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# Copyright (c) 2023, NVIDIA CORPORATION. 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 itertools
import math
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
import torch
from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
from nemo.core.classes import NeuralModule, typecheck
from nemo.core.neural_types import LengthsType, LogprobsType, NeuralType, PredictionsType
class _TokensWrapper:
def __init__(self, vocabulary: List[str], tokenizer: TokenizerSpec):
self.vocabulary = vocabulary
self.tokenizer = tokenizer
if tokenizer is None:
self.reverse_map = {vocabulary[i]: i for i in range(len(vocabulary))}
@property
def blank(self):
return len(self.vocabulary)
@property
def unk_id(self):
if (self.tokenizer is not None) and hasattr(self.tokenizer, 'unk_id') and self.tokenizer.unk_id is not None:
return self.tokenizer.unk_id
if '<unk>' in self.vocabulary:
return self.token_to_id('<unk>')
else:
return -1
@property
def vocab(self):
return self.vocabulary
@property
def vocab_size(self):
# the +1 is because we add the blank id
return len(self.vocabulary) + 1
def token_to_id(self, token: str):
if token == self.blank:
return -1
if self.tokenizer is not None:
return self.tokenizer.token_to_id(token)
else:
return self.reverse_map[token]
class FlashLightKenLMBeamSearchDecoder(NeuralModule):
'''
@property
def input_types(self):
"""Returns definitions of module input ports.
"""
return {
"log_probs": NeuralType(('B', 'T', 'D'), LogprobsType()),
}
@property
def output_types(self):
"""Returns definitions of module output ports.
"""
return {"hypos": NeuralType(('B'), PredictionsType())}
'''
def __init__(
self,
lm_path: str,
vocabulary: List[str],
tokenizer: Optional[TokenizerSpec] = None,
lexicon_path: Optional[str] = None,
beam_size: int = 32,
beam_size_token: int = 32,
beam_threshold: float = 25.0,
lm_weight: float = 2.0,
word_score: float = -1.0,
unk_weight: float = -math.inf,
sil_weight: float = 0.0,
unit_lm: bool = False,
):
try:
from flashlight.lib.text.decoder import (
LM,
CriterionType,
KenLM,
LexiconDecoder,
LexiconDecoderOptions,
SmearingMode,
Trie,
)
from flashlight.lib.text.dictionary import create_word_dict, load_words
except ModuleNotFoundError:
raise ModuleNotFoundError(
"FlashLightKenLMBeamSearchDecoder requires the installation of flashlight python bindings "
"from https://github.com/flashlight/text. Please follow the build instructions there."
)
super().__init__()
self.criterion_type = CriterionType.CTC
self.tokenizer_wrapper = _TokensWrapper(vocabulary, tokenizer)
self.vocab_size = self.tokenizer_wrapper.vocab_size
self.blank = self.tokenizer_wrapper.blank
self.silence = self.tokenizer_wrapper.unk_id
self.unit_lm = unit_lm
if lexicon_path is not None:
self.lexicon = load_words(lexicon_path)
self.word_dict = create_word_dict(self.lexicon)
self.unk_word = self.word_dict.get_index("<unk>")
# loads in the kenlm binary and combines in with the dictionary object from the lexicon
# this gives a mapping between each entry in the kenlm binary and its mapping to whatever
# numeraire is used by the AM, which is explicitly mapped via the lexicon
# this information is ued to build a vocabulary trie for decoding
self.lm = KenLM(lm_path, self.word_dict)
self.trie = Trie(self.vocab_size, self.silence)
start_state = self.lm.start(False)
for i, (word, spellings) in enumerate(self.lexicon.items()):
word_idx = self.word_dict.get_index(word)
_, score = self.lm.score(start_state, word_idx)
for spelling in spellings:
spelling_idxs = [self.tokenizer_wrapper.token_to_id(token) for token in spelling]
if self.tokenizer_wrapper.unk_id in spelling_idxs:
print(f'tokenizer has unknown id for word[ {word} ] {spelling} {spelling_idxs}', flush=True)
continue
self.trie.insert(spelling_idxs, word_idx, score)
self.trie.smear(SmearingMode.MAX)
self.decoder_opts = LexiconDecoderOptions(
beam_size=beam_size,
beam_size_token=int(beam_size_token),
beam_threshold=beam_threshold,
lm_weight=lm_weight,
word_score=word_score,
unk_score=unk_weight,
sil_score=sil_weight,
log_add=False,
criterion_type=self.criterion_type,
)
self.decoder = LexiconDecoder(
self.decoder_opts, self.trie, self.lm, self.silence, self.blank, self.unk_word, [], self.unit_lm,
)
else:
assert self.unit_lm, "lexicon free decoding can only be done with a unit language model"
from flashlight.lib.text.decoder import LexiconFreeDecoder, LexiconFreeDecoderOptions
d = {
w: [[w]]
for w in self.tokenizer_wrapper.vocab + ([] if '<unk>' in self.tokenizer_wrapper.vocab else ['<unk>'])
}
self.word_dict = create_word_dict(d)
self.lm = KenLM(lm_path, self.word_dict)
self.decoder_opts = LexiconFreeDecoderOptions(
beam_size=beam_size,
beam_size_token=int(beam_size_token),
beam_threshold=beam_threshold,
lm_weight=lm_weight,
sil_score=sil_weight,
log_add=False,
criterion_type=self.criterion_type,
)
self.decoder = LexiconFreeDecoder(self.decoder_opts, self.lm, self.silence, self.blank, [])
def _get_tokens(self, idxs: List[int]):
"""Normalize tokens by handling CTC blank, ASG replabels, etc."""
idxs = (g[0] for g in itertools.groupby(idxs))
idxs = filter(lambda x: x != self.blank and x != self.silence, idxs)
return torch.LongTensor(list(idxs))
def _get_timesteps(self, token_idxs: List[int]):
"""Returns frame numbers corresponding to every non-blank token.
Parameters
----------
token_idxs : List[int]
IDs of decoded tokens.
Returns
-------
List[int]
Frame numbers corresponding to every non-blank token.
"""
timesteps = []
for i, token_idx in enumerate(token_idxs):
if token_idx == self.blank:
continue
if i == 0 or token_idx != token_idxs[i - 1]:
timesteps.append(i)
return timesteps
# @typecheck(ignore_collections=True)
@torch.no_grad()
def forward(self, log_probs: Union[np.ndarray, torch.Tensor]):
if isinstance(log_probs, np.ndarray):
log_probs = torch.from_numpy(log_probs).float()
if log_probs.dim() == 2:
log_probs = log_probs.unsqueeze(0)
emissions = log_probs.cpu().contiguous()
B, T, N = emissions.size()
hypos = []
# we iterate over the batch dimension of our input tensor log probabilities
for b in range(B):
# the flashlight C++ expects a C style pointer, so the memory address
# which is what we obtain here. Then we pass it to pybinding method which
# is bound to the underlying C++ code
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
hypos.append(
[
{
"tokens": self._get_tokens(result.tokens),
"score": result.score,
"timesteps": self._get_timesteps(result.tokens),
"words": [self.word_dict.get_entry(x) for x in result.words if x >= 0],
}
for result in results
]
)
return hypos
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