def test_clean_speech_IBM():
"""
check that output of 'complex64' spectrogram is 'float32' mask
"""
## STFT parameters
wlen_sec = 80e-3 # window length in seconds
hop_percent = 0.25 # hop size as a percentage of the window length
win = 'hann' # type of window
dtype = 'complex64'
# Librosa examples
# # AVAILABLE EXAMPLES
# # --------------------------------------------------------------------
# # brahms Brahms - Hungarian Dance #5
# # choice Admiral Bob - Choice (drum+bass)
# # fishin Karissa Hobbs - Let's Go Fishin'
# # nutcracker Tchaikovsky - Dance of the Sugar Plum Fairy
# # trumpet Mihai Sorohan - Trumpet loop
# # vibeace Kevin MacLeod - Vibe Ace
# Take example signal from Librosa
audio_path = example('brahms')
x, fs_x = sf.read(audio_path)
x_len = len(x)
## Ideal binary mask
quantile_fraction = 0.98
quantile_weight = 0.999
# STFT
x_tf = stft(x,
fs=fs_x,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
# binary mask
x_ibm = clean_speech_IBM(x_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
assert x_ibm.dtype == 'float32'
assert np.unique(x_ibm).tolist() == [0., 1.]
#TODO: take masks from Heymann GitHub
def main():
if not os.path.exists(os.path.dirname(output_dataset_file)):
os.makedirs(os.path.dirname(output_dataset_file))
with h5.File(output_dataset_file,
'a',
rdcc_nbytes=rdcc_nbytes,
rdcc_nslots=rdcc_nslots) as f:
# STFT attributes
f.attrs['fs'] = fs
f.attrs['wlen_sec'] = wlen_sec
f.attrs['hop_percent'] = hop_percent
f.attrs['win'] = win
f.attrs['dtype'] = dtype
# label attributes
f.attrs['quantile_fraction'] = quantile_fraction
f.attrs['quantile_weight'] = quantile_weight
# HDF5 attributes
f.attrs['X_chunks'] = X_chunks
f.attrs['Y_chunks'] = Y_chunks
f.attrs['compression'] = compression
for dataset_type in dataset_types:
# Create file list
file_paths = speech_list(input_speech_dir=input_speech_dir,
dataset_type=dataset_type)
if dataset_type == 'train':
noise_types = [
'domestic', 'nature', 'office', 'transportation'
]
if dataset_type == 'validation':
noise_types = ['nature', 'office', 'public', 'transportation']
# Create SNR list
np.random.seed(0)
noise_index = np.random.randint(len(noise_types),
size=len(file_paths))
snrs = [-5, -2.5, 0, 2.5, 5.0]
snrs_index = np.random.randint(len(snrs), size=len(file_paths))
# Create noise_audios from processed noise files
preprocessed_noise_paths = noise_list_preprocessed(
preprocessed_noise_dir=output_noise_dir,
dataset_type=dataset_type)
noise_audios = {}
# Load the noise files
for noise_type, preprocessed_noise_path in preprocessed_noise_paths.items(
):
#if noise already preprocessed, read files directly
if os.path.exists(preprocessed_noise_path):
noise_audio, fs_noise = sf.read(preprocessed_noise_path)
if fs != fs_noise:
raise ValueError(
'Unexpected sampling rate. Did you preprocess the 16kHz version of the DEMAND database?'
)
noise_audios[noise_type] = noise_audio
# Init list of SNR
all_snr_dB = []
# Delete datasets if already exists
if 'X_' + dataset_type in f:
del f['X_' + dataset_type]
del f['Y_' + dataset_type]
# Exact shape of dataset is unknown in advance unfortunately
# Faster writing if you know the shape in advance
# Size of chunks corresponds to one spectrogram frame
f.create_dataset('X_' + dataset_type,
shape=X_shape,
dtype='float32',
maxshape=X_maxshape,
chunks=X_chunks,
compression=compression,
shuffle=shuffle)
f.create_dataset('Y_' + dataset_type,
shape=Y_shape,
dtype='float32',
maxshape=Y_maxshape,
chunks=Y_chunks,
compression=compression,
shuffle=shuffle)
# Store dataset in variables for faster I/O
fx = f['X_' + dataset_type]
fy = f['Y_' + dataset_type]
# Compute mean, std of the train set
if dataset_type == 'train':
# VAR = E[X**2] - E[X]**2
channels_sum, channels_squared_sum = 0., 0.
# Loop over the speech files
for i, file_path in tqdm(enumerate(file_paths)):
speech, fs_speech = sf.read(input_speech_dir + file_path,
samplerate=None)
# Cut burst at begining of file
speech = speech[int(0.1 * fs):]
# Normalize audio
speech = speech / (np.max(np.abs(speech)))
if fs != fs_speech:
raise ValueError('Unexpected sampling rate')
# Select noise_type
noise_type = noise_types[noise_index[i]]
# Extract noise segment
noise = noise_segment(noise_audios, noise_type, speech)
# Select SNR
snr_dB = snrs[snrs_index[i]]
all_snr_dB.append(snr_dB)
# Compute noise gain
speech_power = np.sum(np.power(speech, 2))
noise_power = np.sum(np.power(noise, 2))
noise_power_target = speech_power * np.power(10, -snr_dB / 10)
k = noise_power_target / noise_power
noise = noise * np.sqrt(k)
mixture = speech + noise
# # Normalize by max of speech, noise, speech+noise
# norm = np.max(abs(np.concatenate([speech, noise, speech+noise])))
# mixture = (speech+noise) / norm
# speech /= norm
# noise /= norm
if dataset_size == 'subset':
# Save .wav files, just to check if it working
output_path = output_wav_dir + file_path
output_path = os.path.splitext(output_path)[0]
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
sf.write(output_path + '_s.wav', speech, fs)
sf.write(output_path + '_n.wav', noise, fs)
sf.write(output_path + '_x.wav', mixture, fs)
# TF reprepsentation
mixture_tf = stft(mixture,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
noisy_spectrogram = np.power(abs(mixture_tf), 2)
# TF reprepsentation
speech_tf = stft(speech,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
if labels == 'noisy_wiener_labels':
# TF reprepsentation
noise_tf = stft(noise,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
# wiener mask
speech_wiener_mask = ideal_wiener_mask(
speech_tf, noise_tf, eps)
label = speech_wiener_mask
if labels == 'noisy_labels':
# binary mask
speech_ibm = clean_speech_IBM(
speech_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
label = speech_ibm
if labels == 'noisy_vad_labels':
# binary mask
speech_vad = clean_speech_VAD(
speech_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
label = speech_vad
# Compute mean, std
if dataset_type == 'train':
# VAR = E[X**2] - E[X]**2
channels_sum += np.sum(noisy_spectrogram, axis=-1)
channels_squared_sum += np.sum(noisy_spectrogram**2,
axis=-1)
# Store spectrogram in dataset
fx.resize((fx.shape[1] + noisy_spectrogram.shape[1]), axis=1)
fx[:, -noisy_spectrogram.shape[1]:] = noisy_spectrogram
# Store spectrogram in label
fy.resize((fy.shape[1] + label.shape[1]), axis=1)
fy[:, -label.shape[1]:] = label
# Compute and save mean, std
if dataset_type == 'train':
print('Compute mean and std')
#NB: compute the empirical std (!= regular std)
n_samples = fx.shape[1]
mean = channels_sum / n_samples
std = np.sqrt((1 / (n_samples - 1)) *
(channels_squared_sum - n_samples * mean**2))
# Delete datasets if already exists
if 'X_' + dataset_type + '_mean' in f:
del f['X_' + dataset_type + '_mean']
del f['X_' + dataset_type + '_std']
f.create_dataset('X_' + dataset_type + '_mean',
shape=X_chunks,
dtype='float32',
maxshape=X_chunks,
chunks=None,
compression=compression,
shuffle=shuffle)
f.create_dataset('X_' + dataset_type + '_std',
shape=X_chunks,
dtype='float32',
maxshape=X_chunks,
chunks=None,
compression=compression,
shuffle=shuffle)
f['X_' + dataset_type +
'_mean'][:] = mean[..., None] # Add axis to fit chunks shape
f['X_' + dataset_type +
'_std'][:] = std[..., None] # Add axis to fit chunks shape
print('Mean and std saved in HDF5.')
# TODO: save SNR, level_s, level_n in 1 big csv
write_dataset(all_snr_dB, output_wav_dir, dataset_type, 'snr_db')
def process_utt(mcem, model, classifier, mean, std, file_path, device):
# Input
x_t, fs_x = sf.read(processed_data_dir + os.path.splitext(file_path)[0] + '_x.wav') # mixture
T_orig = len(x_t)
x_tf = stft(x_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype) # (frames, freq_bins)
# Transpose to match PyTorch
x_tf = x_tf.T # (frames, freq_bins)
x = torch.tensor(np.power(np.abs(x_tf), 2), device=device)
# Target
s_t, fs_s = sf.read(processed_data_dir + os.path.splitext(file_path)[0] + '_s.wav') # clean speech
s_tf = stft(s_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype) # (freq_bins, frames)
if classif_type == 'dnn':
# Normalize power spectrogram
if std_norm:
x_norm = x - mean.T
x_norm /= (std + eps).T
y_hat_soft = classifier(x_norm)
else:
y_hat_soft = classifier(x)
y_hat_hard = (y_hat_soft > 0.5).float()
if classif_type == 'oracle':
y_hat_soft = clean_speech_IBM(s_tf, quantile_fraction=quantile_fraction, quantile_weight=quantile_weight)
y_hat_hard = torch.from_numpy(y_hat_soft.T).to(device)
if classif_type == 'timo':
x_numpy = np.power(np.abs(x_tf), 2)
y_hat_soft = timo_mask_estimation(x_numpy.T)
y_hat_hard = (y_hat_soft > 0.5).astype(int)
y_hat_hard = y_hat_hard.T # (frames, freq_bins)
y_hat_hard = torch.tensor(y_hat_hard).to(device)
# Init MCEM
mcem.init_parameters(X=x_tf,
y=y_hat_hard,
vae=model,
nmf_rank=nmf_rank,
eps=eps,
device=device)
cost = mcem.run()
S_hat = mcem.S_hat #+ np.finfo(np.float32).eps
N_hat = mcem.N_hat #+ np.finfo(np.float32).eps
s_hat = istft(S_hat, fs=fs, wlen_sec=wlen_sec, win=win, hop_percent=hop_percent, max_len=T_orig)
n_hat = istft(N_hat, fs=fs, wlen_sec=wlen_sec, win=win, hop_percent=hop_percent, max_len=T_orig)
# Save .wav files
output_path = output_data_dir + file_path
output_path = os.path.splitext(output_path)[0]
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
sf.write(output_path + '_s_est.wav', s_hat, fs)
sf.write(output_path + '_n_est.wav', n_hat, fs)
# Save binary mask
torch.save(y_hat_soft, output_path + ' _ibm_soft_est.pt')
torch.save(y_hat_hard, output_path + '_ibm_hard_est.pt')
def main():
# Load input SNR
all_snr_db = read_dataset(processed_data_dir, dataset_type, 'snr_db')
all_snr_db = np.array(all_snr_db)
device = torch.device("cuda" if cuda else "cpu")
file = open('output.log', 'w')
print('Torch version: {}'.format(torch.__version__))
print('Device: %s' % (device))
if torch.cuda.device_count() >= 1:
print("Number GPUs: ", torch.cuda.device_count())
# Create file list
file_paths = speech_list(input_speech_dir=input_speech_dir,
dataset_type=dataset_type)
classifier = Classifier([x_dim, h_dim_cl, y_dim], batch_norm=batch_norm)
classifier.load_state_dict(
torch.load(classif_dir, map_location=cuda_device))
if cuda: classifier = classifier.cuda()
classifier.eval()
for param in classifier.parameters():
param.requires_grad = False
# Create file list
file_paths = speech_list(input_speech_dir=input_speech_dir,
dataset_type=dataset_type)
for i, file_path in tqdm(enumerate(file_paths)):
# Read files
s_t, fs_s = sf.read(processed_data_dir +
os.path.splitext(file_path)[0] +
'_s.wav') # clean speech
x_t, fs_x = sf.read(processed_data_dir +
os.path.splitext(file_path)[0] +
'_x.wav') # mixture
# x = x/np.max(x)
T_orig = len(x_t)
# TF representation
# Input should be (frames, freq_bibs)
x_tf = stft(x_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
# Transpose to match PyTorch
x_tf = x_tf.T # (frames, freq_bins)
# Power spectrogram (transpose)
x = torch.tensor(np.power(np.abs(x_tf), 2)).to(device)
# Normalize power spectrogram
if std_norm:
x -= mean.T
x /= (std + eps).T
# Classify
y_hat_soft = classifier(x)
y_hat_hard = (y_hat_soft > 0.5).int()
y_hat_hard = y_hat_hard.cpu().numpy()
y_hat_hard = y_hat_hard.T # Transpose to match librosa.display
# plots of target / estimation
s_tf = stft(s_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype) # shape = (freq_bins, frames)
if labels == 'labels':
# binary mask
target = clean_speech_IBM(s_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
if labels == 'vad_labels':
# vad
target = clean_speech_VAD(s_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
# Transpose to match librosa.display
x_tf = x_tf.T
# F1-score
f1score_s_hat = f1_score(target.flatten(),
y_hat_hard.flatten(),
average="binary")
## mixture signal (wav + spectro)
## target signal (wav + spectro + mask)
## estimated signal (wav + spectro + mask)
signal_list = [
[x_t, x_tf, None], # mixture: (waveform, tf_signal, no mask)
[s_t, s_tf, target], # clean speech
[None, None, y_hat_hard]
#[None, None, y_hat_soft]
]
#TODO: modify
fig = display_multiple_signals(signal_list,
fs=fs,
vmin=vmin,
vmax=vmax,
wlen_sec=wlen_sec,
hop_percent=hop_percent,
xticks_sec=xticks_sec,
fontsize=fontsize)
# put all metrics in the title of the figure
title = "Input SNR = {:.1f} dB \n" \
"F1-score = {:.3f} \n".format(all_snr_db[i], f1score_s_hat)
fig.suptitle(title, fontsize=40)
# Save figure
output_path = classif_data_dir + file_path
output_path = os.path.splitext(output_path)[0]
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
fig.savefig(output_path + '_hard_mask.png')
def compute_metrics_utt(args):
# Separate args
file_path, snr_db = args[0], args[1]
# Read files
s_t, fs_s = sf.read(processed_data_dir + os.path.splitext(file_path)[0] + '_s.wav') # clean speech
n_t, fs_n = sf.read(processed_data_dir + os.path.splitext(file_path)[0] + '_n.wav') # noise
x_t, fs_x = sf.read(processed_data_dir + os.path.splitext(file_path)[0] + '_x.wav') # mixture
s_hat_t, fs_s_hat = sf.read(model_data_dir + os.path.splitext(file_path)[0] + '_s_est.wav') # est. speech
# compute metrics
## SI-SDR, SI-SAR, SI-SNR
si_sdr, si_sir, si_sar = energy_ratios(s_hat=s_hat_t, s=s_t, n=n_t)
## STOI (or ESTOI?)
stoi_s_hat = stoi(s_t, s_hat_t, fs, extended=True)
## PESQ
pesq_s_hat = pesq(fs, s_t, s_hat_t, 'wb') # wb = wideband
## POLQA
# polqa_s_hat = polqa(s, s_t, fs)
# all_polqa.append(polqa_s_hat)
## F1 score
# ideal binary mask
y_hat_hard = torch.load(model_data_dir + os.path.splitext(file_path)[0] + '_ibm_hard_est.pt', map_location=lambda storage, location: storage) # shape = (frames, freq_bins)
# y_hat_hard = torch.load(model_data_dir + os.path.splitext(file_path)[0] + '_ibm_soft_est.pt', map_location=lambda storage, location: storage) # shape = (frames, freq_bins)
y_hat_hard = y_hat_hard.T # Transpose to match target y, shape = (freq_bins, frames)
# TF representation
s_tf = stft(s_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype) # shape = (freq_bins, frames)
if labels == 'labels':
y = clean_speech_IBM(s_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
if labels == 'vad_labels':
y = clean_speech_VAD(s_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
# Convert y to Tensor for f1-score
y_hat_hard = y_hat_hard.int()
y = torch.LongTensor(y)
accuracy, precision, recall, f1score_s_hat = f1_loss(y.flatten(), y_hat_hard.flatten(), epsilon=1e-12)
# plots of target / estimation
# TF representation
x_tf = stft(x_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype) # shape = (freq_bins, frames)
s_hat_tf = stft(s_hat_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype) # shape = (freq_bins, frames)
## mixture signal (wav + spectro)
## target signal (wav + spectro + mask)
## estimated signal (wav + spectro + mask)
signal_list = [
[x_t, x_tf, None], # mixture: (waveform, tf_signal, no mask)
[s_t, s_tf, y.numpy()], # clean speech
[s_hat_t, s_hat_tf, y_hat_hard.numpy()]
]
fig = display_multiple_signals(signal_list,
fs=fs, vmin=vmin, vmax=vmax,
wlen_sec=wlen_sec, hop_percent=hop_percent,
xticks_sec=xticks_sec, fontsize=fontsize)
# put all metrics in the title of the figure
title = "Input SNR = {:.1f} dB \n" \
"SI-SDR = {:.1f} dB, " \
"SI-SIR = {:.1f} dB, " \
"SI-SAR = {:.1f} dB\n" \
"STOI = {:.2f}, " \
"PESQ = {:.2f} \n" \
"Accuracy = {:.3f}, "\
"Precision = {:.3f}, "\
"Recall = {:.3f}, "\
"F1-score = {:.3f}\n".format(snr_db, si_sdr, si_sir, si_sar, stoi_s_hat, pesq_s_hat,\
accuracy, precision, recall, f1score_s_hat)
fig.suptitle(title, fontsize=40)
# Save figure
fig.savefig(model_data_dir + os.path.splitext(file_path)[0] + '_fig.png')
# Clear figure
plt.close()
metrics = [si_sdr, si_sir, si_sar, stoi_s_hat, pesq_s_hat,\
accuracy, precision, recall, f1score_s_hat]
return metrics
def test_write_read_labels(dataset_type):
# Parameters
## Dataset
input_speech_dir = 'data/subset/raw/'
output_speech_dir = 'data/subset/processed/'
output_data_dir = 'data/subset/pickle/'
fs = int(16e3) # Sampling rate
## STFT
wlen_sec = 64e-3 # window length in seconds
hop_percent = 0.25 # hop size as a percentage of the window length
win = 'hann' # type of window
dtype = 'complex64'
## Ideal binary mask
quantile_fraction = 0.98
quantile_weight = 0.999
# Create file list
file_paths = speech_list(input_speech_dir=input_speech_dir,
dataset_type=dataset_type)
labels = []
for path in file_paths:
x, fs_x = sf.read(input_speech_dir + path, samplerate=None)
# Cut burst at begining of file
x[:int(0.1 * fs)] = x[int(0.1 * fs):int(0.2 * fs)]
# Normalize audio
x = x / (np.max(np.abs(x)))
#x = x/(np.max(np.abs(x)) + 2)
#x = x/np.linalg.norm(x)
if fs != fs_x:
raise ValueError('Unexpected sampling rate')
# TF reprepsentation
x_tf = stft(x,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
# binary mask
x_ibm = clean_speech_IBM(x_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
labels.append(x_ibm)
labels = np.concatenate(labels, axis=1)
#labels = labels[1]
# write spectrograms
write_dataset(labels,
output_data_dir=output_data_dir,
dataset_type=dataset_type,
suffix='labels')
# Read pickle
pickle_labels = read_dataset(data_dir=output_data_dir,
dataset_type=dataset_type,
suffix='labels')
# Assert stored data is same as spectrograms
assert_array_equal(labels, pickle_labels)
def main():
# Load input SNR
all_snr_db = read_dataset(processed_data_dir, dataset_type, 'snr_db')
all_snr_db = np.array(all_snr_db)
# Create file list
file_paths = speech_list(input_speech_dir=input_speech_dir,
dataset_type=dataset_type)
# Create file list
file_paths = speech_list(input_speech_dir=input_speech_dir,
dataset_type=dataset_type)
for i, file_path in tqdm(enumerate(file_paths)):
# Read files
s_t, fs_s = sf.read(processed_data_dir +
os.path.splitext(file_path)[0] +
'_s.wav') # clean speech
x_t, fs_x = sf.read(processed_data_dir +
os.path.splitext(file_path)[0] +
'_x.wav') # mixture
# x = x/np.max(x)
T_orig = len(x_t)
# TF representation
# Input should be (frames, freq_bibs)
x_tf = stft(x_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
# Power spectrogram (transpose)
x = np.power(np.abs(x_tf), 2)
# Estimate mask
y_hat_soft = timo_mask_estimation(x)
y_hat_hard = (y_hat_soft > 0.5).astype(int)
# plots of target / estimation
s_tf = stft(s_t,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype) # shape = (freq_bins, frames)
# binary mask
s_ibm = clean_speech_IBM(s_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
# F1-score
f1score_s_hat = f1_score(s_ibm.flatten(),
y_hat_hard.flatten(),
average="binary")
## mixture signal (wav + spectro)
## target signal (wav + spectro + mask)
## estimated signal (wav + spectro + mask)
signal_list = [
[x_t, x_tf, None], # mixture: (waveform, tf_signal, no mask)
[s_t, s_tf, s_ibm], # clean speech
#[None, None, y_hat_hard]
[None, None, y_hat_soft]
]
#TODO: modify
fig = display_multiple_signals(signal_list,
fs=fs,
vmin=vmin,
vmax=vmax,
wlen_sec=wlen_sec,
hop_percent=hop_percent,
xticks_sec=xticks_sec,
fontsize=fontsize)
# put all metrics in the title of the figure
title = "Input SNR = {:.1f} dB \n" \
"".format(all_snr_db[i])
fig.suptitle(title, fontsize=40)
# Save figure
output_path = model_data_dir + file_path
output_path = os.path.splitext(output_path)[0]
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
fig.savefig(output_path + '_soft_mask.png')
## mixture signal (wav + spectro)
## target signal (wav + spectro + mask)
## estimated signal (wav + spectro + mask)
signal_list = [
[x_t, x_tf, None], # mixture: (waveform, tf_signal, no mask)
[s_t, s_tf, s_ibm], # clean speech
#[None, None, y_hat_hard]
[None, None, y_hat_hard]
]
#TODO: modify
fig = display_multiple_signals(signal_list,
fs=fs,
vmin=vmin,
vmax=vmax,
wlen_sec=wlen_sec,
hop_percent=hop_percent,
xticks_sec=xticks_sec,
fontsize=fontsize)
# put all metrics in the title of the figure
title = "Input SNR = {:.1f} dB \n" \
"F1-score = {:.3f} \n".format(all_snr_db[i], f1score_s_hat)
fig.suptitle(title, fontsize=40)
# Save figure
output_path = model_data_dir + file_path
output_path = os.path.splitext(output_path)[0]
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
fig.savefig(output_path + '_hard_mask.png')
def main():
if not os.path.exists(os.path.dirname(output_dataset_file)):
os.makedirs(os.path.dirname(output_dataset_file))
with h5.File(output_dataset_file,
'a',
rdcc_nbytes=rdcc_nbytes,
rdcc_nslots=rdcc_nslots) as f:
for dataset_type in dataset_types:
# Create file list
file_paths = speech_list(input_speech_dir=input_speech_dir,
dataset_type=dataset_type)
# Delete datasets if already exists
if 'X_' + dataset_type in f:
del f['X_' + dataset_type]
del f['Y_' + dataset_type]
# Exact shape of dataset is unknown in advance unfortunately
# Faster writing if you know the shape in advance
# Size of chunks corresponds to one spectrogram frame
f.create_dataset('X_' + dataset_type,
shape=X_shape,
dtype='float32',
maxshape=X_maxshape,
chunks=X_chunks,
compression=compression,
shuffle=shuffle)
f.create_dataset('Y_' + dataset_type,
shape=Y_shape,
dtype='float32',
maxshape=Y_maxshape,
chunks=Y_chunks,
compression=compression,
shuffle=shuffle)
# STFT attributes
f.attrs['fs'] = fs
f.attrs['wlen_sec'] = wlen_sec
f.attrs['hop_percent'] = hop_percent
f.attrs['win'] = win
f.attrs['dtype'] = dtype
# label attributes
f.attrs['quantile_fraction'] = quantile_fraction
f.attrs['quantile_weight'] = quantile_weight
# HDF5 attributes
f.attrs['X_chunks'] = X_chunks
f.attrs['Y_chunks'] = Y_chunks
f.attrs['compression'] = compression
# Store dataset in variables for faster I/O
fx = f['X_' + dataset_type]
fy = f['Y_' + dataset_type]
for i, file_path in tqdm(enumerate(file_paths)):
speech, fs_speech = sf.read(input_speech_dir + file_path,
samplerate=None)
# Cut burst at begining of file
speech = speech[int(0.1 * fs):]
# Normalize audio
speech = speech / (np.max(np.abs(speech)))
if fs != fs_speech:
raise ValueError('Unexpected sampling rate')
# TF reprepsentation
speech_tf = stft(speech,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
spectrogram = np.power(abs(speech_tf), 2)
if labels == 'vad_labels':
# vad
speech_vad = clean_speech_VAD(
speech_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
label = speech_vad
if labels == 'labels':
# binary mask
speech_ibm = clean_speech_IBM(
speech_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
label = speech_ibm
# Store spectrogram in dataset
fx.resize((fx.shape[1] + spectrogram.shape[1]), axis=1)
fx[:, -spectrogram.shape[1]:] = spectrogram
# Store spectrogram in label
fy.resize((fy.shape[1] + label.shape[1]), axis=1)
fy[:, -label.shape[1]:] = label
def test_write_read_frames(dataset_type):
# Parameters
## Dataset
input_speech_dir = 'data/subset/raw/'
output_speech_dir = 'data/subset/processed/'
output_data_dir = 'data/subset/h5/'
data_dir = 'CSR-1-WSJ-0'
suffix = 'lzf'
output_h5_dir = output_data_dir + data_dir + '_' + suffix + '.h5'
# Create file list
file_paths = speech_list(input_speech_dir=input_speech_dir,
dataset_type=dataset_type)
# Open hdf5 file
#We are using 400Mb of chunk_cache_mem here ("rdcc_nbytes" and "rdcc_nslots")
with h5.File(output_h5_dir, 'r', rdcc_nbytes=1024**2*400, rdcc_nslots=10e6) as f:
dx = f['X_' + dataset_type]
dy = f['Y_' + dataset_type]
## STFT
fs = f.attrs['fs'] # Sampling rate
wlen_sec = f.attrs['wlen_sec'] # window length in seconds
hop_percent = f.attrs['hop_percent'] # hop size as a percentage of the window length
win = f.attrs['win'] # type of window
dtype = f.attrs['dtype']
## Ideal binary mask
quantile_fraction = f.attrs['quantile_fraction']
quantile_weight = f.attrs['quantile_weight']
frame_begin = 0
frame_end = 0
for path in file_paths:
x, fs_x = sf.read(input_speech_dir + path, samplerate=None)
# Cut burst at begining of file
#x[:int(0.1*fs)] = x[int(0.1*fs):int(0.2*fs)]
x = x[int(0.1*fs):]
# Normalize audio
x = x/(np.max(np.abs(x)))
if fs != fs_x:
raise ValueError('Unexpected sampling rate')
# TF reprepsentation
x_tf = stft(x,
fs=fs,
wlen_sec=wlen_sec,
win=win,
hop_percent=hop_percent,
dtype=dtype)
spectrogram = np.power(abs(x_tf), 2)
# binary mask
label = clean_speech_IBM(x_tf,
quantile_fraction=quantile_fraction,
quantile_weight=quantile_weight)
# Read h5 spectrogram
frame_end += spectrogram.shape[1]
h5_spectrogram = dx[:,frame_begin:frame_end]
h5_label = dy[:,frame_begin:frame_end]
# Assert stored data is same as spectrograms
assert_array_equal(spectrogram, h5_spectrogram)
assert_array_equal(label, h5_label)
# Next iteration
frame_begin += spectrogram.shape[1]
