
|
||||
sr : int
|
||||
The sampling rate for the input audio. It is used to calculate the correct ``fmin`` and ``fmax``.
|
||||
Setting the correct sampling rate is very important for calculating the correct frequency.
|
||||
|
||||
freq_scale: 'linear', 'log', or 'no'
|
||||
Determine the spacing between each frequency bin.
|
||||
When 'linear' or 'log' is used, the bin spacing can be controlled by ``fmin`` and ``fmax``.
|
||||
If 'no' is used, the bin will start at 0Hz and end at Nyquist frequency with linear spacing.
|
||||
|
||||
Returns
|
||||
-------
|
||||
wsin : numpy.array
|
||||
Imaginary Fourier Kernel with the shape ``(freq_bins, 1, n_fft)``
|
||||
|
||||
wcos : numpy.array
|
||||
Real Fourier Kernel with the shape ``(freq_bins, 1, n_fft)``
|
||||
|
||||
bins2freq : list
|
||||
Mapping each frequency bin to frequency in Hz.
|
||||
|
||||
binslist : list
|
||||
The normalized frequency ``k`` in digital domain.
|
||||
This ``k`` is in the Discrete Fourier Transform equation $$
|
||||
|
||||
"""
|
||||
|
||||
if freq_bins==None: freq_bins = n_fft//2+1
|
||||
if win_length==None: win_length = n_fft
|
||||
|
||||
s = np.arange(0, n_fft, 1.)
|
||||
wsin = np.empty((freq_bins,1,n_fft))
|
||||
wcos = np.empty((freq_bins,1,n_fft))
|
||||
start_freq = fmin
|
||||
end_freq = fmax
|
||||
bins2freq = []
|
||||
binslist = []
|
||||
|
||||
# num_cycles = start_freq*d/44000.
|
||||
# scaling_ind = np.log(end_freq/start_freq)/k
|
||||
|
||||
# Choosing window shape
|
||||
|
||||
window_mask = get_window(window,int(win_length), fftbins=True)
|
||||
window_mask = pad_center(window_mask, n_fft)
|
||||
|
||||
if freq_scale == 'linear':
|
||||
if verbose==True:
|
||||
print(f"sampling rate = {sr}. Please make sure the sampling rate is correct in order to"
|
||||
f"get a valid freq range")
|
||||
start_bin = start_freq*n_fft/sr
|
||||
scaling_ind = (end_freq-start_freq)*(n_fft/sr)/freq_bins
|
||||
|
||||
for k in range(freq_bins): # Only half of the bins contain useful info
|
||||
# print("linear freq = {}".format((k*scaling_ind+start_bin)*sr/n_fft))
|
||||
bins2freq.append((k*scaling_ind+start_bin)*sr/n_fft)
|
||||
binslist.append((k*scaling_ind+start_bin))
|
||||
wsin[k,0,:] = np.sin(2*np.pi*(k*scaling_ind+start_bin)*s/n_fft)
|
||||
wcos[k,0,:] = np.cos(2*np.pi*(k*scaling_ind+start_bin)*s/n_fft)
|
||||
|
||||
elif freq_scale == 'log':
|
||||
if verbose==True:
|
||||
print(f"sampling rate = {sr}. Please make sure the sampling rate is correct in order to"
|
||||
f"get a valid freq range")
|
||||
start_bin = start_freq*n_fft/sr
|
||||
scaling_ind = np.log(end_freq/start_freq)/freq_bins
|
||||
|
||||
for k in range(freq_bins): # Only half of the bins contain useful info
|
||||
# print("log freq = {}".format(np.exp(k*scaling_ind)*start_bin*sr/n_fft))
|
||||
bins2freq.append(np.exp(k*scaling_ind)*start_bin*sr/n_fft)
|
||||
binslist.append((np.exp(k*scaling_ind)*start_bin))
|
||||
wsin[k,0,:] = np.sin(2*np.pi*(np.exp(k*scaling_ind)*start_bin)*s/n_fft)
|
||||
wcos[k,0,:] = np.cos(2*np.pi*(np.exp(k*scaling_ind)*start_bin)*s/n_fft)
|
||||
|
||||
elif freq_scale == 'no':
|
||||
for k in range(freq_bins): # Only half of the bins contain useful info
|
||||
bins2freq.append(k*sr/n_fft)
|
||||
binslist.append(k)
|
||||
wsin[k,0,:] = np.sin(2*np.pi*k*s/n_fft)
|
||||
wcos[k,0,:] = np.cos(2*np.pi*k*s/n_fft)
|
||||
else:
|
||||
print("Please select the correct frequency scale, 'linear' or 'log'")
|
||||
return wsin.astype(np.float32),wcos.astype(np.float32), bins2freq, binslist, window_mask.astype(np.float32)
|
||||
|
||||
|
||||
# Tools for CQT
|
||||
|
||||
def create_cqt_kernels(Q, fs, fmin, n_bins=84, bins_per_octave=12, norm=1,
|
||||
window='hann', fmax=None, topbin_check=True):
|
||||
"""
|
||||
Automatically create CQT kernels in time domain
|
||||
"""
|
||||
|
||||
fftLen = 2**nextpow2(np.ceil(Q * fs / fmin))
|
||||
# minWin = 2**nextpow2(np.ceil(Q * fs / fmax))
|
||||
|
||||
if (fmax != None) and (n_bins == None):
|
||||
n_bins = np.ceil(bins_per_octave * np.log2(fmax / fmin)) # Calculate the number of bins
|
||||
freqs = fmin * 2.0 ** (np.r_[0:n_bins] / np.float(bins_per_octave))
|
||||
|
||||
elif (fmax == None) and (n_bins != None):
|
||||
freqs = fmin * 2.0 ** (np.r_[0:n_bins] / np.float(bins_per_octave))
|
||||
|
||||
else:
|
||||
warnings.warn('If fmax is given, n_bins will be ignored',SyntaxWarning)
|
||||
n_bins = np.ceil(bins_per_octave * np.log2(fmax / fmin)) # Calculate the number of bins
|
||||
freqs = fmin * 2.0 ** (np.r_[0:n_bins] / np.float(bins_per_octave))
|
||||
|
||||
if np.max(freqs) > fs/2 and topbin_check==True:
|
||||
raise ValueError('The top bin {}Hz has exceeded the Nyquist frequency, \
|
||||
please reduce the n_bins'.format(np.max(freqs)))
|
||||
|
||||
tempKernel = np.zeros((int(n_bins), int(fftLen)), dtype=np.complex64)
|
||||
specKernel = np.zeros((int(n_bins), int(fftLen)), dtype=np.complex64)
|
||||
|
||||
lengths = np.ceil(Q * fs / freqs)
|
||||
for k in range(0, int(n_bins)):
|
||||
freq = freqs[k]
|
||||
l = np.ceil(Q * fs / freq)
|
||||
|
||||
# Centering the kernels
|
||||
if l%2==1: # pad more zeros on RHS
|
||||
start = int(np.ceil(fftLen / 2.0 - l / 2.0))-1
|
||||
else:
|
||||
start = int(np.ceil(fftLen / 2.0 - l / 2.0))
|
||||
|
||||
sig = get_window_dispatch(window,int(l), fftbins=True)*np.exp(np.r_[-l//2:l//2]*1j*2*np.pi*freq/fs)/l
|
||||
|
||||
if norm: # Normalizing the filter # Trying to normalize like librosa
|
||||
tempKernel[k, start:start + int(l)] = sig/np.linalg.norm(sig, norm)
|
||||
else:
|
||||
tempKernel[k, start:start + int(l)] = sig
|
||||
# specKernel[k, :] = fft(tempKernel[k])
|
||||
|
||||
# return specKernel[:,:fftLen//2+1], fftLen, torch.tensor(lenghts).float()
|
||||
return tempKernel, fftLen, torch.tensor(lengths).float(), freqs
|
||||
|
||||
|
||||
def get_window_dispatch(window, N, fftbins=True):
|
||||
if isinstance(window, str):
|
||||
return get_window(window, N, fftbins=fftbins)
|
||||
elif isinstance(window, tuple):
|
||||
if window[0] == 'gaussian':
|
||||
assert window[1] >= 0
|
||||
sigma = np.floor(- N / 2 / np.sqrt(- 2 * np.log(10**(- window[1] / 20))))
|
||||
return get_window(('gaussian', sigma), N, fftbins=fftbins)
|
||||
else:
|
||||
Warning("Tuple windows may have undesired behaviour regarding Q factor")
|
||||
elif isinstance(window, float):
|
||||
Warning("You are using Kaiser window with beta factor " + str(window) + ". Correct behaviour not checked.")
|
||||
else:
|
||||
raise Exception("The function get_window from scipy only supports strings, tuples and floats.")
|
||||
|
||||
|
||||
|
||||
def get_cqt_complex(x, cqt_kernels_real, cqt_kernels_imag, hop_length, padding):
|
||||
"""Multiplying the STFT result with the cqt_kernel, check out the 1992 CQT paper [1]
|
||||
for how to multiple the STFT result with the CQT kernel
|
||||
[2] Brown, Judith C.C. and Miller Puckette. “An efficient algorithm for the calculation of
|
||||
a constant Q transform.” (1992)."""
|
||||
|
||||
# STFT, converting the audio input from time domain to frequency domain
|
||||
try:
|
||||
x = padding(x) # When center == True, we need padding at the beginning and ending
|
||||
except:
|
||||
warnings.warn(f"\ninput size = {x.shape}\tkernel size = {cqt_kernels_real.shape[-1]}\n"
|
||||
"padding with reflection mode might not be the best choice, try using constant padding",
|
||||
UserWarning)
|
||||
x = torch.nn.functional.pad(x, (cqt_kernels_real.shape[-1]//2, cqt_kernels_real.shape[-1]//2))
|
||||
CQT_real = conv1d(x, cqt_kernels_real, stride=hop_length)
|
||||
CQT_imag = -conv1d(x, cqt_kernels_imag, stride=hop_length)
|
||||
|
||||
return torch.stack((CQT_real, CQT_imag),-1)
|
||||
|
||||
def get_cqt_complex2(x, cqt_kernels_real, cqt_kernels_imag, hop_length, padding, wcos=None, wsin=None):
|
||||
"""Multiplying the STFT result with the cqt_kernel, check out the 1992 CQT paper [1]
|
||||
for how to multiple the STFT result with the CQT kernel
|
||||
[2] Brown, Judith C.C. and Miller Puckette. “An efficient algorithm for the calculation of
|
||||
a constant Q transform.” (1992)."""
|
||||
|
||||
# STFT, converting the audio input from time domain to frequency domain
|
||||
try:
|
||||
x = padding(x) # When center == True, we need padding at the beginning and ending
|
||||
except:
|
||||
warnings.warn(f"\ninput size = {x.shape}\tkernel size = {cqt_kernels_real.shape[-1]}\n"
|
||||
"padding with reflection mode might not be the best choice, try using constant padding",
|
||||
UserWarning)
|
||||
x = torch.nn.functional.pad(x, (cqt_kernels_real.shape[-1]//2, cqt_kernels_real.shape[-1]//2))
|
||||
|
||||
|
||||
|
||||
if wcos==None or wsin==None:
|
||||
CQT_real = conv1d(x, cqt_kernels_real, stride=hop_length)
|
||||
CQT_imag = -conv1d(x, cqt_kernels_imag, stride=hop_length)
|
||||
|
||||
else:
|
||||
fourier_real = conv1d(x, wcos, stride=hop_length)
|
||||
fourier_imag = conv1d(x, wsin, stride=hop_length)
|
||||
# Multiplying input with the CQT kernel in freq domain
|
||||
CQT_real, CQT_imag = complex_mul((cqt_kernels_real, cqt_kernels_imag),
|
||||
(fourier_real, fourier_imag))
|
||||
|
||||
return torch.stack((CQT_real, CQT_imag),-1)
|
||||
|
||||
|
||||
|
||||
|
||||
def create_lowpass_filter(band_center=0.5, kernelLength=256, transitionBandwidth=0.03):
|
||||
"""
|
||||
Calculate the highest frequency we need to preserve and the lowest frequency we allow
|
||||
to pass through.
|
||||
Note that frequency is on a scale from 0 to 1 where 0 is 0 and 1 is Nyquist frequency of
|
||||
the signal BEFORE downsampling.
|
||||
"""
|
||||
|
||||
# transitionBandwidth = 0.03
|
||||
passbandMax = band_center / (1 + transitionBandwidth)
|
||||
stopbandMin = band_center * (1 + transitionBandwidth)
|
||||
|
||||
# Unlike the filter tool we used online yesterday, this tool does
|
||||
# not allow us to specify how closely the filter matches our
|
||||
# specifications. Instead, we specify the length of the kernel.
|
||||
# The longer the kernel is, the more precisely it will match.
|
||||
# kernelLength = 256
|
||||
|
||||
# We specify a list of key frequencies for which we will require
|
||||
# that the filter match a specific output gain.
|
||||
# From [0.0 to passbandMax] is the frequency range we want to keep
|
||||
# untouched and [stopbandMin, 1.0] is the range we want to remove
|
||||
keyFrequencies = [0.0, passbandMax, stopbandMin, 1.0]
|
||||
|
||||
# We specify a list of output gains to correspond to the key
|
||||
# frequencies listed above.
|
||||
# The first two gains are 1.0 because they correspond to the first
|
||||
# two key frequencies. the second two are 0.0 because they
|
||||
# correspond to the stopband frequencies
|
||||
gainAtKeyFrequencies = [1.0, 1.0, 0.0, 0.0]
|
||||
|
||||
# This command produces the filter kernel coefficients
|
||||
filterKernel = signal.firwin2(kernelLength, keyFrequencies, gainAtKeyFrequencies)
|
||||
|
||||
return filterKernel.astype(np.float32)
|
||||
|
||||
def get_early_downsample_params(sr, hop_length, fmax_t, Q, n_octaves, verbose):
|
||||
"""Used in CQT2010 and CQT2010v2"""
|
||||
|
||||
window_bandwidth = 1.5 # for hann window
|
||||
filter_cutoff = fmax_t * (1 + 0.5 * window_bandwidth / Q)
|
||||
sr, hop_length, downsample_factor = early_downsample(sr,
|
||||
hop_length,
|
||||
n_octaves,
|
||||
sr//2,
|
||||
filter_cutoff)
|
||||
if downsample_factor != 1:
|
||||
if verbose==True:
|
||||
print("Can do early downsample, factor = ", downsample_factor)
|
||||
earlydownsample=True
|
||||
# print("new sr = ", sr)
|
||||
# print("new hop_length = ", hop_length)
|
||||
early_downsample_filter = create_lowpass_filter(band_center=1/downsample_factor,
|
||||
kernelLength=256,
|
||||
transitionBandwidth=0.03)
|
||||
early_downsample_filter = torch.tensor(early_downsample_filter)[None, None, :]
|
||||
|
||||
else:
|
||||
if verbose==True:
|
||||
print("No early downsampling is required, downsample_factor = ", downsample_factor)
|
||||
early_downsample_filter = None
|
||||
earlydownsample=False
|
||||
|
||||
return sr, hop_length, downsample_factor, early_downsample_filter, earlydownsample
|
||||
|
||||
def early_downsample(sr, hop_length, n_octaves,
|
||||
nyquist, filter_cutoff):
|
||||
'''Return new sampling rate and hop length after early dowansampling'''
|
||||
downsample_count = early_downsample_count(nyquist, filter_cutoff, hop_length, n_octaves)
|
||||
# print("downsample_count = ", downsample_count)
|
||||
downsample_factor = 2**(downsample_count)
|
||||
|
||||
hop_length //= downsample_factor # Getting new hop_length
|
||||
new_sr = sr / float(downsample_factor) # Getting new sampling rate
|
||||
sr = new_sr
|
||||
|
||||
return sr, hop_length, downsample_factor
|
||||
|
||||
|
||||
# The following two downsampling count functions are obtained from librosa CQT
|
||||
# They are used to determine the number of pre resamplings if the starting and ending frequency
|
||||
# are both in low frequency regions.
|
||||
def early_downsample_count(nyquist, filter_cutoff, hop_length, n_octaves):
|
||||
'''Compute the number of early downsampling operations'''
|
||||
|
||||
downsample_count1 = max(0, int(np.ceil(np.log2(0.85 * nyquist /
|
||||
filter_cutoff)) - 1) - 1)
|
||||
# print("downsample_count1 = ", downsample_count1)
|
||||
num_twos = nextpow2(hop_length)
|
||||
downsample_count2 = max(0, num_twos - n_octaves + 1)
|
||||
# print("downsample_count2 = ",downsample_count2)
|
||||
|
||||
return min(downsample_count1, downsample_count2)
|
||||
|
||||
def early_downsample(sr, hop_length, n_octaves,
|
||||
nyquist, filter_cutoff):
|
||||
'''Return new sampling rate and hop length after early dowansampling'''
|
||||
downsample_count = early_downsample_count(nyquist, filter_cutoff, hop_length, n_octaves)
|
||||
# print("downsample_count = ", downsample_count)
|
||||
downsample_factor = 2**(downsample_count)
|
||||
|
||||
hop_length //= downsample_factor # Getting new hop_length
|
||||
new_sr = sr / float(downsample_factor) # Getting new sampling rate
|
||||
|
||||
sr = new_sr
|
||||
|
||||
return sr, hop_length, downsample_factor
|
||||
@ -0,0 +1,37 @@
|
||||
import setuptools
|
||||
import codecs
|
||||
import os.path
|
||||
|
||||
with open("README.md", "r") as fh:
|
||||
long_description = fh.read()
|
||||
|
||||
def read(rel_path):
|
||||
here = os.path.abspath(os.path.dirname(__file__))
|
||||
with codecs.open(os.path.join(here, rel_path), 'r') as fp:
|
||||
return fp.read()
|
||||
|
||||
def get_version(rel_path):
|
||||
for line in read(rel_path).splitlines():
|
||||
if line.startswith('__version__'):
|
||||
delim = '"' if '"' in line else "'"
|
||||
return line.split(delim)[1]
|
||||
else:
|
||||
raise RuntimeError("Unable to find version string.")
|
||||
|
||||
setuptools.setup(
|
||||
name="nnAudio", # Replace with your own username
|
||||
version=get_version("nnAudio/__init__.py"),
|
||||
author="KinWaiCheuk",
|
||||
author_email="u3500684@connect.hku.hk",
|
||||
description="A fast GPU audio processing toolbox with 1D convolutional neural network",
|
||||
long_description=long_description,
|
||||
long_description_content_type="text/markdown",
|
||||
url="https://github.com/KinWaiCheuk/nnAudio",
|
||||
packages=setuptools.find_packages(),
|
||||
classifiers=[
|
||||
"Programming Language :: Python :: 3",
|
||||
"License :: OSI Approved :: MIT License",
|
||||
"Operating System :: OS Independent",
|
||||
],
|
||||
python_requires='>=3.6',
|
||||
)
|
||||
@ -0,0 +1,38 @@
|
||||
# Creating parameters for STFT test
|
||||
"""
|
||||
It is equivalent to
|
||||
[(1024, 128, 'ones'),
|
||||
(1024, 128, 'hann'),
|
||||
(1024, 128, 'hamming'),
|
||||
(2048, 128, 'ones'),
|
||||
(2048, 512, 'ones'),
|
||||
(2048, 128, 'hann'),
|
||||
(2048, 512, 'hann'),
|
||||
(2048, 128, 'hamming'),
|
||||
(2048, 512, 'hamming'),
|
||||
(None, None, None)]
|
||||
"""
|
||||
|
||||
stft_parameters = []
|
||||
n_fft = [1024,2048]
|
||||
hop_length = {128,512,1024}
|
||||
window = ['ones', 'hann', 'hamming']
|
||||
for i in n_fft:
|
||||
for k in window:
|
||||
for j in hop_length:
|
||||
if j < (i/2):
|
||||
stft_parameters.append((i,j,k))
|
||||
stft_parameters.append((256, None, 'hann'))
|
||||
|
||||
stft_with_win_parameters = []
|
||||
n_fft = [512,1024]
|
||||
win_length = [400, 900]
|
||||
hop_length = {128,256}
|
||||
for i in n_fft:
|
||||
for j in win_length:
|
||||
if j < i:
|
||||
for k in hop_length:
|
||||
if k < (i/2):
|
||||
stft_with_win_parameters.append((i,j,k))
|
||||
|
||||
mel_win_parameters = [(512,400), (1024, 1000)]
|
||||
@ -0,0 +1,373 @@
|
||||
import pytest
|
||||
import librosa
|
||||
import torch
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.signal import chirp, sweep_poly
|
||||
from nnAudio.Spectrogram import *
|
||||
from parameters import *
|
||||
|
||||
gpu_idx=0
|
||||
|
||||
# librosa example audio for testing
|
||||
example_y, example_sr = librosa.load(librosa.util.example_audio_file())
|
||||
|
||||
|
||||
@pytest.mark.parametrize("n_fft, hop_length, window", stft_parameters)
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_inverse2(n_fft, hop_length, window, device):
|
||||
x = torch.tensor(example_y,device=device)
|
||||
stft = STFT(n_fft=n_fft, hop_length=hop_length, window=window).to(device)
|
||||
istft = iSTFT(n_fft=n_fft, hop_length=hop_length, window=window).to(device)
|
||||
X = stft(x.unsqueeze(0), output_format="Complex")
|
||||
x_recon = istft(X, length=x.shape[0], onesided=True).squeeze()
|
||||
assert np.allclose(x.cpu(), x_recon.cpu(), rtol=1e-5, atol=1e-3)
|
||||
|
||||
@pytest.mark.parametrize("n_fft, hop_length, window", stft_parameters)
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_inverse(n_fft, hop_length, window, device):
|
||||
x = torch.tensor(example_y, device=device)
|
||||
stft = STFT(n_fft=n_fft, hop_length=hop_length, window=window, iSTFT=True).to(device)
|
||||
X = stft(x.unsqueeze(0), output_format="Complex")
|
||||
x_recon = stft.inverse(X, length=x.shape[0]).squeeze()
|
||||
assert np.allclose(x.cpu(), x_recon.cpu(), rtol=1e-3, atol=1)
|
||||
|
||||
|
||||
|
||||
# @pytest.mark.parametrize("n_fft, hop_length, window", stft_parameters)
|
||||
|
||||
# def test_inverse_GPU(n_fft, hop_length, window):
|
||||
# x = torch.tensor(example_y,device=f'cuda:{gpu_idx}')
|
||||
# stft = STFT(n_fft=n_fft, hop_length=hop_length, window=window, device=f'cuda:{gpu_idx}')
|
||||
# X = stft(x.unsqueeze(0), output_format="Complex")
|
||||
# x_recon = stft.inverse(X, num_samples=x.shape[0]).squeeze()
|
||||
# assert np.allclose(x.cpu(), x_recon.cpu(), rtol=1e-3, atol=1)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("n_fft, hop_length, window", stft_parameters)
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_stft_complex(n_fft, hop_length, window, device):
|
||||
x = example_y
|
||||
stft = STFT(n_fft=n_fft, hop_length=hop_length, window=window).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0), output_format="Complex")
|
||||
X_real, X_imag = X[:, :, :, 0].squeeze(), X[:, :, :, 1].squeeze()
|
||||
X_librosa = librosa.stft(x, n_fft=n_fft, hop_length=hop_length, window=window)
|
||||
real_diff, imag_diff = np.allclose(X_real.cpu(), X_librosa.real, rtol=1e-3, atol=1e-3), \
|
||||
np.allclose(X_imag.cpu(), X_librosa.imag, rtol=1e-3, atol=1e-3)
|
||||
|
||||
assert real_diff and imag_diff
|
||||
|
||||
# @pytest.mark.parametrize("n_fft, hop_length, window", stft_parameters)
|
||||
# def test_stft_complex_GPU(n_fft, hop_length, window):
|
||||
# x = example_y
|
||||
# stft = STFT(n_fft=n_fft, hop_length=hop_length, window=window, device=f'cuda:{gpu_idx}')
|
||||
# X = stft(torch.tensor(x,device=f'cuda:{gpu_idx}').unsqueeze(0), output_format="Complex")
|
||||
# X_real, X_imag = X[:, :, :, 0].squeeze().detach().cpu(), X[:, :, :, 1].squeeze().detach().cpu()
|
||||
# X_librosa = librosa.stft(x, n_fft=n_fft, hop_length=hop_length, window=window)
|
||||
# real_diff, imag_diff = np.allclose(X_real, X_librosa.real, rtol=1e-3, atol=1e-3), \
|
||||
# np.allclose(X_imag, X_librosa.imag, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# assert real_diff and imag_diff
|
||||
|
||||
@pytest.mark.parametrize("n_fft, win_length, hop_length", stft_with_win_parameters)
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_stft_complex_winlength(n_fft, win_length, hop_length, device):
|
||||
x = example_y
|
||||
stft = STFT(n_fft=n_fft, win_length=win_length, hop_length=hop_length).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0), output_format="Complex")
|
||||
X_real, X_imag = X[:, :, :, 0].squeeze(), X[:, :, :, 1].squeeze()
|
||||
X_librosa = librosa.stft(x, n_fft=n_fft, win_length=win_length, hop_length=hop_length)
|
||||
real_diff, imag_diff = np.allclose(X_real.cpu(), X_librosa.real, rtol=1e-3, atol=1e-3), \
|
||||
np.allclose(X_imag.cpu(), X_librosa.imag, rtol=1e-3, atol=1e-3)
|
||||
assert real_diff and imag_diff
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_stft_magnitude(device):
|
||||
x = example_y
|
||||
stft = STFT(n_fft=2048, hop_length=512).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0), output_format="Magnitude").squeeze()
|
||||
X_librosa, _ = librosa.core.magphase(librosa.stft(x, n_fft=2048, hop_length=512))
|
||||
assert np.allclose(X.cpu(), X_librosa, rtol=1e-3, atol=1e-3)
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_stft_phase(device):
|
||||
x = example_y
|
||||
stft = STFT(n_fft=2048, hop_length=512).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0), output_format="Phase")
|
||||
X_real, X_imag = torch.cos(X).squeeze(), torch.sin(X).squeeze()
|
||||
_, X_librosa = librosa.core.magphase(librosa.stft(x, n_fft=2048, hop_length=512))
|
||||
|
||||
real_diff, imag_diff = np.mean(np.abs(X_real.cpu().numpy() - X_librosa.real)), \
|
||||
np.mean(np.abs(X_imag.cpu().numpy() - X_librosa.imag))
|
||||
|
||||
# I find that np.allclose is too strict for allowing phase to be similar to librosa.
|
||||
# Hence for phase we use average element-wise distance as the test metric.
|
||||
assert real_diff < 2e-4 and imag_diff < 2e-4
|
||||
|
||||
@pytest.mark.parametrize("n_fft, win_length", mel_win_parameters)
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_mel_spectrogram(n_fft, win_length, device):
|
||||
x = example_y
|
||||
melspec = MelSpectrogram(n_fft=n_fft, win_length=win_length, hop_length=512).to(device)
|
||||
X = melspec(torch.tensor(x, device=device).unsqueeze(0)).squeeze()
|
||||
X_librosa = librosa.feature.melspectrogram(x, n_fft=n_fft, win_length=win_length, hop_length=512)
|
||||
assert np.allclose(X.cpu(), X_librosa, rtol=1e-3, atol=1e-3)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_cqt_1992(device):
|
||||
# Log sweep case
|
||||
fs = 44100
|
||||
t = 1
|
||||
f0 = 55
|
||||
f1 = 22050
|
||||
s = np.linspace(0, t, fs*t)
|
||||
x = chirp(s, f0, 1, f1, method='logarithmic')
|
||||
x = x.astype(dtype=np.float32)
|
||||
|
||||
# Magnitude
|
||||
stft = CQT1992(sr=fs, fmin=220, output_format="Magnitude",
|
||||
n_bins=80, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
|
||||
|
||||
# Complex
|
||||
stft = CQT1992(sr=fs, fmin=220, output_format="Complex",
|
||||
n_bins=80, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
|
||||
# Phase
|
||||
stft = CQT1992(sr=fs, fmin=220, output_format="Phase",
|
||||
n_bins=160, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
|
||||
assert True
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_cqt_2010(device):
|
||||
# Log sweep case
|
||||
fs = 44100
|
||||
t = 1
|
||||
f0 = 55
|
||||
f1 = 22050
|
||||
s = np.linspace(0, t, fs*t)
|
||||
x = chirp(s, f0, 1, f1, method='logarithmic')
|
||||
x = x.astype(dtype=np.float32)
|
||||
|
||||
# Magnitude
|
||||
stft = CQT2010(sr=fs, fmin=110, output_format="Magnitude",
|
||||
n_bins=160, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
|
||||
# Complex
|
||||
stft = CQT2010(sr=fs, fmin=110, output_format="Complex",
|
||||
n_bins=160, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
|
||||
# Phase
|
||||
stft = CQT2010(sr=fs, fmin=110, output_format="Phase",
|
||||
n_bins=160, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
assert True
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_cqt_1992_v2_log(device):
|
||||
# Log sweep case
|
||||
fs = 44100
|
||||
t = 1
|
||||
f0 = 55
|
||||
f1 = 22050
|
||||
s = np.linspace(0, t, fs*t)
|
||||
x = chirp(s, f0, 1, f1, method='logarithmic')
|
||||
x = x.astype(dtype=np.float32)
|
||||
|
||||
# Magnitude
|
||||
stft = CQT1992v2(sr=fs, fmin=55, output_format="Magnitude",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
ground_truth = np.load("tests/ground-truths/log-sweep-cqt-1992-mag-ground-truth.npy")
|
||||
X = torch.log(X + 1e-5)
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# Complex
|
||||
stft = CQT1992v2(sr=fs, fmin=55, output_format="Complex",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
ground_truth = np.load("tests/ground-truths/log-sweep-cqt-1992-complex-ground-truth.npy")
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# Phase
|
||||
stft = CQT1992v2(sr=fs, fmin=55, output_format="Phase",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
ground_truth = np.load("tests/ground-truths/log-sweep-cqt-1992-phase-ground-truth.npy")
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_cqt_1992_v2_linear(device):
|
||||
# Linear sweep case
|
||||
fs = 44100
|
||||
t = 1
|
||||
f0 = 55
|
||||
f1 = 22050
|
||||
s = np.linspace(0, t, fs*t)
|
||||
x = chirp(s, f0, 1, f1, method='linear')
|
||||
x = x.astype(dtype=np.float32)
|
||||
|
||||
# Magnitude
|
||||
stft = CQT1992v2(sr=fs, fmin=55, output_format="Magnitude",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
ground_truth = np.load("tests/ground-truths/linear-sweep-cqt-1992-mag-ground-truth.npy")
|
||||
X = torch.log(X + 1e-5)
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# Complex
|
||||
stft = CQT1992v2(sr=fs, fmin=55, output_format="Complex",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
ground_truth = np.load("tests/ground-truths/linear-sweep-cqt-1992-complex-ground-truth.npy")
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# Phase
|
||||
stft = CQT1992v2(sr=fs, fmin=55, output_format="Phase",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
ground_truth = np.load("tests/ground-truths/linear-sweep-cqt-1992-phase-ground-truth.npy")
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_cqt_2010_v2_log(device):
|
||||
# Log sweep case
|
||||
fs = 44100
|
||||
t = 1
|
||||
f0 = 55
|
||||
f1 = 22050
|
||||
s = np.linspace(0, t, fs*t)
|
||||
x = chirp(s, f0, 1, f1, method='logarithmic')
|
||||
x = x.astype(dtype=np.float32)
|
||||
|
||||
# Magnitude
|
||||
stft = CQT2010v2(sr=fs, fmin=55, output_format="Magnitude",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
X = torch.log(X + 1e-2)
|
||||
# np.save("tests/ground-truths/log-sweep-cqt-2010-mag-ground-truth", X.cpu())
|
||||
ground_truth = np.load("tests/ground-truths/log-sweep-cqt-2010-mag-ground-truth.npy")
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# Complex
|
||||
stft = CQT2010v2(sr=fs, fmin=55, output_format="Complex",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
# np.save("tests/ground-truths/log-sweep-cqt-2010-complex-ground-truth", X.cpu())
|
||||
ground_truth = np.load("tests/ground-truths/log-sweep-cqt-2010-complex-ground-truth.npy")
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# # Phase
|
||||
# stft = CQT2010v2(sr=fs, fmin=55, device=device, output_format="Phase",
|
||||
# n_bins=207, bins_per_octave=24)
|
||||
# X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
# # np.save("tests/ground-truths/log-sweep-cqt-2010-phase-ground-truth", X.cpu())
|
||||
# ground_truth = np.load("tests/ground-truths/log-sweep-cqt-2010-phase-ground-truth.npy")
|
||||
# assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_cqt_2010_v2_linear(device):
|
||||
# Linear sweep case
|
||||
fs = 44100
|
||||
t = 1
|
||||
f0 = 55
|
||||
f1 = 22050
|
||||
s = np.linspace(0, t, fs*t)
|
||||
x = chirp(s, f0, 1, f1, method='linear')
|
||||
x = x.astype(dtype=np.float32)
|
||||
|
||||
# Magnitude
|
||||
stft = CQT2010v2(sr=fs, fmin=55, output_format="Magnitude",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
X = torch.log(X + 1e-2)
|
||||
# np.save("tests/ground-truths/linear-sweep-cqt-2010-mag-ground-truth", X.cpu())
|
||||
ground_truth = np.load("tests/ground-truths/linear-sweep-cqt-2010-mag-ground-truth.npy")
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# Complex
|
||||
stft = CQT2010v2(sr=fs, fmin=55, output_format="Complex",
|
||||
n_bins=207, bins_per_octave=24).to(device)
|
||||
X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
# np.save("tests/ground-truths/linear-sweep-cqt-2010-complex-ground-truth", X.cpu())
|
||||
ground_truth = np.load("tests/ground-truths/linear-sweep-cqt-2010-complex-ground-truth.npy")
|
||||
assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
# Phase
|
||||
# stft = CQT2010v2(sr=fs, fmin=55, device=device, output_format="Phase",
|
||||
# n_bins=207, bins_per_octave=24)
|
||||
# X = stft(torch.tensor(x, device=device).unsqueeze(0))
|
||||
# # np.save("tests/ground-truths/linear-sweep-cqt-2010-phase-ground-truth", X.cpu())
|
||||
# ground_truth = np.load("tests/ground-truths/linear-sweep-cqt-2010-phase-ground-truth.npy")
|
||||
# assert np.allclose(X.cpu(), ground_truth, rtol=1e-3, atol=1e-3)
|
||||
|
||||
@pytest.mark.parametrize("device", ['cpu', f'cuda:{gpu_idx}'])
|
||||
def test_mfcc(device):
|
||||
x = example_y
|
||||
mfcc = MFCC(sr=example_sr).to(device)
|
||||
X = mfcc(torch.tensor(x, device=device).unsqueeze(0)).squeeze()
|
||||
X_librosa = librosa.feature.mfcc(x, sr=example_sr)
|
||||
assert np.allclose(X.cpu(), X_librosa, rtol=1e-3, atol=1e-3)
|
||||
|
||||
|
||||
x = torch.randn((4,44100)) # Create a batch of input for the following Data.Parallel test
|
||||
|
||||
@pytest.mark.parametrize("device", [f'cuda:{gpu_idx}'])
|
||||
def test_STFT_Parallel(device):
|
||||
spec_layer = STFT(hop_length=512, n_fft=2048, window='hann',
|
||||
freq_scale='no',
|
||||
output_format='Complex').to(device)
|
||||
inverse_spec_layer = iSTFT(hop_length=512, n_fft=2048, window='hann',
|
||||
freq_scale='no').to(device)
|
||||
|
||||
spec_layer_parallel = torch.nn.DataParallel(spec_layer)
|
||||
inverse_spec_layer_parallel = torch.nn.DataParallel(inverse_spec_layer)
|
||||
spec = spec_layer_parallel(x)
|
||||
x_recon = inverse_spec_layer_parallel(spec, onesided=True, length=x.shape[-1])
|
||||
|
||||
assert np.allclose(x_recon.detach().cpu(), x.detach().cpu(), rtol=1e-3, atol=1e-3)
|
||||
|
||||
@pytest.mark.parametrize("device", [f'cuda:{gpu_idx}'])
|
||||
def test_MelSpectrogram_Parallel(device):
|
||||
spec_layer = MelSpectrogram(sr=22050, n_fft=2048, n_mels=128, hop_length=512,
|
||||
window='hann', center=True, pad_mode='reflect',
|
||||
power=2.0, htk=False, fmin=0.0, fmax=None, norm=1,
|
||||
verbose=True).to(device)
|
||||
spec_layer_parallel = torch.nn.DataParallel(spec_layer)
|
||||
spec = spec_layer_parallel(x)
|
||||
|
||||
@pytest.mark.parametrize("device", [f'cuda:{gpu_idx}'])
|
||||
def test_MFCC_Parallel(device):
|
||||
spec_layer = MFCC().to(device)
|
||||
spec_layer_parallel = torch.nn.DataParallel(spec_layer)
|
||||
spec = spec_layer_parallel(x)
|
||||
|
||||
@pytest.mark.parametrize("device", [f'cuda:{gpu_idx}'])
|
||||
def test_CQT1992_Parallel(device):
|
||||
spec_layer = CQT1992(fmin=110, n_bins=60, bins_per_octave=12).to(device)
|
||||
spec_layer_parallel = torch.nn.DataParallel(spec_layer)
|
||||
spec = spec_layer_parallel(x)
|
||||
|
||||
@pytest.mark.parametrize("device", [f'cuda:{gpu_idx}'])
|
||||
def test_CQT1992v2_Parallel(device):
|
||||
spec_layer = CQT1992v2().to(device)
|
||||
spec_layer_parallel = torch.nn.DataParallel(spec_layer)
|
||||
spec = spec_layer_parallel(x)
|
||||
|
||||
@pytest.mark.parametrize("device", [f'cuda:{gpu_idx}'])
|
||||
def test_CQT2010_Parallel(device):
|
||||
spec_layer = CQT2010().to(device)
|
||||
spec_layer_parallel = torch.nn.DataParallel(spec_layer)
|
||||
spec = spec_layer_parallel(x)
|
||||
|
||||
@pytest.mark.parametrize("device", [f'cuda:{gpu_idx}'])
|
||||
def test_CQT2010v2_Parallel(device):
|
||||
spec_layer = CQT2010v2().to(device)
|
||||
spec_layer_parallel = torch.nn.DataParallel(spec_layer)
|
||||
spec = spec_layer_parallel(x)
|
||||
Loading…
Reference in new issue