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Commit aba2e39e authored by Anthony Larcher's avatar Anthony Larcher
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new dataset

parent e8dc7fc8
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Inline Side-by-side
nnet/sad_rnn.py
View file @ aba2e39e
......@@ -12,77 +12,112 @@ from sidekit.frontend.io import _read_dataset_percentile
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class SAD_Dataset(Dataset):
"""
Object that takes a list of files from a file and initialize a Dataset
"""
def __init__(self, mdtm_file, feature_file, batch_size=512, duration=3.2, step=0.8, uem_file=None, shuffle=False):
self.batch_size = batch_size
self.duration = int(duration * 100)
self.step = int(step * 100)
self.feature_file = h5py.File(feature_file, 'r')
train_list = {}
with open(mdtm_file, 'r') as f:
for line in f:
show, _, start, dur, _, _, _, _ = line.rstrip().split()
if show not in train_list:
train_list[show] = []
train_list[show].append({
"start": int(float(start) * 100),
"stop": int((float(start) + float(dur)) * 100)})
uem_list = {}
if uem_file is not None:
with open(uem_file, 'r') as f:
for line in f:
show, _, start, stop = line.rstrip().split()
if show not in uem_list:
uem_list[show] = []
uem_list[show].append({
def __init__(self, mdtm_file, features_server, batch_size=512, duration=3.2, step=0.8, uem_file=None,
shuffle=False):
def __init__(self, mdtm_file, feature_file, batch_size=512, duration=3.2, step=0.8, uem_file=None,
shuffle=False):
self.batch_size = batch_size
self.duration = int(duration * 100)
self.step = int(step * 100)
self.features_server = features_server
train_list = {}
with open(mdtm_file, 'r') as f:
lines = [l for l in f]
for line in lines[:500]:
show, _, start, dur, _, _, _, _ = line.rstrip().split()
if show not in train_list:
train_list[show] = []
train_list[show].append({
"start": int(float(start) * 100),
"stop": int(float(stop) * 100)})
else:
for show in train_list.keys():
uem_list[show].append({"start": None, "stop": None})
self.vad = {}
self.segments = []
for show in sorted(train_list.keys()):
features = _read_dataset_percentile(self.feature_file, show+"/cep")
labels = numpy.zeros((len(features), 1), dtype=numpy.int)
if show in train_list and show in uem_list:
for seg in train_list[show]:
labels[seg['start']:seg['stop']] = 1
self.vad[show] = labels
for seg in uem_list[show]:
if seg['start'] is not None:
start, stop = seg['start'], seg['stop']
else:
start, stop = 0, len(features)
for i in range(start, min(stop, len(features)) - self.duration, self.step):
self.segments.append((show, i, i + self.duration))
self.input_size = features.shape[1]
if shuffle:
random.shuffle(self.segments)
print("After Shuffle, number of segments is: {}".format(len(self.segments)))
self.len = len(self.segments) // self.batch_size
"stop": int((float(start) + float(dur)) * 100)})
uem_list = {}
if uem_file is not None:
with open(uem_file, 'r') as f:
for line in f:
show, _, start, stop = line.rstrip().split()
if show not in uem_list:
uem_list[show] = []
uem_list[show].append({
"start": int(float(start) * 100),
"stop": int(float(stop) * 100)})
else:
for show in train_list.keys():
uem_list[show].append({"start": None, "stop": None})
self.vad = {}
self.segments = []
# speech_only_segments = []
# speech_nonspeech_segments = []
for show in sorted(train_list.keys()):
features, _ = features_server.load(show)
labels = numpy.zeros((len(features), 1), dtype=numpy.int)
speech_only_segments = []
speech_nonspeech_segments = []
if show in train_list and show in uem_list:
for seg in train_list[show]:
labels[seg['start']:seg['stop']] = 1
self.vad[show] = labels
for seg in uem_list[show]:
if seg['start'] is not None:
start, stop = seg['start'], seg['stop']
else:
start, stop = 0, len(features)
for i in range(start, min(stop, len(features)) - self.duration, self.step):
self.segments.append((show, i, i + self.duration))
# cree les segments ne contenant QUE de la parole (sans recouvrement)
for i in range(start, min(stop, len(features)) - self.duration, self.duration):
if labels[i:i + self.duration].sum() == self.duration:
speech_only_segments.append((show, i, i + self.duration))
# cree les segments contenant de la PAROLE ET DU SILENCE (avec recouvrement pour equilibrer les classes)
for i in range(start, min(stop, len(features)) - self.duration, self.step):
if labels[i:i + self.duration].sum() < self.duration - 1:
speech_nonspeech_segments.append((show, i, i + self.duration))
# for i in range(start, min(stop, len(features)) - self.duration, self.step):
# self.segments.append((show, i, i + self.duration))
tmp = speech_only_segments + speech_nonspeech_segments
random.shuffle(tmp)
self.segments += tmp
print("Show {}, ratio S/NS = {}".format(show, len(speech_only_segments) / (
len(speech_nonspeech_segments) + len(speech_only_segments))))
# tmp = speech_only_segments + speech_nonspeech_segments
# if shuffle:
# print("taille de tmp: {}".format(len(tmp)))
# random.shuffle(tmp)
# print("taille de tmp: {}".format(len(tmp)))
# print(tmp[0])
# for t in tmp:
# self.segments.append(t)
# self.segments = tmp.copy()
self.input_size = features.shape[1]
print("Final ratio S/NS = {}".format(
len(speech_only_segments) / (len(speech_nonspeech_segments) + len(speech_only_segments))))
self.len = len(self.segments) // self.batch_size
def __getitem__(self, index):
batch_X = numpy.zeros((self.batch_size, self.duration, self.input_size))
batch_Y = numpy.zeros((self.batch_size, self.duration, 1))
for i in range(self.batch_size):
show, start, stop = self.segments[index * self.batch_size + i]
features = _read_dataset_percentile(self.feature_file, show + "/cep")
features, _ = self.features_server.load(show)
batch_X[i] = features[start:stop]
batch_Y[i] = self.vad[show][start:stop]
return torch.Tensor(batch_X), torch.Tensor(batch_Y)
......
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