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decoder.py
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decoder.py
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#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright 2015-2016 Nervana Systems Inc.
# 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.
# ----------------------------------------------------------------------------
# Modified to support pytorch Tensors
import Levenshtein as Lev
import torch
from enum import Enum
from six.moves import xrange
try:
from pytorch_ctc import CTCBeamDecoder as CTCBD
from pytorch_ctc import Scorer, KenLMScorer
except ImportError:
print("warn: pytorch_ctc unavailable. Only greedy decoding is supported.")
class Decoder(object):
"""
Basic decoder class from which all other decoders inherit. Implements several
helper functions. Subclasses should implement the decode() method.
Arguments:
labels (string): mapping from integers to characters.
blank_index (int, optional): index for the blank '_' character. Defaults to 0.
space_index (int, optional): index for the space ' ' character. Defaults to 28.
"""
def __init__(self, labels, blank_index=0, space_index=28):
# e.g. labels = "_'ABCDEFGHIJKLMNOPQRSTUVWXYZ#"
self.labels = labels
self.int_to_char = dict([(i, c) for (i, c) in enumerate(labels)])
self.blank_index = blank_index
self.space_index = space_index
def convert_to_strings(self, sequences, sizes=None):
"""Given a list of numeric sequences, returns the corresponding strings"""
strings = []
for x in xrange(len(sequences)):
seq_len = sizes[x] if sizes is not None else len(sequences[x])
string = self._convert_to_string(sequences[x], seq_len)
strings.append(string)
return strings
def _convert_to_string(self, sequence, sizes):
return ''.join([self.int_to_char[sequence[i]] for i in range(sizes)])
def process_strings(self, sequences, remove_repetitions=False):
"""
Given a list of strings, removes blanks and replace space character with space.
Option to remove repetitions (e.g. 'abbca' -> 'abca').
Arguments:
sequences: list of 1-d array of integers
remove_repetitions (boolean, optional): If true, repeating characters
are removed. Defaults to False.
"""
processed_strings = []
for sequence in sequences:
string = self.process_string(remove_repetitions, sequence).strip()
processed_strings.append(string)
return processed_strings
def process_string(self, remove_repetitions, sequence):
string = ''
for i, char in enumerate(sequence):
if char != self.int_to_char[self.blank_index]:
# if this char is a repetition and remove_repetitions=true,
# skip.
if remove_repetitions and i != 0 and char == sequence[i - 1]:
pass
elif char == self.labels[self.space_index]:
string += ' '
else:
string = string + char
return string
def wer(self, s1, s2):
"""
Computes the Word Error Rate, defined as the edit distance between the
two provided sentences after tokenizing to words.
Arguments:
s1 (string): space-separated sentence
s2 (string): space-separated sentence
"""
# build mapping of words to integers
b = set(s1.split() + s2.split())
word2char = dict(zip(b, range(len(b))))
# map the words to a char array (Levenshtein packages only accepts
# strings)
w1 = [chr(word2char[w]) for w in s1.split()]
w2 = [chr(word2char[w]) for w in s2.split()]
return Lev.distance(''.join(w1), ''.join(w2))
def cer(self, s1, s2):
"""
Computes the Character Error Rate, defined as the edit distance.
Arguments:
s1 (string): space-separated sentence
s2 (string): space-separated sentence
"""
return Lev.distance(s1, s2)
def decode(self, probs, sizes=None):
"""
Given a matrix of character probabilities, returns the decoder's
best guess of the transcription
Arguments:
probs: Tensor of character probabilities, where probs[c,t]
is the probability of character c at time t
sizes(optional): Size of each sequence in the mini-batch
Returns:
string: sequence of the model's best guess for the transcription
"""
raise NotImplementedError
class BeamCTCDecoder(Decoder):
def __init__(self, labels, scorer, beam_width=20, top_paths=1, blank_index=0, space_index=28):
super(BeamCTCDecoder, self).__init__(labels, blank_index=blank_index, space_index=space_index)
self._beam_width = beam_width
self._top_n = top_paths
try:
import pytorch_ctc
except ImportError:
raise ImportError("BeamCTCDecoder requires pytorch_ctc package.")
self._decoder = CTCBD(scorer, labels, top_paths=top_paths, beam_width=beam_width,
blank_index=blank_index, space_index=space_index, merge_repeated=False)
def decode(self, probs, sizes=None):
sizes = sizes.cpu() if sizes is not None else None
out, conf, seq_len = self._decoder.decode(probs.cpu(), sizes)
# TODO: support returning multiple paths
strings = self.convert_to_strings(out[0], sizes=seq_len[0])
return self.process_strings(strings)
class GreedyDecoder(Decoder):
def decode(self, probs, sizes=None):
"""
Returns the argmax decoding given the probability matrix. Removes
repeated elements in the sequence, as well as blanks.
Arguments:
probs: Tensor of character probabilities from the network. Expected shape of seq_length x batch x output_dim
sizes(optional): Size of each sequence in the mini-batch
Returns:
strings: sequences of the model's best guess for the transcription on inputs
"""
_, max_probs = torch.max(probs.transpose(0, 1), 2)
strings = self.convert_to_strings(max_probs.view(max_probs.size(0), max_probs.size(1)), sizes)
return self.process_strings(strings, remove_repetitions=True)