def test_kaldiserializer_badfile(tmpdir, mfcc_col, missing):
filename = str(tmpdir.join('foo.ark'))
mfcc_col.save(filename)
os.remove(str(tmpdir.join(missing)))
with pytest.raises(IOError) as err:
FeaturesCollection.load(filename)
assert 'file not found: {}'.format(str(tmpdir.join(missing))) in str(err)
def mfcc_utf8(mfcc):
props = mfcc.properties
props['comments'] = '使用人口について正確な統計はないが、日本国'
feats = FeaturesCollection()
feats['æðÐ'] = Features(mfcc.data, mfcc.times, props)
return feats
def test_save_invalid(tmpdir, mfcc):
f = str(tmpdir.join('foo.json'))
h = serializers.get_serializer(FeaturesCollection, f, None)
feats = FeaturesCollection(
mfcc=Features(data=mfcc.data, times=0, validate=False))
with pytest.raises(ValueError) as err:
h.save(feats)
assert 'features are not valid' in str(err.value)
def test_apply_baddim(features_collection):
feats = FeaturesCollection(
{k: v.copy() for k, v in features_collection.items()})
feats['new'] = Features(
np.random.random((2, 1)), np.asarray([0, 1]))
with pytest.raises(ValueError) as err:
apply_cmvn(feats)
assert 'must have consistent dimensions' in str(err.value)
def features_collection():
# build a collection of 3 random features of same ndims, various
# nframes
dim = 10
feats = FeaturesCollection()
for n in range(3):
nframes = np.random.randint(5, 15)
feats[str(n)] = Features(np.random.random((nframes, dim)),
np.arange(0, nframes))
return feats
def test_collection_isclose():
f1 = Features(np.random.random((10, 2)), np.ones((10, )))
f2 = Features(np.random.random((10, 2)), np.ones((10, )))
fc1 = FeaturesCollection(f1=f1, f2=f2)
fc2 = FeaturesCollection(f1=f1, f2=Features(f2.data + 1, f2.times))
fc3 = FeaturesCollection(f1=f1, f3=f2)
assert fc1.is_close(fc1)
assert not fc1.is_close(fc2)
assert fc1.is_close(fc2, atol=1)
assert not fc1.is_close(fc3)
def test_kaldiserializer(mfcc_col, tmpdir, scp):
mfcc_col.save(str(tmpdir.join('foo.ark')), scp=scp)
assert os.path.isfile(str(tmpdir.join('foo.ark')))
assert os.path.isfile(str(tmpdir.join('foo.times.ark')))
assert os.path.isfile(str(tmpdir.join('foo.properties.json')))
if scp:
assert os.path.isfile(str(tmpdir.join('foo.scp')))
assert os.path.isfile(str(tmpdir.join('foo.times.scp')))
mfcc_col2 = FeaturesCollection.load(str(tmpdir.join('foo.ark')))
assert mfcc_col2 == mfcc_col
def test_times_1d(serializer, tmpdir):
filename = ('feats.ark'
if serializer is serializers.KaldiSerializer else 'feats')
tmpfile = str(tmpdir.join(filename))
p = MfccProcessor()
times = p.times(10)[:, 1]
assert times.shape == (10, )
col = FeaturesCollection(mfcc=Features(np.random.random((10, 5)), times))
serializer(col.__class__, tmpfile).save(col)
col2 = serializer(col.__class__, tmpfile).load()
assert col == col2
def _extract_features(config, utterances, njobs=1, log=get_logger()):
# the manager will instanciate the pipeline components
manager = _Manager(config, utterances, log=log)
# verbosity level for joblib (no joblib verbosity on debug level
# (level <= 10) because each step is already detailed in inner
# loops
verbose = 8 if log.getEffectiveLevel() > 10 else 0
# cmvn : two passes. 1st with features pitch and cmvn
# accumulation, 2nd with cmvn application and delta
if 'cmvn' in config:
# extract features and pitch, accumulate cmvn stats
pass_one = _Parallel(
'features extraction, pass 1', log,
n_jobs=njobs, verbose=verbose, prefer='threads')(
joblib.delayed(_extract_pass_one)(
utterance, manager, log=log) for utterance in utterances)
# apply cmvn and extract deltas
features = FeaturesCollection(**{k: v for k, v in _Parallel(
'features extraction, pass 2', log,
n_jobs=njobs, verbose=verbose, prefer='threads')(
joblib.delayed(_extract_pass_two)(
utterance, manager, features, pitch, log=log)
for utterance, features, pitch in pass_one)})
# no cmvn: single pass
else:
features = FeaturesCollection(**{k: v for k, v in _Parallel(
'features extraction', log,
n_jobs=njobs, verbose=verbose, prefer='threads')(
joblib.delayed(_extract_single_pass)(
utterance, manager, log=log) for utterance in utterances)})
return features
def test_kaldiserializer_baditems(tmpdir, mfcc_col):
mfcc_col2 = FeaturesCollection(one=mfcc_col['mfcc'], two=mfcc_col['mfcc'])
mfcc_col.save(str(tmpdir.join('one.ark')))
mfcc_col2.save(str(tmpdir.join('two.ark')))
os.remove(str(tmpdir.join('two.times.ark')))
shutil.copyfile(str(tmpdir.join('one.times.ark')),
str(tmpdir.join('two.times.ark')))
with pytest.raises(ValueError) as err:
FeaturesCollection.load(str(tmpdir.join('two.ark')))
assert 'items differ in data and times' in str(err.value)
os.remove(str(tmpdir.join('one.properties.json')))
shutil.copyfile(str(tmpdir.join('two.properties.json')),
str(tmpdir.join('one.properties.json')))
with pytest.raises(ValueError) as err:
FeaturesCollection.load(str(tmpdir.join('one.ark')))
assert 'items differ in data and properties' in str(err.value)
def test_heterogeneous(mfcc, serializer, tmpdir):
mfcc_col = FeaturesCollection(mfcc32=mfcc,
mfcc64=mfcc.copy(dtype=np.float64))
filename = ('feats.ark'
if serializer is serializers.KaldiSerializer else 'feats')
h = serializer(mfcc_col.__class__, str(tmpdir.join(filename)))
# h5features doesn't support heteregoneous data
if serializer is serializers.H5featuresSerializer:
with pytest.raises(IOError) as err:
h.save(mfcc_col)
assert 'data is not appendable to the group' in str(err.value)
else:
h.save(mfcc_col)
mfcc2 = h.load()
assert mfcc2 == mfcc_col
def main():
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument('data_dir', help='input directory with wavs')
parser.add_argument(
'output_dir',
default='/tmp',
nargs='?',
help='output directory (created files are deleted at exit)')
args = parser.parse_args()
# load audio data and compute total duration
audio_data = {
os.path.basename(f): Audio.load(f)
for f in list_files_with_extension(args.data_dir, '.wav')
}
total_duration = datetime.timedelta(
seconds=int(sum(a.duration for a in audio_data.values())))
print('found {} wav files, total duration of {}'.format(
len(audio_data), str(total_duration)))
# compute the features (default MFCC)
print('computing MFCC features...')
t1 = datetime.datetime.now()
processor = MfccProcessor()
features = FeaturesCollection(
**{k: processor.process(v)
for k, v in audio_data.items()})
t2 = datetime.datetime.now()
print('took {}'.format(t2 - t1))
# save the features in all the supported formats
data = {
'duration': total_duration,
'data': {
ext: analyze_serializer(features, ext, args.output_dir)
for ext in supported_extensions().keys()
}
}
print_results(data)
def process_all(self, signals, njobs=None):
"""Returns features processed from several input `signals`
This function processes the features in parallel jobs.
Parameters
----------
signals: dict of :class`~shennong.audio.Audio`
A dictionnary of input audio signals to process features
on, where the keys are item names and values are audio
signals.
njobs: int, optional
The number of parallel jobs to run in background. Default
to the number of CPU cores available on the machine.
Returns
-------
features: :class:`~shennong.features.features.FeaturesCollection`
The computed features on each input signal. The keys of
output `features` are the keys of the input `signals`.
Raises
------
ValueError
If the `njobs` parameter is <= 0
"""
# checks the number of background jobs
njobs = get_njobs(njobs, log=self._log)
def _process_one(name, signal):
return name, self.process(signal)
return FeaturesCollection(
**{
k: v
for k, v in joblib.Parallel(
n_jobs=njobs, verbose=0, backend='threading')(
joblib.delayed(_process_one)(name, signal)
for name, signal in signals.items())
})
def analyze_serializer(features, ext, output_dir):
with tempfile.TemporaryDirectory(dir=output_dir) as tmpdir:
filename = os.path.join(tmpdir, 'features' + ext)
print('writing {}...'.format(filename))
t1 = datetime.datetime.now()
features.save(filename)
t2 = datetime.datetime.now()
t_write = t2 - t1
print('took {}'.format(t_write))
f_size = os.path.getsize(filename)
print('filesize: {}'.format(sizeof_fmt(f_size)))
print('reading {}...'.format(filename))
t1 = datetime.datetime.now()
features2 = FeaturesCollection.load(filename)
t2 = datetime.datetime.now()
t_read = t2 - t1
print('took {}'.format(t_read))
print('rw equality: {}'.format(features2 == features))
return (t_write, t_read, f_size)
def test_partition():
f1 = Features(np.random.random((10, 2)), np.ones((10, )))
f2 = Features(np.random.random((5, 2)), np.ones((5, )))
f3 = Features(np.random.random((5, 2)), np.ones((5, )))
fc = FeaturesCollection(f1=f1, f2=f2, f3=f3)
with pytest.raises(ValueError) as err:
fp = fc.partition({'f1': 'p1', 'f2': 'p1'})
assert ('following items are not defined in the partition index: f3'
in str(err))
fp = fc.partition({'f1': 'p1', 'f2': 'p1', 'f3': 'p2'})
assert sorted(fp.keys()) == ['p1', 'p2']
assert sorted(fp['p1'].keys()) == ['f1', 'f2']
assert sorted(fp['p2'].keys()) == ['f3']
assert fc.is_valid()
for fc in fp.values():
assert fc.is_valid()
def test_collection(mfcc):
assert FeaturesCollection._value_type is Features
assert FeaturesCollection().is_valid()
assert FeaturesCollection(mfcc=mfcc).is_valid()
assert not FeaturesCollection(
mfcc=Features(np.asarray([0]), 0, validate=False)).is_valid()
def test_extract_features_full(ext, wav_file, wav_file_8k, wav_file_float32,
capsys, tmpdir):
# difficult case with parallel jobs, different sampling rates,
# speakers and segments
index = [('u1', wav_file, 's1', 0, 1),
('u2', wav_file_float32, 's2', 1, 1.2),
('u3', wav_file_8k, 's1', 1, 3)]
config = pipeline.get_default_config('mfcc')
# disable VAD because it can alter the cmvn result (far from (0,
# 1) when the signal includes non-voiced frames)
config['cmvn']['with_vad'] = False
feats = pipeline.extract_features(config,
index,
njobs=2,
log=utils.get_logger())
# ensure we have the expected log messages
messages = capsys.readouterr().err
assert 'INFO - get 3 utterances from 2 speakers in 3 wavs' in messages
assert 'WARNING - several sample rates found in wav files' in messages
for utt in ('u1', 'u2', 'u3'):
assert utt in feats
assert feats[utt].dtype == np.float32
# check properies
p1 = feats['u1'].properties
p2 = feats['u2'].properties
p3 = feats['u3'].properties
assert p1['audio']['file'] == wav_file
assert p1['audio']['duration'] == 1.0
assert p2['audio']['file'] == wav_file_float32
assert p2['audio']['duration'] == pytest.approx(0.2)
assert p3['audio']['file'] == wav_file_8k
assert p3['audio']['duration'] < 0.5 # ask 3s but get duration-tstart
assert p1['mfcc'] == p2['mfcc']
assert p1['mfcc']['sample_rate'] != p3['mfcc']['sample_rate']
assert p1.keys() == {
'audio', 'mfcc', 'cmvn', 'pitch', 'delta', 'speaker', 'pipeline'
}
assert p1.keys() == p2.keys() == p3.keys()
assert p1['pipeline'] == p2['pipeline'] == p3['pipeline']
# check shape. mfcc*delta + pitch = 13 * 3 + 3 = 42
assert feats['u1'].shape == (98, 42)
assert feats['u2'].shape == (18, 42)
assert feats['u3'].shape == (40, 42)
# check cmvn
assert feats['u2'].data[:, :13].mean() == pytest.approx(0.0, abs=1e-6)
assert feats['u2'].data[:, :13].std() == pytest.approx(1.0, abs=1e-6)
data = np.vstack((feats['u1'].data[:, :13], feats['u3'].data[:, :13]))
assert data.mean() == pytest.approx(0.0, abs=1e-6)
assert data.std() == pytest.approx(1.0, abs=1e-6)
assert np.abs(data.mean()) <= np.abs(feats['u1'].data[:, :13].mean())
assert np.abs(data.std() - 1.0) <= np.abs(feats['u1'].data[:, :13].std() -
1.0)
assert np.abs(data.mean()) <= np.abs(feats['u3'].data[:, :13].mean())
assert np.abs(data.std() - 1.0) <= np.abs(feats['u3'].data[:, :13].std() -
1.0)
# save / load the features
filename = str(tmpdir.join('feats' + ext))
feats.save(filename)
feats2 = FeaturesCollection.load(filename)
assert feats2 == feats
def mfcc_col(mfcc):
return FeaturesCollection(mfcc=mfcc)
def apply_cmvn(feats_collection,
by_collection=True,
norm_vars=True,
weights=None,
skip_dims=None):
"""CMVN normalization of a collection of features
This function is a simple wrapper on the class
:class:`~shennong.features.CmvnPostProcessor` that allows to
accumulate and apply CMVN statistics over a whole collections of
features.
Warnings
--------
The features in the collection must have the same
dimensionality. It is assumed they are all extracted from the same
processor. If this is not the case, a ValueError is raised.
Parameters
----------
feats_collection : :class:`~shennong.FeaturesCollection`
The collection of features on wich to apply CMVN normlization.
Each features in the collection is assumed to have consistent
dimensions.
by_collection : bool, optional
When True, accumulate and apply CMVN over the entire
collection. When False, do it independently for each features
in the collection. Default to True.
norm_vars : bool, optional
If False, do not apply variance normalization (only mean),
default to True.
weights : dict of arrays, optional
For each features in the collection, an array of weights to
apply on the features frames, if specified we must have
``weights.keys() == feats_collections.keys()`` (see
:func:`CmvnPostProcessor.accumulate`). Unweighted by default.
skip_dims : list of integers
The dimensions for which to skip the normalization (see
:func:`CmvnPostProcessor.process`). Default is to normalize
all dimensions.
Returns
-------
cmvn_feats_collection : :class:`~shennong.features.FeaturesCollection`
Raises
------
ValueError
If something goes wrong during CMVN processing.
"""
# extract the features dimension
dim = set(f.ndims for f in feats_collection.values())
if not len(dim) == 1:
raise ValueError(
'features in the collection must have consistent dimensions '
'but dimensions are: {}'.format(sorted(dim)))
dim = list(dim)[0]
# check weights
if weights is not None and weights.keys() != feats_collection.keys():
raise ValueError('keys differ for weights and features collection')
# check skip_dims
if skip_dims is not None:
sdmin, sdmax = min(skip_dims), max(skip_dims)
if sdmin < 0 or sdmax >= dim:
raise ValueError(
'out of bounds dimensions in skip_dims, must be in [0, {}] '
'but are in [{}, {}]'.format(dim - 1, sdmin, sdmax))
if by_collection:
# accumulate CMVN stats over the whole collection
cmvn = CmvnPostProcessor(dim)
for k, f in feats_collection.items():
w = weights[k] if weights is not None else None
cmvn.accumulate(f, weights=w)
# apply CMVN stats
return FeaturesCollection({
k: cmvn.process(f, norm_vars=norm_vars, skip_dims=skip_dims)
for k, f in feats_collection.items()
})
else:
# independently for each features in the collection,
# accumulate and apply CMNV stats
cmvn_collection = FeaturesCollection()
for k, f in feats_collection.items():
cmvn = CmvnPostProcessor(f.ndims)
cmvn.accumulate(
f, weights=weights[k] if weights is not None else None)
cmvn_collection[k] = cmvn.process(f,
norm_vars=norm_vars,
skip_dims=skip_dims)
return cmvn_collection
