def init_model():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# construct the data generator.
params = {
'dim': (257, None, 1),
'nfft': 512,
'min_slice': 720,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
if args.resume:
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
return network_eval
def extract_features(paths, args):
# GPU configuration
toolkits.initialize_GPU(args)
network_eval = model.vggvox_resnet2d_icassp(
input_dim=PARAMS["dim"], num_class=PARAMS["n_classes"], mode="eval", args=args
)
network_eval.load_weights(os.path.join(args.resume), by_name=True)
num_paths = len(paths)
feats = np.zeros((num_paths, PARAMS["feat_dim"]))
for i, path in enumerate(tqdm(paths)):
specs = ut.load_data(
path,
win_length=PARAMS["win_length"],
sr=PARAMS["sampling_rate"],
hop_length=PARAMS["hop_length"],
n_fft=PARAMS["nfft"],
spec_len=PARAMS["spec_len"],
mode="eval",
)
specs = np.expand_dims(np.expand_dims(specs, 0), -1)
feats[i] = network_eval.predict(specs)
return feats
def main(args):
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Train/Val.
# ==================================
total_list = [os.path.join(args.data_path, file) for file in os.listdir(args.data_path)]
unique_list = np.unique(total_list)
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {'dim': (257, None, 1),
'nfft': 512,
'min_slice': 720,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval', args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(args.resume))
else:
raise IOError('==> please type in the model to load')
print('==> start testing.')
# The feature extraction process has to be done sample-by-sample,
# because each sample is of different lengths.
feats = []
for ID in unique_list:
specs = preprocess.load_data(ID, split=False, win_length=params['win_length'], sr=params['sampling_rate'],
hop_length=params['hop_length'], n_fft=params['nfft'],
min_slice=params['min_slice'])
specs = np.expand_dims(np.expand_dims(specs[0], 0), -1)
v = network_eval.predict(specs)
feats += [v]
feats = np.array(feats)[:,0,:]
preprocess.similar(feats)
def diarize(segments, sr=16000, win_len=400, hop_len=160, embedding_per_sec=1.0, overlap_rate=0.1):
logger.debug("[Speaker diarization] Initializing models")
# Initialize ghostvlad
toolkits.initialize_GPU(Expando({"gpu": ""}))
ghostvlad_model = model.vggvox_resnet2d_icassp(input_dim=(257, None, 1),
num_class=5994,
mode="eval",
args=Expando({"net": "resnet34s",
"loss": "softmax",
"vlad_cluster": 8,
"ghost_cluster": 2,
"bottleneck_dim": 512,
"aggregation_mode": "gvlad"}))
ghostvlad_model.load_weights("ghostvlad/pretrained/weights.h5", by_name=True)
# Initialize uisrnn
sys.argv = sys.argv[:1]
model_args, _, inference_args = uisrnn.parse_arguments()
model_args.observation_dim = 512
uisrnn_model = uisrnn.UISRNN(model_args)
uisrnn_model.load("uisrnn/pretrained/saved_model.uisrnn_benchmark")
logger.debug("[Speaker diarization] Calculating utterance features")
utterances_spec = prepare_ghostvlad_data(segments, sr, win_len, hop_len, embedding_per_sec, overlap_rate)
feats = []
for spec in utterances_spec:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = ghostvlad_model.predict(spec)
feats += [v]
feats = np.array(feats)[:, 0, :].astype(float)
logger.debug("[Speaker diarization] Clustering utterance features")
labels = uisrnn_model.predict(feats, inference_args)
logger.debug("[Speaker diarization] Tagging segments speakers")
embedding_duration = (1/embedding_per_sec) * (1.0 - overlap_rate)
labels_count = len(labels)
current = 0
for segment in segments:
begin_index = math.floor(current/embedding_duration)
current += segment.end-segment.begin
end_index = math.ceil(current/embedding_duration)
segment_labels = [labels[index] for index in range(begin_index, min(end_index, labels_count))]
if len(segment_labels) > 0:
segment.speaker = max(segment_labels, key=segment_labels.count)
else:
segment.speaker = 999
return segments
def load_model():
toolkits.initialize_GPU(args)
global network_eval
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
def __init__(self):
netConfig = {
'net': 'resnet34s',
'ghost_cluster': 2,
'vlad_cluster': 8,
'bottleneck_dim': 512,
'aggregation_mode': 'gvlad',
'loss': 'softmax',
'dim': (257, None, 1),
'n_classes': 5994,
}
netConfig = namedtuple("NetConfig",
netConfig.keys())(*netConfig.values())
self.net = model.vggvox_resnet2d_icassp(input_dim=netConfig.dim,
num_class=netConfig.n_classes,
mode='eval',
args=netConfig)
self.net.load_weights(os.path.join(
'../model/gvlad_softmax/resnet34_vlad8_ghost2_bdim512_deploy/weights.h5'
),
by_name=True)
def main():
# gpu configuration
toolkits.initialize_GPU(args)
# get speaker id from folder name
totalList = [os.path.join(dataPath, file) for file in os.listdir(dataPath)]
uniqueList = np.unique(totalList)
speakerList = [extractSpeakerId(u) for u in uniqueList]
# get audio file for each speaker
speakerAudioDict = {}
for speaker in speakerList:
# root path
rootPath = os.path.join(dataPath, speaker)
# get list of files
fileList = getListOfFiles(rootPath)
# add to dict
speakerAudioDict[speaker] = fileList
# get embedding for each audio of speaker
speakerToFeatureDict = {}
for speaker in speakerList:
# construct the data generator.
params = {
'dim': (257, None, 1),
'nfft': 512,
'min_slice': 720,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(
input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume),
by_name=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
feats = []
for ID in speakerAudioDict[speaker]:
specs = preprocess.load_data(ID,
split=False,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
min_slice=params['min_slice'])
specs = np.expand_dims(np.expand_dims(specs[0], 0), -1)
v = network_eval.predict(specs)
feats += [v]
speakerToFeatureDict[speaker] = feats
# save to file
with open('speaker_data.pickle', 'wb') as handle:
pickle.dump(speakerToFeatureDict,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
import generator
# ==================================
# Get Train/Val.
# ==================================
trnlist, trnlb = toolkits.get_hike_datalist(
meta_paths=args.train_meta_data_path,
data_paths=args.train_data_path,
mode=model_config['loss'])
vallist, vallb = toolkits.get_hike_datalist(
meta_paths=args.val_meta_data_path,
data_paths=args.val_data_path,
mode=model_config['loss'])
input_length = int(args.audio_length * 25)
num_class = len(score_rule)
# construct the data generator.
params = {
'dim': (513, input_length, 1),
'mp_pooler': toolkits.set_mp(processes=args.multiprocess),
'nfft': 1024,
'spec_len': input_length,
'win_length': 1024,
'hop_length': 640,
'n_classes': num_class,
'sampling_rate': 16000,
'batch_size': model_config['batch_size'],
'shuffle': True,
'normalize': True,
'loss': model_config['loss'],
'data_format': args.data_format
}
# Datasets
partition = {'train': trnlist.flatten(), 'val': vallist.flatten()}
labels = {'train': trnlb.flatten(), 'val': vallb.flatten()}
# Generators
wandb.init(project='vgg_speaker')
trn_gen = generator.DataGenerator(partition['train'], labels['train'],
**params)
val_gen = generator.DataGenerator(partition['val'], labels['val'],
**params)
network = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='train',
args=model_config)
# # val data
# val_data = [params['mp_pooler'].apply_async(ut.load_data,
# args=(ID, params['win_length'], params['sampling_rate'], params['hop_length'],
# params['nfft'], params['spec_len'], 'train', args.data_format)) for ID in partition['val']]
# val_data = np.expand_dims(np.array([p.get() for p in val_data]), -1)
# ==> load pre-trained model ???
print(keras.backend.tensorflow_backend._get_available_gpus())
if args.resume:
print("Attempting to load", args.resume)
if args.resume:
if os.path.isfile(args.resume):
network.load_weights(os.path.join(args.resume),
by_name=True,
skip_mismatch=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise ValueError("==> no checkpoint found at '{}'".format(
args.resume))
print(network.summary())
print('==> gpu {} is, training {} images, classes: 0-{} '
'loss: {}, aggregation: {}, ohemlevel: {}'.format(
args.gpu, len(partition['train']), np.max(labels['train']),
model_config['loss'], model_config['aggregation_mode'],
model_config['ohem_level']))
model_path, log_path = set_path(args, model_config)
normal_lr = keras.callbacks.LearningRateScheduler(step_decay)
# tbcallbacks = keras.callbacks.TensorBoard(log_dir=log_path, histogram_freq=0, write_graph=True, write_images=False,
# update_freq=model_config['batch_size'] * 16)
callbacks = [
keras.callbacks.ModelCheckpoint(
os.path.join(model_path, 'weights-{epoch:02d}-{loss:.3f}.h5'),
monitor='loss',
mode='min',
save_best_only=True,
period=20,
), normal_lr,
WandbCallback()
]
if model_config[
'ohem_level'] > 1: # online hard negative mining will be used
candidate_steps = int(
len(partition['train']) // model_config['batch_size'])
iters_per_epoch = int(
len(partition['train']) //
(model_config['ohem_level'] * model_config['batch_size']))
ohem_generator = generator.OHEM_generator(
network, trn_gen, candidate_steps, model_config['ohem_level'],
model_config['batch_size'], params['dim'], params['n_classes'])
A = ohem_generator.next(
) # for some reason, I need to warm up the generator
network.fit_generator(generator.OHEM_generator(
network, trn_gen, iters_per_epoch, model_config['ohem_level'],
model_config['batch_size'], params['dim'], params['n_classes']),
steps_per_epoch=iters_per_epoch,
epochs=model_config['epochs'],
max_queue_size=10,
callbacks=callbacks,
use_multiprocessing=False,
workers=1,
verbose=1)
else:
if model_config['loss'] != 'mse':
network.fit_generator(trn_gen,
steps_per_epoch=int(
len(partition['train']) //
model_config['batch_size']),
epochs=model_config['epochs'],
max_queue_size=10,
validation_data=val_gen,
validation_freq=1,
callbacks=callbacks,
use_multiprocessing=False,
workers=1,
verbose=1)
else:
network.fit_generator(trn_gen,
steps_per_epoch=int(
len(partition['train']) //
model_config['batch_size']),
epochs=model_config['epochs'],
max_queue_size=10,
validation_data=val_gen,
validation_freq=1,
callbacks=callbacks,
use_multiprocessing=False,
workers=1,
verbose=1)
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Train/Val.
# ==================================
print('==> calculating test({}) data lists...'.format(args.test_type))
publicTest = pd.read_csv("/content/VoveDataset/public-test.csv")
list1 = addPath(np.array(publicTest["audio_1"]))
list2 = addPath(np.array(publicTest["audio_2"]))
total_list = np.concatenate((list1, list2))
unique_list = np.unique(total_list)
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
result_path = "/content/VGG-Speaker-Recognition/result"
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
print('==> start testing.')
# The feature extraction process has to be done sample-by-sample,
# because each sample is of different lengths.
total_length = len(unique_list)
feats, scores, labels = [], [], []
for c, ID in enumerate(pbar(unique_list)):
specs = ut.load_data(ID,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
spec_len=params['spec_len'],
mode='eval')
specs = np.expand_dims(np.expand_dims(specs, 0), -1)
v = network_eval.predict(specs)
feats += [v]
feats = np.array(feats)
np.save("/content/feats.npy", feats)
# ===========================================
def wav2spec(wav):
wav = np.append(wav, wav[::-1])
wav = wav.astype(np.float)
linear_spect = librosa.stft(wav, n_fft=512, win_length=400, hop_length=160).T
mag, _ = librosa.magphase(linear_spect)
spec_mag = mag.T
mu = np.mean(spec_mag, 0, keepdims=True)
std = np.std(spec_mag, 0, keepdims=True)
specs = (spec_mag - mu) / (std + 1e-5)
specs = np.expand_dims(np.expand_dims(specs, 0), -1)
return specs
if __name__ == '__main__':
#os.environ["CUDA_VISIBLE_DEVICES"] = "0" # 如有GPU的话取消注释该行，GPU会加速特征提取.
network_eval = model.vggvox_resnet2d_icassp(input_dim=(257, None, 1),num_class=5994, mode='eval')
network_eval.load_weights(os.path.join('model/weights.h5'), by_name=True)
my_model = Mymodel(network_eval)
wav1_path = "audio/spk1_1.wav"
wav2_path = "audio/spk2_2.wav"
audio1, sr = sf.read(wav1_path)
audio2, sr = sf.read(wav2_path)
spec1 = wav2spec(audio1)
spec2 = wav2spec(audio2)
t0 = time.time()
feat1 = my_model.get_feats(spec1)
t1 = time.time()
print("{} 语音时长: {}s，提取该语音所需时间: {} s".format(wav1_path, len(audio1)/sr, t1-t0))
feat2 = my_model.get_feats(spec2)
print("{} 语音时长: {}s，提取该语音所需时间: {} s".format(wav2_path, len(audio2)/sr, time.time()-t1))
# gpu configuration
toolkits.initialize_GPU(args)
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = spkModel.vggvox_resnet2d_icassp(
input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
network_eval.load_weights(args.resume, by_name=True)
model_args, _, inference_args = uisrnn.parse_arguments()
model_args.observation_dim = 512
uisrnnModel = uisrnn.UISRNN(model_args)
uisrnnModel.load(SAVED_MODEL_NAME)
while (True):
print("Start speaking")
myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=2)
print(type(myrecording))
sd.wait() # Wait until recording is finished0000
print("Finished recording")
write('wavs/output12.wav', fs, myrecording)
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Train/Val.
# ==================================
print('==> calculating test({}) data lists...'.format(args.test_type))
if args.test_type == 'normal':
verify_list = np.loadtxt('../meta/voxceleb1_veri_test.txt', str)
elif args.test_type == 'hard':
verify_list = np.loadtxt('../meta/voxceleb1_veri_test_hard.txt', str)
elif args.test_type == 'extend':
verify_list = np.loadtxt('../meta/voxceleb1_veri_test_extended.txt',
str)
else:
raise IOError('==> unknown test type.')
verify_lb = np.array([int(i[0]) for i in verify_list])
list1 = np.array([os.path.join(args.data_path, i[1]) for i in verify_list])
list2 = np.array([os.path.join(args.data_path, i[2]) for i in verify_list])
total_list = np.concatenate((list1, list2))
unique_list = np.unique(total_list)
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {
'dim': (257, 100, 1),
'nfft': 512,
'spec_len': 100,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
result_path = set_result_path(args)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
print('==> start testing.')
# The feature extraction process has to be done sample-by-sample,
# because each sample is of different lengths.
total_length = len(unique_list)
feats, scores, labels = [], [], []
for c, ID in enumerate(unique_list):
if c % 50 == 0:
print('Finish extracting features for {}/{}th wav.'.format(
c, total_length))
specs = ut.load_data(ID,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
spec_len=params['spec_len'],
mode='eval')
specs = np.expand_dims(np.expand_dims(specs, 0), -1)
v = network_eval.predict(specs)
feats += [v]
feats = np.array(feats)
# ==> compute the pair-wise similarity.
for c, (p1, p2) in enumerate(zip(list1, list2)):
ind1 = np.where(unique_list == p1)[0][0]
ind2 = np.where(unique_list == p2)[0][0]
v1 = feats[ind1, 0]
v2 = feats[ind2, 0]
scores += [np.sum(v1 * v2)]
labels += [verify_lb[c]]
print('scores : {}, gt : {}'.format(scores[-1], verify_lb[c]))
scores = np.array(scores)
labels = np.array(labels)
np.save(os.path.join(result_path, 'prediction_scores.npy'), scores)
np.save(os.path.join(result_path, 'groundtruth_labels.npy'), labels)
eer, thresh = toolkits.calculate_eer(labels, scores)
print('==> model : {}, EER: {}'.format(args.resume, eer))
def main(args):
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
_ = tf.Session(config=config)
# ==================================
# Get Train/Val.
# ==================================
trnlist, trnlb = utils.get_voxceleb2_datalist(path=args.train_list)
vallist, vallb = utils.get_voxceleb2_datalist(path=args.val_list)
# construct the data generator.
params = {'dim': (257, 250, 1),
'mp_pooler': utils.set_mp(processes=args.multiprocess),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': args.n_classes,
'sampling_rate': 16000,
'batch_size': args.batch_size,
'shuffle': True,
'normalize': True,
}
# Datasets
partition = {'train': trnlist.flatten(), 'val': vallist.flatten()}
labels = {'train': trnlb.flatten(), 'val': vallb.flatten()}
# Generators
trn_gen = generator.DataGenerator(partition['train'], labels['train'], **params)
val_gen = generator.DataGenerator(partition['val'], labels['val'], **params)
network = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='train', args=args)
# ==> load pre-trained model
mgpu = len(keras.backend.tensorflow_backend._get_available_gpus())
initial_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
if mgpu == 1:
network.load_weights(os.path.join(args.resume))
else:
network.layers[mgpu + 1].load_weights(os.path.join(args.resume))
initial_epoch = int(os.path.basename(args.resume).split('-')[1])
print('==> successfully loading model {}.'.format(args.resume))
else:
print("==> no checkpoint found at '{}'".format(args.resume))
print(network.summary())
print('==> gpu {} is, training {} images, classes: 0-{} loss: {}, aggregation: {}'
.format(args.gpu, len(partition['train']), np.max(labels['train']), args.loss, args.aggregation_mode))
model_path, log_path = set_path(args)
normal_lr = keras.callbacks.LearningRateScheduler(step_decay)
tbcallbacks = keras.callbacks.TensorBoard(log_dir=log_path, histogram_freq=0, write_graph=True, write_images=False,
update_freq=args.batch_size * 16)
callbacks = [keras.callbacks.ModelCheckpoint(os.path.join(model_path, 'weights-{epoch:02d}-{acc:.3f}.h5'),
monitor='loss',
mode='min',
save_best_only=True),
normal_lr, tbcallbacks]
network.fit_generator(generator=trn_gen,
steps_per_epoch=int(len(partition['train']) // args.batch_size),
epochs=args.epochs,
initial_epoch=initial_epoch,
max_queue_size=10,
callbacks=callbacks,
use_multiprocessing=True,
validation_data=val_gen,
workers=4,
verbose=1)
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Train/Val.
# ==================================
vallist, vallb = toolkits.get_hike_datalist(
meta_paths=args.test_meta_data_path,
data_paths=args.test_data_path,
mode=model_config['loss'])
_, valscore = toolkits.get_hike_datalist(
meta_paths=args.test_meta_data_path,
data_paths=args.test_data_path,
mode='mse')
# ==================================
# Get Model
# ==================================
# construct the data generator.
num_class = len(score_rule)
input_length = int(args.audio_length * 25)
params = {
'dim': (513, None, 1),
'mp_pooler': toolkits.set_mp(processes=args.multiprocess),
'nfft': 1024,
'spec_len': input_length,
'win_length': 1024,
'hop_length': 640,
'n_classes': num_class,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=model_config)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume),
by_name=True,
skip_mismatch=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
print('==> start testing.')
v = []
for ID in vallist:
val_data = ut.load_data(ID, params['win_length'],
params['sampling_rate'], params['hop_length'],
params['nfft'], params['spec_len'], 'test',
args.data_format)
info = network_eval.predict(np.expand_dims(val_data, (0, -1)))
v += info.tolist()
v = np.array(v)
print('val data shape {}'.format(v.shape))
if model_config['loss'] == 'mse':
v = v.T[0] * 10 + 5
vallb = vallb * 10 + 5
metric = np.square(np.subtract(v, vallb)).mean()
print('mse: ', metric)
v_test = np.vstack([v, vallb]).astype('float').T
df = np.hstack([vallist.reshape(-1, 1), v_test])
df = pd.DataFrame(data=df,
columns=['content', 'score_predict', 'score_true'])
else:
valscore = valscore * 10 + 5
v_predict = ((v < 0.5) * 1)[:, 0]
metric = sum(v_predict == vallb) / len(vallb)
print('confusion matrix: ', confusion_matrix(vallb, v_predict))
print('accuracy ', metric)
v_test = np.hstack([v,
vallb.reshape(-1, 1),
valscore.reshape(-1, 1)]).astype('float')
df = np.hstack([vallist.reshape(-1, 1), v_test])
df = pd.DataFrame(data=df,
columns=[
'content', 'prob_0', 'prob_1', 'true_label',
'score_true'
])
date = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
df.to_csv(
os.path.join(args.save_dir,
'{}_{}_{}.csv'.format(date, model_config['loss'],
metric)))
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
import generator
# ==================================
# Get Train/Val.
# ==================================
trnlist, trnlb, l2i = toolkits.load_from_kaldi_dir(args,
"train",
min_len=300)
vallist, vallb, _ = toolkits.load_from_kaldi_dir(args,
"val",
min_len=300,
label2idx=l2i)
if args.cmvn:
cmvn_stats = kaldiio.load_mat(args.cmvn)
mean_stats = cmvn_stats[0, :-1]
count = cmvn_stats[0, -1]
offset = np.expand_dims(mean_stats, 0) / count
print("offset", offset)
CMVN = offset
else:
CMVN = None
if args.post_cmvn:
cmvn_stats = kaldiio.load_mat(args.post_cmvn)
mean_stats = cmvn_stats[0, :-1]
count = cmvn_stats[0, -1]
offset = np.expand_dims(mean_stats, 0) / count
print("offset", offset)
POSTCMVN = offset
else:
POSTCMVN = None
# construct the data generator.
params = {
'dim': (args.dim, 300, 1),
'mp_pooler': toolkits.set_mp(processes=args.multiprocess),
'nfft': 512,
'spec_len': 300,
'win_length': 400,
'hop_length': 160,
'n_classes': 8,
'sampling_rate': 16000,
'tandem': args.tandem,
'batch_size': args.batch_size,
'shuffle': True,
'normalize': False,
'cmvn': CMVN,
'postcmvn': POSTCMVN
}
# Datasets
partition = {'train': trnlist, 'val': vallist}
labels = {'train': trnlb.flatten(), 'val': vallb.flatten()}
# Generators
trn_gen = generator.DataGenerator(partition['train'], labels['train'],
**params)
val_gen = generator.DataGenerator(partition['val'], labels['val'],
**params)
network = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='train',
args=args)
# ==> load pre-trained model ???
mgpu = len(keras.backend.tensorflow_backend._get_available_gpus())
if args.resume:
print("Attempting to load", args.resume)
if args.resume:
if os.path.isfile(args.resume):
if mgpu == 1:
# by_name=True, skip_mismatch=True
# https://github.com/WeidiXie/VGG-Speaker-Recognition/issues/46
network.load_weights(os.path.join(args.resume),
by_name=True,
skip_mismatch=True)
else:
network.layers[mgpu + 1].load_weights(
os.path.join(args.resume))
print('==> successfully loading model {}.'.format(args.resume))
else:
print("==> no checkpoint found at '{}'".format(args.resume))
print(network.summary())
print('==> gpu {} is, training {} images, classes: 0-{} '
'loss: {}, aggregation: {}, ohemlevel: {}'.format(
args.gpu, len(partition['train']), np.max(labels['train']),
args.loss, args.aggregation_mode, args.ohem_level))
model_path, log_path = set_path(args)
with open(os.path.join(model_path, 'label2idx'), 'w') as f:
for key in l2i.keys():
f.write(key + ' ' + str(l2i[key]) + '\n')
normal_lr = keras.callbacks.LearningRateScheduler(step_decay)
tbcallbacks = keras.callbacks.TensorBoard(log_dir=log_path,
histogram_freq=0,
write_graph=True,
write_images=False,
update_freq=args.batch_size * 16)
callbacks = [
keras.callbacks.ModelCheckpoint(os.path.join(
model_path, 'weights-{epoch:02d}-{val_loss:.3f}.h5'),
monitor='val_loss',
mode='min',
save_best_only=True), normal_lr,
tbcallbacks
]
if args.ohem_level > 1: # online hard negative mining will be used
candidate_steps = int(len(partition['train']) // args.batch_size)
iters_per_epoch = int(
len(partition['train']) // (args.ohem_level * args.batch_size))
ohem_generator = generator.OHEM_generator(
network, trn_gen, candidate_steps, args.ohem_level,
args.batch_size, params['dim'], params['n_classes'])
A = ohem_generator.next(
) # for some reason, I need to warm up the generator
network.fit_generator(generator.OHEM_generator(
network, trn_gen, iters_per_epoch, args.ohem_level,
args.batch_size, params['dim'], params['n_classes']),
steps_per_epoch=iters_per_epoch,
epochs=args.epochs,
max_queue_size=10,
callbacks=callbacks,
use_multiprocessing=False,
workers=1,
verbose=1)
else:
network.fit_generator(trn_gen,
validation_data=val_gen,
steps_per_epoch=int(
len(partition['train']) // args.batch_size),
epochs=args.epochs,
max_queue_size=10,
callbacks=callbacks,
use_multiprocessing=True,
workers=12,
verbose=1)
def main(args):
# 減少显存占用
config = tensorflow.ConfigProto()
config.gpu_options.allow_growth = True
_ = tensorflow.Session(config=config)
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': args.n_classes,
'sampling_rate': 16000,
'normalize': True
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model
network_eval.load_weights(os.path.join(args.resume), by_name=True)
print('==> successfully loading model {}.'.format(args.resume))
start = time.time()
# 获取第一个语音特征
specs1 = utils.load_data(args.audio1_path,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
spec_len=params['spec_len'],
mode='eval')
specs1 = np.expand_dims(np.expand_dims(specs1, 0), -1)
feature1 = network_eval.predict(specs1)[0]
# 获取第二个语音特征
specs2 = utils.load_data(args.audio2_path,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
spec_len=params['spec_len'],
mode='eval')
specs2 = np.expand_dims(np.expand_dims(specs2, 0), -1)
feature2 = network_eval.predict(specs2)[0]
end = time.time()
dist = np.dot(feature1, feature2.T)
if dist > 0.8:
print("%s 和 %s 为同一个人，相似度为：%f，平均预测时间：%dms" %
(args.audio1_path, args.audio2_path, dist,
round((end - start) * 1000) / 2))
else:
print("%s 和 %s 不是同一个人，相似度仅为：%f，平均预测时间：%dms" %
(args.audio1_path, args.audio2_path, dist,
round((end - start) * 1000) / 2))
def main(wav_path, embedding_per_second=1.0, overlap_rate=0.5):
# gpu configuration
toolkits.initialize_GPU(args)
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = spkModel.vggvox_resnet2d_icassp(
input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
network_eval.load_weights(args.resume, by_name=True)
model_args, _, inference_args = uisrnn.parse_arguments()
model_args.observation_dim = 512
uisrnnModel = uisrnn.UISRNN(model_args)
uisrnnModel.load(SAVED_MODEL_NAME)
specs, intervals = load_data(wav_path,
embedding_per_second=embedding_per_second,
overlap_rate=overlap_rate)
#specs1,interval1 = load_data(r'wavs/REC20190716140159.wav', embedding_per_second=1.2, overlap_rate=0.4)
#mapTable1,keys1 =genMap(interval1)
mapTable, keys = genMap(intervals)
feats = []
for spec in specs:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = network_eval.predict(spec)
feats += [v]
# =============================================================================
# for spec1 in specs1:
# spec1 = np.expand_dims(np.expand_dims(spec1, 0), -1)
# v = network_eval.predict(spec1)
# feats += [v]
# =============================================================================
feats = np.array(feats)[:, 0, :].astype(float) # [splits, embedding dim]
#print(len(feats),'00000000')
#predicted_label = uisrnnModel.predict(feats, inference_args)
#silhoutte score
# =============================================================================
# sli=[]
# fromsel=[]
# li=[]
# knum=[]
# for i in range(10):
# li=[]
# range_n_clusters = list (range(2,5))
# for n_clusters in range_n_clusters:
# clusterer = KMeans(n_clusters=n_clusters)
# preds = clusterer.fit_predict(feats)
# centers = clusterer.cluster_centers_
#
# score = silhouette_score (feats, preds, metric='euclidean')
# print ("For n_clusters = {}, silhouette score is {})".format(n_clusters, score))
# li.append([n_clusters,score,clusterer,centers])
# # =============================================================================
# # print([float(str(i[1])[:4]) for i in li])
# # kvalue=(max([float(str(i[1])[:4]) for i in li]))
# # for i in range(len(li)):
# # if kvalue==float(str(li[i][1])[:4]):
# # true_k=li[i][0]
# # break
# # =============================================================================
# maxi=li[0][1]
# for i in range(1,len(li)):
# if li[i][1]-maxi>=0.005:
# maxi=li[i][1]
# for i in li:
# if i[1]==maxi:
# true_k=i[0]
# # =============================================================================
# # maxi=max([i[1] for i in li])
# # for i in li:
# # if i[1]==maxi:
# # true_k=i[0]
# # =============================================================================
# fromsel.append(li[true_k-2])
# print(true_k)
# knum.append(true_k)
# kval=(max(set(knum), key=knum.count))
# print(kval)
# =============================================================================
clusterer = SpectralClusterer(min_clusters=2,
max_clusters=100,
p_percentile=0.95,
gaussian_blur_sigma=1)
predicted_label = clusterer.predict(feats)
# =============================================================================
# clusters = KMeans(n_clusters=40, init='k-means++', max_iter=100, n_init=1, random_state = 0)
# clusters.fit(feats)
# tsne = TSNEVisualizer()
# tsne.fit(feats, ["c{}".format(c) for c in clusters.labels_])
# tsne.poof()
# =============================================================================
global no_speakers
no_speakers = len(set(predicted_label))
#print(predicted_label,'**************************')
time_spec_rate = 1000 * (1.0 / embedding_per_second) * (
1.0 - overlap_rate) # speaker embedding every ?ms
center_duration = int(1000 * (1.0 / embedding_per_second) // 2)
speakerSlice = arrangeResult(predicted_label, time_spec_rate)
for spk, timeDicts in speakerSlice.items(
): # time map to orgin wav(contains mute)
for tid, timeDict in enumerate(timeDicts):
s = 0
e = 0
for i, key in enumerate(keys):
if (s != 0 and e != 0):
break
if (s == 0 and key > timeDict['start']):
offset = timeDict['start'] - keys[i - 1]
s = mapTable[keys[i - 1]] + offset
if (e == 0 and key > timeDict['stop']):
offset = timeDict['stop'] - keys[i - 1]
e = mapTable[keys[i - 1]] + offset
speakerSlice[spk][tid]['start'] = s
speakerSlice[spk][tid]['stop'] = e
for spk, timeDicts in speakerSlice.items():
print('========= ' + str(spk) + ' =========')
for timeDict in timeDicts:
s = timeDict['start']
e = timeDict['stop']
s = fmtTime(s) # change point moves to the center of the slice
e = fmtTime(e)
print(s + ' ==> ' + e)
p = PlotDiar(map=speakerSlice, wav=wav_path, gui=True, size=(25, 6))
p.draw()
p.plot.show()
def extract_embeddings(input_path=time_100_emp_train, mode='train'):
toolkits.initialize_GPU(args)
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
if args.resume:
weight_path = os.path.join(base_path, args.resume)
if os.path.isfile(weight_path):
print('loading graph')
network_eval.load_weights(weight_path, by_name=True)
else:
return 'Issue with loading graph'
else:
return 'Pre-trained graph is required'
if mode == 'train':
audio_files = [
filename for filename in Path(input_path).rglob('*.wav')
]
total_files = len(audio_files) * 10
working_file = 0
emb_store = {}
for audio in audio_files:
print(f'processing {os.path.basename(os.path.dirname(audio))} ')
specs = ut.load_data_aug(audio,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
spec_len=params['spec_len'],
mode='eval')
count_file = 0
for sample in specs:
print(f'Augmentation count is {count_file}')
print(f'Processing file {working_file} of {total_files}')
sample_spec = np.expand_dims(np.expand_dims(sample, 0), -1)
class_label = os.path.basename(os.path.dirname(audio))
v = network_eval.predict(sample_spec)
old_data = []
if class_label in emb_store.keys():
pre_data = emb_store.get(class_label)
pre_data.append(v[0])
old_data = pre_data
else:
old_data.append(v[0])
emb_store[class_label] = old_data
count_file += 1
working_file += 1
logging.info(f'For {audio} label stored is {class_label}')
with open('../data/training_features_augmented.pickle',
'wb') as handle:
pickle.dump(emb_store, handle, protocol=pickle.HIGHEST_PROTOCOL)
else:
specs = ut.load_data(input_path,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
spec_len=params['spec_len'],
mode='eval')
specs = np.expand_dims(np.expand_dims(specs, 0), -1)
vector_embedding = network_eval.predict(specs)[0]
return vector_embedding
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Train/Val.
# ==================================
print('Calculating test data lists...')
# AI project list file
if args.test_type == 'ai':
verify_list = np.loadtxt('model/meta/sets.txt', str)
else:
raise IOError('Unknown test type.')
verify_lb = np.array([int(i[0]) for i in verify_list])
list1 = np.array([os.path.join(args.data_path, i[1]) for i in verify_list])
list2 = np.array([os.path.join(args.data_path, i[2]) for i in verify_list])
total_list = np.concatenate((list1, list2))
unique_list = np.unique(total_list)
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval', args=args)
# ==> load pre-trained model
if args.resume:
# Load pretrained weight
if os.path.isfile('model/src/weights.h5'):
network_eval.load_weights('model/src/weights.h5', by_name=True)
print('Successfully loading model {}.'.format(args.resume))
else:
raise IOError("No checkpoint found at '{}'".format(args.resume))
else:
raise IOError('Please type in the model to load')
print('\nStart testing...')
# The feature extraction process has to be done sample-by-sample,
# because each sample is of different lengths.
total_length = len(unique_list)
feats, scores, labels = [], [], []
for c, ID in enumerate(unique_list):
specs = ut.load_data(ID, win_length=params['win_length'], sr=params['sampling_rate'],
hop_length=params['hop_length'], n_fft=params['nfft'],
spec_len=params['spec_len'], mode='eval')
specs = np.expand_dims(np.expand_dims(specs, 0), -1)
v = network_eval.predict(specs)
feats += [v]
feats = np.array(feats)
allscores = []
match = []
nomatch = []
# ==> compute the pair-wise similarity.
print("Model 1 scores")
for c, (p1, p2) in enumerate(zip(list1, list2)):
ind1 = np.where(unique_list == p1)[0][0]
ind2 = np.where(unique_list == p2)[0][0]
v1 = feats[ind1, 0]
v2 = feats[ind2, 0]
scores += [np.sum(v1*v2)]
labels += [verify_lb[c]]
if c != 0 and verify_lb[c] == 1:
match.append(scores[-1])
elif verify_lb[c] == 0:
nomatch.append(scores[-1])
allscores.append(scores[-1])
print('Score : {}'.format(scores[-1]))
# For evaluation
# match = [str(x) for x in match]
# nomatch = [str(x) for x in nomatch]
# with open("./eval/result.txt", "a") as w:
# matches = ','.join(match)
# nomatches = ','.join(nomatch)
# w.write(matches+'\n')
# w.write(nomatches+'\n')
with open("result1.pickle", "wb") as w:
pickle.dump(scores, w)
def main(wav_path, embedding_per_second=1.0, overlap_rate=0.5):
# gpu configuration
toolkits.initialize_GPU(args)
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = spkModel.vggvox_resnet2d_icassp(
input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
network_eval.load_weights(args.resume, by_name=True)
model_args, _, inference_args = uisrnn.parse_arguments()
model_args.observation_dim = 512
uisrnnModel = uisrnn.UISRNN(model_args)
uisrnnModel.load(SAVED_MODEL_NAME)
specs, intervals = load_data(wav_path,
embedding_per_second=embedding_per_second,
overlap_rate=overlap_rate)
mapTable, keys = genMap(intervals)
feats = []
for spec in specs:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = network_eval.predict(spec)
feats += [v]
feats = np.array(feats)[:, 0, :].astype(float) # [splits, embedding dim]
predicted_label = uisrnnModel.predict(feats, inference_args)
time_spec_rate = 1000 * (1.0 / embedding_per_second) * (
1.0 - overlap_rate) # speaker embedding every ?ms
center_duration = int(1000 * (1.0 / embedding_per_second) // 2)
speakerSlice = arrangeResult(predicted_label, time_spec_rate)
for spk, timeDicts in speakerSlice.items(
): # time map to orgin wav(contains mute)
for tid, timeDict in enumerate(timeDicts):
s = 0
e = 0
for i, key in enumerate(keys):
if (s != 0 and e != 0):
break
if (s == 0 and key > timeDict['start']):
offset = timeDict['start'] - keys[i - 1]
s = mapTable[keys[i - 1]] + offset
if (e == 0 and key > timeDict['stop']):
offset = timeDict['stop'] - keys[i - 1]
e = mapTable[keys[i - 1]] + offset
speakerSlice[spk][tid]['start'] = s
speakerSlice[spk][tid]['stop'] = e
# for spk,timeDicts in speakerSlice.items():
# print('========= ' + str(spk) + ' =========')
# for timeDict in timeDicts:
# s = timeDict['start']
# e = timeDict['stop']
# s = fmtTime(s) # change point moves to the center of the slice
# e = fmtTime(e)
# print(s+' ==> '+e)
# p = PlotDiar(map=speakerSlice, wav=wav_path, gui=True, size=(25, 6))
# p.draw()
# p.plot.show()
speech_r = speech_reg.Recognizer()
sound = AudioSegment.from_wav(wav_path)
for spk in speakerSlice.keys():
print('========= ' + str(spk) + ' =========')
for item_dict in speakerSlice[spk]:
audio_seg = sound[item_dict['start']:item_dict['stop']]
s = item_dict['start']
e = item_dict['stop']
s = fmtTime(s) # change point moves to the center of the slice
e = fmtTime(e)
print(s + ' ==> ' + e)
item_dict.update({'content': audio_seg})
filename = 'speaker' + str(spk) + '-' + str(
item_dict['start'] / 1000) + '-' + str(
item_dict['stop'] / 1000) + '.wav'
audio_seg.export(filename, format="wav")
audio = speech_reg.AudioFile(filename)
# words=speech_reg.AudioData(audio_seg,sample_rate=fs,sample_width=2)
with audio as source:
words = speech_r.record(source)
try:
res = speech_r.recognize_google(words)
except speech_reg.UnknownValueError:
try:
res = speech_r.recognize_sphinx(words)
except speech_reg.UnknownValueError:
res = ''
item_dict.update({'content': res})
print(res)
return speakerSlice
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
params = {
'dim': (513, None, 1),
'n_fft': 1024,
'win_length': 1024,
'hop_length': 640,
'n_classes': 2,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume),
by_name=True,
skip_mismatch=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
print('==> start testing.')
if sum([args.data_path.endswith(i) for i in ['.wav', '.m4a', 'mp3']]) == 1:
wav, sr_ret = librosa.load(args.data_path,
sr=params['sampling_rate'],
offset=5)
linear_spect = ut.lin_spectogram_from_wav(wav, params['hop_length'],
params['win_length'],
params['n_fft'])
print('sample_rate is ', sr_ret)
elif args.data_path.endswith('.npy'):
linear_spect = np.load(args.data_path)
else:
raise IOError('wrong input format')
mag, _ = librosa.magphase(linear_spect) # magnitude
mag_T = mag.T
spec_mag = mag_T
mu = np.mean(spec_mag, 0, keepdims=True)
std = np.std(spec_mag * (10**5), 0, keepdims=True) / (10**5)
spec_mag = (spec_mag - mu) / (std + 1e-3)
spec_mag = np.expand_dims(spec_mag, (0, -1))
print(spec_mag.shape)
if args.loss == 'regression':
v = network_eval.predict(spec_mag) * 10 + 5
print('the predicted score is: {}'.format(v))
else:
v = network_eval.predict(spec_mag)
print(v)
def main():
params = {
'dim': (257, None, 1),
'n_fft': 512,
'win_length': 400,
'hop_length': 160,
'n_classes': 2,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume),
by_name=True,
skip_mismatch=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
print('==> start testing.')
audio_clip_dir = args.data_path
clipname_list = os.listdir(audio_clip_dir)
with open('../../meta_data_100.json', 'r') as f:
meta_data = json.load(f)
new_meta_data = []
for id, score in meta_data:
# find all corresponding audio clip
id_audioclip_name_list = [i for i in clipname_list if id in i]
clip_v = []
for id_audioclip_name in id_audioclip_name_list:
wav, sr_ret = librosa.load(os.path.join(audio_clip_dir,
id_audioclip_name),
sr=params['sampling_rate'])
linear_spect = ut.lin_spectogram_from_wav(wav,
params['hop_length'],
params['win_length'],
params['n_fft'])
mag, _ = librosa.magphase(linear_spect) # magnitude
mag_T = mag.T
spec_mag = mag_T
mu = np.mean(spec_mag, 0, keepdims=True)
std = np.std(spec_mag * (10**5), 0, keepdims=True) / (10**5)
spec_mag = (spec_mag - mu) / (std + 1e-3)
spec_mag = np.expand_dims(spec_mag, (0, -1))
v = network_eval.predict(spec_mag) * 10 + 5
v = round(v[0][0], 2).astype('float')
clip_v.append(v)
if len(id_audioclip_name_list) != 0:
if sum(clip_v) / len(clip_v) < 3:
print('{} is selected, its predicted score is {}'.format(
id,
sum(clip_v) / len(clip_v)))
new_meta_data.append((id, score, sum(clip_v) / len(clip_v)))
# if abs(v-score) < 1.2:
# new_meta_data.append((id_audioclip_name, score, v, id))
# print('{} is selected, its rule score is {}, and its predicted score is {}'.format(id_audioclip_name, score, v))
with open('meta_data_low.json', 'w') as f:
json.dump(new_meta_data, f)
def main(wav_path,
embedding_per_second=1.0,
overlap_rate=0.5,
exportFile=None,
expectedSpeakers=2):
# gpu configuration
toolkits.initialize_GPU(args)
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = spkModel.vggvox_resnet2d_icassp(
input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
network_eval.load_weights(args.resume, by_name=True)
model_args, _, inference_args = uisrnn.parse_arguments()
model_args.observation_dim = 512
uisrnnModel = uisrnn.UISRNN(model_args)
uisrnnModel.load(SAVED_MODEL_NAME)
specs, intervals = load_data(wav_path,
embedding_per_second=embedding_per_second,
overlap_rate=overlap_rate)
mapTable, keys = genMap(intervals)
feats = []
for spec in specs:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = network_eval.predict(spec)
feats += [v]
feats = np.array(feats)[:, 0, :].astype(float) # [splits, embedding dim]
predicted_label = uisrnnModel.predict(feats, inference_args)
time_spec_rate = 1000 * (1.0 / embedding_per_second) * (
1.0 - overlap_rate) # speaker embedding every ?ms
center_duration = int(1000 * (1.0 / embedding_per_second) // 2)
speakerSlice = arrangeResult(predicted_label, time_spec_rate)
for spk, timeDicts in speakerSlice.items(
): # time map to orgin wav(contains mute)
for tid, timeDict in enumerate(timeDicts):
s = 0
e = 0
for i, key in enumerate(keys):
if (s != 0 and e != 0):
break
if (s == 0 and key > timeDict['start']):
offset = timeDict['start'] - keys[i - 1]
s = mapTable[keys[i - 1]] + offset
if (e == 0 and key > timeDict['stop']):
offset = timeDict['stop'] - keys[i - 1]
e = mapTable[keys[i - 1]] + offset
speakerSlice[spk][tid]['start'] = s
speakerSlice[spk][tid]['stop'] = e
n_speakers = len(speakerSlice)
print('N-SPeakers:', n_speakers)
global speaker_final
speaker_final = [pdb.empty()] * n_speakers
for spk, timeDicts in speakerSlice.items():
print('========= ' + str(spk) + ' =========')
for timeDict in timeDicts:
s = timeDict['start']
e = timeDict['stop']
diarization_try(wav_path, s / 1000, e / 1000, spk)
s = fmtTime(s) # change point moves to the center of the slice
e = fmtTime(e)
print(s + ' ==> ' + e)
# Find the Top n Speakers
speaker_final.sort(key=lambda speaker: speaker.duration_seconds,
reverse=True)
speaker_final = speaker_final[0:expectedSpeakers]
# Export the Files
iso_wav_path = wav_path.split(".")[0]
itr = 0
while itr < len(speaker_final):
write_path = exportFile + "_speaker" + str(itr) + ".wav"
speaker_final[itr].export(write_path, format="wav")
itr += 1
del speaker_final
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Train/Val.
# ==================================
total_list = [
os.path.join(args.data_path, file)
for file in os.listdir(args.data_path)
]
unique_list = np.unique(total_list)
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {
'dim': (257, None, 1),
'nfft': 512,
'min_slice': 720,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
print('==> start testing.')
# The feature extraction process has to be done sample-by-sample,
# because each sample is of different lengths.
train_cluster_id = []
train_sequence = []
SRC_PATH = r'/data/dataset/SpkWav120'
wavDir = os.listdir(SRC_PATH)
wavDir.sort()
for i, spkDir in enumerate(wavDir): # Each speaker's directory
spk = spkDir # speaker name
wavPath = os.path.join(SRC_PATH, spkDir, 'audio')
print('Processing speaker({}) : {}'.format(i, spk))
for wav in os.listdir(wavPath): # wavfile
utter_path = os.path.join(wavPath, wav)
feats = []
specs = load_data(utter_path,
split=True,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
min_slice=params['min_slice'])
if (len(specs) < 1):
continue
for spec in specs:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = network_eval.predict(spec)
feats += [v]
feats = np.array(feats)[:, 0, :] # [splits, embedding dim]
train_cluster_id.append([spk] * feats.shape[0])
train_sequence.append(feats)
np.savez('training_data',
train_sequence=train_sequence,
train_cluster_id=train_cluster_id)
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
import generator
import keras
# ==================================
# Get Train/Val.
# ==================================
trnlist, trnlb = toolkits.get_voxceleb2_datalist(
args, path='../meta/vox2_train_wav.txt')
vallist, vallb = toolkits.get_voxceleb2_datalist(
args, path='../meta/vox2_val_wav.txt')
# construct the data generator.
params = {
'dim': (257, 250, 1),
'mp_pooler': toolkits.set_mp(processes=args.multiprocess),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'batch_size': args.batch_size,
'shuffle': True,
'normalize': True,
}
# Datasets
partition = {'train': trnlist.flatten(), 'val': vallist.flatten()}
labels = {'train': trnlb.flatten(), 'val': vallb.flatten()}
# Generators
trn_gen = generator.DataGenerator(partition['train'], labels['train'],
**params)
network = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='train',
args=args)
# ==> load pre-trained model ???
mgpu = len(keras.backend.tensorflow_backend._get_available_gpus())
if args.resume:
if os.path.isfile(args.resume):
if mgpu == 1: network.load_weights(os.path.join(args.resume))
else:
network.layers[mgpu + 1].load_weights(os.path.join(
args.resume))
print('==> successfully loading model {}.'.format(args.resume))
else:
print("==> no checkpoint found at '{}'".format(args.resume))
print(network.summary())
print('==> gpu {} is, training {} images, classes: 0-{} '
'loss: {}, aggregation: {}, ohemlevel: {}'.format(
args.gpu, len(partition['train']), np.max(labels['train']),
args.loss, args.aggregation_mode, args.ohem_level))
model_path, log_path = set_path(args)
normal_lr = keras.callbacks.LearningRateScheduler(step_decay)
tbcallbacks = keras.callbacks.TensorBoard(log_dir=log_path,
histogram_freq=0,
write_graph=True,
write_images=False,
update_freq=args.batch_size * 16)
callbacks = [
keras.callbacks.ModelCheckpoint(os.path.join(
model_path, 'weights-{epoch:02d}-{acc:.3f}.h5'),
monitor='loss',
mode='min',
save_best_only=True), normal_lr,
tbcallbacks
]
if args.ohem_level > 1: # online hard negative mining will be used
candidate_steps = int(len(partition['train']) // args.batch_size)
iters_per_epoch = int(
len(partition['train']) // (args.ohem_level * args.batch_size))
ohem_generator = generator.OHEM_generator(
network, trn_gen, candidate_steps, args.ohem_level,
args.batch_size, params['dim'], params['n_classes'])
A = ohem_generator.next(
) # for some reason, I need to warm up the generator
network.fit_generator(generator.OHEM_generator(
network, trn_gen, iters_per_epoch, args.ohem_level,
args.batch_size, params['dim'], params['n_classes']),
steps_per_epoch=iters_per_epoch,
epochs=args.epochs,
max_queue_size=10,
callbacks=callbacks,
use_multiprocessing=False,
workers=1,
verbose=1)
else:
network.fit_generator(trn_gen,
steps_per_epoch=int(
len(partition['train']) // args.batch_size),
epochs=args.epochs,
max_queue_size=10,
callbacks=callbacks,
use_multiprocessing=False,
workers=1,
verbose=1)
def main(wav_path, check, embedding_per_second=1.0, overlap_rate=0.5):
# gpu configuration
toolkits.initialize_GPU(args)
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = spkModel.vggvox_resnet2d_icassp(
input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
network_eval.load_weights(args.resume, by_name=True)
model_args, _, inference_args = uisrnn.parse_arguments()
model_args.observation_dim = 512
uisrnnModel = uisrnn.UISRNN(model_args)
uisrnnModel.load(SAVED_MODEL_NAME)
specs, intervals = load_data(wav_path,
embedding_per_second=embedding_per_second,
overlap_rate=overlap_rate)
mapTable, keys = genMap(intervals)
if check != '':
specs1, interval1 = load_data(check,
embedding_per_second=1.2,
overlap_rate=0.4)
mapTable1, keys1 = genMap(interval1)
feats = []
for spec in specs:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = network_eval.predict(spec)
feats += [v]
featss = np.array(feats)[:, 0, :].astype(float)
predicted_label = uisrnnModel.predict(featss, inference_args)
total_speaker = len(set(predicted_label))
global no_speakers
print("predicted_label: %s" % predicted_label)
no_speakers = len(set(predicted_label))
print('total no of speakers', no_speakers)
time_spec_rate = 1000 * (1.0 / embedding_per_second) * (
1.0 - overlap_rate) # speaker embedding every ?ms
if check != '':
for spec1 in specs1:
spec1 = np.expand_dims(np.expand_dims(spec1, 0), -1)
v = network_eval.predict(spec1)
feats += [v]
featss = np.array(feats)[:,
0, :].astype(float) # [splits, embedding dim]
print("=====================")
print(feats)
print(featss)
print("=====================")
predicted_label2 = uisrnnModel.predict(featss, inference_args)
check_speaker = len(set(predicted_label2))
print("predicted_label2: %s" % predicted_label2)
print('same Speaker' if total_speaker ==
check_speaker else 'not the same speaker')
print('speaker detected as ' +
str(predicted_label2[-1]) if total_speaker ==
check_speaker else '')
speakerSlice2 = arrangeResult(predicted_label2, time_spec_rate)
print("=============speakerSlice2===============")
for spk, timeDicts in speakerSlice2.items(
): # time map to orgin wav(contains mute)
for tid, timeDict in enumerate(timeDicts):
s = 0
e = 0
for i, key in enumerate(keys):
if (s != 0 and e != 0):
break
if (s == 0 and key > timeDict['start']):
offset = timeDict['start'] - keys[i - 1]
s = mapTable[keys[i - 1]] + offset
if (e == 0 and key > timeDict['stop']):
offset = timeDict['stop'] - keys[i - 1]
e = mapTable[keys[i - 1]] + offset
speakerSlice2[spk][tid]['start'] = s
speakerSlice2[spk][tid]['stop'] = e
for spk, timeDicts in speakerSlice2.items():
print('========= ' + str(spk) + ' =========')
for timeDict in timeDicts:
s = timeDict['start']
e = timeDict['stop']
s = fmtTime(s) # change point moves to the center of the slice
e = fmtTime(e)
print(s + ' ==> ' + e)
print("=============speakerSlice2===============")
#print(predicted_label,'**************************')
center_duration = int(1000 * (1.0 / embedding_per_second) // 2)
speakerSlice = arrangeResult(predicted_label, time_spec_rate)
for spk, timeDicts in speakerSlice.items(
): # time map to orgin wav(contains mute)
for tid, timeDict in enumerate(timeDicts):
s = 0
e = 0
for i, key in enumerate(keys):
if (s != 0 and e != 0):
break
if (s == 0 and key > timeDict['start']):
offset = timeDict['start'] - keys[i - 1]
s = mapTable[keys[i - 1]] + offset
if (e == 0 and key > timeDict['stop']):
offset = timeDict['stop'] - keys[i - 1]
e = mapTable[keys[i - 1]] + offset
speakerSlice[spk][tid]['start'] = s
speakerSlice[spk][tid]['stop'] = e
for spk, timeDicts in speakerSlice.items():
print('========= ' + str(spk) + ' =========')
for timeDict in timeDicts:
s = timeDict['start']
e = timeDict['stop']
s = fmtTime(s) # change point moves to the center of the slice
e = fmtTime(e)
print(s + ' ==> ' + e)
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Train/Val.
# ==================================
total_list = [os.path.join(args.data_path, file) for file in os.listdir(args.data_path)]
unique_list = np.unique(total_list)
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {'dim': (257, None, 1),
'nfft': 512,
'min_slice': 720,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval', args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(args.resume))
else:
raise IOError('==> please type in the model to load')
# The feature extraction process has to be done sample-by-sample,
# because each sample is of different lengths.
SRC_PATH = r'/data/dataset/SpkWav120'
SRC_PATH = r'./ghostvlad/SRC_PATH' # bencq path
print(SRC_PATH)
path_spk_tuples = prepare_data(SRC_PATH)
train_sequence = []
train_cluster_id = []
CNT = 7000 # 7000
for epoch in range(CNT): # Random choice utterances from whole wavfiles
# A merged utterance contains [10,20] utterances
splits_count = np.random.randint(10, 20, 1) # 最小值,最大值,[维度]
path_spks = random.sample(path_spk_tuples, splits_count[0])
utterance_specs, utterance_speakers = load_data(path_spks, min_win_time=500, max_win_time=1600)
feats = []
for spec in utterance_specs:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = network_eval.predict(spec)
feats += [v]
feats = np.array(feats)[:,0,:] # [splits, embedding dim]
train_sequence.append(feats)
train_cluster_id.append(utterance_speakers)
print("epoch:{}, utterance length: {}, speakers: {}".format(epoch, len(utterance_speakers), len(path_spks)))
np.savez('training_data', train_sequence=train_sequence, train_cluster_id=train_cluster_id)
def __init__(self):
self.filename2embedding = {}
arguments = "--net resnet34s --gpu 0 --ghost_cluster 2 --vlad_cluster 8 --loss softmax " \
"--resume " \
"/media/ben/datadrive/Software/VGG-Speaker-Recognition/model/gvlad_softmax" \
"/2020-11-15_resnet34s_bs16_adam_lr0.001_vlad8_ghost2_bdim512_ohemlevel0" \
"/weights-42-0.931.h5 --data_path " \
"/media/ben/datadrive/Zalo/voice-verification/Train-Test-Data/dataset/".split()
ZALO_TEST = "/media/ben/datadrive/Zalo/voice-verification/vgg_db_files/val_trials.txt"
parser = argparse.ArgumentParser()
# set up training configuration.
parser.add_argument("--gpu", default="", type=str)
parser.add_argument("--resume", default="", type=str)
parser.add_argument("--batch_size", default=16, type=int)
parser.add_argument("--data_path", default="/media/weidi/2TB-2/datasets/voxceleb1/wav",
type=str)
# set up network configuration.
parser.add_argument("--net", default="resnet34s", choices=["resnet34s", "resnet34l"],
type=str)
parser.add_argument("--ghost_cluster", default=2, type=int)
parser.add_argument("--vlad_cluster", default=8, type=int)
parser.add_argument("--bottleneck_dim", default=512, type=int)
parser.add_argument("--aggregation_mode", default="gvlad", choices=["avg", "vlad", "gvlad"],
type=str)
# set up learning rate, training loss and optimizer.
parser.add_argument("--loss", default="softmax", choices=["softmax", "amsoftmax"], type=str)
parser.add_argument("--test_type", default="normal", choices=["normal", "hard", "extend"],
type=str)
global args
args = parser.parse_args(arguments)
# gpu configuration
toolkits.initialize_GPU(args)
# ==================================
# Get Train/Val.
# ==================================
print("==> Initialising inference engine...".format(args.test_type))
# ==================================
# Get Model
# ==================================
# construct the data generator.
self.params = {"dim": (257, None, 1),
"nfft": 512,
"spec_len": 250,
"win_length": 400,
"hop_length": 160,
"n_classes": 5994,
"sampling_rate": 16000,
"normalize": True,
}
self.network_eval = model.vggvox_resnet2d_icassp(input_dim=self.params["dim"],
num_class=self.params["n_classes"],
mode="eval", args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the image_model == real_model.
if os.path.isfile(args.resume):
self.network_eval.load_weights(os.path.join(args.resume), by_name=True)
print("==> successfully loading model {}.".format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(args.resume))
else:
raise IOError("==> please type in the model to load")
print("==> start testing.")
def dia_audio(wav_path, embedding_per_second=0.3, overlap_rate=0.33):
# gpu configuration
#toolkits.initialize_GPU(args)
params = {'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = spkModel.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval', args=args)
network_eval.load_weights(args.resume, by_name=True)
model_args.observation_dim = 512
uisrnnModel = uisrnn.UISRNN(model_args)
uisrnnModel.load(SAVED_MODEL_NAME)
specs, intervals = load_data(
wav_path, embedding_per_second=embedding_per_second, overlap_rate=overlap_rate)
mapTable, keys = genMap(intervals)
feats = []
for spec in specs:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = network_eval.predict(spec)
feats += [v]
feats = np.array(feats)[:, 0, :].astype(float) # [splits, embedding dim]
predicted_label = uisrnnModel.predict(feats, inference_args)
time_spec_rate = 1000*(1.0/embedding_per_second) * \
(1.0-overlap_rate) # speaker embedding every ?ms
center_duration = int(1000*(1.0/embedding_per_second)//2)
speakerSlice = arrangeResult(predicted_label, time_spec_rate)
for spk, timeDicts in speakerSlice.items(): # time map to orgin wav(contains mute)
for tid, timeDict in enumerate(timeDicts):
s = 0
e = 0
for i, key in enumerate(keys):
if(s != 0 and e != 0):
break
if(s == 0 and key > timeDict['start']):
offset = timeDict['start'] - keys[i-1]
s = mapTable[keys[i-1]] + offset
if(e == 0 and key > timeDict['stop']):
offset = timeDict['stop'] - keys[i-1]
e = mapTable[keys[i-1]] + offset
speakerSlice[spk][tid]['start'] = s
speakerSlice[spk][tid]['stop'] = e
for spk, timeDicts in speakerSlice.items():
##print('========= ' + str(spk) + ' =========')
for timeDict in timeDicts:
s = timeDict['start']
e = timeDict['stop']
s = fmtTime(s) # change point moves to the center of the slice
e = fmtTime(e)
#print(s+' ==> '+e)
# p = PlotDiar(map=speakerSlice, wav=wav_path, gui=True, size=(25, 6))
# p.draw()
# p.plot.show()
return speakerSlice
def main(wav_path,
embedding_per_second=1.0,
n_classes=5994,
overlap_rate=0.5,
plot_results=True):
# gpu configuration
toolkits.initialize_GPU(args)
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = spkModel.vggvox_resnet2d_icassp(
input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
network_eval.load_weights(args.resume, by_name=True)
#
model_args, _, inference_args = uisrnn.parse_arguments()
model_args.observation_dim = 512
uisrnnModel = uisrnn.UISRNN(model_args)
uisrnnModel.load(SAVED_MODEL_NAME)
specs, intervals = load_data(wav_path,
embedding_per_second=embedding_per_second,
overlap_rate=overlap_rate)
mapTable, keys = genMap(intervals)
print('intervals', intervals, len(intervals))
print('mapTable', mapTable, len(mapTable))
print('keys', keys, len(keys))
# print('mapTable, keys', mapTable, keys)
feats = []
for spec in specs:
spec = np.expand_dims(np.expand_dims(spec, 0), -1)
v = network_eval.predict(spec)
# print('v',v.shape)
#print('feats', feats.shape)
feats += [v]
feats = np.array(feats)[:, 0, :].astype(float) # [splits, embedding dim]
predicted_label = uisrnnModel.predict(feats, inference_args)
print(feats.shape)
print(inference_args)
print('predicted_label', predicted_label)
time_spec_rate = 1000 * (1.0 / embedding_per_second) * (
1.0 - overlap_rate) # speaker embedding every ?ms
print('time_spec_rate', time_spec_rate)
center_duration = int(1000 * (1.0 / embedding_per_second) // 2)
speakerSlice = arrangeResult(predicted_label, time_spec_rate)
print('speakerSlice', speakerSlice)
for spk, timeDicts in speakerSlice.items(
): # time map to orgin wav(contains mute)
print(spk, timeDicts)
for tid, timeDict in enumerate(timeDicts):
print(tid, timeDict)
s = 0
e = 0
for i, key in enumerate(keys):
if (s != 0 and e != 0):
break
if (s == 0 and key > timeDict['start']):
offset = timeDict['start'] - keys[i - 1]
print('offset', offset)
s = mapTable[keys[i - 1]] + offset
if (e == 0 and key > timeDict['stop']):
offset = timeDict['stop'] - keys[i - 1]
e = mapTable[keys[i - 1]] + offset
print('i,s,e')
print(i, s, e, tid, spk)
print('>>>>>', i, s, e, tid, spk)
speakerSlice[spk][tid]['start'] = s
speakerSlice[spk][tid]['stop'] = e
speaker_assingments = []
for spk, timeDicts in speakerSlice.items():
speaker = str(spk)
print('========= ' + str(spk) + ' =========')
for timeDict in timeDicts:
start = timeDict['start']
end = timeDict['stop']
start = fmtTime(
start) # change point moves to the center of the slice
end = fmtTime(end)
print(start + ' ==> ' + end)
speaker_assingments.append((start, end, speaker, wav_path))
if plot_results:
p = PlotDiar(map=speakerSlice, wav=wav_path, gui=True, size=(25, 6))
p.draw()
p.plot.show()
return feats, predicted_label, intervals, speaker_assingments, time_spec_rate