def get_wav_from_stft(stft_modified,
rate_hz,
modified_scale,
fft_size,
hopsamp,
iterations=2000,
outfile=None):
stft_modified_scaled = stft_modified / modified_scale
stft_modified_scaled = stft_modified_scaled**0.5
x_reconstruct = audio_utilities.reconstruct_signal_griffin_lim\
(stft_modified_scaled,
fft_size=fft_size,
hopsamp=hopsamp,
iterations=iterations)
max_sample = np.max(abs(x_reconstruct))
if max_sample > 1.0:
x_reconstruct = x_reconstruct / max_sample
audio_utilities.save_audio_to_file(x_reconstruct, rate_hz, outfile=outfile)
return stft_modified_scaled
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)
def run_recon():
"""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 = Image.open(args.in_file)
#print(in_file.shape)
in_file = in_file.resize((1025, 640), Image.ANTIALIAS)
ext = ".png"
in_file.save("rescaledimage" + ext)
in_file = plt.imread("rescaledimage.png")
print(in_file.shape)
in_file = rgb2gray(in_file)
hopsamp = args.fft_size // 8
print(in_file.shape)
# Use the Griffin&Lim algorithm to reconstruct an audio signal from the
# magnitude spectrogram.
x_reconstruct = audio_utilities.reconstruct_signal_griffin_lim(
in_file, 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)
for wav_path in wavs
]
melSpectrum = np.load('mel-batch_0_sentence_0.npy')
imshow(melSpectrum.T**0.125,
origin='lower',
cmap=cm.hot,
aspect='auto',
interpolation='nearest')
stft_modified = melSpectrum.T
filterbank = audio_utilities.make_mel_filterbank(70, 8000, 80, 123, 44100)
inverted_mel_to_linear_freq_spectrogram = np.dot(filterbank.T,
stft_modified)
spectrogram = inverted_mel_to_linear_freq_spectrogram
print("Linear spectrograms dim: ")
print(spectrogram[0].shape)
spectrogram = spectrogram.astype(np.float32)
spectrogram = spectrogram
# --------------------------------- librosa Version ---------------------------------
# convert back
x_reconstruct = audio_utilities.reconstruct_signal_griffin_lim(
spectrogram, 244, 123, 300)
max_sample = np.max(abs(x_reconstruct))
if max_sample > 1.0:
x_reconstruct = x_reconstruct / max_sample
audio_utilities.save_audio_to_file(x_reconstruct, 44100)
print("Done!")
if not os.path.exists(input_dir):
print("Input directory does not exist")
sys.exit()
if input_dir[-1] != "/":
input_dir = input_dir + '/'
if not os.path.exists(output_dir):
print("Output directory created")
os.makedirs(output_dir)
if output_dir[-1] != "/":
output_dir = output_dir + '/'
for root, subdirs, files in os.walk(input_dir):
for name in tqdm(files):
if name.endswith(".npy"):
iname = "%s/%s" % (root, name)
root_out = root.replace(input_dir, output_dir)
if not os.path.exists(root_out):
os.makedirs(root_out)
oname = "%s/%s-griffinlim-reconstructed.wav" % (root_out,
name[:-4])
S = np.load(iname)
y_inv = lr.griffinlim(S)
audio_utilities.save_audio_to_file(y_inv, sr, outfile=oname)
print("Done!\n")
stft_modified=melSpectrum.T
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
stft_modified_scaled = stft_modified
stft_modified_scaled = stft_modified_scaled**0.5
# Use the Griffin&Lim algorithm to reconstruct an audio signal from the
# magnitude spectrogram.
hopsamp = args.fft_size // 8
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)
if np.ndim(spectrogram) == 2:
# gray
mel_spectrogram = (1 - spectrogram / 65535.)
else:
# color
mel_spectrogram = rgb2gray(spectrogram / 255.)
mel_spectrogram = np.flip(mel_spectrogram, 0)
mel_spectrogram /= scale_mel
mel_spectrogram = np.exp(mel_spectrogram / 20. * np.log(10)) * 10e-6
inverted_mel_to_linear_freq_spectrogram = np.dot(filterbank.T, mel_spectrogram)
stft_modified = inverted_mel_to_linear_freq_spectrogram.T
stft_modified = stft_modified**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, 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,
sample_rate_hz,
outfile=args.out_file)
