def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
if self.asgtransitions is None:
transitions = torch.FloatTensor(N, N).zero_()
else:
transitions = torch.FloatTensor(self.asgtransitions).view(N, N)
viterbi_path = torch.IntTensor(B, T)
workspace = torch.ByteTensor(CpuViterbiPath.get_workspace_size(
B, T, N))
CpuViterbiPath.compute(
B,
T,
N,
get_data_ptr_as_bytes(emissions),
get_data_ptr_as_bytes(transitions),
get_data_ptr_as_bytes(viterbi_path),
get_data_ptr_as_bytes(workspace),
)
return [[{
"tokens": self.get_tokens(viterbi_path[b].tolist()),
"score": 0
}] for b in range(B)]
def decode(self, emissions):
B, T, N = emissions.size()
hypos = []
if self.asg_transitions is None:
transitions = torch.FloatTensor(N, N).zero_()
else:
transitions = torch.FloatTensor(self.asg_transitions).view(N, N)
viterbi_path = torch.IntTensor(B, T)
workspace = torch.ByteTensor(CpuViterbiPath.get_workspace_size(
B, T, N))
CpuViterbiPath.compute(
B,
T,
N,
get_data_ptr_as_bytes(emissions),
get_data_ptr_as_bytes(transitions),
get_data_ptr_as_bytes(viterbi_path),
get_data_ptr_as_bytes(workspace),
)
logging.info("decoder input length: " + str(emissions.shape[1]))
returns = [[{
"tokens": self.get_tokens(viterbi_path[b].tolist()),
"score": 0
}] for b in range(B)]
if self.token_list is not None:
logging.info(
"best hypo: " +
"".join([self.token_list[x]
for x in returns[0][0]["tokens"]]) + "\n")
return returns