def select_device(self):
return utils.select_model_device(self.elements["model"],
self.params["use_gpu"],
gpu_id=self.params["gpu_id"],
benchmark=self.params["benchmark"])
try:
# nnet_config include model_blueprint and model_creation
if args.nnet_config != "":
model_blueprint, model_creation = utils.read_nnet_config(args.nnet_config)
elif args.model_blueprint is not None and args.model_creation is not None:
model_blueprint = args.model_blueprint
model_creation = args.model_creation
else:
raise ValueError("Expected nnet_config or (model_blueprint, model_creation) to exist.")
model = utils.create_model_from_py(model_blueprint, model_creation)
model.load_state_dict(torch.load(args.model_path, map_location='cpu'), strict=False)
# Select device
model = utils.select_model_device(model, args.use_gpu, gpu_id=args.gpu_id)
model.eval()
with kaldi_io.open_or_fd(args.feats_rspecifier, "rb") as r, \
kaldi_io.open_or_fd(args.vectors_wspecifier, 'wb') as w:
for line in r:
# (key, rxfile, chunk_start, chunk_end) = line.decode().split(' ')
# chunk=[chunk_start, chunk_end]
# print("Process utterance for key {0}".format(key))
# feats = kaldi_io.read_mat(rxfile, chunk=chunk)
(key, rxfile) = line.decode().split(' ')
print("Process utterance for key {0}".format(key))
feats = kaldi_io.read_mat(rxfile)
embedding = model.extract_embedding(feats)
kaldi_io.write_vec_flt(w, embedding.numpy(), key=key)