"""RNN decoder module."""
import logging
import math
import random
from argparse import Namespace
import numpy as np
import torch
import torch.nn.functional as F
from espnet.nets.ctc_prefix_score import CTCPrefixScore, CTCPrefixScoreTH
from espnet.nets.e2e_asr_common import end_detect
from espnet.nets.pytorch_backend.nets_utils import (
mask_by_length,
pad_list,
th_accuracy,
to_device,
)
from espnet.nets.pytorch_backend.rnn.attentions import att_to_numpy
from espnet.nets.scorer_interface import ScorerInterface
MAX_DECODER_OUTPUT = 5
CTC_SCORING_RATIO = 1.5
[docs]class Decoder(torch.nn.Module, ScorerInterface):
"""Decoder module
:param int eprojs: encoder projection units
:param int odim: dimension of outputs
:param str dtype: gru or lstm
:param int dlayers: decoder layers
:param int dunits: decoder units
:param int sos: start of sequence symbol id
:param int eos: end of sequence symbol id
:param torch.nn.Module att: attention module
:param int verbose: verbose level
:param list char_list: list of character strings
:param ndarray labeldist: distribution of label smoothing
:param float lsm_weight: label smoothing weight
:param float sampling_probability: scheduled sampling probability
:param float dropout: dropout rate
:param float context_residual: if True, use context vector for token generation
:param float replace_sos: use for multilingual (speech/text) translation
"""
def __init__(
self,
eprojs,
odim,
dtype,
dlayers,
dunits,
sos,
eos,
att,
verbose=0,
char_list=None,
labeldist=None,
lsm_weight=0.0,
sampling_probability=0.0,
dropout=0.0,
context_residual=False,
replace_sos=False,
num_encs=1,
):
torch.nn.Module.__init__(self)
self.dtype = dtype
self.dunits = dunits
self.dlayers = dlayers
self.context_residual = context_residual
self.embed = torch.nn.Embedding(odim, dunits)
self.dropout_emb = torch.nn.Dropout(p=dropout)
self.decoder = torch.nn.ModuleList()
self.dropout_dec = torch.nn.ModuleList()
self.decoder += [
(
torch.nn.LSTMCell(dunits + eprojs, dunits)
if self.dtype == "lstm"
else torch.nn.GRUCell(dunits + eprojs, dunits)
)
]
self.dropout_dec += [torch.nn.Dropout(p=dropout)]
for _ in range(1, self.dlayers):
self.decoder += [
(
torch.nn.LSTMCell(dunits, dunits)
if self.dtype == "lstm"
else torch.nn.GRUCell(dunits, dunits)
)
]
self.dropout_dec += [torch.nn.Dropout(p=dropout)]
# NOTE: dropout is applied only for the vertical connections
# see https://arxiv.org/pdf/1409.2329.pdf
self.ignore_id = -1
if context_residual:
self.output = torch.nn.Linear(dunits + eprojs, odim)
else:
self.output = torch.nn.Linear(dunits, odim)
self.loss = None
self.att = att
self.dunits = dunits
self.sos = sos
self.eos = eos
self.odim = odim
self.verbose = verbose
self.char_list = char_list
# for label smoothing
self.labeldist = labeldist
self.vlabeldist = None
self.lsm_weight = lsm_weight
self.sampling_probability = sampling_probability
self.dropout = dropout
self.num_encs = num_encs
# for multilingual E2E-ST
self.replace_sos = replace_sos
self.logzero = -10000000000.0
[docs] def zero_state(self, hs_pad):
return hs_pad.new_zeros(hs_pad.size(0), self.dunits)
[docs] def rnn_forward(self, ey, z_list, c_list, z_prev, c_prev):
if self.dtype == "lstm":
z_list[0], c_list[0] = self.decoder[0](ey, (z_prev[0], c_prev[0]))
for i in range(1, self.dlayers):
z_list[i], c_list[i] = self.decoder[i](
self.dropout_dec[i - 1](z_list[i - 1]), (z_prev[i], c_prev[i])
)
else:
z_list[0] = self.decoder[0](ey, z_prev[0])
for i in range(1, self.dlayers):
z_list[i] = self.decoder[i](
self.dropout_dec[i - 1](z_list[i - 1]), z_prev[i]
)
return z_list, c_list
[docs] def forward(self, hs_pad, hlens, ys_pad, strm_idx=0, lang_ids=None):
"""Decoder forward
:param torch.Tensor hs_pad: batch of padded hidden state sequences (B, Tmax, D)
[in multi-encoder case,
list of torch.Tensor,
[(B, Tmax_1, D), (B, Tmax_2, D), ..., ] ]
:param torch.Tensor hlens: batch of lengths of hidden state sequences (B)
[in multi-encoder case, list of torch.Tensor,
[(B), (B), ..., ]
:param torch.Tensor ys_pad: batch of padded character id sequence tensor
(B, Lmax)
:param int strm_idx: stream index indicates the index of decoding stream.
:param torch.Tensor lang_ids: batch of target language id tensor (B, 1)
:return: attention loss value
:rtype: torch.Tensor
:return: accuracy
:rtype: float
"""
# to support mutiple encoder asr mode, in single encoder mode,
# convert torch.Tensor to List of torch.Tensor
if self.num_encs == 1:
hs_pad = [hs_pad]
hlens = [hlens]
# TODO(kan-bayashi): need to make more smart way
ys = [y[y != self.ignore_id] for y in ys_pad] # parse padded ys
# attention index for the attention module
# in SPA (speaker parallel attention),
# att_idx is used to select attention module. In other cases, it is 0.
att_idx = min(strm_idx, len(self.att) - 1)
# hlens should be list of list of integer
hlens = [list(map(int, hlens[idx])) for idx in range(self.num_encs)]
self.loss = None
# prepare input and output word sequences with sos/eos IDs
eos = ys[0].new([self.eos])
sos = ys[0].new([self.sos])
if self.replace_sos:
ys_in = [torch.cat([idx, y], dim=0) for idx, y in zip(lang_ids, ys)]
else:
ys_in = [torch.cat([sos, y], dim=0) for y in ys]
ys_out = [torch.cat([y, eos], dim=0) for y in ys]
# padding for ys with -1
# pys: utt x olen
ys_in_pad = pad_list(ys_in, self.eos)
ys_out_pad = pad_list(ys_out, self.ignore_id)
# get dim, length info
batch = ys_out_pad.size(0)
olength = ys_out_pad.size(1)
for idx in range(self.num_encs):
logging.info(
self.__class__.__name__
+ "Number of Encoder:{}; enc{}: input lengths: {}.".format(
self.num_encs, idx + 1, hlens[idx]
)
)
logging.info(
self.__class__.__name__
+ " output lengths: "
+ str([y.size(0) for y in ys_out])
)
# initialization
c_list = [self.zero_state(hs_pad[0])]
z_list = [self.zero_state(hs_pad[0])]
for _ in range(1, self.dlayers):
c_list.append(self.zero_state(hs_pad[0]))
z_list.append(self.zero_state(hs_pad[0]))
z_all = []
if self.num_encs == 1:
att_w = None
self.att[att_idx].reset() # reset pre-computation of h
else:
att_w_list = [None] * (self.num_encs + 1) # atts + han
att_c_list = [None] * (self.num_encs) # atts
for idx in range(self.num_encs + 1):
self.att[idx].reset() # reset pre-computation of h in atts and han
# pre-computation of embedding
eys = self.dropout_emb(self.embed(ys_in_pad)) # utt x olen x zdim
# loop for an output sequence
for i in range(olength):
if self.num_encs == 1:
att_c, att_w = self.att[att_idx](
hs_pad[0], hlens[0], self.dropout_dec[0](z_list[0]), att_w
)
else:
for idx in range(self.num_encs):
att_c_list[idx], att_w_list[idx] = self.att[idx](
hs_pad[idx],
hlens[idx],
self.dropout_dec[0](z_list[0]),
att_w_list[idx],
)
hs_pad_han = torch.stack(att_c_list, dim=1)
hlens_han = [self.num_encs] * len(ys_in)
att_c, att_w_list[self.num_encs] = self.att[self.num_encs](
hs_pad_han,
hlens_han,
self.dropout_dec[0](z_list[0]),
att_w_list[self.num_encs],
)
if i > 0 and random.random() < self.sampling_probability:
logging.info(" scheduled sampling ")
z_out = self.output(z_all[-1])
z_out = np.argmax(z_out.detach().cpu(), axis=1)
z_out = self.dropout_emb(self.embed(to_device(hs_pad[0], z_out)))
ey = torch.cat((z_out, att_c), dim=1) # utt x (zdim + hdim)
else:
ey = torch.cat((eys[:, i, :], att_c), dim=1) # utt x (zdim + hdim)
z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_list, c_list)
if self.context_residual:
z_all.append(
torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1)
) # utt x (zdim + hdim)
else:
z_all.append(self.dropout_dec[-1](z_list[-1])) # utt x (zdim)
z_all = torch.stack(z_all, dim=1).view(batch * olength, -1)
# compute loss
y_all = self.output(z_all)
self.loss = F.cross_entropy(
y_all,
ys_out_pad.view(-1),
ignore_index=self.ignore_id,
reduction="mean",
)
# compute perplexity
ppl = math.exp(self.loss.item())
# -1: eos, which is removed in the loss computation
self.loss *= np.mean([len(x) for x in ys_in]) - 1
acc = th_accuracy(y_all, ys_out_pad, ignore_label=self.ignore_id)
logging.info("att loss:" + "".join(str(self.loss.item()).split("\n")))
# show predicted character sequence for debug
if self.verbose > 0 and self.char_list is not None:
ys_hat = y_all.view(batch, olength, -1)
ys_true = ys_out_pad
for (i, y_hat), y_true in zip(
enumerate(ys_hat.detach().cpu().numpy()), ys_true.detach().cpu().numpy()
):
if i == MAX_DECODER_OUTPUT:
break
idx_hat = np.argmax(y_hat[y_true != self.ignore_id], axis=1)
idx_true = y_true[y_true != self.ignore_id]
seq_hat = [self.char_list[int(idx)] for idx in idx_hat]
seq_true = [self.char_list[int(idx)] for idx in idx_true]
seq_hat = "".join(seq_hat)
seq_true = "".join(seq_true)
logging.info("groundtruth[%d]: " % i + seq_true)
logging.info("prediction [%d]: " % i + seq_hat)
if self.labeldist is not None:
if self.vlabeldist is None:
self.vlabeldist = to_device(hs_pad[0], torch.from_numpy(self.labeldist))
loss_reg = -torch.sum(
(F.log_softmax(y_all, dim=1) * self.vlabeldist).view(-1), dim=0
) / len(ys_in)
self.loss = (1.0 - self.lsm_weight) * self.loss + self.lsm_weight * loss_reg
return self.loss, acc, ppl
[docs] def recognize_beam(self, h, lpz, recog_args, char_list, rnnlm=None, strm_idx=0):
"""beam search implementation
:param torch.Tensor h: encoder hidden state (T, eprojs)
[in multi-encoder case, list of torch.Tensor,
[(T1, eprojs), (T2, eprojs), ...] ]
:param torch.Tensor lpz: ctc log softmax output (T, odim)
[in multi-encoder case, list of torch.Tensor,
[(T1, odim), (T2, odim), ...] ]
:param Namespace recog_args: argument Namespace containing options
:param char_list: list of character strings
:param torch.nn.Module rnnlm: language module
:param int strm_idx:
stream index for speaker parallel attention in multi-speaker case
:return: N-best decoding results
:rtype: list of dicts
"""
# to support mutiple encoder asr mode, in single encoder mode,
# convert torch.Tensor to List of torch.Tensor
if self.num_encs == 1:
h = [h]
lpz = [lpz]
if self.num_encs > 1 and lpz is None:
lpz = [lpz] * self.num_encs
for idx in range(self.num_encs):
logging.info(
"Number of Encoder:{}; enc{}: input lengths: {}.".format(
self.num_encs, idx + 1, h[0].size(0)
)
)
att_idx = min(strm_idx, len(self.att) - 1)
# initialization
c_list = [self.zero_state(h[0].unsqueeze(0))]
z_list = [self.zero_state(h[0].unsqueeze(0))]
for _ in range(1, self.dlayers):
c_list.append(self.zero_state(h[0].unsqueeze(0)))
z_list.append(self.zero_state(h[0].unsqueeze(0)))
if self.num_encs == 1:
a = None
self.att[att_idx].reset() # reset pre-computation of h
else:
a = [None] * (self.num_encs + 1) # atts + han
att_w_list = [None] * (self.num_encs + 1) # atts + han
att_c_list = [None] * (self.num_encs) # atts
for idx in range(self.num_encs + 1):
self.att[idx].reset() # reset pre-computation of h in atts and han
# search parms
beam = recog_args.beam_size
penalty = recog_args.penalty
ctc_weight = getattr(recog_args, "ctc_weight", False) # for NMT
if lpz[0] is not None and self.num_encs > 1:
# weights-ctc,
# e.g. ctc_loss = w_1*ctc_1_loss + w_2 * ctc_2_loss + w_N * ctc_N_loss
weights_ctc_dec = recog_args.weights_ctc_dec / np.sum(
recog_args.weights_ctc_dec
) # normalize
logging.info(
"ctc weights (decoding): " + " ".join([str(x) for x in weights_ctc_dec])
)
else:
weights_ctc_dec = [1.0]
# preprate sos
if self.replace_sos and recog_args.tgt_lang:
y = char_list.index(recog_args.tgt_lang)
else:
y = self.sos
logging.info("<sos> index: " + str(y))
logging.info("<sos> mark: " + char_list[y])
vy = h[0].new_zeros(1).long()
maxlen = np.amin([h[idx].size(0) for idx in range(self.num_encs)])
if recog_args.maxlenratio != 0:
# maxlen >= 1
maxlen = max(1, int(recog_args.maxlenratio * maxlen))
minlen = int(recog_args.minlenratio * maxlen)
logging.info("max output length: " + str(maxlen))
logging.info("min output length: " + str(minlen))
# initialize hypothesis
if rnnlm:
hyp = {
"score": 0.0,
"yseq": [y],
"c_prev": c_list,
"z_prev": z_list,
"a_prev": a,
"rnnlm_prev": None,
}
else:
hyp = {
"score": 0.0,
"yseq": [y],
"c_prev": c_list,
"z_prev": z_list,
"a_prev": a,
}
if lpz[0] is not None:
ctc_prefix_score = [
CTCPrefixScore(lpz[idx].detach().numpy(), 0, self.eos, np)
for idx in range(self.num_encs)
]
hyp["ctc_state_prev"] = [
ctc_prefix_score[idx].initial_state() for idx in range(self.num_encs)
]
hyp["ctc_score_prev"] = [0.0] * self.num_encs
if ctc_weight != 1.0:
# pre-pruning based on attention scores
ctc_beam = min(lpz[0].shape[-1], int(beam * CTC_SCORING_RATIO))
else:
ctc_beam = lpz[0].shape[-1]
hyps = [hyp]
ended_hyps = []
for i in range(maxlen):
logging.debug("position " + str(i))
hyps_best_kept = []
for hyp in hyps:
vy[0] = hyp["yseq"][i]
ey = self.dropout_emb(self.embed(vy)) # utt list (1) x zdim
if self.num_encs == 1:
att_c, att_w = self.att[att_idx](
h[0].unsqueeze(0),
[h[0].size(0)],
self.dropout_dec[0](hyp["z_prev"][0]),
hyp["a_prev"],
)
else:
for idx in range(self.num_encs):
att_c_list[idx], att_w_list[idx] = self.att[idx](
h[idx].unsqueeze(0),
[h[idx].size(0)],
self.dropout_dec[0](hyp["z_prev"][0]),
hyp["a_prev"][idx],
)
h_han = torch.stack(att_c_list, dim=1)
att_c, att_w_list[self.num_encs] = self.att[self.num_encs](
h_han,
[self.num_encs],
self.dropout_dec[0](hyp["z_prev"][0]),
hyp["a_prev"][self.num_encs],
)
ey = torch.cat((ey, att_c), dim=1) # utt(1) x (zdim + hdim)
z_list, c_list = self.rnn_forward(
ey, z_list, c_list, hyp["z_prev"], hyp["c_prev"]
)
# get nbest local scores and their ids
if self.context_residual:
logits = self.output(
torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1)
)
else:
logits = self.output(self.dropout_dec[-1](z_list[-1]))
local_att_scores = F.log_softmax(logits, dim=1)
if rnnlm:
rnnlm_state, local_lm_scores = rnnlm.predict(hyp["rnnlm_prev"], vy)
local_scores = (
local_att_scores + recog_args.lm_weight * local_lm_scores
)
else:
local_scores = local_att_scores
if lpz[0] is not None:
local_best_scores, local_best_ids = torch.topk(
local_att_scores, ctc_beam, dim=1
)
ctc_scores, ctc_states = (
[None] * self.num_encs,
[None] * self.num_encs,
)
for idx in range(self.num_encs):
ctc_scores[idx], ctc_states[idx] = ctc_prefix_score[idx](
hyp["yseq"], local_best_ids[0], hyp["ctc_state_prev"][idx]
)
local_scores = (1.0 - ctc_weight) * local_att_scores[
:, local_best_ids[0]
]
if self.num_encs == 1:
local_scores += ctc_weight * torch.from_numpy(
ctc_scores[0] - hyp["ctc_score_prev"][0]
)
else:
for idx in range(self.num_encs):
local_scores += (
ctc_weight
* weights_ctc_dec[idx]
* torch.from_numpy(
ctc_scores[idx] - hyp["ctc_score_prev"][idx]
)
)
if rnnlm:
local_scores += (
recog_args.lm_weight * local_lm_scores[:, local_best_ids[0]]
)
local_best_scores, joint_best_ids = torch.topk(
local_scores, beam, dim=1
)
local_best_ids = local_best_ids[:, joint_best_ids[0]]
else:
local_best_scores, local_best_ids = torch.topk(
local_scores, beam, dim=1
)
for j in range(beam):
new_hyp = {}
# [:] is needed!
new_hyp["z_prev"] = z_list[:]
new_hyp["c_prev"] = c_list[:]
if self.num_encs == 1:
new_hyp["a_prev"] = att_w[:]
else:
new_hyp["a_prev"] = [
att_w_list[idx][:] for idx in range(self.num_encs + 1)
]
new_hyp["score"] = hyp["score"] + local_best_scores[0, j]
new_hyp["yseq"] = [0] * (1 + len(hyp["yseq"]))
new_hyp["yseq"][: len(hyp["yseq"])] = hyp["yseq"]
new_hyp["yseq"][len(hyp["yseq"])] = int(local_best_ids[0, j])
if rnnlm:
new_hyp["rnnlm_prev"] = rnnlm_state
if lpz[0] is not None:
new_hyp["ctc_state_prev"] = [
ctc_states[idx][joint_best_ids[0, j]]
for idx in range(self.num_encs)
]
new_hyp["ctc_score_prev"] = [
ctc_scores[idx][joint_best_ids[0, j]]
for idx in range(self.num_encs)
]
# will be (2 x beam) hyps at most
hyps_best_kept.append(new_hyp)
hyps_best_kept = sorted(
hyps_best_kept, key=lambda x: x["score"], reverse=True
)[:beam]
# sort and get nbest
hyps = hyps_best_kept
logging.debug("number of pruned hypotheses: " + str(len(hyps)))
logging.debug(
"best hypo: "
+ "".join([char_list[int(x)] for x in hyps[0]["yseq"][1:]])
)
# add eos in the final loop to avoid that there are no ended hyps
if i == maxlen - 1:
logging.info("adding <eos> in the last position in the loop")
for hyp in hyps:
hyp["yseq"].append(self.eos)
# add ended hypotheses to a final list,
# and removed them from current hypotheses
# (this will be a problem, number of hyps < beam)
remained_hyps = []
for hyp in hyps:
if hyp["yseq"][-1] == self.eos:
# only store the sequence that has more than minlen outputs
# also add penalty
if len(hyp["yseq"]) > minlen:
hyp["score"] += (i + 1) * penalty
if rnnlm: # Word LM needs to add final <eos> score
hyp["score"] += recog_args.lm_weight * rnnlm.final(
hyp["rnnlm_prev"]
)
ended_hyps.append(hyp)
else:
remained_hyps.append(hyp)
# end detection
if end_detect(ended_hyps, i) and recog_args.maxlenratio == 0.0:
logging.info("end detected at %d", i)
break
hyps = remained_hyps
if len(hyps) > 0:
logging.debug("remaining hypotheses: " + str(len(hyps)))
else:
logging.info("no hypothesis. Finish decoding.")
break
for hyp in hyps:
logging.debug(
"hypo: " + "".join([char_list[int(x)] for x in hyp["yseq"][1:]])
)
logging.debug("number of ended hypotheses: " + str(len(ended_hyps)))
nbest_hyps = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[
: min(len(ended_hyps), recog_args.nbest)
]
# check number of hypotheses
if len(nbest_hyps) == 0:
logging.warning(
"there is no N-best results, "
"perform recognition again with smaller minlenratio."
)
# should copy because Namespace will be overwritten globally
recog_args = Namespace(**vars(recog_args))
recog_args.minlenratio = max(0.0, recog_args.minlenratio - 0.1)
if self.num_encs == 1:
return self.recognize_beam(h[0], lpz[0], recog_args, char_list, rnnlm)
else:
return self.recognize_beam(h, lpz, recog_args, char_list, rnnlm)
logging.info("total log probability: " + str(nbest_hyps[0]["score"]))
logging.info(
"normalized log probability: "
+ str(nbest_hyps[0]["score"] / len(nbest_hyps[0]["yseq"]))
)
# remove sos
return nbest_hyps
[docs] def recognize_beam_batch(
self,
h,
hlens,
lpz,
recog_args,
char_list,
rnnlm=None,
normalize_score=True,
strm_idx=0,
lang_ids=None,
):
# to support mutiple encoder asr mode, in single encoder mode,
# convert torch.Tensor to List of torch.Tensor
if self.num_encs == 1:
h = [h]
hlens = [hlens]
lpz = [lpz]
if self.num_encs > 1 and lpz is None:
lpz = [lpz] * self.num_encs
att_idx = min(strm_idx, len(self.att) - 1)
for idx in range(self.num_encs):
logging.info(
"Number of Encoder:{}; enc{}: input lengths: {}.".format(
self.num_encs, idx + 1, h[idx].size(1)
)
)
h[idx] = mask_by_length(h[idx], hlens[idx], 0.0)
# search params
batch = len(hlens[0])
beam = recog_args.beam_size
penalty = recog_args.penalty
ctc_weight = getattr(recog_args, "ctc_weight", 0) # for NMT
att_weight = 1.0 - ctc_weight
ctc_margin = getattr(
recog_args, "ctc_window_margin", 0
) # use getattr to keep compatibility
# weights-ctc,
# e.g. ctc_loss = w_1*ctc_1_loss + w_2 * ctc_2_loss + w_N * ctc_N_loss
if lpz[0] is not None and self.num_encs > 1:
weights_ctc_dec = recog_args.weights_ctc_dec / np.sum(
recog_args.weights_ctc_dec
) # normalize
logging.info(
"ctc weights (decoding): " + " ".join([str(x) for x in weights_ctc_dec])
)
else:
weights_ctc_dec = [1.0]
n_bb = batch * beam
pad_b = to_device(h[0], torch.arange(batch) * beam).view(-1, 1)
max_hlen = np.amin([max(hlens[idx]) for idx in range(self.num_encs)])
if recog_args.maxlenratio == 0:
maxlen = max_hlen
else:
maxlen = max(1, int(recog_args.maxlenratio * max_hlen))
minlen = int(recog_args.minlenratio * max_hlen)
logging.info("max output length: " + str(maxlen))
logging.info("min output length: " + str(minlen))
# initialization
c_prev = [
to_device(h[0], torch.zeros(n_bb, self.dunits)) for _ in range(self.dlayers)
]
z_prev = [
to_device(h[0], torch.zeros(n_bb, self.dunits)) for _ in range(self.dlayers)
]
c_list = [
to_device(h[0], torch.zeros(n_bb, self.dunits)) for _ in range(self.dlayers)
]
z_list = [
to_device(h[0], torch.zeros(n_bb, self.dunits)) for _ in range(self.dlayers)
]
vscores = to_device(h[0], torch.zeros(batch, beam))
rnnlm_state = None
if self.num_encs == 1:
a_prev = [None]
att_w_list, ctc_scorer, ctc_state = [None], [None], [None]
self.att[att_idx].reset() # reset pre-computation of h
else:
a_prev = [None] * (self.num_encs + 1) # atts + han
att_w_list = [None] * (self.num_encs + 1) # atts + han
att_c_list = [None] * (self.num_encs) # atts
ctc_scorer, ctc_state = [None] * (self.num_encs), [None] * (self.num_encs)
for idx in range(self.num_encs + 1):
self.att[idx].reset() # reset pre-computation of h in atts and han
if self.replace_sos and recog_args.tgt_lang:
logging.info("<sos> index: " + str(char_list.index(recog_args.tgt_lang)))
logging.info("<sos> mark: " + recog_args.tgt_lang)
yseq = [[char_list.index(recog_args.tgt_lang)] for _ in range(n_bb)]
elif lang_ids is not None:
# NOTE: used for evaluation during training
yseq = [[lang_ids[b // recog_args.beam_size]] for b in range(n_bb)]
else:
logging.info("<sos> index: " + str(self.sos))
logging.info("<sos> mark: " + char_list[self.sos])
yseq = [[self.sos] for _ in range(n_bb)]
accum_odim_ids = [self.sos for _ in range(n_bb)]
stop_search = [False for _ in range(batch)]
nbest_hyps = [[] for _ in range(batch)]
ended_hyps = [[] for _ in range(batch)]
exp_hlens = [
hlens[idx].repeat(beam).view(beam, batch).transpose(0, 1).contiguous()
for idx in range(self.num_encs)
]
exp_hlens = [exp_hlens[idx].view(-1).tolist() for idx in range(self.num_encs)]
exp_h = [
h[idx].unsqueeze(1).repeat(1, beam, 1, 1).contiguous()
for idx in range(self.num_encs)
]
exp_h = [
exp_h[idx].view(n_bb, h[idx].size()[1], h[idx].size()[2])
for idx in range(self.num_encs)
]
if lpz[0] is not None:
scoring_num = min(
(
int(beam * CTC_SCORING_RATIO)
if att_weight > 0.0 and not lpz[0].is_cuda
else 0
),
lpz[0].size(-1),
)
ctc_scorer = [
CTCPrefixScoreTH(
lpz[idx],
hlens[idx],
0,
self.eos,
margin=ctc_margin,
)
for idx in range(self.num_encs)
]
for i in range(maxlen):
logging.debug("position " + str(i))
vy = to_device(h[0], torch.LongTensor(self._get_last_yseq(yseq)))
ey = self.dropout_emb(self.embed(vy))
if self.num_encs == 1:
att_c, att_w = self.att[att_idx](
exp_h[0], exp_hlens[0], self.dropout_dec[0](z_prev[0]), a_prev[0]
)
att_w_list = [att_w]
else:
for idx in range(self.num_encs):
att_c_list[idx], att_w_list[idx] = self.att[idx](
exp_h[idx],
exp_hlens[idx],
self.dropout_dec[0](z_prev[0]),
a_prev[idx],
)
exp_h_han = torch.stack(att_c_list, dim=1)
att_c, att_w_list[self.num_encs] = self.att[self.num_encs](
exp_h_han,
[self.num_encs] * n_bb,
self.dropout_dec[0](z_prev[0]),
a_prev[self.num_encs],
)
ey = torch.cat((ey, att_c), dim=1)
# attention decoder
z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_prev, c_prev)
if self.context_residual:
logits = self.output(
torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1)
)
else:
logits = self.output(self.dropout_dec[-1](z_list[-1]))
local_scores = att_weight * F.log_softmax(logits, dim=1)
# rnnlm
if rnnlm:
rnnlm_state, local_lm_scores = rnnlm.buff_predict(rnnlm_state, vy, n_bb)
local_scores = local_scores + recog_args.lm_weight * local_lm_scores
# ctc
if ctc_scorer[0]:
local_scores[:, 0] = self.logzero # avoid choosing blank
part_ids = (
torch.topk(local_scores, scoring_num, dim=-1)[1]
if scoring_num > 0
else None
)
for idx in range(self.num_encs):
att_w = att_w_list[idx]
att_w_ = att_w if isinstance(att_w, torch.Tensor) else att_w[0]
local_ctc_scores, ctc_state[idx] = ctc_scorer[idx](
yseq, ctc_state[idx], part_ids, att_w_
)
local_scores = (
local_scores
+ ctc_weight * weights_ctc_dec[idx] * local_ctc_scores
)
local_scores = local_scores.view(batch, beam, self.odim)
if i == 0:
local_scores[:, 1:, :] = self.logzero
# accumulate scores
eos_vscores = local_scores[:, :, self.eos] + vscores
vscores = vscores.view(batch, beam, 1).repeat(1, 1, self.odim)
vscores[:, :, self.eos] = self.logzero
vscores = (vscores + local_scores).view(batch, -1)
# global pruning
accum_best_scores, accum_best_ids = torch.topk(vscores, beam, 1)
accum_odim_ids = (
torch.fmod(accum_best_ids, self.odim).view(-1).data.cpu().tolist()
)
accum_padded_beam_ids = (
(accum_best_ids // self.odim + pad_b).view(-1).data.cpu().tolist()
)
y_prev = yseq[:][:]
yseq = self._index_select_list(yseq, accum_padded_beam_ids)
yseq = self._append_ids(yseq, accum_odim_ids)
vscores = accum_best_scores
vidx = to_device(h[0], torch.LongTensor(accum_padded_beam_ids))
a_prev = []
num_atts = self.num_encs if self.num_encs == 1 else self.num_encs + 1
for idx in range(num_atts):
if isinstance(att_w_list[idx], torch.Tensor):
_a_prev = torch.index_select(
att_w_list[idx].view(n_bb, *att_w_list[idx].shape[1:]), 0, vidx
)
elif isinstance(att_w_list[idx], list):
# handle the case of multi-head attention
_a_prev = [
torch.index_select(att_w_one.view(n_bb, -1), 0, vidx)
for att_w_one in att_w_list[idx]
]
else:
# handle the case of location_recurrent when return is a tuple
_a_prev_ = torch.index_select(
att_w_list[idx][0].view(n_bb, -1), 0, vidx
)
_h_prev_ = torch.index_select(
att_w_list[idx][1][0].view(n_bb, -1), 0, vidx
)
_c_prev_ = torch.index_select(
att_w_list[idx][1][1].view(n_bb, -1), 0, vidx
)
_a_prev = (_a_prev_, (_h_prev_, _c_prev_))
a_prev.append(_a_prev)
z_prev = [
torch.index_select(z_list[li].view(n_bb, -1), 0, vidx)
for li in range(self.dlayers)
]
c_prev = [
torch.index_select(c_list[li].view(n_bb, -1), 0, vidx)
for li in range(self.dlayers)
]
# pick ended hyps
if i >= minlen:
k = 0
penalty_i = (i + 1) * penalty
thr = accum_best_scores[:, -1]
for samp_i in range(batch):
if stop_search[samp_i]:
k = k + beam
continue
for beam_j in range(beam):
_vscore = None
if eos_vscores[samp_i, beam_j] > thr[samp_i]:
yk = y_prev[k][:]
if len(yk) <= min(
hlens[idx][samp_i] for idx in range(self.num_encs)
):
_vscore = eos_vscores[samp_i][beam_j] + penalty_i
elif i == maxlen - 1:
yk = yseq[k][:]
_vscore = vscores[samp_i][beam_j] + penalty_i
if _vscore:
yk.append(self.eos)
if rnnlm:
_vscore += recog_args.lm_weight * rnnlm.final(
rnnlm_state, index=k
)
_score = _vscore.data.cpu().numpy()
ended_hyps[samp_i].append(
{"yseq": yk, "vscore": _vscore, "score": _score}
)
k = k + 1
# end detection
stop_search = [
stop_search[samp_i] or end_detect(ended_hyps[samp_i], i)
for samp_i in range(batch)
]
stop_search_summary = list(set(stop_search))
if len(stop_search_summary) == 1 and stop_search_summary[0]:
break
if rnnlm:
rnnlm_state = self._index_select_lm_state(rnnlm_state, 0, vidx)
if ctc_scorer[0]:
for idx in range(self.num_encs):
ctc_state[idx] = ctc_scorer[idx].index_select_state(
ctc_state[idx], accum_best_ids
)
torch.cuda.empty_cache()
dummy_hyps = [
{"yseq": [self.sos, self.eos], "score": np.array([-float("inf")])}
]
ended_hyps = [
ended_hyps[samp_i] if len(ended_hyps[samp_i]) != 0 else dummy_hyps
for samp_i in range(batch)
]
if normalize_score:
for samp_i in range(batch):
for x in ended_hyps[samp_i]:
x["score"] /= len(x["yseq"])
nbest_hyps = [
sorted(ended_hyps[samp_i], key=lambda x: x["score"], reverse=True)[
: min(len(ended_hyps[samp_i]), recog_args.nbest)
]
for samp_i in range(batch)
]
return nbest_hyps
[docs] def calculate_all_attentions(self, hs_pad, hlen, ys_pad, strm_idx=0, lang_ids=None):
"""Calculate all of attentions
:param torch.Tensor hs_pad: batch of padded hidden state sequences
(B, Tmax, D)
in multi-encoder case, list of torch.Tensor,
[(B, Tmax_1, D), (B, Tmax_2, D), ..., ] ]
:param torch.Tensor hlen: batch of lengths of hidden state sequences (B)
[in multi-encoder case, list of torch.Tensor,
[(B), (B), ..., ]
:param torch.Tensor ys_pad:
batch of padded character id sequence tensor (B, Lmax)
:param int strm_idx:
stream index for parallel speaker attention in multi-speaker case
:param torch.Tensor lang_ids: batch of target language id tensor (B, 1)
:return: attention weights with the following shape,
1) multi-head case => attention weights (B, H, Lmax, Tmax),
2) multi-encoder case =>
[(B, Lmax, Tmax1), (B, Lmax, Tmax2), ..., (B, Lmax, NumEncs)]
3) other case => attention weights (B, Lmax, Tmax).
:rtype: float ndarray
"""
# to support mutiple encoder asr mode, in single encoder mode,
# convert torch.Tensor to List of torch.Tensor
if self.num_encs == 1:
hs_pad = [hs_pad]
hlen = [hlen]
# TODO(kan-bayashi): need to make more smart way
ys = [y[y != self.ignore_id] for y in ys_pad] # parse padded ys
att_idx = min(strm_idx, len(self.att) - 1)
# hlen should be list of list of integer
hlen = [list(map(int, hlen[idx])) for idx in range(self.num_encs)]
self.loss = None
# prepare input and output word sequences with sos/eos IDs
eos = ys[0].new([self.eos])
sos = ys[0].new([self.sos])
if self.replace_sos:
ys_in = [torch.cat([idx, y], dim=0) for idx, y in zip(lang_ids, ys)]
else:
ys_in = [torch.cat([sos, y], dim=0) for y in ys]
ys_out = [torch.cat([y, eos], dim=0) for y in ys]
# padding for ys with -1
# pys: utt x olen
ys_in_pad = pad_list(ys_in, self.eos)
ys_out_pad = pad_list(ys_out, self.ignore_id)
# get length info
olength = ys_out_pad.size(1)
# initialization
c_list = [self.zero_state(hs_pad[0])]
z_list = [self.zero_state(hs_pad[0])]
for _ in range(1, self.dlayers):
c_list.append(self.zero_state(hs_pad[0]))
z_list.append(self.zero_state(hs_pad[0]))
att_ws = []
if self.num_encs == 1:
att_w = None
self.att[att_idx].reset() # reset pre-computation of h
else:
att_w_list = [None] * (self.num_encs + 1) # atts + han
att_c_list = [None] * (self.num_encs) # atts
for idx in range(self.num_encs + 1):
self.att[idx].reset() # reset pre-computation of h in atts and han
# pre-computation of embedding
eys = self.dropout_emb(self.embed(ys_in_pad)) # utt x olen x zdim
# loop for an output sequence
for i in range(olength):
if self.num_encs == 1:
att_c, att_w = self.att[att_idx](
hs_pad[0], hlen[0], self.dropout_dec[0](z_list[0]), att_w
)
att_ws.append(att_w)
else:
for idx in range(self.num_encs):
att_c_list[idx], att_w_list[idx] = self.att[idx](
hs_pad[idx],
hlen[idx],
self.dropout_dec[0](z_list[0]),
att_w_list[idx],
)
hs_pad_han = torch.stack(att_c_list, dim=1)
hlen_han = [self.num_encs] * len(ys_in)
att_c, att_w_list[self.num_encs] = self.att[self.num_encs](
hs_pad_han,
hlen_han,
self.dropout_dec[0](z_list[0]),
att_w_list[self.num_encs],
)
att_ws.append(att_w_list.copy())
ey = torch.cat((eys[:, i, :], att_c), dim=1) # utt x (zdim + hdim)
z_list, c_list = self.rnn_forward(ey, z_list, c_list, z_list, c_list)
if self.num_encs == 1:
# convert to numpy array with the shape (B, Lmax, Tmax)
att_ws = att_to_numpy(att_ws, self.att[att_idx])
else:
_att_ws = []
for idx, ws in enumerate(zip(*att_ws)):
ws = att_to_numpy(ws, self.att[idx])
_att_ws.append(ws)
att_ws = _att_ws
return att_ws
@staticmethod
def _get_last_yseq(exp_yseq):
last = []
for y_seq in exp_yseq:
last.append(y_seq[-1])
return last
@staticmethod
def _append_ids(yseq, ids):
if isinstance(ids, list):
for i, j in enumerate(ids):
yseq[i].append(j)
else:
for i in range(len(yseq)):
yseq[i].append(ids)
return yseq
@staticmethod
def _index_select_list(yseq, lst):
new_yseq = []
for i in lst:
new_yseq.append(yseq[i][:])
return new_yseq
@staticmethod
def _index_select_lm_state(rnnlm_state, dim, vidx):
if isinstance(rnnlm_state, dict):
new_state = {}
for k, v in rnnlm_state.items():
new_state[k] = [torch.index_select(vi, dim, vidx) for vi in v]
elif isinstance(rnnlm_state, list):
new_state = []
for i in vidx:
new_state.append(rnnlm_state[int(i)][:])
return new_state
# scorer interface methods
[docs] def init_state(self, x):
# to support mutiple encoder asr mode, in single encoder mode,
# convert torch.Tensor to List of torch.Tensor
if self.num_encs == 1:
x = [x]
c_list = [self.zero_state(x[0].unsqueeze(0))]
z_list = [self.zero_state(x[0].unsqueeze(0))]
for _ in range(1, self.dlayers):
c_list.append(self.zero_state(x[0].unsqueeze(0)))
z_list.append(self.zero_state(x[0].unsqueeze(0)))
# TODO(karita): support strm_index for `asr_mix`
strm_index = 0
att_idx = min(strm_index, len(self.att) - 1)
if self.num_encs == 1:
a = None
self.att[att_idx].reset() # reset pre-computation of h
else:
a = [None] * (self.num_encs + 1) # atts + han
for idx in range(self.num_encs + 1):
self.att[idx].reset() # reset pre-computation of h in atts and han
return dict(
c_prev=c_list[:],
z_prev=z_list[:],
a_prev=a,
workspace=(att_idx, z_list, c_list),
)
[docs] def score(self, yseq, state, x):
# to support mutiple encoder asr mode, in single encoder mode,
# convert torch.Tensor to List of torch.Tensor
if self.num_encs == 1:
x = [x]
att_idx, z_list, c_list = state["workspace"]
vy = yseq[-1].unsqueeze(0)
ey = self.dropout_emb(self.embed(vy)) # utt list (1) x zdim
if self.num_encs == 1:
att_c, att_w = self.att[att_idx](
x[0].unsqueeze(0),
[x[0].size(0)],
self.dropout_dec[0](state["z_prev"][0]),
state["a_prev"],
)
else:
att_w = [None] * (self.num_encs + 1) # atts + han
att_c_list = [None] * (self.num_encs) # atts
for idx in range(self.num_encs):
att_c_list[idx], att_w[idx] = self.att[idx](
x[idx].unsqueeze(0),
[x[idx].size(0)],
self.dropout_dec[0](state["z_prev"][0]),
state["a_prev"][idx],
)
h_han = torch.stack(att_c_list, dim=1)
att_c, att_w[self.num_encs] = self.att[self.num_encs](
h_han,
[self.num_encs],
self.dropout_dec[0](state["z_prev"][0]),
state["a_prev"][self.num_encs],
)
ey = torch.cat((ey, att_c), dim=1) # utt(1) x (zdim + hdim)
z_list, c_list = self.rnn_forward(
ey, z_list, c_list, state["z_prev"], state["c_prev"]
)
if self.context_residual:
logits = self.output(
torch.cat((self.dropout_dec[-1](z_list[-1]), att_c), dim=-1)
)
else:
logits = self.output(self.dropout_dec[-1](z_list[-1]))
logp = F.log_softmax(logits, dim=1).squeeze(0)
return (
logp,
dict(
c_prev=c_list[:],
z_prev=z_list[:],
a_prev=att_w,
workspace=(att_idx, z_list, c_list),
),
)
[docs]def decoder_for(args, odim, sos, eos, att, labeldist):
return Decoder(
args.eprojs,
odim,
args.dtype,
args.dlayers,
args.dunits,
sos,
eos,
att,
args.verbose,
args.char_list,
labeldist,
args.lsm_weight,
args.sampling_probability,
args.dropout_rate_decoder,
getattr(args, "context_residual", False), # use getattr to keep compatibility
getattr(args, "replace_sos", False), # use getattr to keep compatibility
getattr(args, "num_encs", 1),
) # use getattr to keep compatibility