You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
189 lines
7.7 KiB
189 lines
7.7 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import copy
|
|
from collections import defaultdict
|
|
|
|
import paddle
|
|
|
|
from paddlespeech.cli.log import logger
|
|
from paddlespeech.s2t.utils.utility import log_add
|
|
|
|
__all__ = ['CTCPrefixBeamSearch']
|
|
|
|
|
|
class CTCPrefixBeamSearch:
|
|
def __init__(self, config):
|
|
"""Implement the ctc prefix beam search
|
|
|
|
Args:
|
|
config (yacs.config.CfgNode): the ctc prefix beam search configuration
|
|
"""
|
|
self.config = config
|
|
self.reset()
|
|
|
|
@paddle.no_grad()
|
|
def search(self, ctc_probs, device, blank_id=0):
|
|
"""ctc prefix beam search method decode a chunk feature
|
|
|
|
Args:
|
|
xs (paddle.Tensor): feature data
|
|
ctc_probs (paddle.Tensor): the ctc probability of all the tokens
|
|
device (paddle.fluid.core_avx.Place): the feature host device, such as CUDAPlace(0).
|
|
blank_id (int, optional): the blank id in the vocab. Defaults to 0.
|
|
|
|
Returns:
|
|
list: the search result
|
|
"""
|
|
# decode
|
|
logger.info("start to ctc prefix search")
|
|
|
|
batch_size = 1
|
|
beam_size = self.config.beam_size
|
|
maxlen = ctc_probs.shape[0]
|
|
|
|
assert len(ctc_probs.shape) == 2
|
|
|
|
# cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
|
|
# 0. blank_ending_score,
|
|
# 1. none_blank_ending_score,
|
|
# 2. viterbi_blank ending,
|
|
# 3. viterbi_non_blank,
|
|
# 4. current_token_prob,
|
|
# 5. times_viterbi_blank,
|
|
# 6. times_titerbi_non_blank
|
|
if self.cur_hyps is None:
|
|
self.cur_hyps = [(tuple(), (0.0, -float('inf'), 0.0, 0.0,
|
|
-float('inf'), [], []))]
|
|
# self.cur_hyps = [(tuple(), (0.0, -float('inf')))]
|
|
# 2. CTC beam search step by step
|
|
for t in range(0, maxlen):
|
|
logp = ctc_probs[t] # (vocab_size,)
|
|
# next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
|
|
next_hyps = defaultdict(
|
|
lambda: (-float('inf'), -float('inf'), -float('inf'), -float('inf'), -float('inf'), [], []))
|
|
|
|
# 2.1 First beam prune: select topk best
|
|
# do token passing process
|
|
top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,)
|
|
for s in top_k_index:
|
|
s = s.item()
|
|
ps = logp[s].item()
|
|
for prefix, (pb, pnb, v_b_s, v_nb_s, cur_token_prob, times_s,
|
|
times_ns) in self.cur_hyps:
|
|
last = prefix[-1] if len(prefix) > 0 else None
|
|
if s == blank_id: # blank
|
|
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
|
|
prefix]
|
|
n_pb = log_add([n_pb, pb + ps, pnb + ps])
|
|
|
|
pre_times = times_s if v_b_s > v_nb_s else times_ns
|
|
n_times_s = copy.deepcopy(pre_times)
|
|
viterbi_score = v_b_s if v_b_s > v_nb_s else v_nb_s
|
|
n_v_s = viterbi_score + ps
|
|
next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
|
|
n_cur_token_prob, n_times_s,
|
|
n_times_ns)
|
|
elif s == last:
|
|
# Update *ss -> *s;
|
|
# case1: *a + a => *a
|
|
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
|
|
prefix]
|
|
n_pnb = log_add([n_pnb, pnb + ps])
|
|
if n_v_ns < v_nb_s + ps:
|
|
n_v_ns = v_nb_s + ps
|
|
if n_cur_token_prob < ps:
|
|
n_cur_token_prob = ps
|
|
n_times_ns = copy.deepcopy(times_ns)
|
|
n_times_ns[
|
|
-1] = self.abs_time_step # 注意，这里要重新使用绝对时间
|
|
next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
|
|
n_cur_token_prob, n_times_s,
|
|
n_times_ns)
|
|
|
|
# Update *s-s -> *ss, - is for blank
|
|
# Case 2: *aε + a => *aa
|
|
n_prefix = prefix + (s, )
|
|
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
|
|
n_prefix]
|
|
if n_v_ns < v_b_s + ps:
|
|
n_v_ns = v_b_s + ps
|
|
n_cur_token_prob = ps
|
|
n_times_ns = copy.deepcopy(times_s)
|
|
n_times_ns.append(self.abs_time_step)
|
|
n_pnb = log_add([n_pnb, pb + ps])
|
|
next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
|
|
n_cur_token_prob, n_times_s,
|
|
n_times_ns)
|
|
else:
|
|
# Case 3: *a + b => *ab, *aε + b => *ab
|
|
n_prefix = prefix + (s, )
|
|
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
|
|
n_prefix]
|
|
viterbi_score = v_b_s if v_b_s > v_nb_s else v_nb_s
|
|
pre_times = times_s if v_b_s > v_nb_s else times_ns
|
|
if n_v_ns < viterbi_score + ps:
|
|
n_v_ns = viterbi_score + ps
|
|
n_cur_token_prob = ps
|
|
n_times_ns = copy.deepcopy(pre_times)
|
|
n_times_ns.append(self.abs_time_step)
|
|
|
|
n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
|
|
next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
|
|
n_cur_token_prob, n_times_s,
|
|
n_times_ns)
|
|
|
|
# 2.2 Second beam prune
|
|
next_hyps = sorted(
|
|
next_hyps.items(),
|
|
key=lambda x: log_add([x[1][0], x[1][1]]),
|
|
reverse=True)
|
|
self.cur_hyps = next_hyps[:beam_size]
|
|
|
|
# 2.3 update the absolute time step
|
|
self.abs_time_step += 1
|
|
|
|
self.hyps = [(y[0], log_add([y[1][0], y[1][1]]), y[1][2], y[1][3],
|
|
y[1][4], y[1][5], y[1][6]) for y in self.cur_hyps]
|
|
|
|
logger.info("ctc prefix search success")
|
|
return self.hyps
|
|
|
|
def get_one_best_hyps(self):
|
|
"""Return the one best result
|
|
|
|
Returns:
|
|
list: the one best result
|
|
"""
|
|
return [self.hyps[0][0]]
|
|
|
|
def get_hyps(self):
|
|
"""Return the search hyps
|
|
|
|
Returns:
|
|
list: return the search hyps
|
|
"""
|
|
return self.hyps
|
|
|
|
def reset(self):
|
|
"""Rest the search cache value
|
|
"""
|
|
self.cur_hyps = None
|
|
self.hyps = None
|
|
self.abs_time_step = 0
|
|
|
|
def finalize_search(self):
|
|
"""do nothing in ctc_prefix_beam_search
|
|
"""
|
|
pass
|