|
|
|
|
@ -23,8 +23,8 @@ from paddle.io import DataLoader
|
|
|
|
|
from tqdm import tqdm
|
|
|
|
|
from yacs.config import CfgNode
|
|
|
|
|
|
|
|
|
|
from paddleaudio.paddleaudio.datasets import VoxCeleb
|
|
|
|
|
from paddleaudio.paddleaudio.metric import compute_eer
|
|
|
|
|
from paddleaudio.datasets import VoxCeleb
|
|
|
|
|
from paddleaudio.metric import compute_eer
|
|
|
|
|
from paddlespeech.s2t.utils.log import Log
|
|
|
|
|
from paddlespeech.vector.io.batch import batch_feature_normalize
|
|
|
|
|
from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
|
|
|
|
|
@ -48,6 +48,9 @@ def main(args, config):
|
|
|
|
|
backbone=ecapa_tdnn, num_class=config.num_speakers)
|
|
|
|
|
|
|
|
|
|
# stage3: load the pre-trained model
|
|
|
|
|
# we get the last model from the epoch and save_interval
|
|
|
|
|
last_save_epoch = (config.epochs // config.save_interval) * config.save_interval
|
|
|
|
|
args.load_checkpoint = os.path.join(args.load_checkpoint, "epoch_" + str(last_save_epoch))
|
|
|
|
|
args.load_checkpoint = os.path.abspath(
|
|
|
|
|
os.path.expanduser(args.load_checkpoint))
|
|
|
|
|
|
|
|
|
|
@ -63,7 +66,9 @@ def main(args, config):
|
|
|
|
|
target_dir=args.data_dir,
|
|
|
|
|
feat_type='melspectrogram',
|
|
|
|
|
random_chunk=False,
|
|
|
|
|
**config.feature)
|
|
|
|
|
n_mels=config.n_mels,
|
|
|
|
|
window_size=config.window_size,
|
|
|
|
|
hop_length=config.hop_length)
|
|
|
|
|
enroll_sampler = BatchSampler(
|
|
|
|
|
enroll_dataset, batch_size=config.batch_size,
|
|
|
|
|
shuffle=True) # Shuffle to make embedding normalization more robust.
|
|
|
|
|
@ -73,13 +78,14 @@ def main(args, config):
|
|
|
|
|
x, mean_norm=True, std_norm=False),
|
|
|
|
|
num_workers=config.num_workers,
|
|
|
|
|
return_list=True,)
|
|
|
|
|
|
|
|
|
|
test_dataset = VoxCeleb(
|
|
|
|
|
subset='test',
|
|
|
|
|
target_dir=args.data_dir,
|
|
|
|
|
feat_type='melspectrogram',
|
|
|
|
|
random_chunk=False,
|
|
|
|
|
**config.feature)
|
|
|
|
|
n_mels=config.n_mels,
|
|
|
|
|
window_size=config.window_size,
|
|
|
|
|
hop_length=config.hop_length)
|
|
|
|
|
|
|
|
|
|
test_sampler = BatchSampler(
|
|
|
|
|
test_dataset, batch_size=config.batch_size, shuffle=True)
|
|
|
|
|
@ -89,19 +95,19 @@ def main(args, config):
|
|
|
|
|
x, mean_norm=True, std_norm=False),
|
|
|
|
|
num_workers=config.num_workers,
|
|
|
|
|
return_list=True,)
|
|
|
|
|
# stage6: we must set the model to eval mode
|
|
|
|
|
# stage5: we must set the model to eval mode
|
|
|
|
|
model.eval()
|
|
|
|
|
|
|
|
|
|
# stage7: global embedding norm to imporve the performance
|
|
|
|
|
print("global embedding norm: {}".format(args.global_embedding_norm))
|
|
|
|
|
if args.global_embedding_norm:
|
|
|
|
|
# stage6: global embedding norm to imporve the performance
|
|
|
|
|
logger.info(f"global embedding norm: {config.global_embedding_norm}")
|
|
|
|
|
if config.global_embedding_norm:
|
|
|
|
|
global_embedding_mean = None
|
|
|
|
|
global_embedding_std = None
|
|
|
|
|
mean_norm_flag = args.embedding_mean_norm
|
|
|
|
|
std_norm_flag = args.embedding_std_norm
|
|
|
|
|
mean_norm_flag = config.embedding_mean_norm
|
|
|
|
|
std_norm_flag = config.embedding_std_norm
|
|
|
|
|
batch_count = 0
|
|
|
|
|
|
|
|
|
|
# stage8: Compute embeddings of audios in enrol and test dataset from model.
|
|
|
|
|
# stage7: Compute embeddings of audios in enrol and test dataset from model.
|
|
|
|
|
id2embedding = {}
|
|
|
|
|
# Run multi times to make embedding normalization more stable.
|
|
|
|
|
for i in range(2):
|
|
|
|
|
@ -121,7 +127,7 @@ def main(args, config):
|
|
|
|
|
# Global embedding normalization.
|
|
|
|
|
# if we use the global embedding norm
|
|
|
|
|
# eer can reduece about relative 10%
|
|
|
|
|
if args.global_embedding_norm:
|
|
|
|
|
if config.global_embedding_norm:
|
|
|
|
|
batch_count += 1
|
|
|
|
|
current_mean = embeddings.mean(
|
|
|
|
|
axis=0) if mean_norm_flag else 0
|
|
|
|
|
@ -145,21 +151,22 @@ def main(args, config):
|
|
|
|
|
# Update embedding dict.
|
|
|
|
|
id2embedding.update(dict(zip(ids, embeddings)))
|
|
|
|
|
|
|
|
|
|
# stage 9: Compute cosine scores.
|
|
|
|
|
# stage 8: Compute cosine scores.
|
|
|
|
|
labels = []
|
|
|
|
|
enrol_ids = []
|
|
|
|
|
enroll_ids = []
|
|
|
|
|
test_ids = []
|
|
|
|
|
logger.info(f"read the trial from {VoxCeleb.veri_test_file}")
|
|
|
|
|
with open(VoxCeleb.veri_test_file, 'r') as f:
|
|
|
|
|
for line in f.readlines():
|
|
|
|
|
label, enrol_id, test_id = line.strip().split(' ')
|
|
|
|
|
label, enroll_id, test_id = line.strip().split(' ')
|
|
|
|
|
labels.append(int(label))
|
|
|
|
|
enrol_ids.append(enrol_id.split('.')[0].replace('/', '--'))
|
|
|
|
|
test_ids.append(test_id.split('.')[0].replace('/', '--'))
|
|
|
|
|
enroll_ids.append(enroll_id.split('.')[0].replace('/', '-'))
|
|
|
|
|
test_ids.append(test_id.split('.')[0].replace('/', '-'))
|
|
|
|
|
|
|
|
|
|
cos_sim_func = paddle.nn.CosineSimilarity(axis=1)
|
|
|
|
|
enrol_embeddings, test_embeddings = map(lambda ids: paddle.to_tensor(
|
|
|
|
|
np.asarray([id2embedding[id] for id in ids], dtype='float32')),
|
|
|
|
|
[enrol_ids, test_ids
|
|
|
|
|
np.asarray([id2embedding[uttid] for uttid in ids], dtype='float32')),
|
|
|
|
|
[enroll_ids, test_ids
|
|
|
|
|
]) # (N, emb_size)
|
|
|
|
|
scores = cos_sim_func(enrol_embeddings, test_embeddings)
|
|
|
|
|
EER, threshold = compute_eer(np.asarray(labels), scores.numpy())
|
|
|
|
|
@ -187,17 +194,6 @@ if __name__ == "__main__":
|
|
|
|
|
type=str,
|
|
|
|
|
default='',
|
|
|
|
|
help="Directory to load model checkpoint to contiune trainning.")
|
|
|
|
|
parser.add_argument("--global-embedding-norm",
|
|
|
|
|
default=False,
|
|
|
|
|
action="store_true",
|
|
|
|
|
help="Apply global normalization on speaker embeddings.")
|
|
|
|
|
parser.add_argument("--embedding-mean-norm",
|
|
|
|
|
default=True,
|
|
|
|
|
help="Apply mean normalization on speaker embeddings.")
|
|
|
|
|
parser.add_argument("--embedding-std-norm",
|
|
|
|
|
type=bool,
|
|
|
|
|
default=False,
|
|
|
|
|
help="Apply std normalization on speaker embeddings.")
|
|
|
|
|
args = parser.parse_args()
|
|
|
|
|
# yapf: enable
|
|
|
|
|
# https://yaml.org/type/float.html
|
xiongxinlei
3 years ago