Spaces:
Running
Running
| import numpy as np | |
| from scipy.interpolate import interp1d | |
| def normalize(audio, min_y=-1.0, max_y=1.0, eps=1e-8): | |
| assert len(audio.shape) == 1 | |
| max_y -= eps | |
| min_y += eps | |
| amax = audio.max() | |
| amin = audio.min() | |
| audio = (max_y - min_y) * (audio - amin) / (amax - amin) + min_y | |
| return audio | |
| def get_stereo(pop_y, midi_y, pop_scale=0.99): | |
| if len(pop_y) > len(midi_y): | |
| midi_y = np.pad(midi_y, (0, len(pop_y) - len(midi_y))) | |
| elif len(pop_y) < len(midi_y): | |
| pop_y = np.pad(pop_y, (0, -len(pop_y) + len(midi_y))) | |
| stereo = np.stack((midi_y, pop_y * pop_scale)) | |
| return stereo | |
| def generate_variable_f0_sine_wave(f0, len_y, sr): | |
| """ | |
| integrate instant frequencies to get pure tone sine wave | |
| """ | |
| x_sample = np.arange(len(f0)) | |
| intp = interp1d(x_sample, f0, kind="linear") | |
| f0_audiorate = intp(np.linspace(0, len(f0) - 1, len_y)) | |
| pitch_wave = np.sin((np.nan_to_num(f0_audiorate) / sr * 2 * np.pi).cumsum()) | |
| return pitch_wave | |
| def fluidsynth_without_normalize(self, fs=44100, sf2_path=None): | |
| """Synthesize using fluidsynth. without signal normalize | |
| Parameters | |
| ---------- | |
| fs : int | |
| Sampling rate to synthesize at. | |
| sf2_path : str | |
| Path to a .sf2 file. | |
| Default ``None``, which uses the TimGM6mb.sf2 file included with | |
| ``pretty_midi``. | |
| Returns | |
| ------- | |
| synthesized : np.ndarray | |
| Waveform of the MIDI data, synthesized at ``fs``. | |
| """ | |
| # If there are no instruments, or all instruments have no notes, return | |
| # an empty array | |
| if len(self.instruments) == 0 or all(len(i.notes) == 0 for i in self.instruments): | |
| return np.array([]) | |
| # Get synthesized waveform for each instrument | |
| waveforms = [i.fluidsynth(fs=fs, sf2_path=sf2_path) for i in self.instruments] | |
| # Allocate output waveform, with #sample = max length of all waveforms | |
| synthesized = np.zeros(np.max([w.shape[0] for w in waveforms])) | |
| # Sum all waveforms in | |
| for waveform in waveforms: | |
| synthesized[: waveform.shape[0]] += waveform | |
| # Normalize | |
| # synthesized /= np.abs(synthesized).max() | |
| return synthesized | |