def greedy_search(self, h: torch.Tensor) -> List[Hypothesis]:
"""Greedy search implementation for transformer-transducer.
Args:
h: Encoded speech features (T_max, D_enc)
Returns:
hyp: 1-best decoding results
"""
dec_state = self.decoder.init_state(1)
hyp = Hypothesis(score=0.0, yseq=[self.blank], dec_state=dec_state)
cache = {}
y, state, _ = self.decoder.score(hyp, cache)
for i, hi in enumerate(h):
ytu = torch.log_softmax(self.joint_network(hi, y), dim=-1)
logp, pred = torch.max(ytu, dim=-1)
if pred != self.blank:
hyp.yseq.append(int(pred))
hyp.score += float(logp)
hyp.dec_state = state
y, state, _ = self.decoder.score(hyp, cache)
return [hyp]
def greedy_search(self, enc_out: torch.Tensor) -> List[Hypothesis]:
"""Greedy search implementation.
Args:
enc_out: Encoder output sequence. (T, D_enc)
Returns:
hyp: 1-best hypotheses.
"""
dec_state = self.decoder.init_state(1)
hyp = Hypothesis(score=0.0, yseq=[self.blank_id], dec_state=dec_state)
cache = {}
dec_out, state, _ = self.decoder.score(hyp, cache)
for enc_out_t in enc_out:
logp = torch.log_softmax(
self.joint_network(enc_out_t, dec_out, quantization=self.quantization)
/ self.softmax_temperature,
dim=-1,
)
top_logp, pred = torch.max(logp, dim=-1)
if pred != self.blank_id:
hyp.yseq.append(int(pred))
hyp.score += float(top_logp)
hyp.dec_state = state
dec_out, state, _ = self.decoder.score(hyp, cache)
return [hyp]