def align_length_sync_decoding(self, h: torch.Tensor) -> List[Hypothesis]:
"""Alignment-length synchronous beam search implementation.
Based on https://ieeexplore.ieee.org/document/9053040
Args:
h: Encoded speech features (T_max, D_enc)
Returns:
nbest_hyps: N-best decoding results
"""
beam = min(self.beam_size, self.vocab_size)
h_length = int(h.size(0))
u_max = min(self.u_max, (h_length - 1))
init_tensor = h.unsqueeze(0)
beam_state = self.decoder.init_state(
torch.zeros((beam, self.hidden_size)))
B = [
Hypothesis(
yseq=[self.blank],
score=0.0,
dec_state=self.decoder.select_state(beam_state, 0),
)
]
final = []
if self.lm:
if hasattr(self.lm.predictor, "wordlm"):
lm_model = self.lm.predictor.wordlm
lm_type = "wordlm"
else:
lm_model = self.lm.predictor
lm_type = "lm"
B[0].lm_state = init_lm_state(lm_model)
lm_layers = len(lm_model.rnn)
cache = {}
for i in range(h_length + u_max):
A = []
B_ = []
h_states = []
for hyp in B:
u = len(hyp.yseq) - 1
t = i - u + 1
if t > (h_length - 1):
continue
B_.append(hyp)
h_states.append((t, h[t]))
if B_:
beam_y, beam_state, beam_lm_tokens = self.decoder.batch_score(
B_, beam_state, cache, init_tensor)
h_enc = torch.stack([h[1] for h in h_states])
beam_logp = torch.log_softmax(self.decoder.joint_network(
h_enc, beam_y),
dim=-1)
beam_topk = beam_logp[:, 1:].topk(beam, dim=-1)
if self.lm:
beam_lm_states = create_lm_batch_state(
[b.lm_state for b in B_], lm_type, lm_layers)
beam_lm_states, beam_lm_scores = self.lm.buff_predict(
beam_lm_states, beam_lm_tokens, len(B_))
for i, hyp in enumerate(B_):
new_hyp = Hypothesis(
score=(hyp.score + float(beam_logp[i, 0])),
yseq=hyp.yseq[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
)
A.append(new_hyp)
if h_states[i][0] == (h_length - 1):
final.append(new_hyp)
for logp, k in zip(beam_topk[0][i], beam_topk[1][i] + 1):
new_hyp = Hypothesis(
score=(hyp.score + float(logp)),
yseq=(hyp.yseq[:] + [int(k)]),
dec_state=self.decoder.select_state(beam_state, i),
lm_state=hyp.lm_state,
)
if self.lm:
new_hyp.score += self.lm_weight * beam_lm_scores[i,
k]
new_hyp.lm_state = select_lm_state(
beam_lm_states, i, lm_type, lm_layers)
A.append(new_hyp)
B = sorted(A, key=lambda x: x.score, reverse=True)[:beam]
B = recombine_hyps(B)
if final:
return self.sort_nbest(final)
else:
return B
def time_sync_decoding(self, h: torch.Tensor) -> List[Hypothesis]:
"""Time synchronous beam search implementation.
Based on https://ieeexplore.ieee.org/document/9053040
Args:
h: Encoded speech features (T_max, D_enc)
Returns:
nbest_hyps: N-best decoding results
"""
beam = min(self.beam_size, self.vocab_size)
init_tensor = h.unsqueeze(0)
beam_state = self.decoder.init_state(
torch.zeros((beam, self.hidden_size)))
B = [
Hypothesis(
yseq=[self.blank],
score=0.0,
dec_state=self.decoder.select_state(beam_state, 0),
)
]
if self.lm:
if hasattr(self.lm.predictor, "wordlm"):
lm_model = self.lm.predictor.wordlm
lm_type = "wordlm"
else:
lm_model = self.lm.predictor
lm_type = "lm"
B[0].lm_state = init_lm_state(lm_model)
lm_layers = len(lm_model.rnn)
cache = {}
for hi in h:
A = []
C = B
h_enc = hi.unsqueeze(0)
for v in range(self.max_sym_exp):
D = []
beam_y, beam_state, beam_lm_tokens = self.decoder.batch_score(
C, beam_state, cache, init_tensor)
beam_logp = torch.log_softmax(self.decoder.joint_network(
h_enc, beam_y),
dim=-1)
beam_topk = beam_logp[:, 1:].topk(beam, dim=-1)
seq_A = [h.yseq for h in A]
for i, hyp in enumerate(C):
if hyp.yseq not in seq_A:
A.append(
Hypothesis(
score=(hyp.score + float(beam_logp[i, 0])),
yseq=hyp.yseq[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
))
else:
dict_pos = seq_A.index(hyp.yseq)
A[dict_pos].score = np.logaddexp(
A[dict_pos].score,
(hyp.score + float(beam_logp[i, 0])))
if v < self.max_sym_exp:
if self.lm:
beam_lm_states = create_lm_batch_state(
[c.lm_state for c in C], lm_type, lm_layers)
beam_lm_states, beam_lm_scores = self.lm.buff_predict(
beam_lm_states, beam_lm_tokens, len(C))
for i, hyp in enumerate(C):
for logp, k in zip(beam_topk[0][i],
beam_topk[1][i] + 1):
new_hyp = Hypothesis(
score=(hyp.score + float(logp)),
yseq=(hyp.yseq + [int(k)]),
dec_state=self.decoder.select_state(
beam_state, i),
lm_state=hyp.lm_state,
)
if self.lm:
new_hyp.score += self.lm_weight * beam_lm_scores[
i, k]
new_hyp.lm_state = select_lm_state(
beam_lm_states, i, lm_type, lm_layers)
D.append(new_hyp)
C = sorted(D, key=lambda x: x.score, reverse=True)[:beam]
B = sorted(A, key=lambda x: x.score, reverse=True)[:beam]
return self.sort_nbest(B)
def align_length_sync_decoding(self, enc_out: torch.Tensor) -> List[Hypothesis]:
"""Alignment-length synchronous beam search implementation.
Based on https://ieeexplore.ieee.org/document/9053040
Args:
h: Encoder output sequences. (T, D)
Returns:
nbest_hyps: N-best hypothesis.
"""
beam = min(self.beam_size, self.vocab_size)
t_max = int(enc_out.size(0))
u_max = min(self.u_max, (t_max - 1))
beam_state = self.decoder.init_state(beam)
B = [
Hypothesis(
yseq=[self.blank_id],
score=0.0,
dec_state=self.decoder.select_state(beam_state, 0),
)
]
final = []
cache = {}
if self.use_lm and not self.is_wordlm:
B[0].lm_state = init_lm_state(self.lm_predictor)
for i in range(t_max + u_max):
A = []
B_ = []
B_enc_out = []
for hyp in B:
u = len(hyp.yseq) - 1
t = i - u
if t > (t_max - 1):
continue
B_.append(hyp)
B_enc_out.append((t, enc_out[t]))
if B_:
beam_dec_out, beam_state, beam_lm_tokens = self.decoder.batch_score(
B_,
beam_state,
cache,
self.use_lm,
)
beam_enc_out = torch.stack([x[1] for x in B_enc_out])
beam_logp = torch.log_softmax(
self.joint_network(beam_enc_out, beam_dec_out)
/ self.softmax_temperature,
dim=-1,
)
beam_topk = beam_logp[:, 1:].topk(beam, dim=-1)
if self.use_lm:
beam_lm_states = create_lm_batch_states(
[b.lm_state for b in B_], self.lm_layers, self.is_wordlm
)
beam_lm_states, beam_lm_scores = self.lm.buff_predict(
beam_lm_states, beam_lm_tokens, len(B_)
)
for i, hyp in enumerate(B_):
new_hyp = Hypothesis(
score=(hyp.score + float(beam_logp[i, 0])),
yseq=hyp.yseq[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
)
A.append(new_hyp)
if B_enc_out[i][0] == (t_max - 1):
final.append(new_hyp)
for logp, k in zip(beam_topk[0][i], beam_topk[1][i] + 1):
new_hyp = Hypothesis(
score=(hyp.score + float(logp)),
yseq=(hyp.yseq[:] + [int(k)]),
dec_state=self.decoder.select_state(beam_state, i),
lm_state=hyp.lm_state,
)
if self.use_lm:
new_hyp.score += self.lm_weight * beam_lm_scores[i, k]
new_hyp.lm_state = select_lm_state(
beam_lm_states, i, self.lm_layers, self.is_wordlm
)
A.append(new_hyp)
B = sorted(A, key=lambda x: x.score, reverse=True)[:beam]
B = recombine_hyps(B)
if final:
return self.sort_nbest(final)
else:
return B
def time_sync_decoding(self, enc_out: torch.Tensor) -> List[Hypothesis]:
"""Time synchronous beam search implementation.
Based on https://ieeexplore.ieee.org/document/9053040
Args:
enc_out: Encoder output sequence. (T, D)
Returns:
nbest_hyps: N-best hypothesis.
"""
beam = min(self.beam_size, self.vocab_size)
beam_state = self.decoder.init_state(beam)
B = [
Hypothesis(
yseq=[self.blank_id],
score=0.0,
dec_state=self.decoder.select_state(beam_state, 0),
)
]
cache = {}
if self.use_lm and not self.is_wordlm:
B[0].lm_state = init_lm_state(self.lm_predictor)
for enc_out_t in enc_out:
A = []
C = B
enc_out_t = enc_out_t.unsqueeze(0)
for v in range(self.max_sym_exp):
D = []
beam_dec_out, beam_state, beam_lm_tokens = self.decoder.batch_score(
C,
beam_state,
cache,
self.use_lm,
)
beam_logp = torch.log_softmax(
self.joint_network(enc_out_t, beam_dec_out)
/ self.softmax_temperature,
dim=-1,
)
beam_topk = beam_logp[:, 1:].topk(beam, dim=-1)
seq_A = [h.yseq for h in A]
for i, hyp in enumerate(C):
if hyp.yseq not in seq_A:
A.append(
Hypothesis(
score=(hyp.score + float(beam_logp[i, 0])),
yseq=hyp.yseq[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
)
)
else:
dict_pos = seq_A.index(hyp.yseq)
A[dict_pos].score = np.logaddexp(
A[dict_pos].score, (hyp.score + float(beam_logp[i, 0]))
)
if v < (self.max_sym_exp - 1):
if self.use_lm:
beam_lm_states = create_lm_batch_states(
[c.lm_state for c in C], self.lm_layers, self.is_wordlm
)
beam_lm_states, beam_lm_scores = self.lm.buff_predict(
beam_lm_states, beam_lm_tokens, len(C)
)
for i, hyp in enumerate(C):
for logp, k in zip(beam_topk[0][i], beam_topk[1][i] + 1):
new_hyp = Hypothesis(
score=(hyp.score + float(logp)),
yseq=(hyp.yseq + [int(k)]),
dec_state=self.decoder.select_state(beam_state, i),
lm_state=hyp.lm_state,
)
if self.use_lm:
new_hyp.score += self.lm_weight * beam_lm_scores[i, k]
new_hyp.lm_state = select_lm_state(
beam_lm_states, i, self.lm_layers, self.is_wordlm
)
D.append(new_hyp)
C = sorted(D, key=lambda x: x.score, reverse=True)[:beam]
B = sorted(A, key=lambda x: x.score, reverse=True)[:beam]
return self.sort_nbest(B)
def align_length_sync_decoding(decoder, h, recog_args, rnnlm=None):
"""Alignment-length synchronous beam search implementation.
Based on https://ieeexplore.ieee.org/document/9053040
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)
h_length = int(h.size(0))
u_max = min(recog_args.u_max, (h_length - 1))
nbest = recog_args.nbest
init_tensor = h.unsqueeze(0)
beam_state = decoder.init_state(torch.zeros((beam, decoder.dunits)))
B = [
Hypothesis(
yseq=[decoder.blank],
score=0.0,
dec_state=decoder.select_state(beam_state, 0),
)
]
final = []
if rnnlm:
if hasattr(rnnlm.predictor, "wordlm"):
lm_model = rnnlm.predictor.wordlm
lm_type = "wordlm"
else:
lm_model = rnnlm.predictor
lm_type = "lm"
B[0].lm_state = init_lm_state(lm_model)
lm_layers = len(lm_model.rnn)
cache = {}
for i in range(h_length + u_max):
A = []
B_ = []
h_states = []
for hyp in B:
u = len(hyp.yseq) - 1
t = i - u + 1
if t > (h_length - 1):
continue
B_.append(hyp)
h_states.append((t, h[t]))
if B_:
beam_y, beam_state, beam_lm_tokens = decoder.batch_score(
B_, beam_state, cache, init_tensor)
h_enc = torch.stack([h[1] for h in h_states])
beam_logp = F.log_softmax(decoder.joint(h_enc, beam_y), dim=-1)
beam_topk = beam_logp[:, 1:].topk(beam, dim=-1)
if rnnlm:
beam_lm_states = create_lm_batch_state(
[b.lm_state for b in B_], lm_type, lm_layers)
beam_lm_states, beam_lm_scores = rnnlm.buff_predict(
beam_lm_states, beam_lm_tokens, len(B_))
for i, hyp in enumerate(B_):
new_hyp = Hypothesis(
score=(hyp.score + float(beam_logp[i, 0])),
yseq=hyp.yseq[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
)
A.append(new_hyp)
if h_states[i][0] == (h_length - 1):
final.append(new_hyp)
for logp, k in zip(beam_topk[0][i], beam_topk[1][i] + 1):
new_hyp = Hypothesis(
score=(hyp.score + float(logp)),
yseq=(hyp.yseq[:] + [int(k)]),
dec_state=decoder.select_state(beam_state, i),
lm_state=hyp.lm_state,
)
if rnnlm:
new_hyp.score += recog_args.lm_weight * beam_lm_scores[
i, k]
new_hyp.lm_state = select_lm_state(
beam_lm_states, i, lm_type, lm_layers)
A.append(new_hyp)
B = sorted(A, key=lambda x: x.score, reverse=True)[:beam]
B = recombine_hyps(B)
if final:
nbest_hyps = sorted(final, key=lambda x: x.score, reverse=True)[:nbest]
else:
nbest_hyps = B[:nbest]
return [asdict(n) for n in nbest_hyps]
def time_sync_decoding(decoder, h, recog_args, rnnlm=None):
"""Time synchronous beam search implementation.
Based on https://ieeexplore.ieee.org/document/9053040
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)
max_sym_exp = recog_args.max_sym_exp
nbest = recog_args.nbest
init_tensor = h.unsqueeze(0)
beam_state = decoder.init_state(torch.zeros((beam, decoder.dunits)))
B = [
Hypothesis(
yseq=[decoder.blank],
score=0.0,
dec_state=decoder.select_state(beam_state, 0),
)
]
if rnnlm:
if hasattr(rnnlm.predictor, "wordlm"):
lm_model = rnnlm.predictor.wordlm
lm_type = "wordlm"
else:
lm_model = rnnlm.predictor
lm_type = "lm"
B[0].lm_state = init_lm_state(lm_model)
lm_layers = len(lm_model.rnn)
cache = {}
for hi in h:
A = []
C = B
h_enc = hi.unsqueeze(0)
for v in range(max_sym_exp):
D = []
beam_y, beam_state, beam_lm_tokens = decoder.batch_score(
C, beam_state, cache, init_tensor)
beam_logp = F.log_softmax(decoder.joint(h_enc, beam_y), dim=-1)
beam_topk = beam_logp[:, 1:].topk(beam, dim=-1)
seq_A = [h.yseq for h in A]
for i, hyp in enumerate(C):
if hyp.yseq not in seq_A:
A.append(
Hypothesis(
score=(hyp.score + float(beam_logp[i, 0])),
yseq=hyp.yseq[:],
dec_state=hyp.dec_state,
lm_state=hyp.lm_state,
))
else:
dict_pos = seq_A.index(hyp.yseq)
A[dict_pos].score = np.logaddexp(
A[dict_pos].score,
(hyp.score + float(beam_logp[i, 0])))
if v < max_sym_exp:
if rnnlm:
beam_lm_states = create_lm_batch_state(
[c.lm_state for c in C], lm_type, lm_layers)
beam_lm_states, beam_lm_scores = rnnlm.buff_predict(
beam_lm_states, beam_lm_tokens, len(C))
for i, hyp in enumerate(C):
for logp, k in zip(beam_topk[0][i], beam_topk[1][i] + 1):
new_hyp = Hypothesis(
score=(hyp.score + float(logp)),
yseq=(hyp.yseq + [int(k)]),
dec_state=decoder.select_state(beam_state, i),
lm_state=hyp.lm_state,
)
if rnnlm:
new_hyp.score += recog_args.lm_weight * beam_lm_scores[
i, k]
new_hyp.lm_state = select_lm_state(
beam_lm_states, i, lm_type, lm_layers)
D.append(new_hyp)
C = sorted(D, key=lambda x: x.score, reverse=True)[:beam]
B = sorted(A, key=lambda x: x.score, reverse=True)[:beam]
nbest_hyps = sorted(B, key=lambda x: x.score, reverse=True)[:nbest]
return [asdict(n) for n in nbest_hyps]
