tools.py

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib
import librosa
from scipy.io.wavfile import write

k = 1e-16


def np_log10(x):
    numerator = np.log(x + 1e-16)
    denominator = np.log(10)
    return numerator / denominator


def sigmoid(x):
    s = 1 / (1 + np.exp(-x))
    return s


def inv_sigmoid(s):
    x = np.log((s / (1 - s)) + 1e-16)
    return x


def spc_to_VAE_input(spc):
    """Restrict value range from 0 to 1."""
    return spc / (1 + spc)


def VAE_out_put_to_spc(o):
    """Inverse transform of function 'spc_to_VAE_input'."""
    return o / (1 - o + k)


def denoise(spc):
    """Filter back ground noise. (Not used.)"""
    return np.maximum(0, spc - (2e-5))


hop_length = 256
win_length = 1024


def np_power_to_db(S, amin=1e-16, top_db=80.0):
    """Helper method for scaling."""
    ref = np.max(S)

    # set fixed value for ref

    # 每个元素取max
    log_spec = 10.0 * np_log10(np.maximum(amin, S))
    log_spec -= 10.0 * np_log10(np.maximum(amin, ref))

    log_spec = np.maximum(log_spec, np.max(log_spec) - top_db)

    return log_spec


def show_spc(spc, resolution=(512, 256)):
    """Show a spectrogram."""
    spc = np.reshape(spc, resolution)
    magnitude_spectrum = np.abs(spc)
    log_spectrum = np_power_to_db(magnitude_spectrum)
    plt.imshow(np.flipud(log_spectrum))
    plt.show()


def save_results(spectrogram, spectrogram_image_path, waveform_path):
    """Save the input 'spectrogram' and its waveform (reconstructed bu Griffin Lim)
     to path provided by 'spectrogram_image_path' and 'waveform_path'."""
    # save image
    magnitude_spectrum = np.abs(spectrogram)
    log_spc = np_power_to_db(magnitude_spectrum)
    log_spc = np.reshape(log_spc, (512, 256))
    matplotlib.pyplot.imsave(spectrogram_image_path, log_spc, vmin=-100, vmax=0,
                             origin='lower')

    # save waveform
    abs_spec = np.zeros((513, 256))
    abs_spec[:512, :] = abs_spec[:512, :] + np.sqrt(np.reshape(spectrogram, (512, 256)))
    rec_signal = librosa.griffinlim(abs_spec, n_iter=32, hop_length=256, win_length=1024)
    write(waveform_path, 16000, rec_signal)