def do_mfccs():
print("=======MFCC extraction phase========")
for folder_name in list_sets:
cepstral_type = "mfcc" # choose between "mfcc" or "plp"
# Loading id-wavs specified in the labels file
print("\nReading dir:", folder_name)
source_file = '../../data/{}/labels/labels.csv'.format(recipe)
list_of_wavs = util.traverse_dir(audio_dir + folder_name,
file_type='.wav')
list_of_wavs.sort()
list_specific_wavs = ah.load_specific(
source_file=source_file, list_original_audios=list_of_wavs)
list_specific_wavs.sort() # best 33 wavs selected manually
for deltas in [0, 1, 2]:
print("\n Extracting with {} deltas".format(deltas))
extract_mfccs.compute_flevel_feats(list_specific_wavs,
out_dir,
cepstral_type=cepstral_type,
num_feats=23,
recipe=recipe,
folder_name=folder_name,
num_deltas=deltas,
obs='')
def do_resample():
audio_dir = work_dir + 'audio/'
out_dir = work_dir + 'audio/'
list_sets = ['readtext_test', 'monologue_test']
for folder_name in list_sets:
print("Reading dir:", folder_name)
list_of_wavs = util.traverse_dir(audio_dir + folder_name, '.wav')
resample_wav.make_resample(list_of_wavs,
out_dir,
target_sample_rate=16000,
subtype='PCM_16')
def save_labels(list_sets, audio_dir, out_dir):
for task in list_sets:
list_of_wavs = util.traverse_dir(audio_dir + task, '.wav')
labels_task = []
for wav in list_of_wavs:
w, label, task_name = make_labels(wav)
labels_task.append(w + ' ' + label)
labels_task.sort()
np.savetxt(out_dir + "labels_{}.txt".format(task),
labels_task,
delimiter=',',
fmt='%s')
print("labels saved to:", out_dir)
def do_ivecs():
mfccs_dir = work_dir + 'data/{}/'.format(recipe)
out_dir = work_dir + 'data/'
file_ubm = work_dir + 'data/pcgita/ubm/.mfcc' # Naming format is: "featureType_recipeName_numberOfDeltas.mfcc"
for folder_name in list_sets:
print("\nReading dir:", mfccs_dir + folder_name)
list_mfcc_files = util.traverse_dir(mfccs_dir + folder_name, '.mfcc')
extract_ivecs.compute_ivecs(list_n_gauss=[2, 4, 8, 16, 32, 64],
list_mfcc_files=list_mfcc_files,
out_dir=out_dir,
file_ubm_feats=file_ubm,
recipe=recipe,
folder_name=folder_name)
def do_fishers():
mfccs_dir = work_dir + 'data/{}/'.format(recipe)
out_dir = work_dir + 'data/'
file_ubm = work_dir + 'data/pcgita/DDK_analysis/mfccs_pcgita_20_DDK_analysis_2del.mfcc'
for folder_name in list_sets:
print("Reading dir:", mfccs_dir + folder_name)
list_mfcc_files = util.traverse_dir(mfccs_dir + folder_name, '.mfcc')
extract_fishers.compute_fishers(list_n_clusters=[2, 4, 8, 16, 32],
list_mfcc_files=list_mfcc_files,
out_dir=out_dir,
list_files_ubm=file_ubm,
recipe=recipe,
folder_name=folder_name)
def do_ivecs_pretrained_mdls():
mfccs_dir = work_dir + 'data/{}/'.format(recipe)
out_dir = work_dir + 'data/'
ubm_dir = work_dir + 'data/' + recipe + '/UBMs/' # where the ubms live
list_ubm_files = util.traverse_dir(
ubm_dir, '.mdll') # reading all the files with .mdl format
list_sets = ['read_text']
for folder_name in list_sets: # iterating over the list of sets where the features live
print("\nReading dir:", mfccs_dir + folder_name)
for ubm in list_ubm_files: # iterating over the pretrained ubms
n_ubm = util.extract_numbers_from_str(
ubm) # getting the number of ubms of the corresponding file
print("\ni-vecs for {} GMMs".format(n_ubm))
list_mfcc_files = util.traverse_dir(
mfccs_dir + folder_name,
'.mfcc') # reading MFCCs to extracting i-vecs from
extract_ivecs.compute_ivecs_pretr_ubms(
list_mfcc_files,
out_dir, #n_ubm=n_ubm,
file_ubm=ubm,
recipe=recipe,
folder_name=folder_name)
def do_mfccs():
print("=======MFCC extraction phase========")
recipe = 'cold'
audio_dir = '/opt/project/audio/'
out_dir = '/opt/project/data/'
for folder_name in list_sets:
print("\nReading dir:", folder_name)
list_of_wavs = util.traverse_dir(audio_dir + folder_name, '.wav')
# print(list_of_wavs[0])
extract_mfccs.compute_flevel_feats(list_of_wavs,
out_dir,
num_mfccs=13,
recipe=recipe,
folder_name=folder_name)
def sel_spec_wavs():
recipe = 'dementia_new8k'
work_dir = '/media/jose/hk-data/PycharmProjects/the_speech/' # for ubuntu (native bob kaldi)
audio_dir = work_dir + 'audio/dementia_new8k'
source_file = work_dir + 'data/{}/labels/labels.csv'.format(recipe)
list_of_wavs = util.traverse_dir(audio_dir, file_type='.wav')
list_of_wavs.sort()
list_specific_wavs = ah.load_specific(source_file=source_file, list_original_audios=list_of_wavs)
list_specific_wavs.sort()
wav_names_only = []
for wav in list_specific_wavs:
wav_names_only.append(os.path.basename(wav))
return wav_names_only
def do_ivecs():
print("=======i-vector extraction phase========")
recipe = 'cold'
mfccs_dir = '/opt/project/data/{}/'.format(recipe)
out_dir = '/opt/project/data/'
file_ubm = '/opt/project/data/cold/train/mfccs_cold_13_train_2del.mfcc' # Format is: "featureType_recipeName_numberOfDeltas.mfcc"
for folder_name in list_sets:
print("\nReading dir:", mfccs_dir + folder_name)
list_mfcc_files = util.traverse_dir(mfccs_dir + folder_name, '.mfcc')
extract_ivecs.compute_ivecs(list_mfcc_files,
out_dir,
info_num_feats_got=13,
ivec_dims=256,
file_ubm_feats=file_ubm,
recipe=recipe,
folder_name=folder_name)
def do_fishers():
print("=======fisher-vector extraction phase========")
work_dir = 'C:/Users/Win10/PycharmProjects/the_speech'
recipe = 'cold'
mfccs_dir = work_dir + '/data/{}/'.format(recipe)
out_dir = work_dir + '/data/'
file_ubm = work_dir + '/data/cold/train/mfccs_cold_13_train_2del.mfcc' # Format is: "featureType_recipeName_numberOfDeltas.mfcc"
for folder_name in list_sets:
print("\nReading dir:", mfccs_dir + folder_name)
list_mfcc_files = util.traverse_dir(mfccs_dir + folder_name, '.mfcc')
extract_fishers.compute_fishers(list_n_clusters,
list_mfcc_files,
out_dir,
info_num_feats=13,
file_ubm_feats=file_ubm,
recipe=recipe,
folder_name=folder_name)
def do_frame_level():
print("=======Frame-level extraction phase========")
cepstral_type = "mfcc" # choose between "mfcc" or "plp"
for folder_name in list_sets:
print("\nReading dir:", folder_name)
list_of_wavs = util.traverse_dir(audio_dir + folder_name, '.wav')
list_of_wavs.sort()
# print(list_of_wavs)
for deltas in [0, 1, 2]:
extract_mfccs.compute_flevel_feats(list_of_wavs,
out_dir,
cepstral_type=cepstral_type,
num_feats=23,
recipe=recipe,
folder_name=folder_name,
num_deltas=deltas,
obs='')
def do_fishers_pretrained_ubm():
mfccs_dir = work_dir + 'data/{}/'.format(recipe)
out_dir = work_dir + 'data/' # Where the computed features will live in
ubm_dir = work_dir + 'data/' + recipe + '/UBMs/' # where the diagonal ubms live
list_ubm_files = util.traverse_dir(
ubm_dir, '.mdl'
) # reading all the files with .mdl or .dubm as format (latter is more reliable)
mfcc_n_deltas = 2 # Number of deltas of the mfccs
list_sets = ['monologue_erlangen', 'readtext_erlangen']
for folder_name in list_sets: # iterating over the list of sets where the features live
print("\nReading dir:", mfccs_dir + folder_name)
for ubm in list_ubm_files: # iterating over the pretrained ubms
list_mfcc_files = util.traverse_dir_2(
mfccs_dir + folder_name, '*{}del.mfcc'.format(
mfcc_n_deltas)) # reading MFCCs to extracting fishers from
extract_fishers.compute_fishers_pretr_ubm(
list_mfcc_files=list_mfcc_files,
out_dir=out_dir,
file_ubm=ubm,
recipe=recipe,
folder_name=folder_name)
def do_mfccs():
print("=======Frame-level extraction phase========")
audio_dir = work_dir + 'audio/'
out_dir = work_dir + 'data/'
list_sets = ['monologue_erlangen', 'readtext_erlangen']
cepstral_type = "mfcc" # choose between "mfcc" or "plp"
for folder_name in list_sets:
print("Reading dir:", folder_name)
list_of_wavs = util.traverse_dir(audio_dir + folder_name, '.wav')
list_of_wavs.sort()
# save the labels. list of sets/tasks (NAME of the folders containing the audios), dir to the audios, output dir
save_labels(list_sets, audio_dir,
out_dir + recipe + '/') # make labels of the wavs
for deltas in [1, 2]:
extract_mfccs.compute_flevel_feats(list_of_wavs,
out_dir,
cepstral_type=cepstral_type,
num_feats=20,
recipe=recipe,
folder_name=folder_name,
num_deltas=deltas,
obs='')