| import numpy as np | |
| from numpy.lib.stride_tricks import sliding_window_view | |
| def istft(frames, framesize, hopsize): | |
| frames = np.atleast_2d(frames) | |
| assert frames.ndim == 2 | |
| analysis_window_size = np.ravel(framesize)[0] | |
| synthesis_window_size = np.ravel(framesize)[-1] | |
| assert analysis_window_size >= synthesis_window_size | |
| A = asymmetric_analysis_window(analysis_window_size, synthesis_window_size) if analysis_window_size != synthesis_window_size else symmetric_window(analysis_window_size) | |
| S = asymmetric_synthesis_window(analysis_window_size, synthesis_window_size) if analysis_window_size != synthesis_window_size else symmetric_window(synthesis_window_size) | |
| W = S * hopsize / np.sum(A * S) | |
| N = frames.shape[0] * hopsize + analysis_window_size | |
| y = np.zeros((N), float) | |
| frames[:, 0] = 0 | |
| frames[:, -1] = 0 | |
| frames0 = sliding_window_view(y, analysis_window_size, writeable=True)[::hopsize] | |
| frames1 = np.fft.irfft(frames, axis=-1, norm='forward') * W | |
| for i in range(min(len(frames0), len(frames1))): | |
| frames0[i] += frames1[i] | |
| return y | |
| def asymmetric_synthesis_window(analysis_window_size, synthesis_window_size): | |
| n = analysis_window_size | |
| m = synthesis_window_size // 2 | |
| right = symmetric_window(2 * m) | |
| window = np.zeros(n) | |
| window[n-m-m:n-m] = np.square(right[:m]) / symmetric_window(2 * n - 2 * m)[n-m-m:n-m] | |
| window[-m:] = right[-m:] | |
| return window | |
| def asymmetric_analysis_window(analysis_window_size, synthesis_window_size): | |
| n = analysis_window_size | |
| m = synthesis_window_size // 2 | |
| window = np.zeros(n) | |
| window[:n-m] = symmetric_window(2 * n - 2 * m)[:n-m] | |
| window[-m:] = symmetric_window(2 * m)[-m:] | |
| return window | |
| def symmetric_window(symmetric_window_size): | |
| n = symmetric_window_size | |
| window = 0.5 - 0.5 * np.cos(2 * np.pi * np.arange(n) / n) | |
| return window | |
| def stft(x, framesize, hopsize): | |
| x = np.atleast_1d(x) | |
| assert x.ndim == 1 | |
| analysis_window_size = np.ravel(framesize)[0] | |
| synthesis_window_size = np.ravel(framesize)[-1] | |
| assert analysis_window_size >= synthesis_window_size | |
| W = asymmetric_analysis_window(analysis_window_size, synthesis_window_size) if analysis_window_size != synthesis_window_size else symmetric_window(analysis_window_size) | |
| frames0 = sliding_window_view(x, analysis_window_size, writeable=False)[::hopsize] | |
| frames1 = np.fft.rfft(frames0 * W, axis=-1, norm='forward') | |
| return frames1 | |
| def normalize(frames, frames0): | |
| for i in range(len(frames)): | |
| a = np.real(frames0[i]) | |
| b = np.real(frames[i]) | |
| a = np.dot(a, a) | |
| b = np.dot(b, b) | |
| if b == 0: continue | |
| frames[i] = np.real(frames[i]) * np.sqrt(a / b) + 1j * np.imag(frames[i]) | |
| return frames | |
| def lowpass(cepstrum, quefrency): | |
| cepstrum[1:quefrency] *= 2 | |
| cepstrum[quefrency+1:] = 0 | |
| return cepstrum | |
| def lifter(frames, quefrency): | |
| envelopes = np.zeros(frames.shape) | |
| for i, frame in enumerate(frames): | |
| with np.errstate(divide='ignore', invalid='ignore'): | |
| spectrum = np.log10(np.real(frame)) | |
| envelopes[i] = np.power(10, np.real(np.fft.rfft(lowpass(np.fft.irfft(spectrum, norm='forward'), quefrency), norm='forward'))) | |
| return envelopes | |
| def resample(x, factor): | |
| if factor == 1: return x.copy() | |
| y = np.zeros(x.shape, dtype=x.dtype) | |
| n = len(x) | |
| m = int(n * factor) | |
| i = np.arange(min(n, m)) | |
| k = i * (n / m) | |
| j = np.trunc(k).astype(int) | |
| k = k - j | |
| ok = (0 <= j) & (j < n - 1) | |
| y[i[ok]] = k[ok] * x[j[ok] + 1] + (1 - k[ok]) * x[j[ok]] | |
| return y | |
| def shiftpitch(frames, factors, samplerate): | |
| for i in range(len(frames)): | |
| magnitudes = np.vstack([resample(np.real(frames[i]), factor) for factor in factors]) | |
| frequencies = np.vstack([resample(np.imag(frames[i]), factor) * factor for factor in factors]) | |
| magnitudes[(frequencies <= 0) | (frequencies >= samplerate / 2)] = 0 | |
| mask = np.argmax(magnitudes, axis=0) | |
| magnitudes = np.take_along_axis(magnitudes, mask[None,:], axis=0) | |
| frequencies = np.take_along_axis(frequencies, mask[None,:], axis=0) | |
| frames[i] = magnitudes + 1j * frequencies | |
| return frames | |
| def wrap(x): | |
| return (x + np.pi) % (2 * np.pi) - np.pi | |
| def encode(frames, framesize, hopsize, samplerate): | |
| M, N = frames.shape | |
| analysis_framesize = np.ravel(framesize)[0] | |
| freqinc = samplerate / analysis_framesize | |
| phaseinc = 2 * np.pi * hopsize / analysis_framesize | |
| buffer = np.zeros(N) | |
| data = np.zeros((M, N), complex) | |
| for m, frame in enumerate(frames): | |
| arg = np.angle(frame) | |
| buffer = arg | |
| i = np.arange(N) | |
| freq = (i + (wrap((arg - buffer) - i * phaseinc) / phaseinc)) * freqinc | |
| data[m] = np.abs(frame) + 1j * freq | |
| return data | |
| def decode(frames, framesize, hopsize, samplerate): | |
| M, N = frames.shape | |
| analysis_framesize = np.ravel(framesize)[0] | |
| synthesis_framesize = np.ravel(framesize)[-1] | |
| freqinc = samplerate / analysis_framesize | |
| phaseinc = 2 * np.pi * hopsize / analysis_framesize | |
| timeshift = 2 * np.pi * synthesis_framesize * np.arange(N) / N if synthesis_framesize != analysis_framesize else 0 | |
| buffer = np.zeros(N) | |
| data = np.zeros((M, N), complex) | |
| for m, frame in enumerate(frames): | |
| i = np.arange(N) | |
| delta = (i + ((np.imag(frame) - i * freqinc) / freqinc)) * phaseinc | |
| buffer += delta | |
| arg = buffer.copy() | |
| arg -= timeshift | |
| data[m] = np.real(frame) * np.exp(1j * arg) | |
| return data | |
| class StftPitchShift: | |
| def __init__(self, framesize, hopsize, samplerate): | |
| self.framesize = framesize | |
| self.hopsize = hopsize | |
| self.samplerate = samplerate | |
| def shiftpitch(self, input, factors = 1, quefrency = 0, distortion = 1, normalization = False): | |
| input = np.atleast_1d(input) | |
| dtype = input.dtype | |
| shape = input.shape | |
| input = np.squeeze(input) | |
| if input.ndim != 1: raise ValueError('input.ndim != 1') | |
| if np.issubdtype(dtype, np.integer): | |
| a, b = np.iinfo(dtype).min, np.iinfo(dtype).max | |
| input = ((input.astype(float) - a) / (b - a)) * 2 - 1 | |
| elif not np.issubdtype(dtype, np.floating): raise TypeError('not np.issubdtype(dtype, np.floating)') | |
| def isnotnormal(x): | |
| return (np.isinf(x)) | (np.isnan(x)) | (abs(x) < np.finfo(x.dtype).tiny) | |
| framesize = self.framesize | |
| hopsize = self.hopsize | |
| samplerate = self.samplerate | |
| factors = np.asarray(factors).flatten() | |
| quefrency = int(quefrency * samplerate) | |
| frames = encode(stft(input, framesize, hopsize), framesize, hopsize, samplerate) | |
| if normalization: frames0 = frames.copy() | |
| if quefrency: | |
| envelopes = lifter(frames, quefrency) | |
| mask = isnotnormal(envelopes) | |
| frames.real /= envelopes | |
| frames.real[mask] = 0 | |
| if distortion != 1: | |
| envelopes[mask] = 0 | |
| for i in range(len(envelopes)): | |
| envelopes[i] = resample(envelopes[i], distortion) | |
| mask = isnotnormal(envelopes) | |
| frames = shiftpitch(frames, factors, samplerate) | |
| frames.real *= envelopes | |
| frames.real[mask] = 0 | |
| else: frames = shiftpitch(frames, factors, samplerate) | |
| if normalization: frames = normalize(frames, frames0) | |
| output = istft(decode(frames, framesize, hopsize, samplerate), framesize, hopsize) | |
| output.resize(shape, refcheck=False) | |
| if np.issubdtype(dtype, np.integer): | |
| a, b = np.iinfo(dtype).min, np.iinfo(dtype).max | |
| output = (((output + 1) / 2) * (b - a) + a).clip(a, b).astype(dtype) | |
| elif output.dtype != dtype: output = output.astype(dtype) | |
| assert output.dtype == dtype | |
| assert output.shape == shape | |
| return output |