def main(argv):
argv = FLAGS(argv)
timit = load_timit(FLAGS.corpus_path)
extractor = FeatureExtractor()
utterance_names = []
if FLAGS.data_set == "brugnara":
utterance_names = load_brugnara_files(timit)
elif FLAGS.data_set == "hundred":
utterance_names = load_brugnara_files(timit)[:100]
elif FLAGS.data_set == "ten":
utterance_names = load_brugnara_files(timit)[:10]
elif FLAGS.data_set == "one":
utterance_names = load_brugnara_files(timit)[:1]
print utterance_names
speech_problem = SpeechProblem(timit, FLAGS.output_name)
speech_problem.extract_phonemes()
for utterance_file in utterance_names:
features = extractor.extract_features(timit.abspath(utterance_file + ".wav"))
#features = extractor.random_features()
speech_problem.add_utterance(utterance_file, features)
speech_problem.extract_centers()
#speech_problem.random_centers()
speech_problem.write()
def main(argv):
argv = FLAGS(argv)
data_sets = []
timit_dev = load_timit(FLAGS.corpus_path + "TIMITNLTKTEST")
if FLAGS.shrink_data:
files = load_core_dev_files(timit_dev)[:10]
else:
files = load_core_dev_files(timit_dev)
data_sets.append((timit_dev, files, "dev"))
timit_train = load_timit(FLAGS.corpus_path + "TIMITNLTK39")
if FLAGS.shrink_data:
files = load_training_files(timit_train)[:10]
else:
files = load_training_files(timit_train)
data_sets.append((timit_train, files, "train"))
timit_test = load_timit(FLAGS.corpus_path + "TIMITNLTKTEST")
if FLAGS.shrink_data:
files = load_core_test_files(timit_test)[:10]
else:
files = load_core_test_files(timit_test)
data_sets.append((timit_test, files, "test"))
extractor = FeatureExtractor()
all_features = []
all_states = []
utterance_features = []
feature_count = 0
for timit, utterance_names, _ in data_sets:
for utterance_file in utterance_names:
features = extractor.extract_features(timit.abspath(utterance_file + ".wav"))
utterance_data = construct_gold(timit, utterance_file, features)
#states = [state for state, feature in utterance_data]
#assert(len(states) == len(features))
global_indices = []
for state, feature in utterance_data:
all_features.append(feature)
all_states.append(state)
global_indices.append(feature_count)
feature_count += 1
utterance_features.append(global_indices)
vq = VQ(FLAGS.vq_size)
vqs = vq.make_code_book(all_features, all_states)
utterance_ind = 0
for timit, utterance_names, suffix in data_sets:
file_name = FLAGS.output_prefix + "_" + str(FLAGS.vq_size) + "_" + suffix
if FLAGS.shrink_data:
file_name += "_shrink"
out_file = open(file_name, 'w')
all_utterances = []
for utterance_file in utterance_names:
feature_inds = utterance_features[utterance_ind]
utterance_ind += 1
vq_features = [vqs[ind] for ind in feature_inds]
states = [all_states[ind] for ind in feature_inds]
assert(len(vq_features) == len(states))
#utterance_data = construct_gold(timit, utterance_file, vq_features)
#all_utterances += utterance_data
print >>out_file, " ".join(["%s/%s"%(p,code) for (p, code) in izip(states , vq_features)])
if False:
correct_steps = 0
total_steps = 0
phoneme_histogram = {}
vq_histogram = {}
for p, code in all_utterances:
phoneme_histogram.setdefault(p, {})
phoneme_histogram[p].setdefault(code, 0)
phoneme_histogram[p][code] += 1
vq_histogram.setdefault(code, {})
vq_histogram[code].setdefault(p, 0)
vq_histogram[code][p] += 1
total_steps += 1
for p,groups in phoneme_histogram.iteritems():
print p
pairs = groups.items()
pairs.sort(key=lambda a: a[1])
pairs.reverse()
total = sum([num for _,num in pairs])
print "\t",
for code, nums in pairs:
if nums / float(total) < 0.01: continue
print "%3s:%3.2f "%(code, nums / float(total)),
print
for code in range(FLAGS.vq_size):
if code not in vq_histogram: continue
groups = vq_histogram[code]
print code,
pairs = groups.items()
pairs.sort(key=lambda a: a[1])
pairs.reverse()
total = sum([num for _,num in pairs])
correct_steps += pairs[0][1]
print "", total,
print "\t",
print
for p, nums in pairs:
if nums / float(total) < 0.01: continue
print "%3s:%3.2f "%(p, nums/ float(total)),
print
print total_steps, correct_steps, correct_steps / float(total_steps)
print len(phoneme_histogram.keys())
print i, p
# All the male utterances of a region.
brugnara = set([l.strip() for l in open("corpus")])
#f.startswith("dr1-f")
utterance_names = [f for f in timit.utterances() if f.split("-")[1].split("/")[0] in brugnara and "sa1" not in f and "sa2" not in f]
print len(utterance_names)
utterance_set = speech.UtteranceSet()
all_features = []
#for utterance_file in utterance_names:
for utterance_file in utterance_names:
#for utterance_file in [ u for u in utterance_names if u == "dr8-mbcg0/sx57"]:
# extract features from an audio file using AudioFileProcessor
afp = AudioFileProcessor()
afp.processFile(engine, timit.abspath(utterance_file + ".wav"))
phone_times = timit.phone_times(utterance_file)
print phone_times
last = float(phone_times[-1][2])
features = engine.readAllOutputs()
#print timit.sents(utterance_file)
utterance = utterance_set.utterances.add()
for phone in timit.phones(utterance_file):
if phone == "q": continue
utterance.phones.append(phoneme_map[phone])
#print features["mfcc"]
final = len(features["mfcc"])
#print " ".join([ str(p) for (p, s, e) in phone_times])
