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.
PaddleSpeech/deepspeech/decoders/swig_wrapper.py

133 lines
5.5 KiB

# Copyright (c) 2021 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.
"""Wrapper for various CTC decoders in SWIG."""
import swig_decoders
class Scorer(swig_decoders.Scorer):
"""Wrapper for Scorer.
:param alpha: Parameter associated with language model. Don't use
language model when alpha = 0.
:type alpha: float
:param beta: Parameter associated with word count. Don't use word
count when beta = 0.
:type beta: float
:model_path: Path to load language model.
:type model_path: str
"""
def __init__(self, alpha, beta, model_path, vocabulary):
swig_decoders.Scorer.__init__(self, alpha, beta, model_path, vocabulary)
def ctc_greedy_decoder(probs_seq, vocabulary):
"""Wrapper for ctc best path decoder in swig.
:param probs_seq: 2-D list of probability distributions over each time
step, with each element being a list of normalized
probabilities over vocabulary and blank.
:type probs_seq: 2-D list
:param vocabulary: Vocabulary list.
:type vocabulary: list
:return: Decoding result string.
:rtype: str
"""
result = swig_decoders.ctc_greedy_decoder(probs_seq.tolist(), vocabulary)
return result
def ctc_beam_search_decoder(probs_seq,
vocabulary,
beam_size,
cutoff_prob=1.0,
cutoff_top_n=40,
ext_scoring_func=None):
"""Wrapper for the CTC Beam Search Decoder.
:param probs_seq: 2-D list of probability distributions over each time
step, with each element being a list of normalized
probabilities over vocabulary and blank.
:type probs_seq: 2-D list
:param vocabulary: Vocabulary list.
:type vocabulary: list
:param beam_size: Width for beam search.
:type beam_size: int
:param cutoff_prob: Cutoff probability in pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in vocabulary will be
used in beam search, default 40.
:type cutoff_top_n: int
:param ext_scoring_func: External scoring function for
partially decoded sentence, e.g. word count
or language model.
:type external_scoring_func: callable
:return: List of tuples of log probability and sentence as decoding
results, in descending order of the probability.
:rtype: list
"""
beam_results = swig_decoders.ctc_beam_search_decoder(
probs_seq.tolist(), vocabulary, beam_size, cutoff_prob, cutoff_top_n,
ext_scoring_func)
beam_results = [(res[0], res[1].decode('utf-8')) for res in beam_results]
return beam_results
def ctc_beam_search_decoder_batch(probs_split,
vocabulary,
beam_size,
num_processes,
cutoff_prob=1.0,
cutoff_top_n=40,
ext_scoring_func=None):
"""Wrapper for the batched CTC beam search decoder.
:param probs_seq: 3-D list with each element as an instance of 2-D list
of probabilities used by ctc_beam_search_decoder().
:type probs_seq: 3-D list
:param vocabulary: Vocabulary list.
:type vocabulary: list
:param beam_size: Width for beam search.
:type beam_size: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param cutoff_prob: Cutoff probability in vocabulary pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in vocabulary will be
used in beam search, default 40.
:type cutoff_top_n: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param ext_scoring_func: External scoring function for
partially decoded sentence, e.g. word count
or language model.
:type external_scoring_function: callable
:return: List of tuples of log probability and sentence as decoding
results, in descending order of the probability.
:rtype: list
"""
probs_split = [probs_seq.tolist() for probs_seq in probs_split]
batch_beam_results = swig_decoders.ctc_beam_search_decoder_batch(
probs_split, vocabulary, beam_size, num_processes, cutoff_prob,
cutoff_top_n, ext_scoring_func)
batch_beam_results = [[(res[0], res[1]) for res in beam_results]
for beam_results in batch_beam_results]
return batch_beam_results