def get_stft_modified(fname,
hopsamp=hopsamp,
rate_hz=rate_hz,
fft_size=fft_size,
enableMel=False,
enableFilter=False):
input_signal = audio_utilities.get_signal(fname, expected_fs=rate_hz)
stft_full = audio_utilities.stft_for_reconstruction(
input_signal, fft_size, hopsamp)
stft_mag = abs(stft_full)**2.0
scale = 1.0 / np.amax(stft_mag)
stft_mag *= scale
stft_modified = stft_mag
print('[*]stft_modified : ', stft_modified.shape)
'''
imshow(stft_mag.T**0.125, origin='lower', cmap=cm.hot, aspect='auto',
interpolation='nearest')
colorbar()
title('Unmodified spectrogram')
xlabel('time index')
ylabel('frequency bin index')
savefig(fname+'unmodified_spectrogram.png', dpi=150)
'''
if enableMel:
min_freq_hz = 30
max_freq_hz = 1000
mel_bin_count = 200
linear_bin_count = 1 + fft_size // 2
filterbank = audio_utilities.make_mel_filterbank(
min_freq_hz, max_freq_hz, mel_bin_count, linear_bin_count, rate_hz)
mel_spectrogram = np.dot(filterbank, stft_mag.T)
inverted_mel_to_linear_freq_spectrogram = np.dot(
filterbank.T, mel_spectrogram)
stft_modified = inverted_mel_to_linear_freq_spectrogram.T
if enableFilter:
cutoff_freq = 1000
cutoff_bin = round(cutoff_freq * fft_size / rate_hz)
stft_modified[:, cutoff_bin:] = 0
return stft_modified, scale
def run_demo():
"""Test Griffin & Lim method for reconstructing audio from a magnitude spectrogram.
Example of using the Griffin-Lim algorithm. The input file is loaded, the
spectrogram is computed (note that we discard the phase information). Then,
using only the (magnitude) spectrogram, the Griffin-Lim algorithm is run
to reconstruct an audio signal from the spectrogram. The reconstructed audio
is finally saved to a file.
A plot of the spectrogram is also displayed.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--in_file',
type=str,
default="bkvhi.wav",
help='Input WAV file')
parser.add_argument('--sample_rate_hz',
default=44100,
type=int,
help='Sample rate in Hz')
parser.add_argument('--fft_size', default=2048, type=int, help='FFT siz')
parser.add_argument('--iterations',
default=300,
type=int,
help='Number of iterations to run')
parser.add_argument('--enable_filter',
action='store_true',
help='Apply a low-pass filter')
parser.add_argument('--enable_mel_scale',
action='store_true',
help='Convert to mel scale and back')
parser.add_argument(
'--cutoff_freq',
type=int,
default=1000,
help='If filter is enable, the low-pass cutoff frequency in Hz')
args = parser.parse_args()
in_file = args.in_file
# Load an audio file. It must be WAV format. Multi-channel files will be
# converted to mono.
input_signal = audio_utilities.get_signal(in_file,
expected_fs=args.sample_rate_hz)
# Hopsamp is the number of samples that the analysis window is shifted after
# computing the FFT. For example, if the sample rate is 44100 Hz and hopsamp is
# 256, then there will be approximately 44100/256 = 172 FFTs computed per second
# and thus 172 spectral slices (i.e., columns) per second in the spectrogram.
hopsamp = args.fft_size // 8
# Compute the Short-Time Fourier Transform (STFT) from the audio file. This is a 2-dim Numpy array with
# time_slices rows and frequency_bins columns. Thus, you will need to take the
# transpose of this matrix to get the usual STFT which has frequency bins as rows
# and time slices as columns.
stft_full = audio_utilities.stft_for_reconstruction(
input_signal, args.fft_size, hopsamp)
# Note that the STFT is complex-valued. Therefore, to get the (magnitude)
# spectrogram, we need to take the absolute value.
print(stft_full.shape)
stft_mag = abs(stft_full)**2.0
# Note that `stft_mag` only contains the magnitudes and so we have lost the
# phase information.
scale = 1.0 / np.amax(stft_mag)
print('Maximum value in the magnitude spectrogram: ', 1 / scale)
# Rescale to put all values in the range [0, 1].
stft_mag *= scale
print(stft_mag.shape)
# We now have a (magnitude only) spectrogram, `stft_mag` that is normalized to be within [0, 1.0].
# In a practical use case, we would probably want to perform some processing on `stft_mag` here
# which would produce a modified version that we would want to reconstruct audio from.
figure(1)
imshow(stft_mag.T**0.125,
origin='lower',
cmap=cm.hot,
aspect='auto',
interpolation='nearest')
colorbar()
title('Unmodified spectrogram')
xlabel('time index')
ylabel('frequency bin index')
savefig('unmodified_spectrogram.png', dpi=150)
# If the mel scale option is selected, apply a perceptual frequency scale.
if args.enable_mel_scale:
min_freq_hz = 70
max_freq_hz = 8000
mel_bin_count = 200
linear_bin_count = 1 + args.fft_size // 2
filterbank = audio_utilities.make_mel_filterbank(
min_freq_hz, max_freq_hz, mel_bin_count, linear_bin_count,
args.sample_rate_hz)
figure(2)
imshow(filterbank,
origin='lower',
cmap=cm.hot,
aspect='auto',
interpolation='nearest')
colorbar()
title('Mel scale filter bank')
xlabel('linear frequency index')
ylabel('mel frequency index')
savefig('mel_scale_filterbank.png', dpi=150)
mel_spectrogram = np.dot(filterbank, stft_mag.T)
clf()
figure(3)
imshow(mel_spectrogram**0.125,
origin='lower',
cmap=cm.hot,
aspect='auto',
interpolation='nearest')
colorbar()
title('Mel scale spectrogram')
xlabel('time index')
ylabel('mel frequency bin index')
savefig('mel_scale_spectrogram.png', dpi=150)
inverted_mel_to_linear_freq_spectrogram = np.dot(
filterbank.T, mel_spectrogram)
clf()
figure(4)
imshow(inverted_mel_to_linear_freq_spectrogram**0.125,
origin='lower',
cmap=cm.hot,
aspect='auto',
interpolation='nearest')
colorbar()
title('Linear scale spectrogram obtained from mel scale spectrogram')
xlabel('time index')
ylabel('frequency bin index')
savefig('inverted_mel_to_linear_freq_spectrogram.png', dpi=150)
stft_modified = inverted_mel_to_linear_freq_spectrogram.T
else:
stft_modified = stft_mag
savefig('stft_modified.png', dpi=150)
###### Optional: modify the spectrogram
# For example, we can implement a low-pass filter by simply setting all frequency bins above
# some threshold frequency (args.cutoff_freq) to 0 as follows.
if args.enable_filter:
# Calculate corresponding bin index.
cutoff_bin = round(args.cutoff_freq * args.fft_size /
args.sample_rate_hz)
stft_modified[:, cutoff_bin:] = 0
###########
# Undo the rescaling.
stft_modified_scaled = stft_modified / scale
stft_modified_scaled = stft_modified_scaled**0.5
# Use the Griffin&Lim algorithm to reconstruct an audio signal from the
# magnitude spectrogram.
x_reconstruct = audio_utilities.reconstruct_signal_griffin_lim(
stft_modified_scaled, args.fft_size, hopsamp, args.iterations)
# The output signal must be in the range [-1, 1], otherwise we need to clip or normalize.
max_sample = np.max(abs(x_reconstruct))
if max_sample > 1.0:
x_reconstruct = x_reconstruct / max_sample
# Save the reconstructed signal to a WAV file.
audio_utilities.save_audio_to_file(x_reconstruct, args.sample_rate_hz)
# Save the spectrogram image also.
clf()
figure(5)
imshow(stft_modified.T**0.125,
origin='lower',
cmap=cm.hot,
aspect='auto',
interpolation='nearest')
colorbar()
title('Spectrogram used to reconstruct audio')
xlabel('time index')
ylabel('frequency bin index')
savefig('reconstruction_spectrogram.png', dpi=150)
data = {}
print("Loading audio...")
fft_size = 2048
sample_rate = 8000
max_len = 80
mel = False
for root, dirs, files in os.walk("./audio"):
if len(files) == 0:
continue
group = os.path.split(root)[-1]
if group == "audio":
continue
data[group] = []
for f in sorted(files):
if f.endswith(".wav"):
input_signal = audio_utilities.get_signal(os.path.join(root, f),
expected_fs=sample_rate)
# Hopsamp is the number of samples that the analysis window is shifted after
# computing the FFT. For example, if the sample rate is 44100 Hz and hopsamp is
# 256, then there will be approximately 44100/256 = 172 FFTs computed per second
# and thus 172 spectral slices (i.e., columns) per second in the spectrogram.
hopsamp = fft_size // 8
# Compute the Short-Time Fourier Transform (STFT) from the audio file. This is a 2-dim Numpy array with
# time_slices rows and frequency_bins columns. Thus, you will need to take the
# transpose of this matrix to get the usual STFT which has frequency bins as rows
# and time slices as columns.
stft_full = audio_utilities.stft_for_reconstruction(
input_signal, fft_size, hopsamp)
# If maximum length exceeds mfcc lengths then pad the remaining ones
if (max_len > stft_full.shape[0]):
'--pad_length',
type=int,
help='if defined pad up to this length (in frame) or if longer, fail')
args = parser.parse_args()
in_file = args.in_file
out_file_prefix = args.out_file_prefix
if out_file_prefix is None or out_file_prefix.endswith(".wav"):
out_file_prefix = in_file[:-4]
if args.out_file_dir is not None:
out_file_prefix = os.path.join(args.out_file_dir, out_file_prefix)
# Load an audio file. It must be WAV format. Multi-channel files will be
# converted to mono.
input_signal = audio_utilities.get_signal(in_file,
expected_fs=args.sample_rate_hz)
print("processing %s - signal %fs long" %
(in_file, len(input_signal) * 1.0 / args.sample_rate_hz))
if args.pad_length:
if len(input_signal) > args.pad_length:
print("signal too long... skipping")
sys.exit(0)
else:
max = np.amax(input_signal)
whitenoise = np.random.normal(0,
max / 100,
size=args.pad_length - len(input_signal))
input_signal = np.concatenate((input_signal, whitenoise))
# Hopsamp is the number of samples that the analysis window is shifted after
