class W2lKenLMDecoder(W2lDecoder):
def __init__(self, args, tgt_dict):
super().__init__(args, tgt_dict)
self.silence = (tgt_dict.index("<ctc_blank>") if "<ctc_blank>"
in tgt_dict.indices else tgt_dict.bos())
self.lexicon = load_words(args.lexicon)
self.word_dict = create_word_dict(self.lexicon)
self.unk_word = self.word_dict.get_index("<unk>")
self.lm = KenLM(args.kenlm_model, 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 = [tgt_dict.index(token) for token in spelling]
assert (tgt_dict.unk()
not in spelling_idxs), f"{spelling} {spelling_idxs}"
self.trie.insert(spelling_idxs, word_idx, score)
self.trie.smear(SmearingMode.MAX)
self.decoder_opts = LexiconDecoderOptions(
beam_size=args.beam,
beam_size_token=int(getattr(args, "beam_size_token",
len(tgt_dict))),
beam_threshold=args.beam_threshold,
lm_weight=args.lm_weight,
word_score=args.word_score,
unk_score=args.unk_weight,
sil_score=args.sil_weight,
log_add=False,
criterion_type=self.criterion_type,
)
if self.asg_transitions is None:
N = 768
# self.asg_transitions = torch.FloatTensor(N, N).zero_()
self.asg_transitions = []
self.decoder = LexiconDecoder(
self.decoder_opts,
self.trie,
self.lm,
self.silence,
self.blank,
self.unk_word,
self.asg_transitions,
False,
)
def decode(self, emissions):
B, T, N = emissions.size()
hypos = []
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[:self.nbest]
hypos.append([{
"tokens":
self.get_tokens(result.tokens),
"score":
result.score,
"words":
[self.word_dict.get_entry(x) for x in result.words if x >= 0],
} for result in nbest_results])
return hypos
class KenLMDecoder(BaseDecoder):
def __init__(self, cfg: DecoderConfig, tgt_dict: Dictionary) -> None:
super().__init__(cfg, tgt_dict)
if cfg.lexicon:
self.lexicon = load_words(cfg.lexicon)
self.word_dict = create_word_dict(self.lexicon)
self.unk_word = self.word_dict.get_index("<unk>")
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.trie = Trie(self.vocab_size, self.silence)
start_state = self.lm.start(False)
for word, spellings in 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 = [
tgt_dict.index(token) for token in spelling
]
assert tgt_dict.unk() not in spelling_idxs, \
f"{word} {spelling} {spelling_idxs}"
self.trie.insert(spelling_idxs, word_idx, score)
self.trie.smear(SmearingMode.MAX)
self.decoder_opts = LexiconDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
word_score=cfg.wordscore,
unk_score=cfg.unkweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=self.criterion_type,
)
if self.asgtransitions is None:
self.asgtransitions = []
self.decoder = LexiconDecoder(
self.decoder_opts,
self.trie,
self.lm,
self.silence,
self.blank,
self.unk_word,
self.asgtransitions,
self.unitlm,
)
else:
assert self.unitlm, "Lexicon-free decoding requires unit LM"
d = {w: [[w]] for w in tgt_dict.symbols}
self.word_dict = create_word_dict(d)
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.decoder_opts = LexiconFreeDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=self.criterion_type,
)
self.decoder = LexiconFreeDecoder(self.decoder_opts, self.lm,
self.silence, self.blank, [])
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
hypos = []
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[:self.nbest]
hypos.append([{
"tokens":
self.get_tokens(result.tokens),
"score":
result.score,
"words":
[self.word_dict.get_entry(x) for x in result.words if x >= 0],
} for result in nbest_results])
return hypos
class FairseqLMDecoder(BaseDecoder):
def __init__(self, cfg: DecoderConfig, tgt_dict: Dictionary) -> None:
super().__init__(cfg, tgt_dict)
self.lexicon = load_words(cfg.lexicon) if cfg.lexicon else None
self.idx_to_wrd = {}
checkpoint = torch.load(cfg.lmpath, map_location="cpu")
if "cfg" in checkpoint and checkpoint["cfg"] is not None:
lm_args = checkpoint["cfg"]
else:
lm_args = convert_namespace_to_omegaconf(checkpoint["args"])
with open_dict(lm_args.task):
lm_args.task.data = osp.dirname(cfg.lmpath)
task = tasks.setup_task(lm_args.task)
model = task.build_model(lm_args.model)
model.load_state_dict(checkpoint["model"], strict=False)
self.trie = Trie(self.vocab_size, self.silence)
self.word_dict = task.dictionary
self.unk_word = self.word_dict.unk()
self.lm = FairseqLM(self.word_dict, model)
if self.lexicon:
start_state = self.lm.start(False)
for i, (word, spellings) in enumerate(self.lexicon.items()):
if self.unitlm:
word_idx = i
self.idx_to_wrd[i] = word
score = 0
else:
word_idx = self.word_dict.index(word)
_, score = self.lm.score(start_state,
word_idx,
no_cache=True)
for spelling in spellings:
spelling_idxs = [
tgt_dict.index(token) for token in spelling
]
assert tgt_dict.unk() not in spelling_idxs, \
f"{spelling} {spelling_idxs}"
self.trie.insert(spelling_idxs, word_idx, score)
self.trie.smear(SmearingMode.MAX)
self.decoder_opts = LexiconDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
word_score=cfg.wordscore,
unk_score=cfg.unkweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=self.criterion_type,
)
if self.asgtransitions is None:
self.asgtransitions = []
self.decoder = LexiconDecoder(
self.decoder_opts,
self.trie,
self.lm,
self.silence,
self.blank,
self.unk_word,
self.asgtransitions,
self.unitlm,
)
else:
assert self.unitlm, "Lexicon-free decoding requires unit LM"
d = {w: [[w]] for w in tgt_dict.symbols}
self.word_dict = create_word_dict(d)
self.lm = KenLM(cfg.lmpath, self.word_dict)
self.decoder_opts = LexiconFreeDecoderOptions(
beam_size=cfg.beam,
beam_size_token=cfg.beamsizetoken or len(tgt_dict),
beam_threshold=cfg.beamthreshold,
lm_weight=cfg.lmweight,
sil_score=cfg.silweight,
log_add=False,
criterion_type=self.criterion_type,
)
self.decoder = LexiconFreeDecoder(self.decoder_opts, self.lm,
self.silence, self.blank, [])
def decode(
self,
emissions: torch.FloatTensor,
) -> List[List[Dict[str, torch.LongTensor]]]:
B, T, N = emissions.size()
hypos = []
def make_hypo(result: DecodeResult) -> Dict[str, Any]:
hypo = {
"tokens": self.get_tokens(result.tokens),
"score": result.score,
}
if self.lexicon:
hypo["words"] = [
self.idx_to_wrd[x] if self.unitlm else self.word_dict[x]
for x in result.words if x >= 0
]
return hypo
for b in range(B):
emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
results = self.decoder.decode(emissions_ptr, T, N)
nbest_results = results[:self.nbest]
hypos.append([make_hypo(result) for result in nbest_results])
self.lm.empty_cache()
return hypos
# create Kenlm language model
lm = KenLM(os.path.join(data_path, "lm.arpa"), word_dict)
# test LM
sentence = ["the", "cat", "sat", "on", "the", "mat"]
# start LM with nothing, get its current state
lm_state = lm.start(False)
total_score = 0
lm_score_target = [
-1.05971, -4.19448, -3.33383, -2.76726, -1.16237, -4.64589
]
# iterate over words in the sentence
for i in range(len(sentence)):
# score lm, taking current state and index of the word
# returns new state and score for the word
lm_state, lm_score = lm.score(lm_state,
word_dict.get_index(sentence[i]))
assert_near(lm_score, lm_score_target[i], 1e-5)
# add score of the current word to the total sentence score
total_score += lm_score
# move lm to the final state, the score returned is for eos
lm_state, lm_score = lm.finish(lm_state)
total_score += lm_score
assert_near(total_score, -19.5123, 1e-5)
# build trie
# Trie is necessary to do beam-search decoding with word-level lm
# We restrict our search only to the words from the lexicon
# Trie is constructed from the lexicon, each node is a token
# path from the root to a leaf corresponds to a word spelling in the lexicon
# get silence index
