add OTC related scripts using phone as units instead of BPEs (#1602)

* add otc related scripts using phone instead of bpe

egs/librispeech/WSASR/conformer_ctc2/decode_phone.py

@@ -0,0 +1,592 @@
+#!/usr/bin/env python3
+# Copyright 2021 Xiaomi Corporation (Author: Liyong Guo,
+#                                            Fangjun Kuang,
+#                                            Quandong Wang)
+#           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.
+
+
+import argparse
+import logging
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
+from conformer import Conformer
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.decode import get_lattice, one_best_decoding
+from icefall.env import get_env_info
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    get_texts,
+    load_averaged_model,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--otc-token",
+        type=str,
+        default="<star>",
+        help="OTC token",
+    )
+
+    parser.add_argument(
+        "--blank-bias",
+        type=float,
+        default=0,
+        help="bias (log-prob) added to blank token during decoding",
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=20,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=5,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="ctc-greedy-search",
+        help="""Decoding method.
+        Supported values are:
+            - (0) 1best. Extract the best path from the decoding lattice as the
+              decoding result.
+       """,
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--num-decoder-layers",
+        type=int,
+        default=0,
+        help="""Number of decoder layer of transformer decoder.
+        Setting this to 0 will not create the decoder at all (pure CTC model)
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="conformer_ctc2/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_phone",
+        help="The lang dir",
+    )
+
+    parser.add_argument(
+        "--lm-dir",
+        type=str,
+        default="data/lm",
+        help="""The n-gram LM dir.
+        It should contain either G_4_gram.pt or G_4_gram.fst.txt
+        """,
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            # parameters for conformer
+            "subsampling_factor": 4,
+            "feature_dim": 80,
+            "nhead": 8,
+            "dim_feedforward": 2048,
+            "encoder_dim": 512,
+            "num_encoder_layers": 12,
+            # parameters for decoding
+            "search_beam": 20,
+            "output_beam": 8,
+            "min_active_states": 30,
+            "max_active_states": 10000,
+            "use_double_scores": True,
+            "env_info": get_env_info(),
+        }
+    )
+    return params
+
+
+def remove_duplicates_and_blank(hyp: List[int]) -> List[int]:
+    # from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/common.py
+    new_hyp: List[int] = []
+    cur = 0
+    while cur < len(hyp):
+        if hyp[cur] != 0:
+            new_hyp.append(hyp[cur])
+        prev = cur
+        while cur < len(hyp) and hyp[cur] == hyp[prev]:
+            cur += 1
+    return new_hyp
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    HLG: k2.Fsa,
+    batch: dict,
+    word_table: k2.SymbolTable,
+    G: Optional[k2.Fsa] = None,
+) -> Dict[str, List[List[str]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if no rescoring is used, the key is the string `no_rescore`.
+               If LM rescoring is used, the key is the string `lm_scale_xxx`,
+               where `xxx` is the value of `lm_scale`. An example key is
+               `lm_scale_0.7`
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+
+        - params.method is "1best", it uses 1best decoding without LM rescoring.
+
+      model:
+        The neural model.
+      HLG:
+        The decoding graph. Used only when params.method is NOT ctc-decoding.
+      H:
+        The ctc topo. Used only when params.method is ctc-decoding.
+      bpe_model:
+        The BPE model. Used only when params.method is ctc-decoding.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      word_table:
+        The word symbol table.
+      G:
+        An LM. It is not None when params.method is "nbest-rescoring"
+        or "whole-lattice-rescoring". In general, the G in HLG
+        is a 3-gram LM, while this G is a 4-gram LM.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict. Note: If it decodes to nothing, then return None.
+    """
+    device = HLG.device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+
+    nnet_output, memory, memory_key_padding_mask = model(feature, supervisions)
+    # nnet_output is (N, T, C)
+    nnet_output[:, :, 0] += params.blank_bias
+
+    supervision_segments = torch.stack(
+        (
+            supervisions["sequence_idx"],
+            torch.div(
+                supervisions["start_frame"],
+                params.subsampling_factor,
+                rounding_mode="trunc",
+            ),
+            torch.div(
+                supervisions["num_frames"],
+                params.subsampling_factor,
+                rounding_mode="trunc",
+            ),
+        ),
+        1,
+    ).to(torch.int32)
+
+    decoding_graph = HLG
+
+    lattice = get_lattice(
+        nnet_output=nnet_output,
+        decoding_graph=decoding_graph,
+        supervision_segments=supervision_segments,
+        search_beam=params.search_beam,
+        output_beam=params.output_beam,
+        min_active_states=params.min_active_states,
+        max_active_states=params.max_active_states,
+        subsampling_factor=params.subsampling_factor + 2,
+    )
+
+    if params.method in ["1best"]:
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+        key = "no_rescore"
+
+        hyps = get_texts(best_path)
+        hyps = [[word_table[i] for i in ids] for ids in hyps]
+
+        return {key: hyps}
+    else:
+        assert False, f"Unsupported decoding method: {params.method}"
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    HLG: k2.Fsa,
+    word_table: k2.SymbolTable,
+    G: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      HLG:
+        The decoding graph. Used only when params.method is NOT ctc-decoding.
+      H:
+        The ctc topo. Used only when params.method is ctc-decoding.
+      bpe_model:
+        The BPE model. Used only when params.method is ctc-decoding.
+      word_table:
+        It is the word symbol table.
+      sos_id:
+        The token ID for SOS.
+      eos_id:
+        The token ID for EOS.
+      G:
+        An LM. It is not None when params.method is "nbest-rescoring"
+        or "whole-lattice-rescoring". In general, the G in HLG
+        is a 3-gram LM, while this G is a 4-gram LM.
+    Returns:
+      Return a dict, whose key may be "no-rescore" if no LM rescoring
+      is used, or it may be "lm_scale_0.7" if LM rescoring is used.
+      Its value is a list of tuples. Each tuple contains two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            HLG=HLG,
+            batch=batch,
+            word_table=word_table,
+            G=G,
+        )
+
+        if hyps_dict is not None:
+            for lm_scale, hyps in hyps_dict.items():
+                this_batch = []
+                assert len(hyps) == len(texts)
+                for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
+                    ref_words = ref_text.split()
+                    this_batch.append((cut_id, ref_words, hyp_words))
+
+                results[lm_scale].extend(this_batch)
+        else:
+            assert len(results) > 0, "It should not decode to empty in the first batch!"
+            this_batch = []
+            hyp_words = []
+            for ref_text in texts:
+                ref_words = ref_text.split()
+                this_batch.append((ref_words, hyp_words))
+
+            for lm_scale in results.keys():
+                results[lm_scale].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % 100 == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+):
+    if params.method in ("attention-decoder", "rnn-lm"):
+        # Set it to False since there are too many logs.
+        enable_log = False
+    else:
+        enable_log = True
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = params.exp_dir / f"recogs-{test_set_name}-{key}.txt"
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        if enable_log:
+            logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = params.exp_dir / f"errs-{test_set_name}-{key}.txt"
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=enable_log
+            )
+            test_set_wers[key] = wer
+
+        if enable_log:
+            logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = params.exp_dir / f"wer-summary-{test_set_name}.txt"
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+    args.lang_dir = Path(args.lang_dir)
+    args.lm_dir = Path(args.lm_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    setup_logger(f"{params.exp_dir}/log-{params.method}/log-decode")
+    logging.info("Decoding started")
+    logging.info(params)
+
+    lexicon = Lexicon(params.lang_dir)
+    # remove otc_token from decoding units
+    max_token_id = len(lexicon.tokens) - 1
+    num_classes = max_token_id + 1  # +1 for the blank
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    params.num_classes = num_classes
+
+    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
+    HLG = HLG.to(device)
+    assert HLG.requires_grad is False
+
+    if not hasattr(HLG, "lm_scores"):
+        HLG.lm_scores = HLG.scores.clone()
+
+    G = None
+
+    model = Conformer(
+        num_features=params.feature_dim,
+        nhead=params.nhead,
+        d_model=params.encoder_dim,
+        num_classes=num_classes,
+        subsampling_factor=params.subsampling_factor,
+        num_encoder_layers=params.num_encoder_layers,
+        num_decoder_layers=params.num_decoder_layers,
+    )
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg + 1
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to(device)
+    model.eval()
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+
+    test_sets = ["test-clean", "test-other"]
+    test_dl = [test_clean_dl, test_other_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            HLG=HLG,
+            word_table=lexicon.word_table,
+        )
+
+        save_results(params=params, test_set_name=test_set, results_dict=results_dict)
+
+    logging.info("Done!")
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()

egs/librispeech/WSASR/local/download_lm.py

@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# 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.
+
+
+"""
+This file downloads the following LibriSpeech LM files:
+
+    - 3-gram.pruned.1e-7.arpa.gz
+    - 4-gram.arpa.gz
+    - librispeech-vocab.txt
+    - librispeech-lexicon.txt
+    - librispeech-lm-norm.txt.gz
+
+from http://www.openslr.org/resources/11
+and save them in the user provided directory.
+
+Files are not re-downloaded if they already exist.
+
+Usage:
+    ./local/download_lm.py --out-dir ./download/lm
+"""
+
+import argparse
+import gzip
+import logging
+import os
+import shutil
+from pathlib import Path
+
+from tqdm.auto import tqdm
+
+
+# This function is copied from lhotse
+def tqdm_urlretrieve_hook(t):
+    """Wraps tqdm instance.
+    Don't forget to close() or __exit__()
+    the tqdm instance once you're done with it (easiest using `with` syntax).
+    Example
+    -------
+    >>> from urllib.request import urlretrieve
+    >>> with tqdm(...) as t:
+    ...     reporthook = tqdm_urlretrieve_hook(t)
+    ...     urlretrieve(..., reporthook=reporthook)
+
+    Source: https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py
+    """
+    last_b = [0]
+
+    def update_to(b=1, bsize=1, tsize=None):
+        """
+        b  : int, optional
+            Number of blocks transferred so far [default: 1].
+        bsize  : int, optional
+            Size of each block (in tqdm units) [default: 1].
+        tsize  : int, optional
+            Total size (in tqdm units). If [default: None] or -1,
+            remains unchanged.
+        """
+        if tsize not in (None, -1):
+            t.total = tsize
+        displayed = t.update((b - last_b[0]) * bsize)
+        last_b[0] = b
+        return displayed
+
+    return update_to
+
+
+# This function is copied from lhotse
+def urlretrieve_progress(url, filename=None, data=None, desc=None):
+    """
+    Works exactly like urllib.request.urlretrieve, but attaches a tqdm hook to
+    display a progress bar of the download.
+    Use "desc" argument to display a user-readable string that informs what is
+    being downloaded.
+    """
+    from urllib.request import urlretrieve
+
+    with tqdm(unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=desc) as t:
+        reporthook = tqdm_urlretrieve_hook(t)
+        return urlretrieve(url=url, filename=filename, reporthook=reporthook, data=data)
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--out-dir", type=str, help="Output directory.")
+
+    args = parser.parse_args()
+    return args
+
+
+def main(out_dir: str):
+    url = "http://www.openslr.org/resources/11"
+    out_dir = Path(out_dir)
+
+    files_to_download = (
+        "3-gram.pruned.1e-7.arpa.gz",
+        "4-gram.arpa.gz",
+        "librispeech-vocab.txt",
+        "librispeech-lexicon.txt",
+        "librispeech-lm-norm.txt.gz",
+    )
+
+    for f in tqdm(files_to_download, desc="Downloading LibriSpeech LM files"):
+        filename = out_dir / f
+        if filename.is_file() is False:
+            urlretrieve_progress(
+                f"{url}/{f}",
+                filename=filename,
+                desc=f"Downloading {filename}",
+            )
+        else:
+            logging.info(f"{filename} already exists - skipping")
+
+        if ".gz" in str(filename):
+            unzipped = Path(os.path.splitext(filename)[0])
+            if unzipped.is_file() is False:
+                with gzip.open(filename, "rb") as f_in:
+                    with open(unzipped, "wb") as f_out:
+                        shutil.copyfileobj(f_in, f_out)
+            else:
+                logging.info(f"{unzipped} already exist - skipping")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_args()
+    logging.info(f"out_dir: {args.out_dir}")
+
+    main(out_dir=args.out_dir)

egs/librispeech/WSASR/local/prepare_otc_lang.py

@@ -0,0 +1,469 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#              2024  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.
+
+
+"""
+This script takes as input a lexicon file "data/lang_phone/lexicon.txt"
+consisting of words and tokens (i.e., phones) and does the following:
+
+1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt
+
+2. Generate tokens.txt, the token table mapping a token to a unique integer.
+
+3. Generate words.txt, the word table mapping a word to a unique integer.
+
+4. Generate L.pt, in k2 format. It can be loaded by
+
+        d = torch.load("L.pt")
+        lexicon = k2.Fsa.from_dict(d)
+
+5. Generate L_disambig.pt, in k2 format.
+"""
+import argparse
+import logging
+import math
+import re
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+import k2
+import torch
+
+from icefall.lexicon import write_lexicon
+from icefall.utils import str2bool
+
+Lexicon = List[Tuple[str, List[str]]]
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        It should contain a file lexicon.txt.
+        Generated files by this script are saved into this directory.
+        """,
+    )
+
+    parser.add_argument(
+        "--otc-token",
+        type=str,
+        default="<star>",
+        help="The OTC token in lexicon",
+    )
+
+    parser.add_argument(
+        "--debug",
+        type=str2bool,
+        default=False,
+        help="""True for debugging, which will generate
+        a visualization of the lexicon FST.
+
+        Caution: If your lexicon contains hundreds of thousands
+        of lines, please set it to False!
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def read_lexicon(
+    filename: str,
+) -> List[Tuple[str, List[str]]]:
+    """Read a lexicon from `filename`.
+
+    Each line in the lexicon contains "word p1 p2 p3 ...".
+    That is, the first field is a word and the remaining
+    fields are tokens. Fields are separated by space(s).
+
+    Args:
+      filename:
+        Path to the lexicon.txt
+
+    Returns:
+      A list of tuples., e.g., [('w', ['p1', 'p2']), ('w1', ['p3, 'p4'])]
+    """
+    ans = []
+
+    with open(filename, "r", encoding="utf-8") as f:
+        whitespace = re.compile("[ \t]+")
+        for line in f:
+            a = whitespace.split(line.strip(" \t\r\n"))
+            if len(a) == 0:
+                continue
+
+            if len(a) < 2:
+                logging.info(f"Found bad line {line} in lexicon file {filename}")
+                logging.info("Every line is expected to contain at least 2 fields")
+                continue
+            word = a[0]
+            if word == "<eps>":
+                logging.info(f"Found bad line {line} in lexicon file {filename}")
+                logging.info("<eps> should not be a valid word")
+                continue
+
+            tokens = a[1:]
+            ans.append((word, tokens))
+
+    return ans
+
+
+def write_mapping(filename: str, sym2id: Dict[str, int]) -> None:
+    """Write a symbol to ID mapping to a file.
+
+    Note:
+      No need to implement `read_mapping` as it can be done
+      through :func:`k2.SymbolTable.from_file`.
+
+    Args:
+      filename:
+        Filename to save the mapping.
+      sym2id:
+        A dict mapping symbols to IDs.
+    Returns:
+      Return None.
+    """
+    with open(filename, "w", encoding="utf-8") as f:
+        for sym, i in sym2id.items():
+            f.write(f"{sym} {i}\n")
+
+
+def get_tokens(lexicon: Lexicon) -> List[str]:
+    """Get tokens from a lexicon.
+
+    Args:
+      lexicon:
+        It is the return value of :func:`read_lexicon`.
+    Returns:
+      Return a list of unique tokens.
+    """
+    ans = set()
+    for _, tokens in lexicon:
+        ans.update(tokens)
+    sorted_ans = sorted(list(ans))
+    return sorted_ans
+
+
+def get_words(lexicon: Lexicon) -> List[str]:
+    """Get words from a lexicon.
+
+    Args:
+      lexicon:
+        It is the return value of :func:`read_lexicon`.
+    Returns:
+      Return a list of unique words.
+    """
+    ans = set()
+    for word, _ in lexicon:
+        ans.add(word)
+    sorted_ans = sorted(list(ans))
+    return sorted_ans
+
+
+def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]:
+    """It adds pseudo-token disambiguation symbols #1, #2 and so on
+    at the ends of tokens to ensure that all pronunciations are different,
+    and that none is a prefix of another.
+
+    See also add_lex_disambig.pl from kaldi.
+
+    Args:
+      lexicon:
+        It is returned by :func:`read_lexicon`.
+    Returns:
+      Return a tuple with two elements:
+
+        - The output lexicon with disambiguation symbols
+        - The ID of the max disambiguation symbol that appears
+          in the lexicon
+    """
+
+    # (1) Work out the count of each token-sequence in the
+    # lexicon.
+    count = defaultdict(int)
+    for _, tokens in lexicon:
+        count[" ".join(tokens)] += 1
+
+    # (2) For each left sub-sequence of each token-sequence, note down
+    # that it exists (for identifying prefixes of longer strings).
+    issubseq = defaultdict(int)
+    for _, tokens in lexicon:
+        tokens = tokens.copy()
+        tokens.pop()
+        while tokens:
+            issubseq[" ".join(tokens)] = 1
+            tokens.pop()
+
+    # (3) For each entry in the lexicon:
+    # if the token sequence is unique and is not a
+    # prefix of another word, no disambig symbol.
+    # Else output #1, or #2, #3, ... if the same token-seq
+    # has already been assigned a disambig symbol.
+    ans = []
+
+    # We start with #1 since #0 has its own purpose
+    first_allowed_disambig = 1
+    max_disambig = first_allowed_disambig - 1
+    last_used_disambig_symbol_of = defaultdict(int)
+
+    for word, tokens in lexicon:
+        tokenseq = " ".join(tokens)
+        assert tokenseq != ""
+        if issubseq[tokenseq] == 0 and count[tokenseq] == 1:
+            ans.append((word, tokens))
+            continue
+
+        cur_disambig = last_used_disambig_symbol_of[tokenseq]
+        if cur_disambig == 0:
+            cur_disambig = first_allowed_disambig
+        else:
+            cur_disambig += 1
+
+        if cur_disambig > max_disambig:
+            max_disambig = cur_disambig
+        last_used_disambig_symbol_of[tokenseq] = cur_disambig
+        tokenseq += f" #{cur_disambig}"
+        ans.append((word, tokenseq.split()))
+    return ans, max_disambig
+
+
+def generate_id_map(
+    symbols: List[str],
+) -> Dict[str, int]:
+    """Generate ID maps, i.e., map a symbol to a unique ID.
+
+    Args:
+      symbols:
+        A list of unique symbols.
+    Returns:
+      A dict containing the mapping between symbols and IDs.
+    """
+    return {sym: i for i, sym in enumerate(symbols)}
+
+
+def add_self_loops(
+    arcs: List[List[Any]], disambig_token: int, disambig_word: int
+) -> List[List[Any]]:
+    """Adds self-loops to states of an FST to propagate disambiguation symbols
+    through it. They are added on each state with non-epsilon output symbols
+    on at least one arc out of the state.
+
+    See also fstaddselfloops.pl from Kaldi. One difference is that
+    Kaldi uses OpenFst style FSTs and it has multiple final states.
+    This function uses k2 style FSTs and it does not need to add self-loops
+    to the final state.
+
+    The input label of a self-loop is `disambig_token`, while the output
+    label is `disambig_word`.
+
+    Args:
+      arcs:
+        A list-of-list. The sublist contains
+        `[src_state, dest_state, label, aux_label, score]`
+      disambig_token:
+        It is the token ID of the symbol `#0`.
+      disambig_word:
+        It is the word ID of the symbol `#0`.
+
+    Return:
+      Return new `arcs` containing self-loops.
+    """
+    states_needs_self_loops = set()
+    for arc in arcs:
+        src, dst, ilabel, olabel, score = arc
+        if olabel != 0:
+            states_needs_self_loops.add(src)
+
+    ans = []
+    for s in states_needs_self_loops:
+        ans.append([s, s, disambig_token, disambig_word, 0])
+
+    return arcs + ans
+
+
+def lexicon_to_fst(
+    lexicon: Lexicon,
+    token2id: Dict[str, int],
+    word2id: Dict[str, int],
+    sil_token: str = "SIL",
+    sil_prob: float = 0.5,
+    need_self_loops: bool = False,
+) -> k2.Fsa:
+    """Convert a lexicon to an FST (in k2 format) with optional silence at
+    the beginning and end of each word.
+
+    Args:
+      lexicon:
+        The input lexicon. See also :func:`read_lexicon`
+      token2id:
+        A dict mapping tokens to IDs.
+      word2id:
+        A dict mapping words to IDs.
+      sil_token:
+        The silence token.
+      sil_prob:
+        The probability for adding a silence at the beginning and end
+        of the word.
+      need_self_loops:
+        If True, add self-loop to states with non-epsilon output symbols
+        on at least one arc out of the state. The input label for this
+        self loop is `token2id["#0"]` and the output label is `word2id["#0"]`.
+    Returns:
+      Return an instance of `k2.Fsa` representing the given lexicon.
+    """
+    assert sil_prob > 0.0 and sil_prob < 1.0
+    # CAUTION: we use score, i.e, negative cost.
+    sil_score = math.log(sil_prob)
+    no_sil_score = math.log(1.0 - sil_prob)
+
+    start_state = 0
+    loop_state = 1  # words enter and leave from here
+    sil_state = 2  # words terminate here when followed by silence; this state
+    # has a silence transition to loop_state.
+    next_state = 3  # the next un-allocated state, will be incremented as we go.
+    arcs = []
+
+    assert token2id["<eps>"] == 0
+    assert word2id["<eps>"] == 0
+
+    eps = 0
+
+    sil_token = token2id[sil_token]
+
+    arcs.append([start_state, loop_state, eps, eps, no_sil_score])
+    arcs.append([start_state, sil_state, eps, eps, sil_score])
+    arcs.append([sil_state, loop_state, sil_token, eps, 0])
+
+    for word, tokens in lexicon:
+        assert len(tokens) > 0, f"{word} has no pronunciations"
+        cur_state = loop_state
+
+        word = word2id[word]
+        tokens = [token2id[i] for i in tokens]
+
+        for i in range(len(tokens) - 1):
+            w = word if i == 0 else eps
+            arcs.append([cur_state, next_state, tokens[i], w, 0])
+
+            cur_state = next_state
+            next_state += 1
+
+        # now for the last token of this word
+        # It has two out-going arcs, one to the loop state,
+        # the other one to the sil_state.
+        i = len(tokens) - 1
+        w = word if i == 0 else eps
+        arcs.append([cur_state, loop_state, tokens[i], w, no_sil_score])
+        arcs.append([cur_state, sil_state, tokens[i], w, sil_score])
+
+    if need_self_loops:
+        disambig_token = token2id["#0"]
+        disambig_word = word2id["#0"]
+        arcs = add_self_loops(
+            arcs,
+            disambig_token=disambig_token,
+            disambig_word=disambig_word,
+        )
+
+    final_state = next_state
+    arcs.append([loop_state, final_state, -1, -1, 0])
+    arcs.append([final_state])
+
+    arcs = sorted(arcs, key=lambda arc: arc[0])
+    arcs = [[str(i) for i in arc] for arc in arcs]
+    arcs = [" ".join(arc) for arc in arcs]
+    arcs = "\n".join(arcs)
+
+    fsa = k2.Fsa.from_str(arcs, acceptor=False)
+    return fsa
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+    lexicon_filename = lang_dir / "lexicon.txt"
+    otc_token = args.otc_token
+    sil_token = "SIL"
+    sil_prob = 0.5
+
+    lexicon = read_lexicon(lexicon_filename)
+    tokens = get_tokens(lexicon)
+    words = get_words(lexicon)
+
+    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
+
+    lexicon.append((otc_token, [otc_token]))
+    tokens.append(otc_token)
+    for i in range(max_disambig + 1):
+        disambig = f"#{i}"
+        assert disambig not in tokens
+        tokens.append(f"#{i}")
+
+    assert "<eps>" not in tokens
+    tokens = ["<eps>"] + tokens
+
+    assert "<eps>" not in words
+    assert "#0" not in words
+    assert "<s>" not in words
+    assert "</s>" not in words
+
+    words = ["<eps>"] + words + [otc_token, "#0", "<s>", "</s>"]
+
+    token2id = generate_id_map(tokens)
+    word2id = generate_id_map(words)
+
+    write_mapping(lang_dir / "tokens.txt", token2id)
+    write_mapping(lang_dir / "words.txt", word2id)
+    write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig)
+
+    L = lexicon_to_fst(
+        lexicon,
+        token2id=token2id,
+        word2id=word2id,
+        sil_token=sil_token,
+        sil_prob=sil_prob,
+    )
+
+    L_disambig = lexicon_to_fst(
+        lexicon_disambig,
+        token2id=token2id,
+        word2id=word2id,
+        sil_token=sil_token,
+        sil_prob=sil_prob,
+        need_self_loops=True,
+    )
+    torch.save(L.as_dict(), lang_dir / "L.pt")
+    torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt")
+
+    if args.debug:
+        labels_sym = k2.SymbolTable.from_file(lang_dir / "tokens.txt")
+        aux_labels_sym = k2.SymbolTable.from_file(lang_dir / "words.txt")
+
+        L.labels_sym = labels_sym
+        L.aux_labels_sym = aux_labels_sym
+        L.draw(f"{lang_dir / 'L.svg'}", title="L.pt")
+
+        L_disambig.labels_sym = labels_sym
+        L_disambig.aux_labels_sym = aux_labels_sym
+        L_disambig.draw(f"{lang_dir / 'L_disambig.svg'}", title="L_disambig.pt")
+
+
+if __name__ == "__main__":
+    main()

egs/librispeech/WSASR/prepare.sh

@@ -30,7 +30,8 @@ stop_stage=100
 #        - librispeech-lm-norm.txt.gz
 #
 otc_token="<star>"
-feature_type="ssl"
+# ssl or fbank
+feature_type="fbank"
 
 dl_dir=$PWD/download
 manifests_dir="data/manifests"
@@ -40,9 +41,6 @@ lm_dir="data/lm"
 
 perturb_speed=false
 
-# ssl or fbank
-
-. ./cmd.sh
 . shared/parse_options.sh || exit 1
 
 # vocab size for sentence piece models.
@@ -192,7 +190,23 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
 fi
 
 if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
-  log "Stage 5: Prepare G"
+  log "Stage 5: Prepare phone based lang"
+  lang_dir="data/lang_phone"
+  mkdir -p ${lang_dir}
+
+  if [ ! -f $lang_dir/lexicon.txt ]; then
+    (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
+      cat - $dl_dir/lm/librispeech-lexicon.txt |
+      sort | uniq > $lang_dir/lexicon.txt
+  fi
+
+  if [ ! -f $lang_dir/L_disambig.pt ]; then
+    ./local/prepare_otc_lang.py --lang-dir $lang_dir
+  fi
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Prepare G"
   # We assume you have installed kaldilm, if not, please install
   # it using: pip install kaldilm
 
@@ -216,18 +230,30 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
   fi
 fi
 
-if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
-  log "Stage 6: Compile HLG"
+  log "Stage 7: Compile HLG"
   # Note If ./local/compile_hlg.py throws OOM,
   # please switch to the following command
   #
   # ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone
-
   for vocab_size in ${vocab_sizes[@]}; do
-    bpe_lang_dir="data/lang_bpe_${vocab_size}"
+    lang_dir="data/lang_bpe_${vocab_size}"
     echo "LM DIR: ${lm_dir}"
     ./local/compile_hlg.py \
         --lm-dir "${lm_dir}" \
         --lang-dir "${bpe_lang_dir}"
   done
 fi
+
+if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
+  log "Stage 7: Compile HLG"
+  # Note If ./local/compile_hlg.py throws OOM,
+  # please switch to the following command
+  #
+  # ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone
+  lang_dir="data/lang_phone"
+  echo "LM DIR: ${lm_dir}"
+  ./local/compile_hlg.py \
+      --lm-dir "${lm_dir}" \
+      --lang-dir "${lang_dir}"
+fi

egs/librispeech/WSASR/shared

@@ -0,0 +1 @@
+../../../icefall/shared/

icefall/otc_phone_graph_compiler.py

@@ -0,0 +1,232 @@
+# 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 torch
+
+from icefall.lexicon import Lexicon
+from icefall.utils import str2bool
+
+
+class OtcPhoneTrainingGraphCompiler(object):
+    def __init__(
+        self,
+        lexicon: Lexicon,
+        otc_token: str,
+        oov: str = "<UNK>",
+        device: Union[str, torch.device] = "cpu",
+        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:
+          lexicon:
+            It is built from `data/lang/lexicon.txt`.
+          otc_token:
+            The special token in OTC that represent all non-blank tokens
+          device:
+            It indicates CPU or CUDA.
+        """
+        self.device = device
+        L_inv = lexicon.L_inv.to(self.device)
+        assert L_inv.requires_grad is False
+        assert oov in lexicon.word_table
+
+        self.L_inv = k2.arc_sort(L_inv)
+        self.oov_id = lexicon.word_table[oov]
+        self.otc_id = lexicon.word_table[otc_token]
+        self.word_table = lexicon.word_table
+
+        max_token_id = max(lexicon.tokens)
+        ctc_topo = k2.ctc_topo(max_token_id, modified=False)
+        self.ctc_topo = ctc_topo.to(self.device)
+        self.max_token_id = max_token_id
+
+        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):
+        return self.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 word 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 word IDs.
+        """
+        word_ids_list = []
+        for text in texts:
+            word_ids = []
+            for word in text.split():
+                if word in self.word_table:
+                    word_ids.append(self.word_table[word])
+                else:
+                    word_ids.append(self.oov_id)
+            word_ids_list.append(word_ids)
+        return word_ids_list
+
+    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,
+            allow_bypass_arc,
+            allow_self_loop_arc,
+            bypass_weight,
+            self_loop_weight,
+        )
+        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],
+        allow_bypass_arc: str2bool = True,
+        allow_self_loop_arc: str2bool = True,
+        bypass_weight: float = 0.0,
+        self_loop_weight: float = 0.0,
+    ):
+
+        word_fsa_list = []
+        for text in texts:
+            word_ids = []
+
+            for word in text.split():
+                if word in self.word_table:
+                    word_ids.append(self.word_table[word])
+                else:
+                    word_ids.append(self.oov_id)
+
+            arcs = []
+            start_state = 0
+            cur_state = start_state
+            next_state = 1
+
+            for word_id in word_ids:
+                if allow_self_loop_arc:
+                    self_loop_arc = self.make_arc(
+                        cur_state,
+                        cur_state,
+                        self.otc_id,
+                        self_loop_weight,
+                    )
+                    arcs.append(self_loop_arc)
+
+                arc = self.make_arc(cur_state, next_state, word_id, 0.0)
+                arcs.append(arc)
+
+                if allow_bypass_arc:
+                    bypass_arc = self.make_arc(
+                        cur_state,
+                        next_state,
+                        self.otc_id,
+                        bypass_weight,
+                    )
+                    arcs.append(bypass_arc)
+
+                cur_state = next_state
+                next_state += 1
+
+            if allow_self_loop_arc:
+                self_loop_arc = self.make_arc(
+                    cur_state,
+                    cur_state,
+                    self.otc_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]))
+
+            word_fsa = k2.Fsa.from_str("\n".join(sorted_arcs))
+            word_fsa = k2.arc_sort(word_fsa)
+            word_fsa_list.append(word_fsa)
+
+        word_fsa_vec = k2.create_fsa_vec(word_fsa_list).to(self.device)
+        word_fsa_vec_with_self_loop = k2.add_epsilon_self_loops(word_fsa_vec)
+
+        fsa = k2.intersect(
+            self.L_inv, word_fsa_vec_with_self_loop, treat_epsilons_specially=False
+        )
+        ans_fsa = fsa.invert_()
+        return k2.arc_sort(ans_fsa)