mirror of
https://github.com/k2-fsa/icefall.git
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247 lines
8.0 KiB
Python
247 lines
8.0 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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# 2023 Johns Hopkins University (author: Dongji Gao)
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#
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# See ../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pathlib import Path
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from typing import List, Union
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import k2
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import sentencepiece as spm
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import torch
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from icefall.utils import str2bool
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class OtcTrainingGraphCompiler(object):
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def __init__(
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self,
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lang_dir: Path,
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otc_token: str,
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device: Union[str, torch.device] = "cpu",
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sos_token: str = "<sos/eos>",
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eos_token: str = "<sos/eos>",
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initial_bypass_weight: float = 0.0,
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initial_self_loop_weight: float = 0.0,
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bypass_weight_decay: float = 0.0,
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self_loop_weight_decay: float = 0.0,
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) -> None:
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"""
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Args:
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lang_dir:
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This directory is expected to contain the following files:
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- bpe.model
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- words.txt
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otc_token:
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The special token in OTC that represent all non-blank tokens
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device:
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It indicates CPU or CUDA.
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sos_token:
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The word piece that represents sos.
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eos_token:
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The word piece that represents eos.
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"""
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lang_dir = Path(lang_dir)
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bpe_model_file = lang_dir / "bpe.model"
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sp = spm.SentencePieceProcessor()
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sp.load(str(bpe_model_file))
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self.sp = sp
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self.token_table = k2.SymbolTable.from_file(lang_dir / "tokens.txt")
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self.otc_token = otc_token
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assert self.otc_token in self.token_table
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self.device = device
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self.sos_id = self.sp.piece_to_id(sos_token)
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self.eos_id = self.sp.piece_to_id(eos_token)
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assert self.sos_id != self.sp.unk_id()
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assert self.eos_id != self.sp.unk_id()
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max_token_id = self.get_max_token_id()
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ctc_topo = k2.ctc_topo(max_token_id, modified=False)
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self.ctc_topo = ctc_topo.to(self.device)
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self.initial_bypass_weight = initial_bypass_weight
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self.initial_self_loop_weight = initial_self_loop_weight
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self.bypass_weight_decay = bypass_weight_decay
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self.self_loop_weight_decay = self_loop_weight_decay
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def get_max_token_id(self):
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max_token_id = 0
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for symbol in self.token_table.symbols:
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if not symbol.startswith("#"):
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max_token_id = max(self.token_table[symbol], max_token_id)
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assert max_token_id > 0
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return max_token_id
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def make_arc(
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self,
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from_state: int,
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to_state: int,
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symbol: Union[str, int],
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weight: float,
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):
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return f"{from_state} {to_state} {symbol} {weight}"
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def texts_to_ids(self, texts: List[str]) -> List[List[int]]:
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"""Convert a list of texts to a list-of-list of piece IDs.
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Args:
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texts:
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It is a list of strings. Each string consists of space(s)
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separated words. An example containing two strings is given below:
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['HELLO ICEFALL', 'HELLO k2']
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Returns:
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Return a list-of-list of piece IDs.
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"""
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return self.sp.encode(texts, out_type=int)
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def compile(
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self,
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texts: List[str],
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allow_bypass_arc: str2bool = True,
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allow_self_loop_arc: str2bool = True,
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bypass_weight: float = 0.0,
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self_loop_weight: float = 0.0,
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) -> k2.Fsa:
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"""Build a OTC graph from a texts (list of words).
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Args:
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texts:
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A list of strings. Each string contains a sentence for an utterance.
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A sentence consists of spaces separated words. An example `texts`
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looks like:
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['hello icefall', 'CTC training with k2']
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allow_bypass_arc:
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Whether to add bypass arc to training graph for substitution
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and insertion errors (wrong or extra words in the transcript).
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allow_self_loop_arc:
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Whether to add self-loop arc to training graph for deletion
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errors (missing words in the transcript).
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bypass_weight:
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Weight associated with bypass arc.
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self_loop_weight:
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Weight associated with self-loop arc.
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Return:
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Return an FsaVec, which is the result of composing a
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CTC topology with OTC FSAs constructed from the given texts.
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"""
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transcript_fsa = self.convert_transcript_to_fsa(
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texts,
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self.otc_token,
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allow_bypass_arc,
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allow_self_loop_arc,
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bypass_weight,
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self_loop_weight,
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)
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transcript_fsa = transcript_fsa.to(self.device)
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fsa_with_self_loop = k2.remove_epsilon_and_add_self_loops(transcript_fsa)
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fsa_with_self_loop = k2.arc_sort(fsa_with_self_loop)
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graph = k2.compose(
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self.ctc_topo,
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fsa_with_self_loop,
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treat_epsilons_specially=False,
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)
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assert graph.requires_grad is False
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return graph
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def convert_transcript_to_fsa(
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self,
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texts: List[str],
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otc_token: str,
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allow_bypass_arc: str2bool = True,
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allow_self_loop_arc: str2bool = True,
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bypass_weight: float = 0.0,
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self_loop_weight: float = 0.0,
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):
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otc_token_id = self.token_table[otc_token]
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transcript_fsa_list = []
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for text in texts:
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text_piece_ids = []
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for word in text.split():
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piece_ids = self.sp.encode(word, out_type=int)
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text_piece_ids.append(piece_ids)
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arcs = []
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start_state = 0
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cur_state = start_state
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next_state = 1
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for piece_ids in text_piece_ids:
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bypass_cur_state = cur_state
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if allow_self_loop_arc:
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self_loop_arc = self.make_arc(
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cur_state,
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cur_state,
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otc_token_id,
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self_loop_weight,
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)
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arcs.append(self_loop_arc)
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for piece_id in piece_ids:
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arc = self.make_arc(cur_state, next_state, piece_id, 0.0)
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arcs.append(arc)
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cur_state = next_state
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next_state += 1
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bypass_next_state = cur_state
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if allow_bypass_arc:
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bypass_arc = self.make_arc(
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bypass_cur_state,
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bypass_next_state,
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otc_token_id,
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bypass_weight,
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)
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arcs.append(bypass_arc)
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bypass_cur_state = cur_state
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if allow_self_loop_arc:
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self_loop_arc = self.make_arc(
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cur_state,
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cur_state,
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otc_token_id,
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self_loop_weight,
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)
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arcs.append(self_loop_arc)
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# Deal with final state
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final_state = next_state
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final_arc = self.make_arc(cur_state, final_state, -1, 0.0)
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arcs.append(final_arc)
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arcs.append(f"{final_state}")
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sorted_arcs = sorted(arcs, key=lambda a: int(a.split()[0]))
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transcript_fsa = k2.Fsa.from_str("\n".join(sorted_arcs))
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transcript_fsa = k2.arc_sort(transcript_fsa)
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transcript_fsa_list.append(transcript_fsa)
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transcript_fsa_vec = k2.create_fsa_vec(transcript_fsa_list)
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return transcript_fsa_vec
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