def create_weights_lab(
labpath,
fids,
outfilepath,
lineheadregexp=r'([^\^]+)\^([^-]+)-([^\+]+)\+([^=]+)=([^@]+)@(.+)',
silencesymbol='sil',
shift=0.005):
"""
This function creates a one-column vector with one weight value per frame.
This weight is created based on the silence symbol that is at the head of
each lab line.
Some lab file formats uses: r'([^\~]+)\~([^-]+)-([^\+]+)\+([^=]+)=([^:]+):(.+)'
"""
makedirs(os.path.dirname(outfilepath))
outfilepath, _ = data.getpathandshape(outfilepath)
for fid in readids(fids):
print_tty(
'\r Processing feature file {} '.format(fid))
with open(labpath.replace('*', fid)) as f:
lines = f.readlines()
lineels = re.findall(r'([0-9]+)\s+([0-9]+)\s+(.+)', lines[-1])[0]
tend = float(lineels[1]) * 1e-7
weight = np.ones(int(np.ceil(tend / shift)), dtype='float32')
for line in lines:
lineels = re.findall(r'([0-9]+)\s+([0-9]+)\s+(.+)', line)[0]
tstart = float(lineels[0]) * 1e-7
tend = float(lineels[1]) * 1e-7
# print('{}-{}'.format(tstart, tend))
phones = re.findall(lineheadregexp, lineels[2])[0]
if phones[2] == silencesymbol:
weight[int(np.floor(tstart /
shift)):int(np.ceil(tend /
shift))] = 0.0
weight.astype('float32').tofile(outfilepath.replace('*', fid))
print_tty(
'\r \r')
def test_data(self):
import data
fids = readids(cptest + 'file_id_list.scp')
path, shape = data.getpathandshape('dummy.fwlspec')
self.assertTrue(path == 'dummy.fwlspec')
self.assertTrue(shape == None)
path, shape = data.getpathandshape('dummy.fwlspec:(-1,129)')
self.assertTrue(path == 'dummy.fwlspec')
self.assertTrue(shape == (-1, 129))
path, shape = data.getpathandshape('dummy.fwlspec:(-1,129)', (-1, 12))
self.assertTrue(path == 'dummy.fwlspec')
self.assertTrue(shape == (-1, 12))
path, shape = data.getpathandshape('dummy.fwlspec', (-1, 12))
self.assertTrue(path == 'dummy.fwlspec')
self.assertTrue(shape == (-1, 12))
dim = data.getlastdim('dummy.fwlspec')
self.assertTrue(dim == 1)
dim = data.getlastdim('dummy.fwlspec:(-1,129)')
self.assertTrue(dim == 129)
indir = cptest + 'binary_label_' + str(
lab_size) + '_norm_minmaxm11/*.lab:(-1,' + str(lab_size) + ')'
Xs = data.load(indir,
fids,
shape=None,
frameshift=0.005,
verbose=1,
label='Xs: ')
self.assertTrue(len(Xs) == 10)
print(Xs[0].shape)
self.assertTrue(Xs[0].shape == (667, lab_size))
print(data.gettotallen(Xs))
self.assertTrue(data.gettotallen(Xs) == 5694)
outdir = cptest + 'wav_cmp_lf0_fwlspec65_fwnm17_bndnmnoscale/*.cmp:(-1,83)'
Ys = data.load(outdir,
fids,
shape=None,
frameshift=0.005,
verbose=1,
label='Ys: ')
print('len(Ys)=' + str(len(Ys)))
self.assertTrue(len(Ys) == 10)
print('Ys[0].shape' + str(Ys[0].shape))
self.assertTrue(Ys[0].shape == (666, 83))
wdir = cptest + 'wav_fwlspec65_weights/*.w:(-1,1)'
Ws = data.load(wdir,
fids,
shape=None,
frameshift=0.005,
verbose=1,
label='Ws: ')
self.assertTrue(len(Ws) == 10)
Xs, Ys, Ws = data.croplen([Xs, Ys, Ws])
[Xs, Ys], Ws = data.croplen_weight([Xs, Ys], Ws, thresh=0.5)
Xs_w_stop = data.addstop(Xs)
X_train, MX_train, Y_train, MY_train, W_train = data.load_inoutset(
indir,
outdir,
wdir,
fids,
length=None,
lengthmax=100,
maskpadtype='randshift',
inouttimesync=False)
X_train, MX_train, Y_train, MY_train, W_train = data.load_inoutset(
indir,
outdir,
wdir,
fids,
length=None,
lengthmax=100,
maskpadtype='randshift')
X_train, MX_train, Y_train, MY_train, W_train = data.load_inoutset(
indir,
outdir,
wdir,
fids,
length=None,
lengthmax=100,
maskpadtype='randshift',
cropmode='begendbigger')
X_train, MX_train, Y_train, MY_train, W_train = data.load_inoutset(
indir,
outdir,
wdir,
fids,
length=None,
lengthmax=100,
maskpadtype='randshift',
cropmode='all')
worst_val = data.cost_0pred_rmse(Ys)
print('worst_val={}'.format(worst_val))
worst_val = data.cost_0pred_rmse(Ys[0])
print('worst_val={}'.format(worst_val))
def data_cost_model_mfn(Xs, Ys):
return np.std(Ys) # TODO More usefull
X_vals = data.load(indir, fids)
Y_vals = data.load(outdir, fids)
X_vals, Y_vals = data.croplen([X_vals, Y_vals])
cost = data.cost_model_mfn(data_cost_model_mfn, [X_vals, Y_vals])
print(cost)
class SmokyModel:
def predict(self, Xs):
return np.zeros([1, Xs.shape[1], 83])
mod = SmokyModel()
cost = data.cost_model_prediction_rmse(mod, [Xs], Ys)
print(cost)
std = data.prediction_mstd(mod, [Xs])
print(std)
rms = data.prediction_rms(mod, [Xs])
print(rms)
def test_compose(self):
import data
import compose
fids = readids(cptest + '/file_id_list.scp')
wav_dir = 'wav'
f0_path = cptest + wav_dir + '_lf0/*.lf0'
spec_path = cptest + wav_dir + '_fwlspec' + str(
spec_size) + '/*.fwlspec'
nm_path = cptest + wav_dir + '_fwnm' + str(nm_size) + '/*.fwnm'
compose.compose([
cptest + 'binary_label_' + str(lab_size) + '/*.lab:(-1,' +
str(lab_size) + ')'
],
fids,
'tests/test_made__smoke_compose_compose_lab0/*.lab',
id_valid_start=8,
normfn=None,
wins=[],
dropzerovardims=False)
compose.compose([
cptest + 'binary_label_' + str(lab_size) + '/*.lab:(-1,' +
str(lab_size) + ')'
],
fids,
'tests/test_made__smoke_compose_compose_lab1/*.lab',
id_valid_start=8,
normfn=compose.normalise_minmax,
wins=[],
dropzerovardims=False)
path2, shape2 = data.getpathandshape(
'tests/test_made__smoke_compose_compose_lab1/*.lab:(mean.dat,' +
str(lab_size) + ')')
compose.compose([
cptest + 'binary_label_' + str(lab_size) + '/*.lab:(-1,' +
str(lab_size) + ')'
],
fids,
'tests/test_made__smoke_compose_compose_lab2/*.lab',
id_valid_start=8,
normfn=compose.normalise_minmax,
wins=[],
dropzerovardims=True)
compose.compose([
f0_path, spec_path + ':(-1,' + str(spec_size) + ')',
nm_path + ':(-1,' + str(nm_size) + ')'
],
fids,
'tests/test_made__smoke_compose_compose2_cmp1/*.cmp',
id_valid_start=8,
normfn=compose.normalise_minmax,
wins=[])
compose.compose([
f0_path, spec_path + ':(-1,' + str(spec_size) + ')',
nm_path + ':(-1,' + str(nm_size) + ')'
],
fids,
'tests/test_made__smoke_compose_compose2_cmp2/*.cmp',
id_valid_start=8,
normfn=compose.normalise_meanstd,
wins=[])
compose.compose([
f0_path, spec_path + ':(-1,' + str(spec_size) + ')',
nm_path + ':(-1,' + str(nm_size) + ')'
],
fids,
'tests/test_made__smoke_compose_compose2_cmp4/*.cmp',
id_valid_start=8,
normfn=compose.normalise_meanstd_nmnoscale,
wins=[])
compose.compose(
[
f0_path, spec_path + ':(-1,' + str(spec_size) + ')',
nm_path + ':(-1,' + str(nm_size) + ')'
],
fids,
'tests/test_made__smoke_compose_compose2_cmp_deltas/*.cmp',
id_valid_start=8,
normfn=compose.normalise_meanstd_nmnoscale,
wins=[[-0.5, 0.0, 0.5], [1.0, -2.0, 1.0]])
# WORLD vocoder features
compose.compose(
[
cptest + wav_dir + '_world_lf0/*.lf0', cptest + wav_dir +
'_world_fwlspec/*.fwlspec:(-1,' + str(spec_size) + ')',
cptest + wav_dir + '_world_fwdbaper/*.fwdbaper:(-1,' +
str(nm_size) + ')', cptest + wav_dir + '_world_vuv/*.vuv'
],
fids,
'tests/test_made__smoke_compose_compose2_cmp_WORLD/*.cmp',
id_valid_start=8,
normfn=compose.normalise_meanstd,
wins=[])
compose.compose(
[
cptest + wav_dir + '_world_lf0/*.lf0', cptest + wav_dir +
'_world_fwlspec/*.fwlspec:(-1,' + str(spec_size) + ')',
cptest + wav_dir + '_world_fwdbaper/*.fwdbaper:(-1,' +
str(nm_size) + ')', cptest + wav_dir + '_world_vuv/*.vuv'
],
fids,
'tests/test_made__smoke_compose_compose2_cmp_WORLD_mlpg/*.cmp',
id_valid_start=8,
normfn=compose.normalise_meanstd,
wins=[[-0.5, 0.0, 0.5], [1.0, -2.0, 1.0]])
compose.create_weights_spec(
spec_path + ':(-1,' + str(spec_size) + ')',
fids,
'tests/test_made__smoke_compose_compose2_w1/*.w',
spec_type='fwlspec',
thresh=-32)
def create_weights_spec(specfeaturepath,
fids,
outfilepath,
thresh=-32,
dftlen=4096,
spec_type='fwlspec'):
"""
This function creates a one-column vector with one weight value per frame.
This weight is computed as a silence coefficient. During training, silent
segments will be dropped (i.e. dropped if weight<0.5), if present at the
very begining or very end of the sample (or optionnaly within a sentence if
the silence is particularly long).
thresh : [dB] The weight of the frames whose energy < threshold are set
weight = 0, and 1 otherwise.
"""
def mag2db(a):
return 20.0 * np.log10(np.abs(a))
outfilepath = re.sub(
r':[^:]+$', "",
outfilepath) # ignore any shape suffix in the output path
if not os.path.isdir(os.path.dirname(outfilepath)):
os.mkdir(os.path.dirname(outfilepath))
for nf, fid in enumerate(fids):
print_tty(
'\r Processing feature files {} for {} '.format(
nf, fid))
infilepath, shape = data.getpathandshape(specfeaturepath)
if shape is None: shape = (-1, 1)
infilepath = infilepath.replace('*', fid)
if spec_type == 'fwlspec':
Yspec = np.fromfile(infilepath, dtype='float32')
Yspec = Yspec.reshape(shape)
ener = mag2db(np.exp(np.mean(Yspec, axis=1)))
elif spec_type == 'mcep':
Ymcep = np.fromfile(infilepath, dtype='float32')
Ymcep = Ymcep.reshape(shape)
ener = mag2db(np.exp(Ymcep[:, 0])) # Just need the first coef
elif spec_type == 'fwcep':
Ymcep = np.fromfile(infilepath, dtype='float32')
Ymcep = Ymcep.reshape(shape)
ener = mag2db(np.exp(Ymcep[:, 0])) # Just need the first coef
# Normalise by the strongest value
# That might not be very reliable if the estimated spec env is very noisy.
ener -= np.max(ener)
weight = ener.copy()
weight[ener >= thresh] = 1.0
weight[ener < thresh] = 0.0
weight.astype('float32').tofile(outfilepath.replace('*', fid))
if 0:
import matplotlib.pyplot as plt
plt.plot(ener, 'k')
plt.plot(np.log10(weight), 'b')
plt.plot([0, len(ener)], thresh * np.array([1, 1]), 'k')
from IPython.core.debugger import Pdb
Pdb().set_trace()
print_tty(
'\r \r')
def compose(featurepaths,
fids,
outfilepath,
wins=None,
id_valid_start=-1,
normfn=None,
shift=0.005,
dropzerovardims=False,
do_finalcheck=False,
verbose=1):
"""
For each file index in fids, compose a set of features (can be input or
output data) into a single file and normalise it according to statistics and
normfn.
The outfilepath will be populated by the composed/normlised files, and,
by statistics files that can be used for de-composition.
Parameters
----------
featurepaths : path of features to concatenate for each file
fids : file IDs
outfilepath : outputpath of the resulted composition and normalisation.
wins : list of numpy arrays
E.g. values in Merlin are wins=[[-0.5, 0.0, 0.5], [1.0, -2.0, 1.0]]
"""
print('Compose data (id_valid_start={})'.format(id_valid_start))
if wins is None: wins = []
outfilepath = re.sub(
r':[^:]+$', "",
outfilepath) # ignore any shape suffix in the output path
if not os.path.isdir(os.path.dirname(outfilepath)):
os.mkdir(os.path.dirname(outfilepath))
size = None
mins = None
maxs = None
means = None
nbframes = 0
for nf, fid in enumerate(fids):
print_tty('\r Composing file {}/{} {} '.format(
1 + nf, len(fids), fid))
features = []
minlen = None
for featurepath in featurepaths:
infilepath, shape = data.getpathandshape(featurepath)
if shape is None: shape = (-1, 1)
infilepath = infilepath.replace('*', fid)
feature = np.fromfile(infilepath, dtype='float32')
feature = feature.reshape(shape)
features.append(feature)
if minlen is None: minlen = feature.shape[0]
else: minlen = np.min((minlen, feature.shape[0]))
# Crop features to same length
for feati in xrange(len(features)):
features[feati] = features[feati][:minlen, ]
Y = np.hstack(features)
if len(wins) > 0:
YWs = [Y] # Always add first the static values
for win in wins:
# Then concatenate the windowed values
YW = np.ones(Y.shape)
win_p = (len(win) + 1) / 2
for d in xrange(Y.shape[1]):
YW[win_p - 1:-(win_p - 1), d] = -scipy.signal.convolve(
Y[:, d], win)[win_p:-win_p] # The fastest
YW[:win_p - 1, d] = YW[win_p - 1, d]
YW[-(win_p - 1):, d] = YW[-(win_p - 1) - 1, d]
YWs.append(YW)
Y = np.hstack(YWs)
#if 0:
#from merlin.mlpg_fast import MLParameterGenerationFast as MLParameterGeneration
#mlpg_algo = MLParameterGeneration()
#var = np.tile(np.ones(CMP.shape[1]),(CMP.shape[0],1)) # Simplification!
#YGEN = mlpg_algo.generation(CMP, var, 1)
#plt.plot(Y, 'k')
#plt.plot(YGEN, 'b')
#from IPython.core.debugger import Pdb; Pdb().set_trace()
size = Y.shape[1]
if nf < id_valid_start:
if mins is None: mins = Y.min(axis=0)
else: mins = np.minimum(mins, Y.min(axis=0))
if maxs is None: maxs = Y.max(axis=0)
else: maxs = np.maximum(maxs, Y.max(axis=0))
if means is None: means = Y.sum(axis=0).astype('float64')
else: means += Y.sum(axis=0).astype('float64')
nbframes += Y.shape[0]
#print('\r Write data file {}: {} '.format(nf, fid)),
Y.astype('float32').tofile(outfilepath.replace('*', fid))
print_tty(
'\r \r')
means /= nbframes
zerovaridx = np.where(
(maxs - mins) == 0.0)[0] # Indices of dimensions having zero-variance
mins.astype('float32').tofile(os.path.dirname(outfilepath) + '/min.dat')
if verbose > 1: print(' mins={}'.format(mins)) # pragma: no cover
maxs.astype('float32').tofile(os.path.dirname(outfilepath) + '/max.dat')
if verbose > 1: print(' maxs={}'.format(maxs)) # pragma: no cover
means.astype('float32').tofile(os.path.dirname(outfilepath) + '/mean.dat')
if verbose > 1: print(' means={}'.format(means)) # pragma: no cover
# Now that we have the mean, we can do the std
stds = None
for nf, fid in enumerate(fids):
Y = np.fromfile(outfilepath.replace('*', fid), dtype='float32')
Y = Y.reshape((-1, size))
if nf < id_valid_start:
if stds is None:
stds = ((Y - means)**2).sum(axis=0).astype('float64')
else:
stds += ((Y - means)**2).sum(axis=0).astype('float64')
stds /= nbframes - 1 # unbiased variance estimator
stds = np.sqrt(stds)
stds.astype('float32').tofile(os.path.dirname(outfilepath) + '/std.dat')
if verbose > 1: print(' stds={}'.format(stds))
keepidx = np.arange(len(means))
if dropzerovardims:
keepidx = np.setdiff1d(np.arange(len(means)), zerovaridx)
size = len(keepidx)
keepidx.astype('int32').tofile(
os.path.dirname(outfilepath) + '/keepidx.dat')
print('Dropped dimensions with zero variance. Remains {} dims'.format(
size))
print('{} files'.format(len(fids)))
print('{} frames ({}s assuming {}s time shift)'.format(
nbframes, datetime.timedelta(seconds=nbframes * shift), shift))
strsize = ''
for fpath in featurepaths:
dummy, shape = data.getpathandshape(fpath)
if shape is None: strsize += '1+'
else: strsize += str(shape[1]) + '+'
strsize = strsize[:-1]
if dropzerovardims:
strsize += '-' + str(len(zerovaridx))
print('nb dimensions={} (features: ({})x{})'.format(
size, strsize, 1 + len(wins)))
print('{} dimensions with zero-variance ({}){}'.format(
len(zerovaridx), zerovaridx, ', which have been dropped'
if dropzerovardims else ', which have been kept'))
if normfn is not None:
print('normalisation done using: {}'.format(normfn.__name__))
else:
print('no normalisation called')
print('output path: {}'.format(outfilepath))
# Maybe this shouldn't be called within compose, it should come afterwards. No see #30
if not normfn is None:
normfn(outfilepath,
fids,
featurepaths=featurepaths,
keepidx=keepidx,
verbose=verbose)
if do_finalcheck:
print('Check data final statistics')
verif_means = None
verif_stds = None
verif_mins = None
verif_maxs = None
verif_nbframes = 0
for nf, fid in enumerate(fids):
if nf >= id_valid_start: continue
fpath = outfilepath.replace('*', fid)
Y = np.fromfile(fpath, dtype='float32')
Y = Y.reshape((-1, size))
if verif_means is None:
verif_means = Y.sum(axis=0).astype('float64')
else:
verif_means += Y.sum(axis=0).astype('float64')
if verif_mins is None: verif_mins = Y.min(axis=0)
else: verif_mins = np.minimum(verif_mins, Y.min(axis=0))
if verif_maxs is None: verif_maxs = Y.max(axis=0)
else: verif_maxs = np.maximum(verif_maxs, Y.max(axis=0))
verif_nbframes += Y.shape[0]
verif_means /= verif_nbframes
for nf, fid in enumerate(fids):
if nf >= id_valid_start: continue
fpath = outfilepath.replace('*', fid)
Y = np.fromfile(fpath, dtype='float32')
Y = Y.reshape((-1, size))
if verif_stds is None:
verif_stds = ((Y -
verif_means)**2).sum(axis=0).astype('float64')
else:
verif_stds += ((Y -
verif_means)**2).sum(axis=0).astype('float64')
verif_stds /= verif_nbframes - 1
if verbose > 0: # pragma: no cover
print('verif_min={}'.format(verif_mins))
print('verif_max={}'.format(verif_maxs))
print('verif_means={}'.format(verif_means))
print('verif_stds={}'.format(verif_stds))