def __init__(self, csv):
self.s = spectral.Spectral()
self.s.read_data(csv)
self.centers = 2
self.kernel_type = 1
self.normalise = 1
self.max_iters = 1000
self.gamma = 0.001
self.constant = 1.0
self.order = 2.0
def construct_encoder(config_dict):
d = {}
d['nfilt'] = config_dict['nfilters']
d['do_dct'] = config_dict['cepstral']
d['compression'] = config_dict['compression']
d['do_deltas'] = config_dict['deltas']
d['do_deltasdeltas'] = config_dict['deltas']
d['fs'] = config_dict['samplerate']
if 'nceps' in config_dict:
d['nceps'] = config_dict['nceps']
return spectral.Spectral(**d)
def _load_spec(fname, nfilt, frate, highpass, fs=16000):
sig = _load_wav(fname, fs)
if highpass:
sig = hpfilter(sig, fs, highpass)
encoder = spectral.Spectral(nfilt=nfilt,
fs=fs,
wlen=0.025,
frate=frate,
compression='log',
do_dct=False,
do_deltas=False,
do_deltasdeltas=False)
return encoder.transform(sig)
def extract_features_single(filename, config):
sig, fs, _ = wavread(filename)
expected_fs = config['features']['preprocessing']['samplerate']
if fs != expected_fs:
if config['features']['preprocessing']['resample']:
try:
import scikits.samplerate
except ImportError:
print 'cannot resample because scikits.samplerate is not ' \
'installed. Either resample all audio files externally or ' \
'install it.'
sig = scikits.samplerate.resample(sig, fs / expected_fs,
'sinc_best')
else:
print 'samplerate error in file {2}: expected {0}, got {1}.\n' \
'Try to resample all audio files to the samplerate specified' \
' in the config file. If you can\'t resample yourself, set the' \
' value of "reset" to "true" in the configuration file.'.format(
fs, expected_fs, filename)
nfilt = config['features']['spectral'].get('filterbanks', 40)
ncep = config['features']['spectral'].get('nceps', 13)
do_dct = config['features']['spectral'].get('dct', True)
lowerf = config['features']['spectral'].get('lowerf', 120)
upperf = config['features']['spectral'].get('upperf', 6900)
alpha = config['features']['preprocessing'].get('preemph', 0.97)
fs = config['features']['spectral'].get('samplerate', 16000)
frate = config['features']['spectral'].get('framerate', 100)
wlen = config['features']['spectral'].get('winlen', 0.025)
nfft = config['features']['spectral'].get('nfft', 512)
compression = config['features']['spectral'].get('compression', 'log')
do_deltas = config['features']['spectral'].get('deltas', True)
do_deltasdeltas = config['features']['spectral'].get('deltas', True)
encoder = spectral.Spectral(nfilt=nfilt,
ncep=ncep,
do_dct=do_dct,
lowerf=lowerf,
upperf=upperf,
alpha=alpha,
fs=fs,
frate=frate,
wlen=wlen,
nfft=nfft,
compression=compression,
do_deltas=do_deltas,
do_deltasdeltas=do_deltasdeltas)
return encoder.transform(sig)
def _load_spec(monkey, fname, nfilt, frate, highpass, fs=16000):
key = (monkey, fname, nfilt, frate, highpass)
if not key in _spec_cache:
sig = _load_wav(monkey, fname)
if highpass:
sig = hpfilter(sig, fs, highpass)
encoder = spectral.Spectral(nfilt=nfilt,
fs=fs,
wlen=0.025,
frate=frate,
compression='log',
do_dct=False,
do_deltas=False,
do_deltasdeltas=False)
_spec_cache[key] = encoder.transform(sig)
return _spec_cache[key]
def load_data_stacked(monkey,
nframes=30,
nfilt=40,
include_noise=False,
min_samples=50):
"""Loads audio data for monkey as stacked. Only intervals.
Arguments:
:param monkey: name of the monkey
:param nframes: number of frames to stack
:param nfilt: number of filterbanks
:param include_noise: do not exclude noise intervals
:param min_samples: minimum number of samples for a class to be used
:return
X: audio representation, ndarray (nsamples x nfilt * nframes)
y: labels as int, ndarray (nsamples)
labelset: list of call names (maps onto ints in y)
"""
annot = get_annotation(monkey, include_noise=include_noise)
counts = reduce(add,
(Counter(f.mark for f in annot[fname]) for fname in annot))
annot = {
k: [f for f in v if counts[f.mark] >= min_samples]
for k, v in annot.iteritems()
}
# labelset = sorted(list(set(f.mark for fname in annot for f in annot[fname])))
# # labelset = sorted(k for k in counts if counts[k] >= min_samples)
# label2idx = dict(zip(labelset, range(len(labelset))))
# nsamples = sum(imap(len, annot.itervalues()))
frate = 100
encoder = spectral.Spectral(nfilt=nfilt,
fs=16000,
wlen=0.025,
frate=frate,
compression='log',
nfft=1024,
do_dct=False,
do_deltas=False,
do_deltasdeltas=False)
X, y, labelset = load_data_stacked_annot(monkey, annot, encoder, nframes)
return X, y, labelset
'Use option -f to force resampling of the audio file.'.
format(encoder.config['fs'], fs, f))
exit()
# feats = np.hstack(encoder.transform(sig))
feats = encoder.transform(sig)
bname = path.splitext(path.basename(f))[0]
np.save(path.join(outdir, bname + '.npy'), feats)
if __name__ == '__main__':
args = parse_args()
config_file = args['config']
try:
with open(config_file, 'r') as fid:
config = json.load(fid)
except IOError:
print 'No such file:', config_file
exit()
outdir = args['outdir']
if not os.path.exists(outdir):
print 'No such directory:', outdir
exit()
encoder = spectral.Spectral(**config)
force = args['force']
files = args['files']
convert(files, outdir, encoder, force)
def load_data_full_stacks(monkey,
nfilt=40,
stacksize=30,
highpass=2000,
min_samples=50):
"""
Arguments:
:param monkey:
:param nfilt:
:param stacksize:
:param highpass:
:param min_samples:
"""
annot = reduced_annotation(monkey, min_samples=min_samples)
X_train = {}
X_test = {}
y_train = {}
y_test = {}
frate = 100
encoder = spectral.Spectral(nfilt=nfilt,
fs=16000,
wlen=0.025,
frate=frate,
compression='log',
nfft=1024,
do_dct=False,
do_deltas=False,
do_deltasdeltas=False)
train_files, test_files = train_test_split_files(annot)
labelset = sorted(
list(
set((f.mark if f.mark != '' else 'NOISE') for fname in annot
for f in annot[fname])) + ['NOISE_ACT'])
with open(path.join(BASEDIR, 'pred_lambdas_{0}.pkl'.format(monkey)),
'rb') as fid:
pred_lambda = pickle.load(fid)
act_intervals = {}
for fname in pred_lambda:
act_intervals[fname] = speech_activity_to_intervals(pred_lambda[fname],
threshold=0.5,
winhop=0.025)
annot_train = {fname: annot[fname] for fname in train_files}
for fname in annot_train:
X, y = load_Xy(monkey, fname, encoder, annot_train[fname],
act_intervals[fname], labelset, frate, highpass,
stacksize)
X_train[fname] = X
y_train[fname] = y
annot_test = {fname: annot[fname] for fname in test_files}
for fname in annot_test:
X, y = load_Xy(monkey, fname, encoder, annot_test[fname],
act_intervals[fname], labelset, frate, highpass,
stacksize)
X_test[fname] = X
y_test[fname] = y
return X_train, X_test, y_train, y_test, labelset
# read in the wave file
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import spectral
import scipy.misc
import wave
import sys
import struct
import numpy as np
fid = wave.open(sys.argv[1], 'r')
_, _, fs, nframes, _, _ = fid.getparams()
sig = np.array(struct.unpack_from("%dh" % nframes,
fid.readframes(nframes)))
fid.close()
config = dict(fs=fs, dct=False, scale='bark', deltas=False)
extractor = spectral.Spectral(**config)
data = extractor.transform(sig)
min = np.min(data)
max = np.max(data)
print(min)
print(max)
data = data - min
data = data / (max - min)
np.savetxt(sys.argv[1] + '.mfsc', data)
scipy.misc.imsave(sys.argv[1] + '.png', data)
def load_all_intervals(nfilt=40, stacksize=30, highpass=200):
print 'loading annotations...',
annot_all = {}
t0 = time.time()
for monkey in MONKEYS:
annot_all[monkey] = data.reduced_annotation(monkey)
print 'done. time taken: {0:.3f}s'.format(time.time() - t0)
sys.stdout.flush()
print 'splitting files...',
sys.stdout.flush()
t0 = time.time()
train_test_files = {
k: data.train_test_split_files(annot_all[k])
for k in annot_all
}
print 'done. time taken: {0:.3f}s'.format(time.time() - t0)
sys.stdout.flush()
frate = 100
encoder = spectral.Spectral(nfilt=nfilt,
fs=16000,
wlen=0.025,
frate=frate,
compression='log',
nfft=1024,
do_dct=False,
do_deltas=False,
do_deltasdeltas=False)
X_train = {}
X_test = {}
y_train = {}
y_test = {}
labelset = {}
for monkey in MONKEYS:
print monkey
sys.stdout.flush()
train_files, test_files = train_test_files[monkey]
annot = annot_all[monkey]
annot_train = {fname: annot[fname] for fname in train_files}
print ' loading train data...',
sys.stdout.flush()
t0 = time.time()
X_train_, y_train_, labels_train = data.load_data_stacked_annot(
monkey, annot_train, encoder, stacksize, highpass=highpass)
print 'done. time taken: {0:.3f}s'.format(time.time() - t0)
sys.stdout.flush()
print ' loading test data...',
sys.stdout.flush()
t0 = time.time()
annot_test = {fname: annot[fname] for fname in test_files}
X_test_, y_test_, labels_test = data.load_data_stacked_annot(
monkey, annot_test, encoder, stacksize, highpass=highpass)
print 'done. time taken: {0:.3f}s'.format(time.time() - t0)
sys.stdout.flush()
# print ' scaling...',
# sys.stdout.flush()
# t0 = time.time()
# scaler = StandardScaler().fit(np.vstack((X_train_, X_test_)))
# X_train[monkey] = scaler.transform(X_train_)
# y_train[monkey] = y_train_
# X_test[monkey] = scaler.transform(X_test_)
# y_test[monkey] = y_test_
# print 'done. time taken: {0:.3f}s'.format(time.time() - t0)
X_train[monkey] = X_train_
y_train[monkey] = y_train_
X_test[monkey] = X_test_
y_test[monkey] = y_test_
assert (labels_test == labels_train)
labelset[monkey] = labels_train
return X_train, X_test, y_train, y_test, labelset
