def prefix_search(self, hyps: List[ExtendedHypothesis],
enc_out_t: torch.Tensor) -> List[ExtendedHypothesis]:
"""Prefix search for NSC and mAES strategies.
Based on https://arxiv.org/pdf/1211.3711.pdf
"""
for j, hyp_j in enumerate(hyps[:-1]):
for hyp_i in hyps[(j + 1):]:
curr_id = len(hyp_j.yseq)
pref_id = len(hyp_i.yseq)
if (is_prefix(hyp_j.yseq, hyp_i.yseq)
and (curr_id - pref_id) <= self.prefix_alpha):
logp = torch.log_softmax(
self.joint_network(enc_out_t, hyp_i.dec_out[-1]),
dim=-1,
)
curr_score = hyp_i.score + float(logp[hyp_j.yseq[pref_id]])
for k in range(pref_id, (curr_id - 1)):
logp = torch.log_softmax(
self.joint_network(enc_out_t, hyp_j.dec_out[k]),
dim=-1,
)
curr_score += float(logp[hyp_j.yseq[k + 1]])
hyp_j.score = np.logaddexp(hyp_j.score, curr_score)
return hyps
def nsc_beam_search(self, h: torch.Tensor) -> List[NSCHypothesis]:
"""N-step constrained beam search implementation.
Based and modified from https://arxiv.org/pdf/2002.03577.pdf.
Please reference ESPnet (b-flo, PR #2444) for any usage outside ESPnet
until further modifications.
Note: the algorithm is not in his "complete" form but works almost as
intended.
Args:
h: Encoded speech features (T_max, D_enc)
Returns:
nbest_hyps: N-best decoding results
"""
beam = min(self.beam_size, self.vocab_size)
beam_k = min(beam, (self.vocab_size - 1))
beam_state = self.decoder.init_state(beam)
init_tokens = [
NSCHypothesis(
yseq=[self.blank],
score=0.0,
dec_state=self.decoder.select_state(beam_state, 0),
)
]
cache = {}
beam_y, beam_state, beam_lm_tokens = self.decoder.batch_score(
init_tokens,
beam_state,
cache,
self.use_lm,
)
state = self.decoder.select_state(beam_state, 0)
if self.use_lm:
beam_lm_states, beam_lm_scores = self.lm.buff_predict(
None, beam_lm_tokens, 1)
lm_state = select_lm_state(beam_lm_states, 0, self.lm_layers,
self.is_wordlm)
lm_scores = beam_lm_scores[0]
else:
lm_state = None
lm_scores = None
kept_hyps = [
NSCHypothesis(
yseq=[self.blank],
score=0.0,
dec_state=state,
y=[beam_y[0]],
lm_state=lm_state,
lm_scores=lm_scores,
)
]
for hi in h:
hyps = sorted(kept_hyps, key=lambda x: len(x.yseq), reverse=True)
kept_hyps = []
h_enc = hi.unsqueeze(0)
for j, hyp_j in enumerate(hyps[:-1]):
for hyp_i in hyps[(j + 1):]:
curr_id = len(hyp_j.yseq)
next_id = len(hyp_i.yseq)
if (is_prefix(hyp_j.yseq, hyp_i.yseq)
and (curr_id - next_id) <= self.prefix_alpha):
ytu = torch.log_softmax(self.joint_network(
hi, hyp_i.y[-1]),
dim=-1)
curr_score = hyp_i.score + float(
ytu[hyp_j.yseq[next_id]])
for k in range(next_id, (curr_id - 1)):
ytu = torch.log_softmax(self.joint_network(
hi, hyp_j.y[k]),
dim=-1)
curr_score += float(ytu[hyp_j.yseq[k + 1]])
hyp_j.score = np.logaddexp(hyp_j.score, curr_score)
S = []
V = []
for n in range(self.nstep):
beam_y = torch.stack([hyp.y[-1] for hyp in hyps])
beam_logp = torch.log_softmax(self.joint_network(
h_enc, beam_y),
dim=-1)
beam_topk = beam_logp[:, 1:].topk(beam_k, dim=-1)
for i, hyp in enumerate(hyps):
S.append(
NSCHypothesis(
yseq=hyp.yseq[:],
score=hyp.score + float(beam_logp[i, 0:1]),
y=hyp.y[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
lm_scores=hyp.lm_scores,
))
V.append(S[-1])
for logp, k in zip(beam_topk[0][i], beam_topk[1][i] + 1):
score = hyp.score + float(logp)
if self.use_lm:
score += self.lm_weight * float(hyp.lm_scores[k])
V.append(
NSCHypothesis(
yseq=hyp.yseq[:] + [int(k)],
score=score,
y=hyp.y[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
lm_scores=hyp.lm_scores,
))
V.sort(key=lambda x: x.score, reverse=True),
V = substract(V, hyps)[:beam]
beam_state = self.decoder.create_batch_states(
beam_state,
[v.dec_state for v in V],
[v.yseq for v in V],
)
beam_y, beam_state, beam_lm_tokens = self.decoder.batch_score(
V,
beam_state,
cache,
self.use_lm,
)
if self.use_lm:
beam_lm_states = create_lm_batch_state(
[v.lm_state for v in V], self.lm_layers,
self.is_wordlm)
beam_lm_states, beam_lm_scores = self.lm.buff_predict(
beam_lm_states, beam_lm_tokens, len(V))
if n < (self.nstep - 1):
for i, v in enumerate(V):
v.y.append(beam_y[i])
v.dec_state = self.decoder.select_state(beam_state, i)
if self.use_lm:
v.lm_state = select_lm_state(
beam_lm_states, i, self.lm_layers,
self.is_wordlm)
v.lm_scores = beam_lm_scores[i]
hyps = V[:]
else:
beam_logp = torch.log_softmax(self.joint_network(
h_enc, beam_y),
dim=-1)
for i, v in enumerate(V):
if self.nstep != 1:
v.score += float(beam_logp[i, 0])
v.y.append(beam_y[i])
v.dec_state = self.decoder.select_state(beam_state, i)
if self.use_lm:
v.lm_state = select_lm_state(
beam_lm_states, i, self.lm_layers,
self.is_wordlm)
v.lm_scores = beam_lm_scores[i]
kept_hyps = sorted((S + V), key=lambda x: x.score,
reverse=True)[:beam]
return self.sort_nbest(kept_hyps)
def nsc_beam_search(self, h: torch.Tensor) -> List[Hypothesis]:
"""N-step constrained beam search implementation.
Based and modified from https://arxiv.org/pdf/2002.03577.pdf.
Please reference ESPnet (b-flo, PR #2444) for any usage outside ESPnet
until further modifications.
Note: the algorithm is not in his "complete" form but works almost as
intended.
Args:
h: Encoded speech features (T_max, D_enc)
Returns:
nbest_hyps: N-best decoding results
"""
beam = min(self.beam_size, self.vocab_size)
beam_k = min(beam, (self.vocab_size - 1))
init_tensor = h.unsqueeze(0)
blank_tensor = init_tensor.new_zeros(1, dtype=torch.long)
beam_state = self.decoder.init_state(
torch.zeros((beam, self.hidden_size)))
init_tokens = [
Hypothesis(
yseq=[self.blank],
score=0.0,
dec_state=self.decoder.select_state(beam_state, 0),
)
]
cache = {}
beam_y, beam_state, beam_lm_tokens = self.decoder.batch_score(
init_tokens, beam_state, cache, init_tensor)
state = self.decoder.select_state(beam_state, 0)
if self.lm:
beam_lm_states, beam_lm_scores = self.lm.buff_predict(
None, beam_lm_tokens, 1)
if hasattr(self.lm.predictor, "wordlm"):
lm_model = self.lm.predictor.wordlm
lm_type = "wordlm"
else:
lm_model = self.lm.predictor
lm_type = "lm"
lm_layers = len(lm_model.rnn)
lm_state = select_lm_state(beam_lm_states, 0, lm_type, lm_layers)
lm_scores = beam_lm_scores[0]
else:
lm_state = None
lm_scores = None
kept_hyps = [
Hypothesis(
yseq=[self.blank],
score=0.0,
dec_state=state,
y=[beam_y[0]],
lm_state=lm_state,
lm_scores=lm_scores,
)
]
for hi in h:
hyps = sorted(kept_hyps, key=lambda x: len(x.yseq), reverse=True)
kept_hyps = []
h_enc = hi.unsqueeze(0)
for j in range(len(hyps) - 1):
for i in range((j + 1), len(hyps)):
if (is_prefix(hyps[j].yseq, hyps[i].yseq)
and (len(hyps[j].yseq) - len(hyps[i].yseq)) <=
self.prefix_alpha):
next_id = len(hyps[i].yseq)
ytu = torch.log_softmax(self.decoder.joint_network(
hi, hyps[i].y[-1]),
dim=0)
curr_score = hyps[i].score + float(
ytu[hyps[j].yseq[next_id]])
for k in range(next_id, (len(hyps[j].yseq) - 1)):
ytu = torch.log_softmax(self.decoder.joint_network(
hi, hyps[j].y[k]),
dim=0)
curr_score += float(ytu[hyps[j].yseq[k + 1]])
hyps[j].score = np.logaddexp(hyps[j].score, curr_score)
S = []
V = []
for n in range(self.nstep):
beam_y = torch.stack([hyp.y[-1] for hyp in hyps])
beam_logp = torch.log_softmax(self.decoder.joint_network(
h_enc, beam_y),
dim=-1)
beam_topk = beam_logp[:, 1:].topk(beam_k, dim=-1)
if self.lm:
beam_lm_scores = torch.stack(
[hyp.lm_scores for hyp in hyps])
for i, hyp in enumerate(hyps):
i_topk = (
torch.cat((beam_topk[0][i], beam_logp[i, 0:1])),
torch.cat((beam_topk[1][i] + 1, blank_tensor)),
)
for logp, k in zip(*i_topk):
new_hyp = Hypothesis(
yseq=hyp.yseq[:],
score=(hyp.score + float(logp)),
y=hyp.y[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
lm_scores=hyp.lm_scores,
)
if k == self.blank:
S.append(new_hyp)
else:
new_hyp.yseq.append(int(k))
if self.lm:
new_hyp.score += self.lm_weight * float(
beam_lm_scores[i, k])
V.append(new_hyp)
V = sorted(V, key=lambda x: x.score, reverse=True)
V = substract(V, hyps)[:beam]
l_state = [v.dec_state for v in V]
l_tokens = [v.yseq for v in V]
beam_state = self.decoder.create_batch_states(
beam_state, l_state, l_tokens)
beam_y, beam_state, beam_lm_tokens = self.decoder.batch_score(
V, beam_state, cache, init_tensor)
if self.lm:
beam_lm_states = create_lm_batch_state(
[v.lm_state for v in V], lm_type, lm_layers)
beam_lm_states, beam_lm_scores = self.lm.buff_predict(
beam_lm_states, beam_lm_tokens, len(V))
if n < (self.nstep - 1):
for i, v in enumerate(V):
v.y.append(beam_y[i])
v.dec_state = self.decoder.select_state(beam_state, i)
if self.lm:
v.lm_state = select_lm_state(
beam_lm_states, i, lm_type, lm_layers)
v.lm_scores = beam_lm_scores[i]
hyps = V[:]
else:
beam_logp = torch.log_softmax(self.decoder.joint_network(
h_enc, beam_y),
dim=-1)
for i, v in enumerate(V):
if self.nstep != 1:
v.score += float(beam_logp[i, 0])
v.y.append(beam_y[i])
v.dec_state = self.decoder.select_state(beam_state, i)
if self.lm:
v.lm_state = select_lm_state(
beam_lm_states, i, lm_type, lm_layers)
v.lm_scores = beam_lm_scores[i]
kept_hyps = sorted((S + V), key=lambda x: x.score,
reverse=True)[:beam]
return self.sort_nbest(kept_hyps)
def nsc_beam_search(decoder, h, recog_args, rnnlm=None):
"""N-step constrained beam search implementation.
Based and modified from https://arxiv.org/pdf/2002.03577.pdf.
Please reference ESPnet (b-flo, PR #2444) for any usage outside ESPnet
until further modifications.
Note: the algorithm is not in his "complete" form but works almost as
intended.
Args:
decoder (class): decoder class
h (torch.Tensor): encoder hidden state sequences (Tmax, Henc)
recog_args (Namespace): argument Namespace containing options
rnnlm (torch.nn.Module): language module
Returns:
nbest_hyps (list of dicts): n-best decoding results
"""
beam = min(recog_args.beam_size, decoder.odim)
beam_k = min(beam, (decoder.odim - 1))
nstep = recog_args.nstep
prefix_alpha = recog_args.prefix_alpha
nbest = recog_args.nbest
cache = {}
init_tensor = h.unsqueeze(0)
blank_tensor = init_tensor.new_zeros(1, dtype=torch.long)
beam_state = decoder.init_state(torch.zeros((beam, decoder.dunits)))
init_tokens = [
Hypothesis(
yseq=[decoder.blank],
score=0.0,
dec_state=decoder.select_state(beam_state, 0),
)
]
beam_y, beam_state, beam_lm_tokens = decoder.batch_score(
init_tokens, beam_state, cache, init_tensor)
state = decoder.select_state(beam_state, 0)
if rnnlm:
beam_lm_states, beam_lm_scores = rnnlm.buff_predict(
None, beam_lm_tokens, 1)
if hasattr(rnnlm.predictor, "wordlm"):
lm_model = rnnlm.predictor.wordlm
lm_type = "wordlm"
else:
lm_model = rnnlm.predictor
lm_type = "lm"
lm_layers = len(lm_model.rnn)
lm_state = select_lm_state(beam_lm_states, 0, lm_type, lm_layers)
lm_scores = beam_lm_scores[0]
else:
lm_state = None
lm_scores = None
kept_hyps = [
Hypothesis(
yseq=[decoder.blank],
score=0.0,
dec_state=state,
y=[beam_y[0]],
lm_state=lm_state,
lm_scores=lm_scores,
)
]
for hi in h:
hyps = sorted(kept_hyps, key=lambda x: len(x.yseq), reverse=True)
kept_hyps = []
h_enc = hi.unsqueeze(0)
for j in range(len(hyps) - 1):
for i in range((j + 1), len(hyps)):
if (is_prefix(hyps[j].yseq, hyps[i].yseq) and
(len(hyps[j].yseq) - len(hyps[i].yseq)) <= prefix_alpha):
next_id = len(hyps[i].yseq)
ytu = F.log_softmax(decoder.joint(hi, hyps[i].y[-1]),
dim=0)
curr_score = hyps[i].score + float(
ytu[hyps[j].yseq[next_id]])
for k in range(next_id, (len(hyps[j].yseq) - 1)):
ytu = F.log_softmax(decoder.joint(hi, hyps[j].y[k]),
dim=0)
curr_score += float(ytu[hyps[j].yseq[k + 1]])
hyps[j].score = np.logaddexp(hyps[j].score, curr_score)
S = []
V = []
for n in range(nstep):
beam_y = torch.stack([hyp.y[-1] for hyp in hyps])
beam_logp = F.log_softmax(decoder.joint(h_enc, beam_y), dim=-1)
beam_topk = beam_logp[:, 1:].topk(beam_k, dim=-1)
if rnnlm:
beam_lm_scores = torch.stack([hyp.lm_scores for hyp in hyps])
for i, hyp in enumerate(hyps):
i_topk = (
torch.cat((beam_topk[0][i], beam_logp[i, 0:1])),
torch.cat((beam_topk[1][i] + 1, blank_tensor)),
)
for logp, k in zip(*i_topk):
new_hyp = Hypothesis(
yseq=hyp.yseq[:],
score=(hyp.score + float(logp)),
y=hyp.y[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
lm_scores=hyp.lm_scores,
)
if k == decoder.blank:
S.append(new_hyp)
else:
new_hyp.yseq.append(int(k))
if rnnlm:
new_hyp.score += recog_args.lm_weight * float(
beam_lm_scores[i, k])
V.append(new_hyp)
V = sorted(V, key=lambda x: x.score, reverse=True)
V = substract(V, hyps)[:beam]
l_state = [v.dec_state for v in V]
l_tokens = [v.yseq for v in V]
beam_state = decoder.create_batch_states(beam_state, l_state,
l_tokens)
beam_y, beam_state, beam_lm_tokens = decoder.batch_score(
V, beam_state, cache, init_tensor)
if rnnlm:
beam_lm_states = create_lm_batch_state([v.lm_state for v in V],
lm_type, lm_layers)
beam_lm_states, beam_lm_scores = rnnlm.buff_predict(
beam_lm_states, beam_lm_tokens, len(V))
if n < (nstep - 1):
for i, v in enumerate(V):
v.y.append(beam_y[i])
v.dec_state = decoder.select_state(beam_state, i)
if rnnlm:
v.lm_state = select_lm_state(beam_lm_states, i,
lm_type, lm_layers)
v.lm_scores = beam_lm_scores[i]
hyps = V[:]
else:
beam_logp = F.log_softmax(decoder.joint(h_enc, beam_y), dim=-1)
for i, v in enumerate(V):
if nstep != 1:
v.score += float(beam_logp[i, 0])
v.y.append(beam_y[i])
v.dec_state = decoder.select_state(beam_state, i)
if rnnlm:
v.lm_state = select_lm_state(beam_lm_states, i,
lm_type, lm_layers)
v.lm_scores = beam_lm_scores[i]
kept_hyps = sorted((S + V), key=lambda x: x.score, reverse=True)[:beam]
nbest_hyps = sorted(kept_hyps,
key=lambda x: (x.score / len(x.yseq)),
reverse=True)[:nbest]
return [asdict(n) for n in nbest_hyps]
