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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
# 2023 Johns Hopkins University (author: Dongji Gao)
#
# See ../../LICENSE for clarification regarding multiple authors
#
# 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.
from pathlib import Path
from typing import List, Union
import k2
import sentencepiece as spm
import torch
from icefall.utils import str2bool
class OtcTrainingGraphCompiler(object):
def __init__(
self,
lang_dir: Path,
otc_token: str,
device: Union[str, torch.device] = "cpu",
sos_token: str = "<sos/eos>",
eos_token: str = "<sos/eos>",
initial_bypass_weight: float = 0.0,
initial_self_loop_weight: float = 0.0,
bypass_weight_decay: float = 0.0,
self_loop_weight_decay: float = 0.0,
) -> None:
"""
Args:
lang_dir:
This directory is expected to contain the following files:
- bpe.model
- words.txt
otc_token:
The special token in OTC that represent all non-blank tokens
device:
It indicates CPU or CUDA.
sos_token:
The word piece that represents sos.
eos_token:
The word piece that represents eos.
"""
lang_dir = Path(lang_dir)
bpe_model_file = lang_dir / "bpe.model"
sp = spm.SentencePieceProcessor()
sp.load(str(bpe_model_file))
self.sp = sp
self.token_table = k2.SymbolTable.from_file(lang_dir / "tokens.txt")
self.otc_token = otc_token
assert self.otc_token in self.token_table
self.device = device
self.sos_id = self.sp.piece_to_id(sos_token)
self.eos_id = self.sp.piece_to_id(eos_token)
assert self.sos_id != self.sp.unk_id()
assert self.eos_id != self.sp.unk_id()
max_token_id = self.get_max_token_id()
ctc_topo = k2.ctc_topo(max_token_id, modified=False)
self.ctc_topo = ctc_topo.to(self.device)
self.initial_bypass_weight = initial_bypass_weight
self.initial_self_loop_weight = initial_self_loop_weight
self.bypass_weight_decay = bypass_weight_decay
self.self_loop_weight_decay = self_loop_weight_decay
def get_max_token_id(self):
max_token_id = 0
for symbol in self.token_table.symbols:
if not symbol.startswith("#"):
max_token_id = max(self.token_table[symbol], max_token_id)
assert max_token_id > 0
return max_token_id
def make_arc(
self,
from_state: int,
to_state: int,
symbol: Union[str, int],
weight: float,
):
return f"{from_state} {to_state} {symbol} {weight}"
def texts_to_ids(self, texts: List[str]) -> List[List[int]]:
"""Convert a list of texts to a list-of-list of piece IDs.
Args:
texts:
It is a list of strings. Each string consists of space(s)
separated words. An example containing two strings is given below:
['HELLO ICEFALL', 'HELLO k2']
Returns:
Return a list-of-list of piece IDs.
"""
return self.sp.encode(texts, out_type=int)
def compile(
self,
texts: List[str],
allow_bypass_arc: str2bool = True,
allow_self_loop_arc: str2bool = True,
bypass_weight: float = 0.0,
self_loop_weight: float = 0.0,
) -> k2.Fsa:
"""Build a OTC graph from a texts (list of words).
Args:
texts:
A list of strings. Each string contains a sentence for an utterance.
A sentence consists of spaces separated words. An example `texts`
looks like:
['hello icefall', 'CTC training with k2']
allow_bypass_arc:
Whether to add bypass arc to training graph for substitution
and insertion errors (wrong or extra words in the transcript).
allow_self_loop_arc:
Whether to add self-loop arc to training graph for deletion
errors (missing words in the transcript).
bypass_weight:
Weight associated with bypass arc.
self_loop_weight:
Weight associated with self-loop arc.
Return:
Return an FsaVec, which is the result of composing a
CTC topology with OTC FSAs constructed from the given texts.
"""
transcript_fsa = self.convert_transcript_to_fsa(
texts,
self.otc_token,
allow_bypass_arc,
allow_self_loop_arc,
bypass_weight,
self_loop_weight,
)
transcript_fsa = transcript_fsa.to(self.device)
fsa_with_self_loop = k2.remove_epsilon_and_add_self_loops(transcript_fsa)
fsa_with_self_loop = k2.arc_sort(fsa_with_self_loop)
graph = k2.compose(
self.ctc_topo,
fsa_with_self_loop,
treat_epsilons_specially=False,
)
assert graph.requires_grad is False
return graph
def convert_transcript_to_fsa(
self,
texts: List[str],
otc_token: str,
allow_bypass_arc: str2bool = True,
allow_self_loop_arc: str2bool = True,
bypass_weight: float = 0.0,
self_loop_weight: float = 0.0,
):
otc_token_id = self.token_table[otc_token]
transcript_fsa_list = []
for text in texts:
text_piece_ids = []
for word in text.split():
piece_ids = self.sp.encode(word, out_type=int)
text_piece_ids.append(piece_ids)
arcs = []
start_state = 0
cur_state = start_state
next_state = 1
for piece_ids in text_piece_ids:
bypass_cur_state = cur_state
if allow_self_loop_arc:
self_loop_arc = self.make_arc(
cur_state,
cur_state,
otc_token_id,
self_loop_weight,
)
arcs.append(self_loop_arc)
for piece_id in piece_ids:
arc = self.make_arc(cur_state, next_state, piece_id, 0.0)
arcs.append(arc)
cur_state = next_state
next_state += 1
bypass_next_state = cur_state
if allow_bypass_arc:
bypass_arc = self.make_arc(
bypass_cur_state,
bypass_next_state,
otc_token_id,
bypass_weight,
)
arcs.append(bypass_arc)
bypass_cur_state = cur_state
if allow_self_loop_arc:
self_loop_arc = self.make_arc(
cur_state,
cur_state,
otc_token_id,
self_loop_weight,
)
arcs.append(self_loop_arc)
# Deal with final state
final_state = next_state
final_arc = self.make_arc(cur_state, final_state, -1, 0.0)
arcs.append(final_arc)
arcs.append(f"{final_state}")
sorted_arcs = sorted(arcs, key=lambda a: int(a.split()[0]))
transcript_fsa = k2.Fsa.from_str("\n".join(sorted_arcs))
transcript_fsa = k2.arc_sort(transcript_fsa)
transcript_fsa_list.append(transcript_fsa)
transcript_fsa_vec = k2.create_fsa_vec(transcript_fsa_list)
return transcript_fsa_vec
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