Merge pull request #916 from PaddlePaddle/decoder
[decoder] scorer interface and beam searchpull/919/head
Hui Zhang
3 years ago
committed by
GitHub
commit
7402c12092
@ -0,0 +1,528 @@
|
||||
"""Beam search module."""
|
||||
|
||||
from itertools import chain
|
||||
import logger
|
||||
from typing import Any
|
||||
from typing import Dict
|
||||
from typing import List
|
||||
from typing import NamedTuple
|
||||
from typing import Tuple
|
||||
from typing import Union
|
||||
|
||||
import paddle
|
||||
|
||||
from .utils import end_detect
|
||||
from .scorers.scorer_interface import PartialScorerInterface
|
||||
from .scorers.scorer_interface import ScorerInterface
|
||||
|
||||
from deepspeech.utils.log import Log
|
||||
|
||||
logger = Log(__name__).getlog()
|
||||
|
||||
class Hypothesis(NamedTuple):
|
||||
"""Hypothesis data type."""
|
||||
|
||||
yseq: paddle.Tensor # (T,)
|
||||
score: Union[float, paddle.Tensor] = 0
|
||||
scores: Dict[str, Union[float, paddle.Tensor]] = dict()
|
||||
states: Dict[str, Any] = dict()
|
||||
|
||||
def asdict(self) -> dict:
|
||||
"""Convert data to JSON-friendly dict."""
|
||||
return self._replace(
|
||||
yseq=self.yseq.tolist(),
|
||||
score=float(self.score),
|
||||
scores={k: float(v) for k, v in self.scores.items()},
|
||||
)._asdict()
|
||||
|
||||
|
||||
class BeamSearch(paddle.nn.Layer):
|
||||
"""Beam search implementation."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
scorers: Dict[str, ScorerInterface],
|
||||
weights: Dict[str, float],
|
||||
beam_size: int,
|
||||
vocab_size: int,
|
||||
sos: int,
|
||||
eos: int,
|
||||
token_list: List[str] = None,
|
||||
pre_beam_ratio: float = 1.5,
|
||||
pre_beam_score_key: str = None,
|
||||
):
|
||||
"""Initialize beam search.
|
||||
|
||||
Args:
|
||||
scorers (dict[str, ScorerInterface]): Dict of decoder modules
|
||||
e.g., Decoder, CTCPrefixScorer, LM
|
||||
The scorer will be ignored if it is `None`
|
||||
weights (dict[str, float]): Dict of weights for each scorers
|
||||
The scorer will be ignored if its weight is 0
|
||||
beam_size (int): The number of hypotheses kept during search
|
||||
vocab_size (int): The number of vocabulary
|
||||
sos (int): Start of sequence id
|
||||
eos (int): End of sequence id
|
||||
token_list (list[str]): List of tokens for debug log
|
||||
pre_beam_score_key (str): key of scores to perform pre-beam search
|
||||
pre_beam_ratio (float): beam size in the pre-beam search
|
||||
will be `int(pre_beam_ratio * beam_size)`
|
||||
|
||||
"""
|
||||
super().__init__()
|
||||
# set scorers
|
||||
self.weights = weights
|
||||
self.scorers = dict() # all = full + partial
|
||||
self.full_scorers = dict() # full tokens
|
||||
self.part_scorers = dict() # partial tokens
|
||||
# this module dict is required for recursive cast
|
||||
# `self.to(device, dtype)` in `recog.py`
|
||||
self.nn_dict = paddle.nn.LayerDict() # nn.Layer
|
||||
for k, v in scorers.items():
|
||||
w = weights.get(k, 0)
|
||||
if w == 0 or v is None:
|
||||
continue
|
||||
assert isinstance(
|
||||
v, ScorerInterface
|
||||
), f"{k} ({type(v)}) does not implement ScorerInterface"
|
||||
self.scorers[k] = v
|
||||
if isinstance(v, PartialScorerInterface):
|
||||
self.part_scorers[k] = v
|
||||
else:
|
||||
self.full_scorers[k] = v
|
||||
if isinstance(v, paddle.nn.Layer):
|
||||
self.nn_dict[k] = v
|
||||
|
||||
# set configurations
|
||||
self.sos = sos
|
||||
self.eos = eos
|
||||
self.token_list = token_list
|
||||
# pre_beam_size > beam_size
|
||||
self.pre_beam_size = int(pre_beam_ratio * beam_size)
|
||||
self.beam_size = beam_size
|
||||
self.n_vocab = vocab_size
|
||||
if (
|
||||
pre_beam_score_key is not None
|
||||
and pre_beam_score_key != "full"
|
||||
and pre_beam_score_key not in self.full_scorers
|
||||
):
|
||||
raise KeyError(f"{pre_beam_score_key} is not found in {self.full_scorers}")
|
||||
# selected `key` scorer to do pre beam search
|
||||
self.pre_beam_score_key = pre_beam_score_key
|
||||
# do_pre_beam when need, valid and has part_scorers
|
||||
self.do_pre_beam = (
|
||||
self.pre_beam_score_key is not None
|
||||
and self.pre_beam_size < self.n_vocab
|
||||
and len(self.part_scorers) > 0
|
||||
)
|
||||
|
||||
def init_hyp(self, x: paddle.Tensor) -> List[Hypothesis]:
|
||||
"""Get an initial hypothesis data.
|
||||
|
||||
Args:
|
||||
x (paddle.Tensor): The encoder output feature, (T, D)
|
||||
|
||||
Returns:
|
||||
Hypothesis: The initial hypothesis.
|
||||
|
||||
"""
|
||||
init_states = dict()
|
||||
init_scores = dict()
|
||||
for k, d in self.scorers.items():
|
||||
init_states[k] = d.init_state(x)
|
||||
init_scores[k] = 0.0
|
||||
return [
|
||||
Hypothesis(
|
||||
yseq=paddle.to_tensor([self.sos], place=x.place),
|
||||
score=0.0,
|
||||
scores=init_scores,
|
||||
states=init_states,
|
||||
)
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def append_token(xs: paddle.Tensor, x: int) -> paddle.Tensor:
|
||||
"""Append new token to prefix tokens.
|
||||
|
||||
Args:
|
||||
xs (paddle.Tensor): The prefix token, (T,)
|
||||
x (int): The new token to append
|
||||
|
||||
Returns:
|
||||
paddle.Tensor: (T+1,), New tensor contains: xs + [x] with xs.dtype and xs.device
|
||||
|
||||
"""
|
||||
x = paddle.to_tensor([x], dtype=xs.dtype, place=xs.place)
|
||||
return paddle.cat((xs, x))
|
||||
|
||||
def score_full(
|
||||
self, hyp: Hypothesis, x: paddle.Tensor
|
||||
) -> Tuple[Dict[str, paddle.Tensor], Dict[str, Any]]:
|
||||
"""Score new hypothesis by `self.full_scorers`.
|
||||
|
||||
Args:
|
||||
hyp (Hypothesis): Hypothesis with prefix tokens to score
|
||||
x (paddle.Tensor): Corresponding input feature, (T, D)
|
||||
|
||||
Returns:
|
||||
Tuple[Dict[str, paddle.Tensor], Dict[str, Any]]: Tuple of
|
||||
score dict of `hyp` that has string keys of `self.full_scorers`
|
||||
and tensor score values of shape: `(self.n_vocab,)`,
|
||||
and state dict that has string keys
|
||||
and state values of `self.full_scorers`
|
||||
|
||||
"""
|
||||
scores = dict()
|
||||
states = dict()
|
||||
for k, d in self.full_scorers.items():
|
||||
# scores[k] shape (self.n_vocab,)
|
||||
scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x)
|
||||
return scores, states
|
||||
|
||||
def score_partial(
|
||||
self, hyp: Hypothesis, ids: paddle.Tensor, x: paddle.Tensor
|
||||
) -> Tuple[Dict[str, paddle.Tensor], Dict[str, Any]]:
|
||||
"""Score new hypothesis by `self.part_scorers`.
|
||||
|
||||
Args:
|
||||
hyp (Hypothesis): Hypothesis with prefix tokens to score
|
||||
ids (paddle.Tensor): 1D tensor of new partial tokens to score,
|
||||
len(ids) < n_vocab
|
||||
x (paddle.Tensor): Corresponding input feature, (T, D)
|
||||
|
||||
Returns:
|
||||
Tuple[Dict[str, paddle.Tensor], Dict[str, Any]]: Tuple of
|
||||
score dict of `hyp` that has string keys of `self.part_scorers`
|
||||
and tensor score values of shape: `(len(ids),)`,
|
||||
and state dict that has string keys
|
||||
and state values of `self.part_scorers`
|
||||
|
||||
"""
|
||||
scores = dict()
|
||||
states = dict()
|
||||
for k, d in self.part_scorers.items():
|
||||
# scores[k] shape (len(ids),)
|
||||
scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x)
|
||||
return scores, states
|
||||
|
||||
def beam(
|
||||
self, weighted_scores: paddle.Tensor, ids: paddle.Tensor
|
||||
) -> Tuple[paddle.Tensor, paddle.Tensor]:
|
||||
"""Compute topk full token ids and partial token ids.
|
||||
|
||||
Args:
|
||||
weighted_scores (paddle.Tensor): The weighted sum scores for each tokens.
|
||||
Its shape is `(self.n_vocab,)`.
|
||||
ids (paddle.Tensor): The partial token ids(Global) to compute topk.
|
||||
|
||||
Returns:
|
||||
Tuple[paddle.Tensor, paddle.Tensor]:
|
||||
The topk full token ids and partial token ids.
|
||||
Their shapes are `(self.beam_size,)`.
|
||||
i.e. (global ids, global relative local ids).
|
||||
|
||||
"""
|
||||
# no pre beam performed, `ids` equal to `weighted_scores`
|
||||
if weighted_scores.size(0) == ids.size(0):
|
||||
top_ids = weighted_scores.topk(self.beam_size)[1] # index in n_vocab
|
||||
return top_ids, top_ids
|
||||
|
||||
# mask pruned in pre-beam not to select in topk
|
||||
tmp = weighted_scores[ids]
|
||||
weighted_scores[:] = -float("inf")
|
||||
weighted_scores[ids] = tmp
|
||||
# top_ids no equal to local_ids, since ids shape not same
|
||||
top_ids = weighted_scores.topk(self.beam_size)[1] # index in n_vocab
|
||||
local_ids = weighted_scores[ids].topk(self.beam_size)[1] # index in len(ids)
|
||||
return top_ids, local_ids
|
||||
|
||||
@staticmethod
|
||||
def merge_scores(
|
||||
prev_scores: Dict[str, float],
|
||||
next_full_scores: Dict[str, paddle.Tensor],
|
||||
full_idx: int,
|
||||
next_part_scores: Dict[str, paddle.Tensor],
|
||||
part_idx: int,
|
||||
) -> Dict[str, paddle.Tensor]:
|
||||
"""Merge scores for new hypothesis.
|
||||
|
||||
Args:
|
||||
prev_scores (Dict[str, float]):
|
||||
The previous hypothesis scores by `self.scorers`
|
||||
next_full_scores (Dict[str, paddle.Tensor]): scores by `self.full_scorers`
|
||||
full_idx (int): The next token id for `next_full_scores`
|
||||
next_part_scores (Dict[str, paddle.Tensor]):
|
||||
scores of partial tokens by `self.part_scorers`
|
||||
part_idx (int): The new token id for `next_part_scores`
|
||||
|
||||
Returns:
|
||||
Dict[str, paddle.Tensor]: The new score dict.
|
||||
Its keys are names of `self.full_scorers` and `self.part_scorers`.
|
||||
Its values are scalar tensors by the scorers.
|
||||
|
||||
"""
|
||||
new_scores = dict()
|
||||
for k, v in next_full_scores.items():
|
||||
new_scores[k] = prev_scores[k] + v[full_idx]
|
||||
for k, v in next_part_scores.items():
|
||||
new_scores[k] = prev_scores[k] + v[part_idx]
|
||||
return new_scores
|
||||
|
||||
def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any:
|
||||
"""Merge states for new hypothesis.
|
||||
|
||||
Args:
|
||||
states: states of `self.full_scorers`
|
||||
part_states: states of `self.part_scorers`
|
||||
part_idx (int): The new token id for `part_scores`
|
||||
|
||||
Returns:
|
||||
Dict[str, paddle.Tensor]: The new score dict.
|
||||
Its keys are names of `self.full_scorers` and `self.part_scorers`.
|
||||
Its values are states of the scorers.
|
||||
|
||||
"""
|
||||
new_states = dict()
|
||||
for k, v in states.items():
|
||||
new_states[k] = v
|
||||
for k, d in self.part_scorers.items():
|
||||
new_states[k] = d.select_state(part_states[k], part_idx)
|
||||
return new_states
|
||||
|
||||
def search(
|
||||
self, running_hyps: List[Hypothesis], x: paddle.Tensor
|
||||
) -> List[Hypothesis]:
|
||||
"""Search new tokens for running hypotheses and encoded speech x.
|
||||
|
||||
Args:
|
||||
running_hyps (List[Hypothesis]): Running hypotheses on beam
|
||||
x (paddle.Tensor): Encoded speech feature (T, D)
|
||||
|
||||
Returns:
|
||||
List[Hypotheses]: Best sorted hypotheses
|
||||
|
||||
"""
|
||||
best_hyps = []
|
||||
part_ids = paddle.arange(self.n_vocab) # no pre-beam
|
||||
for hyp in running_hyps:
|
||||
# scoring
|
||||
weighted_scores = paddle.zeros(self.n_vocab, dtype=x.dtype)
|
||||
scores, states = self.score_full(hyp, x)
|
||||
for k in self.full_scorers:
|
||||
weighted_scores += self.weights[k] * scores[k]
|
||||
# partial scoring
|
||||
if self.do_pre_beam:
|
||||
pre_beam_scores = (
|
||||
weighted_scores
|
||||
if self.pre_beam_score_key == "full"
|
||||
else scores[self.pre_beam_score_key]
|
||||
)
|
||||
part_ids = paddle.topk(pre_beam_scores, self.pre_beam_size)[1]
|
||||
part_scores, part_states = self.score_partial(hyp, part_ids, x)
|
||||
for k in self.part_scorers:
|
||||
weighted_scores[part_ids] += self.weights[k] * part_scores[k]
|
||||
# add previous hyp score
|
||||
weighted_scores += hyp.score
|
||||
|
||||
# update hyps
|
||||
for j, part_j in zip(*self.beam(weighted_scores, part_ids)):
|
||||
# `part_j` is `j` relative id in `part_scores`
|
||||
# will be (2 x beam at most)
|
||||
best_hyps.append(
|
||||
Hypothesis(
|
||||
score=weighted_scores[j],
|
||||
yseq=self.append_token(hyp.yseq, j),
|
||||
scores=self.merge_scores(
|
||||
hyp.scores, scores, j, part_scores, part_j
|
||||
),
|
||||
states=self.merge_states(states, part_states, part_j),
|
||||
)
|
||||
)
|
||||
|
||||
# sort and prune 2 x beam -> beam
|
||||
best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[
|
||||
: min(len(best_hyps), self.beam_size)
|
||||
]
|
||||
return best_hyps
|
||||
|
||||
def forward(
|
||||
self, x: paddle.Tensor, maxlenratio: float = 0.0, minlenratio: float = 0.0
|
||||
) -> List[Hypothesis]:
|
||||
"""Perform beam search.
|
||||
|
||||
Args:
|
||||
x (paddle.Tensor): Encoded speech feature (T, D)
|
||||
maxlenratio (float): Input length ratio to obtain max output length.
|
||||
If maxlenratio=0.0 (default), it uses a end-detect function
|
||||
to automatically find maximum hypothesis lengths
|
||||
If maxlenratio<0.0, its absolute value is interpreted
|
||||
as a constant max output length.
|
||||
minlenratio (float): Input length ratio to obtain min output length.
|
||||
|
||||
Returns:
|
||||
list[Hypothesis]: N-best decoding results
|
||||
|
||||
"""
|
||||
# set length bounds
|
||||
if maxlenratio == 0:
|
||||
maxlen = x.shape[0]
|
||||
elif maxlenratio < 0:
|
||||
maxlen = -1 * int(maxlenratio)
|
||||
else:
|
||||
maxlen = max(1, int(maxlenratio * x.size(0)))
|
||||
minlen = int(minlenratio * x.size(0))
|
||||
logger.info("decoder input length: " + str(x.shape[0]))
|
||||
logger.info("max output length: " + str(maxlen))
|
||||
logger.info("min output length: " + str(minlen))
|
||||
|
||||
# main loop of prefix search
|
||||
running_hyps = self.init_hyp(x)
|
||||
ended_hyps = []
|
||||
for i in range(maxlen):
|
||||
logger.debug("position " + str(i))
|
||||
best = self.search(running_hyps, x)
|
||||
# post process of one iteration
|
||||
running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps)
|
||||
# end detection
|
||||
if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i):
|
||||
logger.info(f"end detected at {i}")
|
||||
break
|
||||
if len(running_hyps) == 0:
|
||||
logger.info("no hypothesis. Finish decoding.")
|
||||
break
|
||||
else:
|
||||
logger.debug(f"remained hypotheses: {len(running_hyps)}")
|
||||
|
||||
nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True)
|
||||
# check the number of hypotheses reaching to eos
|
||||
if len(nbest_hyps) == 0:
|
||||
logger.warning(
|
||||
"there is no N-best results, perform recognition "
|
||||
"again with smaller minlenratio."
|
||||
)
|
||||
return (
|
||||
[]
|
||||
if minlenratio < 0.1
|
||||
else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1))
|
||||
)
|
||||
|
||||
# report the best result
|
||||
best = nbest_hyps[0]
|
||||
for k, v in best.scores.items():
|
||||
logger.info(
|
||||
f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}"
|
||||
)
|
||||
logger.info(f"total log probability: {best.score:.2f}")
|
||||
logger.info(f"normalized log probability: {best.score / len(best.yseq):.2f}")
|
||||
logger.info(f"total number of ended hypotheses: {len(nbest_hyps)}")
|
||||
if self.token_list is not None:
|
||||
logger.info(
|
||||
"best hypo: "
|
||||
+ "".join([self.token_list[x] for x in best.yseq[1:-1]])
|
||||
+ "\n"
|
||||
)
|
||||
return nbest_hyps
|
||||
|
||||
def post_process(
|
||||
self,
|
||||
i: int,
|
||||
maxlen: int,
|
||||
maxlenratio: float,
|
||||
running_hyps: List[Hypothesis],
|
||||
ended_hyps: List[Hypothesis],
|
||||
) -> List[Hypothesis]:
|
||||
"""Perform post-processing of beam search iterations.
|
||||
|
||||
Args:
|
||||
i (int): The length of hypothesis tokens.
|
||||
maxlen (int): The maximum length of tokens in beam search.
|
||||
maxlenratio (int): The maximum length ratio in beam search.
|
||||
running_hyps (List[Hypothesis]): The running hypotheses in beam search.
|
||||
ended_hyps (List[Hypothesis]): The ended hypotheses in beam search.
|
||||
|
||||
Returns:
|
||||
List[Hypothesis]: The new running hypotheses.
|
||||
|
||||
"""
|
||||
logger.debug(f"the number of running hypotheses: {len(running_hyps)}")
|
||||
if self.token_list is not None:
|
||||
logger.debug(
|
||||
"best hypo: "
|
||||
+ "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]])
|
||||
)
|
||||
# add eos in the final loop to avoid that there are no ended hyps
|
||||
if i == maxlen - 1:
|
||||
logger.info("adding <eos> in the last position in the loop")
|
||||
running_hyps = [
|
||||
h._replace(yseq=self.append_token(h.yseq, self.eos))
|
||||
for h in running_hyps
|
||||
]
|
||||
|
||||
# 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 running_hyps:
|
||||
if hyp.yseq[-1] == self.eos:
|
||||
# e.g., Word LM needs to add final <eos> score
|
||||
for k, d in chain(self.full_scorers.items(), self.part_scorers.items()):
|
||||
s = d.final_score(hyp.states[k])
|
||||
hyp.scores[k] += s
|
||||
hyp = hyp._replace(score=hyp.score + self.weights[k] * s)
|
||||
ended_hyps.append(hyp)
|
||||
else:
|
||||
remained_hyps.append(hyp)
|
||||
return remained_hyps
|
||||
|
||||
|
||||
def beam_search(
|
||||
x: paddle.Tensor,
|
||||
sos: int,
|
||||
eos: int,
|
||||
beam_size: int,
|
||||
vocab_size: int,
|
||||
scorers: Dict[str, ScorerInterface],
|
||||
weights: Dict[str, float],
|
||||
token_list: List[str] = None,
|
||||
maxlenratio: float = 0.0,
|
||||
minlenratio: float = 0.0,
|
||||
pre_beam_ratio: float = 1.5,
|
||||
pre_beam_score_key: str = "full",
|
||||
) -> list:
|
||||
"""Perform beam search with scorers.
|
||||
|
||||
Args:
|
||||
x (paddle.Tensor): Encoded speech feature (T, D)
|
||||
sos (int): Start of sequence id
|
||||
eos (int): End of sequence id
|
||||
beam_size (int): The number of hypotheses kept during search
|
||||
vocab_size (int): The number of vocabulary
|
||||
scorers (dict[str, ScorerInterface]): Dict of decoder modules
|
||||
e.g., Decoder, CTCPrefixScorer, LM
|
||||
The scorer will be ignored if it is `None`
|
||||
weights (dict[str, float]): Dict of weights for each scorers
|
||||
The scorer will be ignored if its weight is 0
|
||||
token_list (list[str]): List of tokens for debug log
|
||||
maxlenratio (float): Input length ratio to obtain max output length.
|
||||
If maxlenratio=0.0 (default), it uses a end-detect function
|
||||
to automatically find maximum hypothesis lengths
|
||||
minlenratio (float): Input length ratio to obtain min output length.
|
||||
pre_beam_score_key (str): key of scores to perform pre-beam search
|
||||
pre_beam_ratio (float): beam size in the pre-beam search
|
||||
will be `int(pre_beam_ratio * beam_size)`
|
||||
|
||||
Returns:
|
||||
List[Dict]: N-best decoding results
|
||||
|
||||
"""
|
||||
ret = BeamSearch(
|
||||
scorers,
|
||||
weights,
|
||||
beam_size=beam_size,
|
||||
vocab_size=vocab_size,
|
||||
pre_beam_ratio=pre_beam_ratio,
|
||||
pre_beam_score_key=pre_beam_score_key,
|
||||
sos=sos,
|
||||
eos=eos,
|
||||
token_list=token_list,
|
||||
).forward(x=x, maxlenratio=maxlenratio, minlenratio=minlenratio)
|
||||
return [h.asdict() for h in ret]
|
||||
Loading…
Reference in new issue