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Commit 6343379c authored by Shreyan Chowdhury's avatar Shreyan Chowdhury
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remove griffin_lim file which was added by mistake

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griffin_lim.py deleted 100644 → 0
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import math
import sys
import time
import numpy as np
import wave
import scipy
import scipy.signal
from pylab import *
import array
import os
from os.path import expanduser
import scipy.io.wavfile
# Author: Brian K. Vogel
# brian.vogel@gmail.com
def hz_to_mel(f_hz):
"""Convert Hz to mel scale.
This uses the formula from O'Shaugnessy's book.
Args:
f_hz (float): The value in Hz.
Returns:
The value in mels.
"""
return 2595*np.log10(1.0 + f_hz/700.0)
def mel_to_hz(m_mel):
"""Convert mel scale to Hz.
This uses the formula from O'Shaugnessy's book.
Args:
m_mel (float): The value in mels
Returns:
The value in Hz
"""
return 700*(10**(m_mel/2595) - 1.0)
def fft_bin_to_hz(n_bin, sample_rate_hz, fft_size):
"""Convert FFT bin index to frequency in Hz.
Args:
n_bin (int or float): The FFT bin index.
sample_rate_hz (int or float): The sample rate in Hz.
fft_size (int or float): The FFT size.
Returns:
The value in Hz.
"""
n_bin = float(n_bin)
sample_rate_hz = float(sample_rate_hz)
fft_size = float(fft_size)
return n_bin*sample_rate_hz/(2.0*fft_size)
def hz_to_fft_bin(f_hz, sample_rate_hz, fft_size):
"""Convert frequency in Hz to FFT bin index.
Args:
f_hz (int or float): The frequency in Hz.
sample_rate_hz (int or float): The sample rate in Hz.
fft_size (int or float): The FFT size.
Returns:
The FFT bin index as an int.
"""
f_hz = float(f_hz)
sample_rate_hz = float(sample_rate_hz)
fft_size = float(fft_size)
fft_bin = int(np.round((f_hz*2.0*fft_size/sample_rate_hz)))
if fft_bin >= fft_size:
fft_bin = fft_size-1
return fft_bin
def make_mel_filterbank(min_freq_hz, max_freq_hz, mel_bin_count,
linear_bin_count, sample_rate_hz):
"""Create a mel filterbank matrix.
Create and return a mel filterbank matrix `filterbank` of shape (`mel_bin_count`,
`linear_bin_couont`). The `filterbank` matrix can be used to transform a
(linear scale) spectrum or spectrogram into a mel scale spectrum or
spectrogram as follows:
`mel_scale_spectrum` = `filterbank`*'linear_scale_spectrum'
where linear_scale_spectrum' is a shape (`linear_bin_count`, `m`) and
`mel_scale_spectrum` is shape ('mel_bin_count', `m`) where `m` is the number
of spectral time slices.
Likewise, the reverse-direction transform can be performed as:
'linear_scale_spectrum' = filterbank.T`*`mel_scale_spectrum`
Note that the process of converting to mel scale and then back to linear
scale is lossy.
This function computes the mel-spaced filters such that each filter is triangular
(in linear frequency) with response 1 at the center frequency and decreases linearly
to 0 upon reaching an adjacent filter's center frequency. Note that any two adjacent
filters will overlap having a response of 0.5 at the mean frequency of their
respective center frequencies.
Args:
min_freq_hz (float): The frequency in Hz corresponding to the lowest
mel scale bin.
max_freq_hz (flloat): The frequency in Hz corresponding to the highest
mel scale bin.
mel_bin_count (int): The number of mel scale bins.
linear_bin_count (int): The number of linear scale (fft) bins.
sample_rate_hz (float): The sample rate in Hz.
Returns:
The mel filterbank matrix as an 2-dim Numpy array.
"""
min_mels = hz_to_mel(min_freq_hz)
max_mels = hz_to_mel(max_freq_hz)
# Create mel_bin_count linearly spaced values between these extreme mel values.
mel_lin_spaced = np.linspace(min_mels, max_mels, num=mel_bin_count)
# Map each of these mel values back into linear frequency (Hz).
center_frequencies_hz = np.array([mel_to_hz(n) for n in mel_lin_spaced])
mels_per_bin = float(max_mels - min_mels)/float(mel_bin_count - 1)
mels_start = min_mels - mels_per_bin
hz_start = mel_to_hz(mels_start)
fft_bin_start = hz_to_fft_bin(hz_start, sample_rate_hz, linear_bin_count)
#print('fft_bin_start: ', fft_bin_start)
mels_end = max_mels + mels_per_bin
hz_stop = mel_to_hz(mels_end)
fft_bin_stop = hz_to_fft_bin(hz_stop, sample_rate_hz, linear_bin_count)
#print('fft_bin_stop: ', fft_bin_stop)
# Map each center frequency to the closest fft bin index.
linear_bin_indices = np.array([hz_to_fft_bin(f_hz, sample_rate_hz, linear_bin_count) for f_hz in center_frequencies_hz])
# Create filterbank matrix.
filterbank = np.zeros((mel_bin_count, linear_bin_count))
for mel_bin in range(mel_bin_count):
center_freq_linear_bin = linear_bin_indices[mel_bin]
# Create a triangular filter having the current center freq.
# The filter will start with 0 response at left_bin (if it exists)
# and ramp up to 1.0 at center_freq_linear_bin, and then ramp
# back down to 0 response at right_bin (if it exists).
# Create the left side of the triangular filter that ramps up
# from 0 to a response of 1 at the center frequency.
if center_freq_linear_bin > 1:
# It is possible to create the left triangular filter.
if mel_bin == 0:
# Since this is the first center frequency, the left side
# must start ramping up from linear bin 0 or 1 mel bin before the center freq.
left_bin = max(0, fft_bin_start)
else:
# Start ramping up from the previous center frequency bin.
left_bin = linear_bin_indices[mel_bin - 1]
for f_bin in range(left_bin, center_freq_linear_bin+1):
if (center_freq_linear_bin - left_bin) > 0:
response = float(f_bin - left_bin)/float(center_freq_linear_bin - left_bin)
filterbank[mel_bin, f_bin] = response
# Create the right side of the triangular filter that ramps down
# from 1 to 0.
if center_freq_linear_bin < linear_bin_count-2:
# It is possible to create the right triangular filter.
if mel_bin == mel_bin_count - 1:
# Since this is the last mel bin, we must ramp down to response of 0
# at the last linear freq bin.
right_bin = min(linear_bin_count - 1, fft_bin_stop)
else:
right_bin = linear_bin_indices[mel_bin + 1]
for f_bin in range(center_freq_linear_bin, right_bin+1):
if (right_bin - center_freq_linear_bin) > 0:
response = float(right_bin - f_bin)/float(right_bin - center_freq_linear_bin)
filterbank[mel_bin, f_bin] = response
filterbank[mel_bin, center_freq_linear_bin] = 1.0
return filterbank
def stft_for_reconstruction(x, fft_size, hopsamp):
"""Compute and return the STFT of the supplied time domain signal x.
Args:
x (1-dim Numpy array): A time domain signal.
fft_size (int): FFT size. Should be a power of 2, otherwise DFT will be used.
hopsamp (int):
Returns:
The STFT. The rows are the time slices and columns are the frequency bins.
"""
window = np.hanning(fft_size)
fft_size = int(fft_size)
hopsamp = int(hopsamp)
return np.array([np.fft.rfft(window*x[i:i+fft_size])
for i in range(0, len(x)-fft_size, hopsamp)])
def istft_for_reconstruction(X, fft_size, hopsamp):
"""Invert a STFT into a time domain signal.
Args:
X (2-dim Numpy array): Input spectrogram. The rows are the time slices and columns are the frequency bins.
fft_size (int):
hopsamp (int): The hop size, in samples.
Returns:
The inverse STFT.
"""
fft_size = int(fft_size)
hopsamp = int(hopsamp)
window = np.hanning(fft_size)
time_slices = X.shape[0]
len_samples = int(time_slices*hopsamp + fft_size)
x = np.zeros(len_samples)
for n,i in enumerate(range(0, len(x)-fft_size, hopsamp)):
x[i:i+fft_size] += window*np.real(np.fft.irfft(X[n]))
return x
def get_signal(in_file, expected_fs=44100):
"""Load a wav file.
If the file contains more than one channel, return a mono file by taking
the mean of all channels.
If the sample rate differs from the expected sample rate (default is 44100 Hz),
raise an exception.
Args:
in_file: The input wav file, which should have a sample rate of `expected_fs`.
expected_fs (int): The expected sample rate of the input wav file.
Returns:
The audio siganl as a 1-dim Numpy array. The values will be in the range [-1.0, 1.0]. fixme ( not yet)
"""
fs, y = scipy.io.wavfile.read(in_file)
num_type = y[0].dtype
if num_type == 'int16':
y = y*(1.0/32768)
elif num_type == 'int32':
y = y*(1.0/2147483648)
elif num_type == 'float32':
# Nothing to do
pass
elif num_type == 'uint8':
raise Exception('8-bit PCM is not supported.')
else:
raise Exception('Unknown format.')
if fs != expected_fs:
raise Exception('Invalid sample rate.')
if y.ndim == 1:
return y
else:
return y.mean(axis=1)
def reconstruct_signal_griffin_lim(magnitude_spectrogram, fft_size, hopsamp, iterations):
"""Reconstruct an audio signal from a magnitude spectrogram.
Given a magnitude spectrogram as input, reconstruct
the audio signal and return it using the Griffin-Lim algorithm from the paper:
"Signal estimation from modified short-time fourier transform" by Griffin and Lim,
in IEEE transactions on Acoustics, Speech, and Signal Processing. Vol ASSP-32, No. 2, April 1984.
Args:
magnitude_spectrogram (2-dim Numpy array): The magnitude spectrogram. The rows correspond to the time slices
and the columns correspond to frequency bins.
fft_size (int): The FFT size, which should be a power of 2.
hopsamp (int): The hope size in samples.
iterations (int): Number of iterations for the Griffin-Lim algorithm. Typically a few hundred
is sufficient.
Returns:
The reconstructed time domain signal as a 1-dim Numpy array.
"""
time_slices = magnitude_spectrogram.shape[0]
len_samples = int(time_slices*hopsamp + fft_size)
# Initialize the reconstructed signal to noise.
x_reconstruct = np.random.randn(len_samples)
n = iterations # number of iterations of Griffin-Lim algorithm.
while n > 0:
n -= 1
reconstruction_spectrogram = stft_for_reconstruction(x_reconstruct, fft_size, hopsamp)
reconstruction_angle = np.angle(reconstruction_spectrogram)
# Discard magnitude part of the reconstruction and use the supplied magnitude spectrogram instead.
proposal_spectrogram = magnitude_spectrogram*np.exp(1.0j*reconstruction_angle)
prev_x = x_reconstruct
x_reconstruct = istft_for_reconstruction(proposal_spectrogram, fft_size, hopsamp)
diff = sqrt(sum((x_reconstruct - prev_x)**2)/x_reconstruct.size)
print('Reconstruction iteration: {}/{} RMSE: {} '.format(iterations - n, iterations, diff))
return x_reconstruct
def save_audio_to_file(x, sample_rate, outfile='out.wav'):
"""Save a mono signal to a file.
Args:
x (1-dim Numpy array): The audio signal to save. The signal values should be in the range [-1.0, 1.0].
sample_rate (int): The sample rate of the signal, in Hz.
outfile: Name of the file to save.
"""
x_max = np.max(abs(x))
assert x_max <= 1.0, 'Input audio value is out of range. Should be in the range [-1.0, 1.0].'
x = x*32767.0
data = array.array('h')
for i in range(len(x)):
cur_samp = int(round(x[i]))
data.append(cur_samp)
f = wave.open(outfile, 'w')
f.setparams((1, 2, sample_rate, 0, "NONE", "Uncompressed"))
f.writeframes(data.tostring())
f.close()
\ No newline at end of file
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