Merge branch 'k2-fsa:master' into dev/k2ssl

egs/multi_ja_en/ASR/README.md

@@ -0,0 +1,17 @@
+# Introduction
+
+A bilingual Japanese-English ASR model that utilizes ReazonSpeech, developed by the developers of ReazonSpeech.
+
+**ReazonSpeech** is an open-source dataset that contains a diverse set of natural Japanese speech, collected from terrestrial television streams. It contains more than 35,000 hours of audio.
+
+
+# Included Training Sets
+
+1. LibriSpeech (English)
+2. ReazonSpeech (Japanese)
+
+|Datset| Number of hours| URL|
+|---|---:|---|
+|**TOTAL**|35,960|---|
+|LibriSpeech|960|https://www.openslr.org/12/|
+|ReazonSpeech (all) |35,000|https://huggingface.co/datasets/reazon-research/reazonspeech|

egs/multi_ja_en/ASR/RESULTS.md

@@ -0,0 +1,52 @@
+## Results
+
+### Zipformer
+
+#### Non-streaming
+
+The training command is:
+
+```shell
+./zipformer/train.py \
+  --bilingual 1 \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp \
+  --max-duration 600
+```
+
+The decoding command is:
+
+```shell
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
+```
+
+To export the model with onnx:
+
+```shell
+./zipformer/export-onnx.py   --tokens data/lang_bbpe_2000/tokens.txt   --use-averaged-model 0   --epoch 35   --avg 1   --exp-dir zipformer/exp   --num-encoder-layers "2,2,3,4,3,2"   --downsampling-factor "1,2,4,8,4,2"   --feedforward-dim "512,768,1024,1536,1024,768"   --num-heads "4,4,4,8,4,4"   --encoder-dim "192,256,384,512,384,256"   --query-head-dim 32   --value-head-dim 12   --pos-head-dim 4   --pos-dim 48   --encoder-unmasked-dim "192,192,256,256,256,192"   --cnn-module-kernel "31,31,15,15,15,31"   --decoder-dim 512   --joiner-dim 512   --causal False   --chunk-size "16,32,64,-1"   --left-context-frames "64,128,256,-1"   --fp16 True
+```
+Word Error Rates (WERs) listed below:
+
+|       Datasets       | ReazonSpeech |  ReazonSpeech |     LibriSpeech    |    LibriSpeech    |
+|----------------------|--------------|---------------|--------------------|-------------------|
+|   Zipformer WER (%)  |     dev      |     test      |     test-clean     |    test-other     |
+|     greedy_search    |     5.9      |     4.07      |        3.46        |       8.35        |
+| modified_beam_search |    4.87      |     3.61      |        3.28        |       8.07        |
+
+
+Character Error Rates (CERs) for Japanese listed below:
+|   Decoding Method    | In-Distribution CER | JSUT | CommonVoice | TEDx  |
+| :------------------: | :-----------------: | :--: | :---------: | :---: | 
+|    greedy search     |        12.56        | 6.93 |    9.75     | 9.67  | 
+| modified beam search |        11.59        | 6.97 |    9.55     | 9.51  | 
+
+Pre-trained model can be found here: https://huggingface.co/reazon-research/reazonspeech-k2-v2-ja-en/tree/main
+

egs/multi_ja_en/ASR/local/compute_fbank_reazonspeech.py

@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+# Copyright    2023  The University of Electro-Communications  (Author: Teo Wen Shen)  # noqa
+#
+# 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
+import os
+from pathlib import Path
+from typing import List, Tuple
+
+import torch
+
+# fmt: off
+from lhotse import (  # See the following for why LilcomChunkyWriter is preferred; https://github.com/k2-fsa/icefall/pull/404; https://github.com/lhotse-speech/lhotse/pull/527
+    CutSet,
+    Fbank,
+    FbankConfig,
+    LilcomChunkyWriter,
+    RecordingSet,
+    SupervisionSet,
+)
+
+# fmt: on
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+RNG_SEED = 42
+concat_params = {"gap": 1.0, "maxlen": 10.0}
+
+
+def make_cutset_blueprints(
+    manifest_dir: Path,
+) -> List[Tuple[str, CutSet]]:
+    cut_sets = []
+
+    # Create test dataset
+    logging.info("Creating test cuts.")
+    cut_sets.append(
+        (
+            "test",
+            CutSet.from_manifests(
+                recordings=RecordingSet.from_file(
+                    manifest_dir / "reazonspeech_recordings_test.jsonl.gz"
+                ),
+                supervisions=SupervisionSet.from_file(
+                    manifest_dir / "reazonspeech_supervisions_test.jsonl.gz"
+                ),
+            ),
+        )
+    )
+
+    # Create dev dataset
+    logging.info("Creating dev cuts.")
+    cut_sets.append(
+        (
+            "dev",
+            CutSet.from_manifests(
+                recordings=RecordingSet.from_file(
+                    manifest_dir / "reazonspeech_recordings_dev.jsonl.gz"
+                ),
+                supervisions=SupervisionSet.from_file(
+                    manifest_dir / "reazonspeech_supervisions_dev.jsonl.gz"
+                ),
+            ),
+        )
+    )
+
+    # Create train dataset
+    logging.info("Creating train cuts.")
+    cut_sets.append(
+        (
+            "train",
+            CutSet.from_manifests(
+                recordings=RecordingSet.from_file(
+                    manifest_dir / "reazonspeech_recordings_train.jsonl.gz"
+                ),
+                supervisions=SupervisionSet.from_file(
+                    manifest_dir / "reazonspeech_supervisions_train.jsonl.gz"
+                ),
+            ),
+        )
+    )
+    return cut_sets
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("-m", "--manifest-dir", type=Path)
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    extractor = Fbank(FbankConfig(num_mel_bins=80))
+    num_jobs = min(16, os.cpu_count())
+
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    if (args.manifest_dir / ".reazonspeech-fbank.done").exists():
+        logging.info(
+            "Previous fbank computed for ReazonSpeech found. "
+            f"Delete {args.manifest_dir / '.reazonspeech-fbank.done'} to allow recomputing fbank."
+        )
+        return
+    else:
+        cut_sets = make_cutset_blueprints(args.manifest_dir)
+        for part, cut_set in cut_sets:
+            logging.info(f"Processing {part}")
+            cut_set = cut_set.compute_and_store_features(
+                extractor=extractor,
+                num_jobs=num_jobs,
+                storage_path=(args.manifest_dir / f"feats_{part}").as_posix(),
+                storage_type=LilcomChunkyWriter,
+            )
+            cut_set.to_file(args.manifest_dir / f"reazonspeech_cuts_{part}.jsonl.gz")
+
+        logging.info("All fbank computed for ReazonSpeech.")
+        (args.manifest_dir / ".reazonspeech-fbank.done").touch()
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/local/display_manifest_statistics.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#              2022  The University of Electro-Communications (author: Teo Wen Shen)  # noqa
+#
+# 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
+from pathlib import Path
+
+from lhotse import CutSet, load_manifest
+
+ARGPARSE_DESCRIPTION = """
+This file displays duration statistics of utterances in a manifest.
+You can use the displayed value to choose minimum/maximum duration
+to remove short and long utterances during the training.
+
+See the function `remove_short_and_long_utt()` in
+pruned_transducer_stateless5/train.py for usage.
+"""
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description=ARGPARSE_DESCRIPTION,
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument("--manifest-dir", type=Path, help="Path to cutset manifests")
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_parser()
+
+    for part in ["train", "dev"]:
+        path = args.manifest_dir / f"reazonspeech_cuts_{part}.jsonl.gz"
+        cuts: CutSet = load_manifest(path)
+
+        print("\n---------------------------------\n")
+        print(path.name + ":")
+        cuts.describe()
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/local/prepare_char.py

@@ -0,0 +1 @@
+../../../aishell/ASR/local/prepare_char.py

egs/multi_ja_en/ASR/local/prepare_for_bpe_model.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Zengrui Jin)
+#
+# 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 tokenizes the training transcript by CJK characters
+# and saves the result to transcript_chars.txt, which is used
+# to train the BPE model later.
+
+import argparse
+import re
+from pathlib import Path
+
+from tqdm.auto import tqdm
+
+from icefall.utils import tokenize_by_ja_char
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Output directory.
+        The generated transcript_chars.txt is saved to this directory.
+        """,
+    )
+
+    parser.add_argument(
+        "--text",
+        type=str,
+        help="Training transcript.",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+    text = Path(args.text)
+
+    assert lang_dir.exists() and text.exists(), f"{lang_dir} or {text} does not exist!"
+
+    transcript_path = lang_dir / "transcript_chars.txt"
+
+    with open(text, "r", encoding="utf-8") as fin:
+        with open(transcript_path, "w+", encoding="utf-8") as fout:
+            for line in tqdm(fin):
+                fout.write(tokenize_by_ja_char(line) + "\n")
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/local/prepare_lang.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/prepare_lang.py

egs/multi_ja_en/ASR/local/prepare_lang_bbpe.py

@@ -0,0 +1,268 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang
+#                                                  Wei Kang)
+#
+# 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 `lang_dir`, which should contain::
+
+    - lang_dir/bbpe.model,
+    - lang_dir/words.txt
+
+and generates the following files in the directory `lang_dir`:
+
+    - lexicon.txt
+    - lexicon_disambig.txt
+    - L.pt
+    - L_disambig.pt
+    - tokens.txt
+"""
+
+import argparse
+import re
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+from prepare_lang import (
+    Lexicon,
+    add_disambig_symbols,
+    add_self_loops,
+    write_lexicon,
+    write_mapping,
+)
+
+from icefall.byte_utils import byte_encode
+from icefall.utils import str2bool, tokenize_by_ja_char
+
+
+def lexicon_to_fst_no_sil(
+    lexicon: Lexicon,
+    token2id: Dict[str, int],
+    word2id: Dict[str, int],
+    need_self_loops: bool = False,
+) -> k2.Fsa:
+    """Convert a lexicon to an FST (in k2 format).
+
+    Args:
+      lexicon:
+        The input lexicon. See also :func:`read_lexicon`
+      token2id:
+        A dict mapping tokens to IDs.
+      word2id:
+        A dict mapping words to IDs.
+      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.
+    """
+    loop_state = 0  # words enter and leave from here
+    next_state = 1  # the next un-allocated state, will be incremented as we go
+
+    arcs = []
+
+    # The blank symbol <blk> is defined in local/train_bpe_model.py
+    assert token2id["<blk>"] == 0
+    assert word2id["<eps>"] == 0
+
+    eps = 0
+
+    for word, pieces in lexicon:
+        assert len(pieces) > 0, f"{word} has no pronunciations"
+        cur_state = loop_state
+
+        word = word2id[word]
+        pieces = [token2id[i] for i in pieces]
+
+        for i in range(len(pieces) - 1):
+            w = word if i == 0 else eps
+            arcs.append([cur_state, next_state, pieces[i], w, 0])
+
+            cur_state = next_state
+            next_state += 1
+
+        # now for the last piece of this word
+        i = len(pieces) - 1
+        w = word if i == 0 else eps
+        arcs.append([cur_state, loop_state, pieces[i], w, 0])
+
+    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 generate_lexicon(
+    model_file: str, words: List[str], oov: str
+) -> Tuple[Lexicon, Dict[str, int]]:
+    """Generate a lexicon from a BPE model.
+
+    Args:
+      model_file:
+        Path to a sentencepiece model.
+      words:
+        A list of strings representing words.
+      oov:
+        The out of vocabulary word in lexicon.
+    Returns:
+      Return a tuple with two elements:
+        - A dict whose keys are words and values are the corresponding
+          word pieces.
+        - A dict representing the token symbol, mapping from tokens to IDs.
+    """
+    sp = spm.SentencePieceProcessor()
+    sp.load(str(model_file))
+
+    # Convert word to word piece IDs instead of word piece strings
+    # to avoid OOV tokens.
+    encode_words = [byte_encode(tokenize_by_ja_char(w)) for w in words]
+    words_pieces_ids: List[List[int]] = sp.encode(encode_words, out_type=int)
+
+    # Now convert word piece IDs back to word piece strings.
+    words_pieces: List[List[str]] = [sp.id_to_piece(ids) for ids in words_pieces_ids]
+
+    lexicon = []
+    for word, pieces in zip(words, words_pieces):
+        lexicon.append((word, pieces))
+
+    lexicon.append((oov, ["▁", sp.id_to_piece(sp.unk_id())]))
+
+    token2id: Dict[str, int] = {sp.id_to_piece(i): i for i in range(sp.vocab_size())}
+
+    return lexicon, token2id
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        It should contain the bpe.model and words.txt
+        """,
+    )
+
+    parser.add_argument(
+        "--oov",
+        type=str,
+        default="<UNK>",
+        help="The out of vocabulary word 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!
+
+        See "test/test_bpe_lexicon.py" for usage.
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+    model_file = lang_dir / "bbpe.model"
+
+    word_sym_table = k2.SymbolTable.from_file(lang_dir / "words.txt")
+
+    words = word_sym_table.symbols
+
+    excluded = ["<eps>", "!SIL", "<SPOKEN_NOISE>", args.oov, "#0", "<s>", "</s>"]
+
+    for w in excluded:
+        if w in words:
+            words.remove(w)
+
+    lexicon, token_sym_table = generate_lexicon(model_file, words, args.oov)
+
+    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
+
+    next_token_id = max(token_sym_table.values()) + 1
+    for i in range(max_disambig + 1):
+        disambig = f"#{i}"
+        assert disambig not in token_sym_table
+        token_sym_table[disambig] = next_token_id
+        next_token_id += 1
+
+    word_sym_table.add("#0")
+    word_sym_table.add("<s>")
+    word_sym_table.add("</s>")
+
+    write_mapping(lang_dir / "tokens.txt", token_sym_table)
+
+    write_lexicon(lang_dir / "lexicon.txt", lexicon)
+    write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig)
+
+    L = lexicon_to_fst_no_sil(
+        lexicon,
+        token2id=token_sym_table,
+        word2id=word_sym_table,
+    )
+
+    L_disambig = lexicon_to_fst_no_sil(
+        lexicon_disambig,
+        token2id=token_sym_table,
+        word2id=word_sym_table,
+        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/multi_ja_en/ASR/local/prepare_lang_char.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+# Copyright    2022  The University of Electro-Communications  (Author: Teo Wen Shen)  # noqa
+#
+# 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 pathlib import Path
+
+from lhotse import CutSet
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument(
+        "train_cut", metavar="train-cut", type=Path, help="Path to the train cut"
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default=Path("data/lang_char"),
+        help=(
+            "Name of lang dir. "
+            "If not set, this will default to lang_char_{trans-mode}"
+        ),
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    logging.basicConfig(
+        format=("%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"),
+        level=logging.INFO,
+    )
+
+    sysdef_string = set(["<blk>", "<unk>", "<sos/eos>", " "])
+
+    token_set = set()
+    logging.info(f"Creating vocabulary from {args.train_cut}.")
+    train_cut: CutSet = CutSet.from_file(args.train_cut)
+    for cut in train_cut:
+        for sup in cut.supervisions:
+            token_set.update(sup.text)
+
+    token_set = ["<blk>"] + sorted(token_set - sysdef_string) + ["<unk>", "<sos/eos>"]
+    args.lang_dir.mkdir(parents=True, exist_ok=True)
+    (args.lang_dir / "tokens.txt").write_text(
+        "\n".join(f"{t}\t{i}" for i, t in enumerate(token_set))
+    )
+
+    (args.lang_dir / "lang_type").write_text("char")
+    logging.info("Done.")
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/local/prepare_words.py

@@ -0,0 +1 @@
+../../../aishell2/ASR/local/prepare_words.py

egs/multi_ja_en/ASR/local/text2segments.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright    2021  Xiaomi Corp.        (authors: Mingshuang Luo)
+#              2022  Xiaomi Corp.        (authors: Weiji Zhuang)
+#
+# 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 "text", which refers to the transcript file:
+    - text
+and generates the output file with word segmentation implemented using MeCab:
+    - text_words_segmentation
+"""
+
+import argparse
+from multiprocessing import Pool
+
+import MeCab
+from tqdm import tqdm
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="Japanese Word Segmentation for text",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--num-process",
+        "-n",
+        default=20,
+        type=int,
+        help="the number of processes",
+    )
+    parser.add_argument(
+        "--input-file",
+        "-i",
+        default="data/lang_char/text",
+        type=str,
+        help="the input text file",
+    )
+    parser.add_argument(
+        "--output-file",
+        "-o",
+        default="data/lang_char/text_words_segmentation",
+        type=str,
+        help="the text implemented with word segmentation using MeCab",
+    )
+
+    return parser
+
+
+def cut(lines):
+    if lines is not None:
+        mecab = MeCab.Tagger("-Owakati")  # Use '-Owakati' option for word segmentation
+        segmented_line = mecab.parse(lines).strip()
+        return segmented_line.split()  # Return as a list of words
+    else:
+        return None
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    num_process = args.num_process
+    input_file = args.input_file
+    output_file = args.output_file
+
+    with open(input_file, "r", encoding="utf-8") as fr:
+        lines = fr.readlines()
+
+    with Pool(processes=num_process) as p:
+        new_lines = list(tqdm(p.imap(cut, lines), total=len(lines)))
+
+    with open(output_file, "w", encoding="utf-8") as fw:
+        for line in new_lines:
+            fw.write(" ".join(line) + "\n")
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/local/text2token.py

@@ -0,0 +1,177 @@
+#!/usr/bin/env python3
+# Copyright    2017  Johns Hopkins University   (authors: Shinji Watanabe)
+#              2022  Xiaomi Corp.               (authors: Mingshuang Luo)
+#
+# 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 codecs
+import re
+import sys
+from typing import List
+
+from romkan import to_roma  # Replace with python-romkan v0.2.1
+
+is_python2 = sys.version_info[0] == 2
+
+
+def exist_or_not(i, match_pos):
+    start_pos = None
+    end_pos = None
+    for pos in match_pos:
+        if pos[0] <= i < pos[1]:
+            start_pos = pos[0]
+            end_pos = pos[1]
+            break
+
+    return start_pos, end_pos
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="convert raw text to tokenized text",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--nchar",
+        "-n",
+        default=1,
+        type=int,
+        help="number of characters to split, i.e., \
+                        aabb -> a a b b with -n 1 and aa bb with -n 2",
+    )
+    parser.add_argument(
+        "--skip-ncols", "-s", default=0, type=int, help="skip first n columns"
+    )
+    parser.add_argument("--space", default="<space>", type=str, help="space symbol")
+    parser.add_argument(
+        "--non-lang-syms",
+        "-l",
+        default=None,
+        type=str,
+        help="list of non-linguistic symbols, e.g., <NOISE> etc.",
+    )
+    parser.add_argument("text", type=str, default=False, nargs="?", help="input text")
+    parser.add_argument(
+        "--trans_type",
+        "-t",
+        type=str,
+        default="char",
+        choices=["char", "romaji"],
+        help="Transcript type. char/romaji",
+    )
+    return parser
+
+
+def token2id(
+    texts, token_table, token_type: str = "romaji", oov: str = "<unk>"
+) -> List[List[int]]:
+    """Convert token to id.
+    Args:
+      texts:
+        The input texts, it refers to the Japanese text here.
+      token_table:
+        The token table is built based on "data/lang_xxx/token.txt"
+      token_type:
+        The type of token, such as "romaji".
+      oov:
+        Out of vocabulary token. When a word(token) in the transcript
+        does not exist in the token list, it is replaced with `oov`.
+
+    Returns:
+      The list of ids for the input texts.
+    """
+    if texts is None:
+        raise ValueError("texts can't be None!")
+    else:
+        oov_id = token_table[oov]
+        ids: List[List[int]] = []
+        for text in texts:
+            chars_list = list(str(text))
+            if token_type == "romaji":
+                text = [to_roma(c) for c in chars_list]
+                sub_ids = [
+                    token_table[txt] if txt in token_table else oov_id for txt in text
+                ]
+                ids.append(sub_ids)
+        return ids
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    rs = []
+    if args.non_lang_syms is not None:
+        with codecs.open(args.non_lang_syms, "r", encoding="utf-8") as f:
+            nls = [x.rstrip() for x in f.readlines()]
+            rs = [re.compile(re.escape(x)) for x in nls]
+
+    if args.text:
+        f = codecs.open(args.text, encoding="utf-8")
+    else:
+        f = codecs.getreader("utf-8")(sys.stdin if is_python2 else sys.stdin.buffer)
+
+    sys.stdout = codecs.getwriter("utf-8")(
+        sys.stdout if is_python2 else sys.stdout.buffer
+    )
+    line = f.readline()
+    n = args.nchar
+    while line:
+        x = line.split()
+        print(" ".join(x[: args.skip_ncols]), end=" ")
+        a = " ".join(x[args.skip_ncols :])  # noqa E203
+
+        # get all matched positions
+        match_pos = []
+        for r in rs:
+            i = 0
+            while i >= 0:
+                m = r.search(a, i)
+                if m:
+                    match_pos.append([m.start(), m.end()])
+                    i = m.end()
+                else:
+                    break
+        if len(match_pos) > 0:
+            chars = []
+            i = 0
+            while i < len(a):
+                start_pos, end_pos = exist_or_not(i, match_pos)
+                if start_pos is not None:
+                    chars.append(a[start_pos:end_pos])
+                    i = end_pos
+                else:
+                    chars.append(a[i])
+                    i += 1
+            a = chars
+
+        if args.trans_type == "romaji":
+            a = [to_roma(c) for c in list(str(a))]
+
+        a = [a[j : j + n] for j in range(0, len(a), n)]  # noqa E203
+
+        a_flat = []
+        for z in a:
+            a_flat.append("".join(z))
+
+        a_chars = "".join(a_flat)
+        print(a_chars)
+        line = f.readline()
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/local/train_bbpe_model.py

@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Wei Kang)
+#
+# 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.
+
+# You can install sentencepiece via:
+#
+#  pip install sentencepiece
+#
+# Due to an issue reported in
+# https://github.com/google/sentencepiece/pull/642#issuecomment-857972030
+#
+# Please install a version >=0.1.96
+
+import argparse
+import re
+import shutil
+import tempfile
+from pathlib import Path
+
+import sentencepiece as spm
+
+from icefall import byte_encode
+from icefall.utils import tokenize_by_ja_char
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        The generated bpe.model is saved to this directory.
+        """,
+    )
+
+    parser.add_argument(
+        "--transcript",
+        type=str,
+        help="Training transcript.",
+    )
+
+    parser.add_argument(
+        "--vocab-size",
+        type=int,
+        help="Vocabulary size for BPE training",
+    )
+
+    return parser.parse_args()
+
+
+def _convert_to_bchar(in_path: str, out_path: str):
+    with open(out_path, "w") as f:
+        for line in open(in_path, "r").readlines():
+            f.write(byte_encode(tokenize_by_ja_char(line)) + "\n")
+
+
+def main():
+    args = get_args()
+    vocab_size = args.vocab_size
+    lang_dir = Path(args.lang_dir)
+
+    model_type = "unigram"
+
+    model_prefix = f"{lang_dir}/{model_type}_{vocab_size}"
+    model_file = Path(model_prefix + ".model")
+    if model_file.is_file():
+        print(f"{model_file} exists - skipping")
+        return
+
+    character_coverage = 1.0
+    input_sentence_size = 100000000
+
+    user_defined_symbols = ["<blk>", "<sos/eos>"]
+    unk_id = len(user_defined_symbols)
+    # Note: unk_id is fixed to 2.
+    # If you change it, you should also change other
+    # places that are using it.
+
+    temp = tempfile.NamedTemporaryFile()
+    train_text = temp.name
+
+    _convert_to_bchar(args.transcript, train_text)
+
+    spm.SentencePieceTrainer.train(
+        input=train_text,
+        vocab_size=vocab_size,
+        model_type=model_type,
+        model_prefix=model_prefix,
+        input_sentence_size=input_sentence_size,
+        character_coverage=character_coverage,
+        user_defined_symbols=user_defined_symbols,
+        unk_id=unk_id,
+        bos_id=-1,
+        eos_id=-1,
+    )
+
+    shutil.copyfile(model_file, f"{lang_dir}/bbpe.model")
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/local/utils/asr_datamodule.py

@@ -0,0 +1,355 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022  Xiaomi Corporation     (Author: Mingshuang Luo)
+#
+# 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 inspect
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
+from lhotse.dataset import (
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import OnTheFlyFeatures
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class ReazonSpeechAsrDataModule:
+    """
+    DataModule for k2 ASR experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - augmentation,
+    - on-the-fly feature extraction
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="ASR data related options",
+            description="These options are used for the preparation of "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/fbank"),
+            help="Path to directory with train/dev/test cuts.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=200.0,
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=30,
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+        group.add_argument(
+            "--concatenate-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
+        )
+        group.add_argument(
+            "--duration-factor",
+            type=float,
+            default=1.0,
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
+        )
+        group.add_argument(
+            "--gap",
+            type=float,
+            default=1.0,
+            help="The amount of padding (in seconds) inserted between "
+            "concatenated cuts. This padding is filled with noise when "
+            "noise augmentation is used.",
+        )
+        group.add_argument(
+            "--on-the-fly-feats",
+            type=str2bool,
+            default=False,
+            help="When enabled, use on-the-fly cut mixing and feature "
+            "extraction. Will drop existing precomputed feature manifests "
+            "if available.",
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--drop-last",
+            type=str2bool,
+            default=True,
+            help="Whether to drop last batch. Used by sampler.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, each batch will have the "
+            "field: batch['supervisions']['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=2,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--enable-spec-aug",
+            type=str2bool,
+            default=True,
+            help="When enabled, use SpecAugment for training dataset.",
+        )
+
+        group.add_argument(
+            "--spec-aug-time-warp-factor",
+            type=int,
+            default=80,
+            help="Used only when --enable-spec-aug is True. "
+            "It specifies the factor for time warping in SpecAugment. "
+            "Larger values mean more warping. "
+            "A value less than 1 means to disable time warp.",
+        )
+
+        group.add_argument(
+            "--enable-musan",
+            type=str2bool,
+            default=False,
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
+        )
+
+    def train_dataloaders(
+        self, cuts_train: CutSet, sampler_state_dict: Optional[Dict[str, Any]] = None
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+
+        transforms = []
+        input_transforms = []
+
+        if self.args.enable_spec_aug:
+            logging.info("Enable SpecAugment")
+            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
+            input_transforms.append(
+                SpecAugment(
+                    time_warp_factor=self.args.spec_aug_time_warp_factor,
+                    num_frame_masks=num_frame_masks,
+                    features_mask_size=27,
+                    num_feature_masks=2,
+                    frames_mask_size=100,
+                )
+            )
+        else:
+            logging.info("Disable SpecAugment")
+
+        logging.info("About to create train dataset")
+        train = K2SpeechRecognitionDataset(
+            cut_transforms=transforms,
+            input_transforms=input_transforms,
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            # NOTE: the PerturbSpeed transform should be added only if we
+            # remove it from data prep stage.
+            # Add on-the-fly speed perturbation; since originally it would
+            # have increased epoch size by 3, we will apply prob 2/3 and use
+            # 3x more epochs.
+            # Speed perturbation probably should come first before
+            # concatenation, but in principle the transforms order doesn't have
+            # to be strict (e.g. could be randomized)
+            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
+            # Drop feats to be on the safe side.
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        transforms = []
+        if self.args.concatenate_cuts:
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                return_cuts=self.args.return_cuts,
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create dev dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=2,
+            persistent_workers=False,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.info("About to create test dataset")
+        test = K2SpeechRecognitionDataset(
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            if self.args.on_the_fly_feats
+            else PrecomputedFeatures(),
+            return_cuts=self.args.return_cuts,
+        )
+        sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "reazonspeech_cuts_train.jsonl.gz"
+        )
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to get dev cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "reazonspeech_cuts_dev.jsonl.gz"
+        )
+
+    @lru_cache()
+    def test_cuts(self) -> List[CutSet]:
+        logging.info("About to get test cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "reazonspeech_cuts_test.jsonl.gz"
+        )

egs/multi_ja_en/ASR/local/utils/tokenizer.py

@@ -0,0 +1,252 @@
+import argparse
+from pathlib import Path
+from typing import Callable, List, Union
+
+import sentencepiece as spm
+from k2 import SymbolTable
+
+
+class Tokenizer:
+    text2word: Callable[[str], List[str]]
+
+    @staticmethod
+    def add_arguments(parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(title="Lang related options")
+        group.add_argument("--lang", type=Path, help="Path to lang directory.")
+
+        group.add_argument(
+            "--lang-type",
+            type=str,
+            default=None,
+            help=(
+                "Either 'bpe' or 'char'. If not provided, it expects lang_dir/lang_type to exists. "
+                "Note: 'bpe' directly loads sentencepiece.SentencePieceProcessor"
+            ),
+        )
+
+    @staticmethod
+    def Load(lang_dir: Path, lang_type="", oov="<unk>"):
+
+        if not lang_type:
+            assert (lang_dir / "lang_type").exists(), "lang_type not specified."
+            lang_type = (lang_dir / "lang_type").read_text().strip()
+
+        tokenizer = None
+
+        if lang_type == "bpe":
+            assert (
+                lang_dir / "bpe.model"
+            ).exists(), f"No BPE .model could be found in {lang_dir}."
+            tokenizer = spm.SentencePieceProcessor()
+            tokenizer.Load(str(lang_dir / "bpe.model"))
+        elif lang_type == "char":
+            tokenizer = CharTokenizer(lang_dir, oov=oov)
+        else:
+            raise NotImplementedError(f"{lang_type} not supported at the moment.")
+
+        return tokenizer
+
+    load = Load
+
+    def PieceToId(self, piece: str) -> int:
+        raise NotImplementedError(
+            "You need to implement this function in the child class."
+        )
+
+    piece_to_id = PieceToId
+
+    def IdToPiece(self, id: int) -> str:
+        raise NotImplementedError(
+            "You need to implement this function in the child class."
+        )
+
+    id_to_piece = IdToPiece
+
+    def GetPieceSize(self) -> int:
+        raise NotImplementedError(
+            "You need to implement this function in the child class."
+        )
+
+    get_piece_size = GetPieceSize
+
+    def __len__(self) -> int:
+        return self.get_piece_size()
+
+    def EncodeAsIdsBatch(self, input: List[str]) -> List[List[int]]:
+        raise NotImplementedError(
+            "You need to implement this function in the child class."
+        )
+
+    def EncodeAsPiecesBatch(self, input: List[str]) -> List[List[str]]:
+        raise NotImplementedError(
+            "You need to implement this function in the child class."
+        )
+
+    def EncodeAsIds(self, input: str) -> List[int]:
+        return self.EncodeAsIdsBatch([input])[0]
+
+    def EncodeAsPieces(self, input: str) -> List[str]:
+        return self.EncodeAsPiecesBatch([input])[0]
+
+    def Encode(
+        self, input: Union[str, List[str]], out_type=int
+    ) -> Union[List, List[List]]:
+        if not input:
+            return []
+
+        if isinstance(input, list):
+            if out_type is int:
+                return self.EncodeAsIdsBatch(input)
+            if out_type is str:
+                return self.EncodeAsPiecesBatch(input)
+
+        if out_type is int:
+            return self.EncodeAsIds(input)
+        if out_type is str:
+            return self.EncodeAsPieces(input)
+
+    encode = Encode
+
+    def DecodeIdsBatch(self, input: List[List[int]]) -> List[str]:
+        raise NotImplementedError(
+            "You need to implement this function in the child class."
+        )
+
+    def DecodePiecesBatch(self, input: List[List[str]]) -> List[str]:
+        raise NotImplementedError(
+            "You need to implement this function in the child class."
+        )
+
+    def DecodeIds(self, input: List[int]) -> str:
+        return self.DecodeIdsBatch([input])[0]
+
+    def DecodePieces(self, input: List[str]) -> str:
+        return self.DecodePiecesBatch([input])[0]
+
+    def Decode(
+        self,
+        input: Union[int, List[int], List[str], List[List[int]], List[List[str]]],
+    ) -> Union[List[str], str]:
+
+        if not input:
+            return ""
+
+        if isinstance(input, int):
+            return self.id_to_piece(input)
+        elif isinstance(input, str):
+            raise TypeError(
+                "Unlike spm.SentencePieceProcessor, cannot decode from type str."
+            )
+
+        if isinstance(input[0], list):
+            if not input[0] or isinstance(input[0][0], int):
+                return self.DecodeIdsBatch(input)
+
+            if isinstance(input[0][0], str):
+                return self.DecodePiecesBatch(input)
+
+        if isinstance(input[0], int):
+            return self.DecodeIds(input)
+        if isinstance(input[0], str):
+            return self.DecodePieces(input)
+
+        raise RuntimeError("Unknown input type")
+
+    decode = Decode
+
+    def SplitBatch(self, input: List[str]) -> List[List[str]]:
+        raise NotImplementedError(
+            "You need to implement this function in the child class."
+        )
+
+    def Split(self, input: Union[List[str], str]) -> Union[List[List[str]], List[str]]:
+        if isinstance(input, list):
+            return self.SplitBatch(input)
+        elif isinstance(input, str):
+            return self.SplitBatch([input])[0]
+        raise RuntimeError("Unknown input type")
+
+    split = Split
+
+
+class CharTokenizer(Tokenizer):
+    def __init__(self, lang_dir: Path, oov="<unk>", sep=""):
+        assert (
+            lang_dir / "tokens.txt"
+        ).exists(), f"tokens.txt could not be found in {lang_dir}."
+        token_table = SymbolTable.from_file(lang_dir / "tokens.txt")
+        assert (
+            "#0" not in token_table
+        ), "This tokenizer does not support disambig symbols."
+        self._id2sym = token_table._id2sym
+        self._sym2id = token_table._sym2id
+        self.oov = oov
+        self.oov_id = self._sym2id[oov]
+        self.sep = sep
+        if self.sep:
+            self.text2word = lambda x: x.split(self.sep)
+        else:
+            self.text2word = lambda x: list(x.replace(" ", ""))
+
+    def piece_to_id(self, piece: str) -> int:
+        try:
+            return self._sym2id[piece]
+        except KeyError:
+            return self.oov_id
+
+    def id_to_piece(self, id: int) -> str:
+        return self._id2sym[id]
+
+    def get_piece_size(self) -> int:
+        return len(self._sym2id)
+
+    def EncodeAsIdsBatch(self, input: List[str]) -> List[List[int]]:
+        return [[self.piece_to_id(i) for i in self.text2word(text)] for text in input]
+
+    def EncodeAsPiecesBatch(self, input: List[str]) -> List[List[str]]:
+        return [
+            [i if i in self._sym2id else self.oov for i in self.text2word(text)]
+            for text in input
+        ]
+
+    def DecodeIdsBatch(self, input: List[List[int]]) -> List[str]:
+        return [self.sep.join(self.id_to_piece(i) for i in text) for text in input]
+
+    def DecodePiecesBatch(self, input: List[List[str]]) -> List[str]:
+        return [self.sep.join(text) for text in input]
+
+    def SplitBatch(self, input: List[str]) -> List[List[str]]:
+        return [self.text2word(text) for text in input]
+
+
+def test_CharTokenizer():
+    test_single_string = "こんにちは"
+    test_multiple_string = [
+        "今日はいい天気ですよね",
+        "諏訪湖は綺麗でしょう",
+        "这在词表外",
+        "分かち 書き に し た 文章 です",
+        "",
+    ]
+    test_empty_string = ""
+    sp = Tokenizer.load(Path("lang_char"), "char", oov="<unk>")
+    splitter = sp.split
+    print(sp.encode(test_single_string, out_type=str))
+    print(sp.encode(test_single_string, out_type=int))
+    print(sp.encode(test_multiple_string, out_type=str))
+    print(sp.encode(test_multiple_string, out_type=int))
+    print(sp.encode(test_empty_string, out_type=str))
+    print(sp.encode(test_empty_string, out_type=int))
+    print(sp.decode(sp.encode(test_single_string, out_type=str)))
+    print(sp.decode(sp.encode(test_single_string, out_type=int)))
+    print(sp.decode(sp.encode(test_multiple_string, out_type=str)))
+    print(sp.decode(sp.encode(test_multiple_string, out_type=int)))
+    print(sp.decode(sp.encode(test_empty_string, out_type=str)))
+    print(sp.decode(sp.encode(test_empty_string, out_type=int)))
+    print(splitter(test_single_string))
+    print(splitter(test_multiple_string))
+    print(splitter(test_empty_string))
+
+
+if __name__ == "__main__":
+    test_CharTokenizer()

egs/multi_ja_en/ASR/local/validate_bpe_lexicon.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/validate_bpe_lexicon.py

egs/multi_ja_en/ASR/local/validate_manifest.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+# Copyright    2022  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 script checks the following assumptions of the generated manifest:
+
+- Single supervision per cut
+- Supervision time bounds are within cut time bounds
+
+We will add more checks later if needed.
+
+Usage example:
+
+    python3 ./local/validate_manifest.py \
+            ./data/fbank/librispeech_cuts_train-clean-100.jsonl.gz
+
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+from lhotse import CutSet, load_manifest
+from lhotse.cut import Cut
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--manifest",
+        type=Path,
+        help="Path to the manifest file",
+    )
+
+    return parser.parse_args()
+
+
+def validate_one_supervision_per_cut(c: Cut):
+    if len(c.supervisions) != 1:
+        raise ValueError(f"{c.id} has {len(c.supervisions)} supervisions")
+
+
+def validate_supervision_and_cut_time_bounds(c: Cut):
+    s = c.supervisions[0]
+
+    # Removed because when the cuts were trimmed from supervisions,
+    # the start time of the supervision can be lesser than cut start time.
+    # https://github.com/lhotse-speech/lhotse/issues/813
+    # if s.start < c.start:
+    #     raise ValueError(
+    #         f"{c.id}: Supervision start time {s.start} is less "
+    #         f"than cut start time {c.start}"
+    #     )
+
+    if s.end > c.end:
+        raise ValueError(
+            f"{c.id}: Supervision end time {s.end} is larger "
+            f"than cut end time {c.end}"
+        )
+
+
+def main():
+    args = get_args()
+
+    manifest = Path(args.manifest)
+    logging.info(f"Validating {manifest}")
+
+    assert manifest.is_file(), f"{manifest} does not exist"
+    cut_set = load_manifest(manifest)
+    assert isinstance(cut_set, CutSet)
+
+    for c in cut_set:
+        validate_one_supervision_per_cut(c)
+        validate_supervision_and_cut_time_bounds(c)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/multi_ja_en/ASR/prepare.sh

@@ -0,0 +1,185 @@
+#!/usr/bin/env bash
+
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+set -eou pipefail
+
+stage=-1
+stop_stage=100
+
+dl_dir=$PWD/download
+
+. shared/parse_options.sh || exit 1
+
+vocab_sizes=(
+  2000
+)
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+log "Dataset: musan"
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Soft link fbank of musan"
+  mkdir -p data/fbank
+  if [ -e ../../librispeech/ASR/data/fbank/.musan.done ]; then
+    cd data/fbank
+    ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/musan_feats) .
+    ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/musan_cuts.jsonl.gz) .
+    cd ../..
+  else
+    log "Abort! Please run ../../librispeech/ASR/prepare.sh --stage 4 --stop-stage 4"
+    exit 1
+  fi
+fi
+
+log "Dataset: LibriSpeech"
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 1: Soft link fbank of LibriSpeech"
+  mkdir -p data/fbank
+  if [ -e ../../librispeech/ASR/data/fbank/.librispeech.done ]; then
+    cd data/fbank
+    ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_cuts*) .
+    ln -svf $(realpath ../../../../librispeech/ASR/data/fbank/librispeech_feats*) .
+    cd ../..
+  else
+    log "Abort! Please run ../../librispeech/ASR/prepare.sh --stage 1 --stop-stage 1 and ../../librispeech/ASR/prepare.sh --stage 3 --stop-stage 3"
+    exit 1
+  fi
+fi
+
+log "Dataset: ReazonSpeech"
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 2: Soft link fbank of ReazonSpeech"
+  mkdir -p data/fbank
+  if [ -e ../../reazonspeech/ASR/data/manifests/.reazonspeech.done ]; then
+    cd data/fbank
+    ln -svf $(realpath ../../../../reazonspeech/ASR/data/manifests/reazonspeech_cuts*) .
+    cd ..
+    mkdir -p manifests
+    cd manifests
+    ln -svf $(realpath ../../../../reazonspeech/ASR/data/manifests/feats_*) .
+    cd ../..
+  else
+    log "Abort! Please run ../../reazonspeech/ASR/prepare.sh --stage 0 --stop-stage 2"
+    exit 1
+  fi
+fi
+
+# New Stage 3: Prepare char based lang for ReazonSpeech
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  lang_char_dir=data/lang_char
+  log "Stage 3: Prepare char based lang for ReazonSpeech"
+  mkdir -p $lang_char_dir
+
+  # Prepare text
+  if [ ! -f $lang_char_dir/text ]; then
+    gunzip -c ../../reazonspeech/ASR/data/manifests/reazonspeech_supervisions_train.jsonl.gz \
+      | jq '.text' | sed 's/"//g' \
+      | ./local/text2token.py -t "char" > $lang_char_dir/text
+  fi
+
+  # jp word segmentation for text
+  if [ ! -f $lang_char_dir/text_words_segmentation ]; then
+    python3 ./local/text2segments.py \
+      --input-file $lang_char_dir/text \
+      --output-file $lang_char_dir/text_words_segmentation
+  fi
+
+  cat $lang_char_dir/text_words_segmentation | sed 's/ /\n/g' \
+    | sort -u | sed '/^$/d' | uniq > $lang_char_dir/words_no_ids.txt
+
+  if [ ! -f $lang_char_dir/words.txt ]; then
+    python3 ./local/prepare_words.py \
+      --input-file $lang_char_dir/words_no_ids.txt \
+      --output-file $lang_char_dir/words.txt
+  fi
+
+  if [ ! -f $lang_char_dir/L_disambig.pt ]; then
+    python3 ./local/prepare_char.py --lang-dir data/lang_char
+  fi
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Prepare Byte BPE based lang"
+  mkdir -p data/fbank
+  if [ ! -d ../../reazonspeech/ASR/data/lang_char ] && [ ! -d ./data/lang_char ]; then
+    log "Abort! Please run ../../reazonspeech/ASR/prepare.sh --stage 3 --stop-stage 3"
+    exit 1
+  fi
+
+  if [ ! -d ../../librispeech/ASR/data/lang_bpe_500 ] && [ ! -d ./data/lang_bpe_500 ]; then
+    log "Abort! Please run ../../librispeech/ASR/prepare.sh --stage 5 --stop-stage 5"
+    exit 1
+  fi
+
+  cd data/
+  # if [ ! -d ./lang_char ]; then
+  #   ln -svf $(realpath ../../../reazonspeech/ASR/data/lang_char) .
+  # fi
+  if [ ! -d ./lang_bpe_500 ]; then
+    ln -svf $(realpath ../../../librispeech/ASR/data/lang_bpe_500) .
+  fi
+  cd ../
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_bbpe_${vocab_size}
+    mkdir -p $lang_dir
+
+    cat data/lang_char/text data/lang_bpe_500/transcript_words.txt \
+      > $lang_dir/text
+
+    if [ ! -f $lang_dir/transcript_chars.txt ]; then
+      ./local/prepare_for_bpe_model.py \
+        --lang-dir ./$lang_dir \
+        --text $lang_dir/text
+    fi
+
+    if [ ! -f $lang_dir/text_words_segmentation ]; then
+      python3 ./local/text2segments.py \
+        --input-file ./data/lang_char/text \
+        --output-file $lang_dir/text_words_segmentation
+
+      cat ./data/lang_bpe_500/transcript_words.txt \
+        >> $lang_dir/text_words_segmentation
+    fi
+
+    cat $lang_dir/text_words_segmentation | sed 's/ /\n/g' \
+      | sort -u | sed '/^$/d' | uniq > $lang_dir/words_no_ids.txt
+
+    if [ ! -f $lang_dir/words.txt ]; then
+      python3 ./local/prepare_words.py \
+        --input-file $lang_dir/words_no_ids.txt \
+        --output-file $lang_dir/words.txt
+    fi
+
+    if [ ! -f $lang_dir/bbpe.model ]; then
+      ./local/train_bbpe_model.py \
+        --lang-dir $lang_dir \
+        --vocab-size $vocab_size \
+        --transcript $lang_dir/text
+    fi
+
+    if [ ! -f $lang_dir/L_disambig.pt ]; then
+      ./local/prepare_lang_bbpe.py --lang-dir $lang_dir
+
+      log "Validating $lang_dir/lexicon.txt"
+      ln -svf $(realpath ../../multi_zh_en/ASR/local/validate_bpe_lexicon.py) local/
+      ./local/validate_bpe_lexicon.py \
+        --lexicon $lang_dir/lexicon.txt \
+        --bpe-model $lang_dir/bbpe.model
+    fi
+  done
+fi
+
+log "prepare.sh: PREPARATION DONE"

egs/multi_ja_en/ASR/shared

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

egs/multi_ja_en/ASR/zipformer/asr_datamodule.py

@@ -0,0 +1 @@
+../local/utils/asr_datamodule.py

egs/multi_ja_en/ASR/zipformer/beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/beam_search.py

egs/multi_ja_en/ASR/zipformer/ctc_decode.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/ctc_decode.py

egs/multi_ja_en/ASR/zipformer/decode.py

@@ -0,0 +1,792 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao)
+#
+# 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.
+"""
+Usage:
+(1) greedy search
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
+
+(2) beam search (not recommended)
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method beam_search \
+    --beam-size 4
+
+(3) modified beam search
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+
+(4) fast beam search (one best)
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
+"""
+
+import argparse
+import logging
+import math
+import re
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import ReazonSpeechAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from lhotse.cut import Cut
+from multi_dataset import MultiDataset
+from train import add_model_arguments, get_model, get_params
+
+from icefall import byte_encode, smart_byte_decode
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    tokenize_by_ja_char,
+    write_error_stats,
+)
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        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=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    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(
+        "--exp-dir",
+        type=str,
+        default="zipformer/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bbpe_2000/bbpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bbpe_2000",
+        help="The lang dir containing word table and LG graph",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=20.0,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=64,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; " "2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: 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 greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_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`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      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.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    if params.causal:
+        # this seems to cause insertions at the end of the utterance if used with zipformer.
+        pad_len = 30
+        feature_lens += pad_len
+        feature = torch.nn.functional.pad(
+            feature,
+            pad=(0, 0, 0, pad_len),
+            value=LOG_EPS,
+        )
+
+    encoder_out, encoder_out_lens = model.forward_encoder(feature, feature_lens)
+
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(smart_byte_decode(hyp).split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in hyp_tokens:
+            hyps.append([word_table[i] for i in hyp])
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(smart_byte_decode(hyp).split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(byte_encode(tokenize_by_ja_char(supervisions["text"]))),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(smart_byte_decode(hyp).split())
+    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(smart_byte_decode(hyp).split())
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(smart_byte_decode(hyp).split())
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append(smart_byte_decode(sp.decode(hyp)).split())
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": hyps}
+    elif "fast_beam_search" in params.decoding_method:
+        key = f"beam_{params.beam}_"
+        key += f"max_contexts_{params.max_contexts}_"
+        key += f"max_states_{params.max_states}"
+        if "nbest" in params.decoding_method:
+            key += f"_num_paths_{params.num_paths}_"
+            key += f"nbest_scale_{params.nbest_scale}"
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
+    else:
+        return {f"beam_size_{params.beam_size}": hyps}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: 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.
+      sp:
+        The BPE model.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 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 = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 50
+    else:
+        log_interval = 20
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+        texts = [tokenize_by_ja_char(str(text)).split() for text in texts]
+        # print(texts)
+        # exit()
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            word_table=word_table,
+            batch=batch,
+        )
+
+        for name, 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):
+                this_batch.append((cut_id, ref_text, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % log_interval == 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]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        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.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        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.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.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()
+    ReazonSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if params.causal:
+        assert (
+            "," not in params.chunk_size
+        ), "chunk_size should be one value in decoding."
+        assert (
+            "," not in params.left_context_frames
+        ), "left_context_frames should be one value in decoding."
+        params.suffix += f"-chunk-{params.chunk_size}"
+        params.suffix += f"-left-context-{params.left_context_frames}"
+
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+
+    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()
+
+    if "fast_beam_search" in params.decoding_method:
+        if params.decoding_method == "fast_beam_search_nbest_LG":
+            lexicon = Lexicon(params.lang_dir)
+            word_table = lexicon.word_table
+            lg_filename = params.lang_dir / "LG.pt"
+            logging.info(f"Loading {lg_filename}")
+            decoding_graph = k2.Fsa.from_dict(
+                torch.load(lg_filename, map_location=device)
+            )
+            decoding_graph.scores *= params.ngram_lm_scale
+        else:
+            word_table = None
+            decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+        word_table = None
+
+    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
+    data_module = ReazonSpeechAsrDataModule(args)
+    multi_dataset = MultiDataset(args)
+
+    def remove_short_utt(c: Cut):
+        T = ((c.num_frames - 7) // 2 + 1) // 2
+        if T <= 0:
+            logging.warning(
+                f"Excluding cut with ID: {c.id} from decoding, num_frames: {c.num_frames}"
+            )
+        return T > 0
+
+    test_sets_cuts = multi_dataset.test_cuts()
+
+    test_sets = test_sets_cuts.keys()
+    test_dl = [
+        data_module.test_dataloaders(test_sets_cuts[cuts_name].filter(remove_short_utt))
+        for cuts_name in test_sets
+    ]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        logging.info(f"Start decoding test set: {test_set}")
+
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/zipformer/decode_stream.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/decode_stream.py

egs/multi_ja_en/ASR/zipformer/decoder.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/decoder.py

egs/multi_ja_en/ASR/zipformer/encoder_interface.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/encoder_interface.py

egs/multi_ja_en/ASR/zipformer/export-onnx.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/export-onnx.py

egs/multi_ja_en/ASR/zipformer/export.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/export.py

egs/multi_ja_en/ASR/zipformer/generate_averaged_model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/generate_averaged_model.py

egs/multi_ja_en/ASR/zipformer/joiner.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/joiner.py

egs/multi_ja_en/ASR/zipformer/model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/model.py

egs/multi_ja_en/ASR/zipformer/multi_dataset.py

@@ -0,0 +1,143 @@
+import argparse
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Dict
+
+from lhotse import CutSet, load_manifest_lazy
+
+
+class MultiDataset:
+    def __init__(self, args: argparse.Namespace):
+        """
+        Args:
+          manifest_dir:
+            It is expected to contain the following files:
+            - reazonspeech_cuts_train.jsonl.gz
+            - librispeech_cuts_train-clean-100.jsonl.gz
+            - librispeech_cuts_train-clean-360.jsonl.gz
+            - librispeech_cuts_train-other-500.jsonl.gz
+        """
+        self.fbank_dir = Path(args.manifest_dir)
+
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get multidataset train cuts")
+
+        logging.info("Loading Reazonspeech in lazy mode")
+        reazonspeech_cuts = load_manifest_lazy(
+            self.fbank_dir / "reazonspeech_cuts_train.jsonl.gz"
+        )
+
+        logging.info("Loading LibriSpeech in lazy mode")
+        train_clean_100_cuts = self.train_clean_100_cuts()
+        train_clean_360_cuts = self.train_clean_360_cuts()
+        train_other_500_cuts = self.train_other_500_cuts()
+
+        return CutSet.mux(
+            reazonspeech_cuts,
+            train_clean_100_cuts,
+            train_clean_360_cuts,
+            train_other_500_cuts,
+            weights=[
+                len(reazonspeech_cuts),
+                len(train_clean_100_cuts),
+                len(train_clean_360_cuts),
+                len(train_other_500_cuts),
+            ],
+        )
+
+    def dev_cuts(self) -> CutSet:
+        logging.info("About to get multidataset dev cuts")
+
+        logging.info("Loading Reazonspeech DEV set in lazy mode")
+        reazonspeech_dev_cuts = load_manifest_lazy(
+            self.fbank_dir / "reazonspeech_cuts_dev.jsonl.gz"
+        )
+
+        logging.info("Loading LibriSpeech DEV set in lazy mode")
+        dev_clean_cuts = self.dev_clean_cuts()
+        dev_other_cuts = self.dev_other_cuts()
+
+        return CutSet.mux(
+            reazonspeech_dev_cuts,
+            dev_clean_cuts,
+            dev_other_cuts,
+            weights=[
+                len(reazonspeech_dev_cuts),
+                len(dev_clean_cuts),
+                len(dev_other_cuts),
+            ],
+        )
+
+    def test_cuts(self) -> Dict[str, CutSet]:
+        logging.info("About to get multidataset test cuts")
+
+        logging.info("Loading Reazonspeech set in lazy mode")
+        reazonspeech_test_cuts = load_manifest_lazy(
+            self.fbank_dir / "reazonspeech_cuts_test.jsonl.gz"
+        )
+        reazonspeech_dev_cuts = load_manifest_lazy(
+            self.fbank_dir / "reazonspeech_cuts_dev.jsonl.gz"
+        )
+
+        logging.info("Loading LibriSpeech set in lazy mode")
+        test_clean_cuts = self.test_clean_cuts()
+        test_other_cuts = self.test_other_cuts()
+
+        test_cuts = {
+            "reazonspeech_test": reazonspeech_test_cuts,
+            "reazonspeech_dev": reazonspeech_dev_cuts,
+            "librispeech_test_clean": test_clean_cuts,
+            "librispeech_test_other": test_other_cuts,
+        }
+
+        return test_cuts
+
+    @lru_cache()
+    def train_clean_100_cuts(self) -> CutSet:
+        logging.info("About to get train-clean-100 cuts")
+        return load_manifest_lazy(
+            self.fbank_dir / "librispeech_cuts_train-clean-100.jsonl.gz"
+        )
+
+    @lru_cache()
+    def train_clean_360_cuts(self) -> CutSet:
+        logging.info("About to get train-clean-360 cuts")
+        return load_manifest_lazy(
+            self.fbank_dir / "librispeech_cuts_train-clean-360.jsonl.gz"
+        )
+
+    @lru_cache()
+    def train_other_500_cuts(self) -> CutSet:
+        logging.info("About to get train-other-500 cuts")
+        return load_manifest_lazy(
+            self.fbank_dir / "librispeech_cuts_train-other-500.jsonl.gz"
+        )
+
+    @lru_cache()
+    def dev_clean_cuts(self) -> CutSet:
+        logging.info("About to get dev-clean cuts")
+        return load_manifest_lazy(
+            self.fbank_dir / "librispeech_cuts_dev-clean.jsonl.gz"
+        )
+
+    @lru_cache()
+    def dev_other_cuts(self) -> CutSet:
+        logging.info("About to get dev-other cuts")
+        return load_manifest_lazy(
+            self.fbank_dir / "librispeech_cuts_dev-other.jsonl.gz"
+        )
+
+    @lru_cache()
+    def test_clean_cuts(self) -> CutSet:
+        logging.info("About to get test-clean cuts")
+        return load_manifest_lazy(
+            self.fbank_dir / "librispeech_cuts_test-clean.jsonl.gz"
+        )
+
+    @lru_cache()
+    def test_other_cuts(self) -> CutSet:
+        logging.info("About to get test-other cuts")
+        return load_manifest_lazy(
+            self.fbank_dir / "librispeech_cuts_test-other.jsonl.gz"
+        )

egs/multi_ja_en/ASR/zipformer/my_profile.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/my_profile.py

egs/multi_ja_en/ASR/zipformer/onnx_decode.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_decode.py

egs/multi_ja_en/ASR/zipformer/onnx_pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_pretrained.py

egs/multi_ja_en/ASR/zipformer/optim.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/optim.py

egs/multi_ja_en/ASR/zipformer/pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/pretrained.py

egs/multi_ja_en/ASR/zipformer/scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/scaling.py

egs/multi_ja_en/ASR/zipformer/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/scaling_converter.py

egs/multi_ja_en/ASR/zipformer/streaming_beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/streaming_beam_search.py

egs/multi_ja_en/ASR/zipformer/streaming_decode.py

@@ -0,0 +1,935 @@
+#!/usr/bin/env python3
+# Copyright 2022-2023 Xiaomi Corporation (Authors: Wei Kang,
+#                                                  Fangjun Kuang,
+#                                                  Zengwei Yao)
+# 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.
+
+"""
+Usage:
+
+Monolingual:
+./zipformer/streaming_decode.py \
+    --epoch 28 \
+     --avg 15 \
+    --causal 1 \
+    --chunk-size 32 \
+    --left-context-frames 256 \
+    --exp-dir ./zipformer/exp-large \
+    --lang data/lang_char \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 512,768,1536,2048,1536,768 \
+    --encoder-dim 192,256,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192
+
+Bilingual:
+./zipformer/streaming_decode.py \
+    --bilingual 1 \
+    --epoch 28 \
+     --avg 15 \
+    --causal 1 \
+    --chunk-size 32 \
+    --left-context-frames 256 \
+    --exp-dir ./zipformer/exp-large \
+    --lang data/lang_char \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 512,768,1536,2048,1536,768 \
+    --encoder-dim 192,256,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+
+"""
+
+import argparse
+import logging
+import math
+import os
+import pdb
+import subprocess as sp
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import numpy as np
+import sentencepiece as spm
+import torch
+from asr_datamodule import ReazonSpeechAsrDataModule
+from decode_stream import DecodeStream
+from kaldifeat import Fbank, FbankOptions
+from lhotse import CutSet
+from lhotse.cut import Cut
+from multi_dataset import MultiDataset
+from streaming_beam_search import (
+    fast_beam_search_one_best,
+    greedy_search,
+    modified_beam_search,
+)
+from tokenizer import Tokenizer
+from torch import Tensor, nn
+from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_model, get_params
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import (
+    AttributeDict,
+    make_pad_mask,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--bilingual",
+        type=str2bool,
+        default=False,
+        help="Whether the model is bilingual or not. 1 = bilingual.",
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        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=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    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(
+        "--exp-dir",
+        type=str,
+        default="zipformer/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_char",
+        help="The lang dir containing word table and LG graph",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Supported decoding methods are:
+        greedy_search
+        modified_beam_search
+        fast_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "--num_active_paths",
+        type=int,
+        default=4,
+        help="""An interger indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=4,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=32,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    parser.add_argument(
+        "--num-decode-streams",
+        type=int,
+        default=2000,
+        help="The number of streams that can be decoded parallel.",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def get_init_states(
+    model: nn.Module,
+    batch_size: int = 1,
+    device: torch.device = torch.device("cpu"),
+) -> List[torch.Tensor]:
+    """
+    Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6]
+    is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2).
+    states[-2] is the cached left padding for ConvNeXt module,
+    of shape (batch_size, num_channels, left_pad, num_freqs)
+    states[-1] is processed_lens of shape (batch,), which records the number
+    of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
+    """
+    states = model.encoder.get_init_states(batch_size, device)
+
+    embed_states = model.encoder_embed.get_init_states(batch_size, device)
+    states.append(embed_states)
+
+    processed_lens = torch.zeros(batch_size, dtype=torch.int32, device=device)
+    states.append(processed_lens)
+
+    return states
+
+
+def stack_states(state_list: List[List[torch.Tensor]]) -> List[torch.Tensor]:
+    """Stack list of zipformer states that correspond to separate utterances
+    into a single emformer state, so that it can be used as an input for
+    zipformer when those utterances are formed into a batch.
+
+    Args:
+      state_list:
+        Each element in state_list corresponding to the internal state
+        of the zipformer model for a single utterance. For element-n,
+        state_list[n] is a list of cached tensors of all encoder layers. For layer-i,
+        state_list[n][i*6:(i+1)*6] is (cached_key, cached_nonlin_attn, cached_val1,
+        cached_val2, cached_conv1, cached_conv2).
+        state_list[n][-2] is the cached left padding for ConvNeXt module,
+          of shape (batch_size, num_channels, left_pad, num_freqs)
+        state_list[n][-1] is processed_lens of shape (batch,), which records the number
+        of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
+
+    Note:
+      It is the inverse of :func:`unstack_states`.
+    """
+    batch_size = len(state_list)
+    assert (len(state_list[0]) - 2) % 6 == 0, len(state_list[0])
+    tot_num_layers = (len(state_list[0]) - 2) // 6
+
+    batch_states = []
+    for layer in range(tot_num_layers):
+        layer_offset = layer * 6
+        # cached_key: (left_context_len, batch_size, key_dim)
+        cached_key = torch.cat(
+            [state_list[i][layer_offset] for i in range(batch_size)], dim=1
+        )
+        # cached_nonlin_attn: (num_heads, batch_size, left_context_len, head_dim)
+        cached_nonlin_attn = torch.cat(
+            [state_list[i][layer_offset + 1] for i in range(batch_size)], dim=1
+        )
+        # cached_val1: (left_context_len, batch_size, value_dim)
+        cached_val1 = torch.cat(
+            [state_list[i][layer_offset + 2] for i in range(batch_size)], dim=1
+        )
+        # cached_val2: (left_context_len, batch_size, value_dim)
+        cached_val2 = torch.cat(
+            [state_list[i][layer_offset + 3] for i in range(batch_size)], dim=1
+        )
+        # cached_conv1: (#batch, channels, left_pad)
+        cached_conv1 = torch.cat(
+            [state_list[i][layer_offset + 4] for i in range(batch_size)], dim=0
+        )
+        # cached_conv2: (#batch, channels, left_pad)
+        cached_conv2 = torch.cat(
+            [state_list[i][layer_offset + 5] for i in range(batch_size)], dim=0
+        )
+        batch_states += [
+            cached_key,
+            cached_nonlin_attn,
+            cached_val1,
+            cached_val2,
+            cached_conv1,
+            cached_conv2,
+        ]
+
+    cached_embed_left_pad = torch.cat(
+        [state_list[i][-2] for i in range(batch_size)], dim=0
+    )
+    batch_states.append(cached_embed_left_pad)
+
+    processed_lens = torch.cat([state_list[i][-1] for i in range(batch_size)], dim=0)
+    batch_states.append(processed_lens)
+
+    return batch_states
+
+
+def unstack_states(batch_states: List[Tensor]) -> List[List[Tensor]]:
+    """Unstack the zipformer state corresponding to a batch of utterances
+    into a list of states, where the i-th entry is the state from the i-th
+    utterance in the batch.
+
+    Note:
+      It is the inverse of :func:`stack_states`.
+
+    Args:
+        batch_states: A list of cached tensors of all encoder layers. For layer-i,
+          states[i*6:(i+1)*6] is (cached_key, cached_nonlin_attn, cached_val1, cached_val2,
+          cached_conv1, cached_conv2).
+          state_list[-2] is the cached left padding for ConvNeXt module,
+          of shape (batch_size, num_channels, left_pad, num_freqs)
+          states[-1] is processed_lens of shape (batch,), which records the number
+          of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
+
+    Returns:
+        state_list: A list of list. Each element in state_list corresponding to the internal state
+        of the zipformer model for a single utterance.
+    """
+    assert (len(batch_states) - 2) % 6 == 0, len(batch_states)
+    tot_num_layers = (len(batch_states) - 2) // 6
+
+    processed_lens = batch_states[-1]
+    batch_size = processed_lens.shape[0]
+
+    state_list = [[] for _ in range(batch_size)]
+
+    for layer in range(tot_num_layers):
+        layer_offset = layer * 6
+        # cached_key: (left_context_len, batch_size, key_dim)
+        cached_key_list = batch_states[layer_offset].chunk(chunks=batch_size, dim=1)
+        # cached_nonlin_attn: (num_heads, batch_size, left_context_len, head_dim)
+        cached_nonlin_attn_list = batch_states[layer_offset + 1].chunk(
+            chunks=batch_size, dim=1
+        )
+        # cached_val1: (left_context_len, batch_size, value_dim)
+        cached_val1_list = batch_states[layer_offset + 2].chunk(
+            chunks=batch_size, dim=1
+        )
+        # cached_val2: (left_context_len, batch_size, value_dim)
+        cached_val2_list = batch_states[layer_offset + 3].chunk(
+            chunks=batch_size, dim=1
+        )
+        # cached_conv1: (#batch, channels, left_pad)
+        cached_conv1_list = batch_states[layer_offset + 4].chunk(
+            chunks=batch_size, dim=0
+        )
+        # cached_conv2: (#batch, channels, left_pad)
+        cached_conv2_list = batch_states[layer_offset + 5].chunk(
+            chunks=batch_size, dim=0
+        )
+        for i in range(batch_size):
+            state_list[i] += [
+                cached_key_list[i],
+                cached_nonlin_attn_list[i],
+                cached_val1_list[i],
+                cached_val2_list[i],
+                cached_conv1_list[i],
+                cached_conv2_list[i],
+            ]
+
+    cached_embed_left_pad_list = batch_states[-2].chunk(chunks=batch_size, dim=0)
+    for i in range(batch_size):
+        state_list[i].append(cached_embed_left_pad_list[i])
+
+    processed_lens_list = batch_states[-1].chunk(chunks=batch_size, dim=0)
+    for i in range(batch_size):
+        state_list[i].append(processed_lens_list[i])
+
+    return state_list
+
+
+def streaming_forward(
+    features: Tensor,
+    feature_lens: Tensor,
+    model: nn.Module,
+    states: List[Tensor],
+    chunk_size: int,
+    left_context_len: int,
+) -> Tuple[Tensor, Tensor, List[Tensor]]:
+    """
+    Returns encoder outputs, output lengths, and updated states.
+    """
+    cached_embed_left_pad = states[-2]
+    (x, x_lens, new_cached_embed_left_pad,) = model.encoder_embed.streaming_forward(
+        x=features,
+        x_lens=feature_lens,
+        cached_left_pad=cached_embed_left_pad,
+    )
+    assert x.size(1) == chunk_size, (x.size(1), chunk_size)
+
+    src_key_padding_mask = make_pad_mask(x_lens)
+
+    # processed_mask is used to mask out initial states
+    processed_mask = torch.arange(left_context_len, device=x.device).expand(
+        x.size(0), left_context_len
+    )
+    processed_lens = states[-1]  # (batch,)
+    # (batch, left_context_size)
+    processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1)
+    # Update processed lengths
+    new_processed_lens = processed_lens + x_lens
+
+    # (batch, left_context_size + chunk_size)
+    src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1)
+
+    x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+    encoder_states = states[:-2]
+    (
+        encoder_out,
+        encoder_out_lens,
+        new_encoder_states,
+    ) = model.encoder.streaming_forward(
+        x=x,
+        x_lens=x_lens,
+        states=encoder_states,
+        src_key_padding_mask=src_key_padding_mask,
+    )
+    encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+    new_states = new_encoder_states + [
+        new_cached_embed_left_pad,
+        new_processed_lens,
+    ]
+    return encoder_out, encoder_out_lens, new_states
+
+
+def decode_one_chunk(
+    params: AttributeDict,
+    model: nn.Module,
+    decode_streams: List[DecodeStream],
+) -> List[int]:
+    """Decode one chunk frames of features for each decode_streams and
+    return the indexes of finished streams in a List.
+
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      decode_streams:
+        A List of DecodeStream, each belonging to a utterance.
+    Returns:
+      Return a List containing which DecodeStreams are finished.
+    """
+    chunk_size = int(params.chunk_size)
+    left_context_len = int(params.left_context_frames)
+
+    features = []
+    feature_lens = []
+    states = []
+    processed_lens = []  # Used in fast-beam-search
+
+    for stream in decode_streams:
+        feat, feat_len = stream.get_feature_frames(chunk_size * 2)
+        features.append(feat)
+        feature_lens.append(feat_len)
+        states.append(stream.states)
+        processed_lens.append(stream.done_frames)
+
+    feature_lens = torch.tensor(feature_lens, device=model.device)
+    features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
+
+    # Make sure the length after encoder_embed is at least 1.
+    # The encoder_embed subsample features (T - 7) // 2
+    # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling
+    tail_length = chunk_size * 2 + 7 + 2 * 3
+    if features.size(1) < tail_length:
+        pad_length = tail_length - features.size(1)
+        feature_lens += pad_length
+        features = torch.nn.functional.pad(
+            features,
+            (0, 0, 0, pad_length),
+            mode="constant",
+            value=LOG_EPS,
+        )
+
+    states = stack_states(states)
+
+    encoder_out, encoder_out_lens, new_states = streaming_forward(
+        features=features,
+        feature_lens=feature_lens,
+        model=model,
+        states=states,
+        chunk_size=chunk_size,
+        left_context_len=left_context_len,
+    )
+
+    encoder_out = model.joiner.encoder_proj(encoder_out)
+
+    if params.decoding_method == "greedy_search":
+        greedy_search(model=model, encoder_out=encoder_out, streams=decode_streams)
+    elif params.decoding_method == "fast_beam_search":
+        processed_lens = torch.tensor(processed_lens, device=model.device)
+        processed_lens = processed_lens + encoder_out_lens
+        fast_beam_search_one_best(
+            model=model,
+            encoder_out=encoder_out,
+            processed_lens=processed_lens,
+            streams=decode_streams,
+            beam=params.beam,
+            max_states=params.max_states,
+            max_contexts=params.max_contexts,
+        )
+    elif params.decoding_method == "modified_beam_search":
+        modified_beam_search(
+            model=model,
+            streams=decode_streams,
+            encoder_out=encoder_out,
+            num_active_paths=params.num_active_paths,
+        )
+    else:
+        raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
+
+    states = unstack_states(new_states)
+
+    finished_streams = []
+    for i in range(len(decode_streams)):
+        decode_streams[i].states = states[i]
+        decode_streams[i].done_frames += encoder_out_lens[i]
+        if decode_streams[i].done:
+            finished_streams.append(i)
+        # finished_streams.append(i)
+
+    return finished_streams
+
+
+def decode_dataset(
+    cuts: CutSet,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: Tokenizer,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      cuts:
+        Lhotse Cutset containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 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.
+    """
+    device = model.device
+
+    opts = FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+
+    log_interval = 100
+
+    decode_results = []
+    # Contain decode streams currently running.
+    decode_streams = []
+    for num, cut in enumerate(cuts):
+        # each utterance has a DecodeStream.
+        initial_states = get_init_states(model=model, batch_size=1, device=device)
+        decode_stream = DecodeStream(
+            params=params,
+            cut_id=cut.id,
+            initial_states=initial_states,
+            decoding_graph=decoding_graph,
+            device=device,
+        )
+
+        audio: np.ndarray = cut.load_audio()
+        # audio.shape: (1, num_samples)
+        assert len(audio.shape) == 2
+        assert audio.shape[0] == 1, "Should be single channel"
+        assert audio.dtype == np.float32, audio.dtype
+
+        # The trained model is using normalized samples
+        # - this is to avoid sending [-32k,+32k] signal in...
+        # - some lhotse AudioTransform classes can make the signal
+        #   be out of range [-1, 1], hence the tolerance 10
+        assert (
+            np.abs(audio).max() <= 10
+        ), "Should be normalized to [-1, 1], 10 for tolerance..."
+
+        samples = torch.from_numpy(audio).squeeze(0)
+
+        fbank = Fbank(opts)
+        feature = fbank(samples.to(device))
+        decode_stream.set_features(feature, tail_pad_len=30)
+        decode_stream.ground_truth = cut.supervisions[0].text
+
+        decode_streams.append(decode_stream)
+
+        while len(decode_streams) >= params.num_decode_streams:
+            finished_streams = decode_one_chunk(
+                params=params, model=model, decode_streams=decode_streams
+            )
+            for i in sorted(finished_streams, reverse=True):
+                decode_results.append(
+                    (
+                        decode_streams[i].id,
+                        decode_streams[i].ground_truth.split(),
+                        sp.decode(decode_streams[i].decoding_result()).split(),
+                    )
+                )
+                del decode_streams[i]
+
+        if num % log_interval == 0:
+            logging.info(f"Cuts processed until now is {num}.")
+
+    # decode final chunks of last sequences
+    while len(decode_streams):
+        finished_streams = decode_one_chunk(
+            params=params, model=model, decode_streams=decode_streams
+        )
+
+        if not finished_streams:
+            print("No finished streams, breaking the loop")
+            break
+
+        for i in sorted(finished_streams, reverse=True):
+            try:
+                decode_results.append(
+                    (
+                        decode_streams[i].id,
+                        decode_streams[i].ground_truth.split(),
+                        sp.decode(decode_streams[i].decoding_result()).split(),
+                    )
+                )
+                del decode_streams[i]
+            except IndexError as e:
+                print(f"IndexError: {e}")
+                print(f"decode_streams length: {len(decode_streams)}")
+                print(f"finished_streams: {finished_streams}")
+                print(f"i: {i}")
+                continue
+
+    if params.decoding_method == "greedy_search":
+        key = "greedy_search"
+    elif params.decoding_method == "fast_beam_search":
+        key = (
+            f"beam_{params.beam}_"
+            f"max_contexts_{params.max_contexts}_"
+            f"max_states_{params.max_states}"
+        )
+    elif params.decoding_method == "modified_beam_search":
+        key = f"num_active_paths_{params.num_active_paths}"
+    else:
+        raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
+    torch.cuda.synchronize()
+    return {key: decode_results}
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[List[str], List[str]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        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.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        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.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.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()
+    ReazonSpeechAsrDataModule.add_arguments(parser)
+    Tokenizer.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    params.res_dir = params.exp_dir / "streaming" / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    assert params.causal, params.causal
+    assert "," not in params.chunk_size, "chunk_size should be one value in decoding."
+    assert (
+        "," not in params.left_context_frames
+    ), "left_context_frames should be one value in decoding."
+    params.suffix += f"-chunk-{params.chunk_size}"
+    params.suffix += f"-left-context-{params.left_context_frames}"
+
+    # for fast_beam_search
+    if params.decoding_method == "fast_beam_search":
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    if not params.bilingual:
+        sp = Tokenizer.load(params.lang, params.lang_type)
+    else:
+        sp = spm.SentencePieceProcessor()
+        sp.load(params.bpe_model)
+
+    # <blk> and <unk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+
+    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 start >= 0:
+                    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()
+    model.device = device
+
+    decoding_graph = None
+    if params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+
+    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
+    reazonspeech_corpus = ReazonSpeechAsrDataModule(args)
+
+    if params.bilingual:
+        multi_dataset = MultiDataset(args)
+
+        def remove_short_utt(c: Cut):
+            T = ((c.num_frames - 7) // 2 + 1) // 2
+            if T <= 0:
+                logging.warning(
+                    f"Excluding cut with ID: {c.id} from decoding, num_frames: {c.num_frames}"
+                )
+            return T > 0
+
+        test_sets_cuts = multi_dataset.test_cuts()
+        test_sets = test_sets_cuts.keys()
+        test_cuts = [test_sets_cuts[k] for k in test_sets]
+
+    valid_cuts = reazonspeech_corpus.valid_cuts()
+    test_cuts = reazonspeech_corpus.test_cuts()
+
+    test_sets = ["valid", "test"]
+    test_cuts = [valid_cuts, test_cuts]
+
+    for test_set, test_cut in zip(test_sets, test_cuts):
+        logging.info(f"Decoding {test_set}")
+        if params.bilingual:
+            test_cut = test_cut.filter(remove_short_utt)
+        results_dict = decode_dataset(
+            cuts=test_cut,
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/multi_ja_en/ASR/zipformer/subsampling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/subsampling.py

egs/multi_ja_en/ASR/zipformer/test_scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/test_scaling.py

egs/multi_ja_en/ASR/zipformer/test_subsampling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/test_subsampling.py

egs/multi_ja_en/ASR/zipformer/tokenizer.py

@@ -0,0 +1 @@
+../local/utils/tokenizer.py

egs/multi_ja_en/ASR/zipformer/zipformer.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/zipformer.py

egs/reazonspeech/ASR/RESULTS.md

@@ -47,3 +47,41 @@ The decoding command is:
     --blank-penalty 0
 ```
 
+#### Streaming
+
+We have not completed evaluation of our models yet and will add evaluation results here once it's completed.
+
+The training command is:
+```shell
+./zipformer/train.py \
+  --world-size 8 \
+  --num-epochs 40 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer/exp-large \
+  --causal 1 \
+  --num-encoder-layers 2,2,4,5,4,2 \
+  --feedforward-dim 512,768,1536,2048,1536,768 \
+  --encoder-dim 192,256,512,768,512,256 \
+  --encoder-unmasked-dim 192,192,256,320,256,192 \
+  --lang data/lang_char \
+  --max-duration 1600
+```
+
+The decoding command is:
+
+```shell
+./zipformer/streaming_decode.py \
+  --epoch 28 \
+  --avg 15 \
+  --causal 1 \
+  --chunk-size 32 \
+  --left-context-frames 256 \
+  --exp-dir ./zipformer/exp-large \
+  --lang data/lang_char \
+  --num-encoder-layers 2,2,4,5,4,2 \
+  --feedforward-dim 512,768,1536,2048,1536,768 \
+  --encoder-dim 192,256,512,768,512,256 \
+  --encoder-unmasked-dim 192,192,256,320,256,192
+```
+

egs/reazonspeech/ASR/local/utils/tokenizer.py

@@ -12,7 +12,6 @@ class Tokenizer:
     @staticmethod
     def add_arguments(parser: argparse.ArgumentParser):
         group = parser.add_argument_group(title="Lang related options")
-
         group.add_argument("--lang", type=Path, help="Path to lang directory.")
 
         group.add_argument(

egs/reazonspeech/ASR/zipformer/streaming_decode.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
-# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang)
+# Copyright 2022-2023 Xiaomi Corporation (Authors: Wei Kang,
-#
+#                                                  Fangjun Kuang,
+#                                                  Zengwei Yao)
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -17,28 +18,23 @@
 
 """
 Usage:
-./pruned_transducer_stateless7_streaming/streaming_decode.py \
+./zipformer/streaming_decode.py--epoch 28   --avg 15   --causal 1   --chunk-size 32   --left-context-frames 256   --exp-dir ./zipformer/exp-large --lang data/lang_char --num-encoder-layers 2,2,4,5,4,2 --feedforward-dim 512,768,1536,2048,1536,768 --encoder-dim 192,256,512,768,512,256 --encoder-unmasked-dim 192,192,256,320,256,192
-  --epoch 28 \
+
-  --avg 15 \
-  --decode-chunk-len 32 \
-  --exp-dir ./pruned_transducer_stateless7_streaming/exp \
-  --decoding_method greedy_search \
-  --lang data/lang_char \
-  --num-decode-streams 2000
 """
 
 import argparse
 import logging
 import math
+import os
+import pdb
+import subprocess as sp
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import k2
 import numpy as np
 import torch
-import torch.nn as nn
 from asr_datamodule import ReazonSpeechAsrDataModule
-from decode import save_results
 from decode_stream import DecodeStream
 from kaldifeat import Fbank, FbankOptions
 from lhotse import CutSet
@@ -48,9 +44,9 @@ from streaming_beam_search import (
     modified_beam_search,
 )
 from tokenizer import Tokenizer
+from torch import Tensor, nn
 from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_transducer_model
+from train import add_model_arguments, get_model, get_params
-from zipformer import stack_states, unstack_states
 
 from icefall.checkpoint import (
     average_checkpoints,
@@ -58,7 +54,14 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import AttributeDict, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    make_pad_mask,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
 
 LOG_EPS = math.log(1e-10)
 
@@ -73,7 +76,7 @@ def get_parser():
         type=int,
         default=28,
         help="""It specifies the checkpoint to use for decoding.
-        Note: Epoch counts from 0.
+        Note: Epoch counts from 1.
         You can specify --avg to use more checkpoints for model averaging.""",
     )
 
@@ -87,12 +90,6 @@ def get_parser():
         """,
     )
 
-    parser.add_argument(
-        "--gpu",
-        type=int,
-        default=0,
-    )
-
     parser.add_argument(
         "--avg",
         type=int,
@@ -116,7 +113,7 @@ def get_parser():
     parser.add_argument(
         "--exp-dir",
         type=str,
-        default="pruned_transducer_stateless2/exp",
+        default="zipformer/exp",
         help="The experiment dir",
     )
 
@@ -127,6 +124,13 @@ def get_parser():
         help="Path to the BPE model",
     )
 
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_char",
+        help="The lang dir containing word table and LG graph",
+    )
+
     parser.add_argument(
         "--decoding-method",
         type=str,
@@ -138,14 +142,6 @@ def get_parser():
         """,
     )
 
-    parser.add_argument(
-        "--decoding-graph",
-        type=str,
-        default="",
-        help="""Used only when --decoding-method is
-        fast_beam_search""",
-    )
-
     parser.add_argument(
         "--num_active_paths",
         type=int,
@@ -157,7 +153,7 @@ def get_parser():
     parser.add_argument(
         "--beam",
         type=float,
-        default=4.0,
+        default=4,
         help="""A floating point value to calculate the cutoff score during beam
         search (i.e., `cutoff = max-score - beam`), which is the same as the
         `beam` in Kaldi.
@@ -194,18 +190,235 @@ def get_parser():
         help="The number of streams that can be decoded parallel.",
     )
 
-    parser.add_argument(
-        "--res-dir",
-        type=Path,
-        default=None,
-        help="The path to save results.",
-    )
-
     add_model_arguments(parser)
 
     return parser
 
 
+def get_init_states(
+    model: nn.Module,
+    batch_size: int = 1,
+    device: torch.device = torch.device("cpu"),
+) -> List[torch.Tensor]:
+    """
+    Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6]
+    is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2).
+    states[-2] is the cached left padding for ConvNeXt module,
+    of shape (batch_size, num_channels, left_pad, num_freqs)
+    states[-1] is processed_lens of shape (batch,), which records the number
+    of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
+    """
+    states = model.encoder.get_init_states(batch_size, device)
+
+    embed_states = model.encoder_embed.get_init_states(batch_size, device)
+    states.append(embed_states)
+
+    processed_lens = torch.zeros(batch_size, dtype=torch.int32, device=device)
+    states.append(processed_lens)
+
+    return states
+
+
+def stack_states(state_list: List[List[torch.Tensor]]) -> List[torch.Tensor]:
+    """Stack list of zipformer states that correspond to separate utterances
+    into a single emformer state, so that it can be used as an input for
+    zipformer when those utterances are formed into a batch.
+
+    Args:
+      state_list:
+        Each element in state_list corresponding to the internal state
+        of the zipformer model for a single utterance. For element-n,
+        state_list[n] is a list of cached tensors of all encoder layers. For layer-i,
+        state_list[n][i*6:(i+1)*6] is (cached_key, cached_nonlin_attn, cached_val1,
+        cached_val2, cached_conv1, cached_conv2).
+        state_list[n][-2] is the cached left padding for ConvNeXt module,
+          of shape (batch_size, num_channels, left_pad, num_freqs)
+        state_list[n][-1] is processed_lens of shape (batch,), which records the number
+        of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
+
+    Note:
+      It is the inverse of :func:`unstack_states`.
+    """
+    batch_size = len(state_list)
+    assert (len(state_list[0]) - 2) % 6 == 0, len(state_list[0])
+    tot_num_layers = (len(state_list[0]) - 2) // 6
+
+    batch_states = []
+    for layer in range(tot_num_layers):
+        layer_offset = layer * 6
+        # cached_key: (left_context_len, batch_size, key_dim)
+        cached_key = torch.cat(
+            [state_list[i][layer_offset] for i in range(batch_size)], dim=1
+        )
+        # cached_nonlin_attn: (num_heads, batch_size, left_context_len, head_dim)
+        cached_nonlin_attn = torch.cat(
+            [state_list[i][layer_offset + 1] for i in range(batch_size)], dim=1
+        )
+        # cached_val1: (left_context_len, batch_size, value_dim)
+        cached_val1 = torch.cat(
+            [state_list[i][layer_offset + 2] for i in range(batch_size)], dim=1
+        )
+        # cached_val2: (left_context_len, batch_size, value_dim)
+        cached_val2 = torch.cat(
+            [state_list[i][layer_offset + 3] for i in range(batch_size)], dim=1
+        )
+        # cached_conv1: (#batch, channels, left_pad)
+        cached_conv1 = torch.cat(
+            [state_list[i][layer_offset + 4] for i in range(batch_size)], dim=0
+        )
+        # cached_conv2: (#batch, channels, left_pad)
+        cached_conv2 = torch.cat(
+            [state_list[i][layer_offset + 5] for i in range(batch_size)], dim=0
+        )
+        batch_states += [
+            cached_key,
+            cached_nonlin_attn,
+            cached_val1,
+            cached_val2,
+            cached_conv1,
+            cached_conv2,
+        ]
+
+    cached_embed_left_pad = torch.cat(
+        [state_list[i][-2] for i in range(batch_size)], dim=0
+    )
+    batch_states.append(cached_embed_left_pad)
+
+    processed_lens = torch.cat([state_list[i][-1] for i in range(batch_size)], dim=0)
+    batch_states.append(processed_lens)
+
+    return batch_states
+
+
+def unstack_states(batch_states: List[Tensor]) -> List[List[Tensor]]:
+    """Unstack the zipformer state corresponding to a batch of utterances
+    into a list of states, where the i-th entry is the state from the i-th
+    utterance in the batch.
+
+    Note:
+      It is the inverse of :func:`stack_states`.
+
+    Args:
+        batch_states: A list of cached tensors of all encoder layers. For layer-i,
+          states[i*6:(i+1)*6] is (cached_key, cached_nonlin_attn, cached_val1, cached_val2,
+          cached_conv1, cached_conv2).
+          state_list[-2] is the cached left padding for ConvNeXt module,
+          of shape (batch_size, num_channels, left_pad, num_freqs)
+          states[-1] is processed_lens of shape (batch,), which records the number
+          of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
+
+    Returns:
+        state_list: A list of list. Each element in state_list corresponding to the internal state
+        of the zipformer model for a single utterance.
+    """
+    assert (len(batch_states) - 2) % 6 == 0, len(batch_states)
+    tot_num_layers = (len(batch_states) - 2) // 6
+
+    processed_lens = batch_states[-1]
+    batch_size = processed_lens.shape[0]
+
+    state_list = [[] for _ in range(batch_size)]
+
+    for layer in range(tot_num_layers):
+        layer_offset = layer * 6
+        # cached_key: (left_context_len, batch_size, key_dim)
+        cached_key_list = batch_states[layer_offset].chunk(chunks=batch_size, dim=1)
+        # cached_nonlin_attn: (num_heads, batch_size, left_context_len, head_dim)
+        cached_nonlin_attn_list = batch_states[layer_offset + 1].chunk(
+            chunks=batch_size, dim=1
+        )
+        # cached_val1: (left_context_len, batch_size, value_dim)
+        cached_val1_list = batch_states[layer_offset + 2].chunk(
+            chunks=batch_size, dim=1
+        )
+        # cached_val2: (left_context_len, batch_size, value_dim)
+        cached_val2_list = batch_states[layer_offset + 3].chunk(
+            chunks=batch_size, dim=1
+        )
+        # cached_conv1: (#batch, channels, left_pad)
+        cached_conv1_list = batch_states[layer_offset + 4].chunk(
+            chunks=batch_size, dim=0
+        )
+        # cached_conv2: (#batch, channels, left_pad)
+        cached_conv2_list = batch_states[layer_offset + 5].chunk(
+            chunks=batch_size, dim=0
+        )
+        for i in range(batch_size):
+            state_list[i] += [
+                cached_key_list[i],
+                cached_nonlin_attn_list[i],
+                cached_val1_list[i],
+                cached_val2_list[i],
+                cached_conv1_list[i],
+                cached_conv2_list[i],
+            ]
+
+    cached_embed_left_pad_list = batch_states[-2].chunk(chunks=batch_size, dim=0)
+    for i in range(batch_size):
+        state_list[i].append(cached_embed_left_pad_list[i])
+
+    processed_lens_list = batch_states[-1].chunk(chunks=batch_size, dim=0)
+    for i in range(batch_size):
+        state_list[i].append(processed_lens_list[i])
+
+    return state_list
+
+
+def streaming_forward(
+    features: Tensor,
+    feature_lens: Tensor,
+    model: nn.Module,
+    states: List[Tensor],
+    chunk_size: int,
+    left_context_len: int,
+) -> Tuple[Tensor, Tensor, List[Tensor]]:
+    """
+    Returns encoder outputs, output lengths, and updated states.
+    """
+    cached_embed_left_pad = states[-2]
+    (x, x_lens, new_cached_embed_left_pad,) = model.encoder_embed.streaming_forward(
+        x=features,
+        x_lens=feature_lens,
+        cached_left_pad=cached_embed_left_pad,
+    )
+    assert x.size(1) == chunk_size, (x.size(1), chunk_size)
+
+    src_key_padding_mask = make_pad_mask(x_lens)
+
+    # processed_mask is used to mask out initial states
+    processed_mask = torch.arange(left_context_len, device=x.device).expand(
+        x.size(0), left_context_len
+    )
+    processed_lens = states[-1]  # (batch,)
+    # (batch, left_context_size)
+    processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1)
+    # Update processed lengths
+    new_processed_lens = processed_lens + x_lens
+
+    # (batch, left_context_size + chunk_size)
+    src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1)
+
+    x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+    encoder_states = states[:-2]
+    (
+        encoder_out,
+        encoder_out_lens,
+        new_encoder_states,
+    ) = model.encoder.streaming_forward(
+        x=x,
+        x_lens=x_lens,
+        states=encoder_states,
+        src_key_padding_mask=src_key_padding_mask,
+    )
+    encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+    new_states = new_encoder_states + [
+        new_cached_embed_left_pad,
+        new_processed_lens,
+    ]
+    return encoder_out, encoder_out_lens, new_states
+
+
 def decode_one_chunk(
     params: AttributeDict,
     model: nn.Module,
@@ -224,27 +437,32 @@ def decode_one_chunk(
     Returns:
       Return a List containing which DecodeStreams are finished.
     """
-    device = model.device
+    # pdb.set_trace()
+    # print(model)
+    # print(model.device)
+    # device = model.device
+    chunk_size = int(params.chunk_size)
+    left_context_len = int(params.left_context_frames)
 
     features = []
     feature_lens = []
     states = []
-    processed_lens = []
+    processed_lens = []  # Used in fast-beam-search
 
     for stream in decode_streams:
-        feat, feat_len = stream.get_feature_frames(params.decode_chunk_len)
+        feat, feat_len = stream.get_feature_frames(chunk_size * 2)
         features.append(feat)
         feature_lens.append(feat_len)
         states.append(stream.states)
         processed_lens.append(stream.done_frames)
 
-    feature_lens = torch.tensor(feature_lens, device=device)
+    feature_lens = torch.tensor(feature_lens, device=model.device)
     features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
 
-    # We subsample features with ((x_len - 7) // 2 + 1) // 2 and the max downsampling
+    # Make sure the length after encoder_embed is at least 1.
-    # factor in encoders is 8.
+    # The encoder_embed subsample features (T - 7) // 2
-    # After feature embedding (x_len - 7) // 2, we have (23 - 7) // 2 = 8.
+    # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling
-    tail_length = 23
+    tail_length = chunk_size * 2 + 7 + 2 * 3
     if features.size(1) < tail_length:
         pad_length = tail_length - features.size(1)
         feature_lens += pad_length
@@ -256,12 +474,14 @@ def decode_one_chunk(
         )
 
     states = stack_states(states)
-    processed_lens = torch.tensor(processed_lens, device=device)
 
-    encoder_out, encoder_out_lens, new_states = model.encoder.streaming_forward(
+    encoder_out, encoder_out_lens, new_states = streaming_forward(
-        x=features,
+        features=features,
-        x_lens=feature_lens,
+        feature_lens=feature_lens,
+        model=model,
         states=states,
+        chunk_size=chunk_size,
+        left_context_len=left_context_len,
     )
 
     encoder_out = model.joiner.encoder_proj(encoder_out)
@@ -269,6 +489,7 @@ def decode_one_chunk(
     if params.decoding_method == "greedy_search":
         greedy_search(model=model, encoder_out=encoder_out, streams=decode_streams)
     elif params.decoding_method == "fast_beam_search":
+        processed_lens = torch.tensor(processed_lens, device=model.device)
         processed_lens = processed_lens + encoder_out_lens
         fast_beam_search_one_best(
             model=model,
@@ -295,8 +516,9 @@ def decode_one_chunk(
     for i in range(len(decode_streams)):
         decode_streams[i].states = states[i]
         decode_streams[i].done_frames += encoder_out_lens[i]
-        if decode_streams[i].done:
+        # if decode_streams[i].done:
-            finished_streams.append(i)
+        # finished_streams.append(i)
+        finished_streams.append(i)
 
     return finished_streams
 
@@ -305,7 +527,7 @@ def decode_dataset(
     cuts: CutSet,
     params: AttributeDict,
     model: nn.Module,
-    sp: Tokenizer,
+    tokenizer: Tokenizer,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -317,7 +539,7 @@ def decode_dataset(
         It is returned by :func:`get_params`.
       model:
         The neural model.
-      sp:
+      tokenizer:
         The BPE model.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
@@ -338,14 +560,14 @@ def decode_dataset(
     opts.frame_opts.samp_freq = 16000
     opts.mel_opts.num_bins = 80
 
-    log_interval = 50
+    log_interval = 100
 
     decode_results = []
     # Contain decode streams currently running.
     decode_streams = []
     for num, cut in enumerate(cuts):
         # each utterance has a DecodeStream.
-        initial_states = model.encoder.get_init_state(device=device)
+        initial_states = get_init_states(model=model, batch_size=1, device=device)
         decode_stream = DecodeStream(
             params=params,
             cut_id=cut.id,
@@ -361,15 +583,19 @@ def decode_dataset(
         assert audio.dtype == np.float32, audio.dtype
 
         # The trained model is using normalized samples
-        assert audio.max() <= 1, "Should be normalized to [-1, 1])"
+        # - this is to avoid sending [-32k,+32k] signal in...
+        # - some lhotse AudioTransform classes can make the signal
+        #   be out of range [-1, 1], hence the tolerance 10
+        assert (
+            np.abs(audio).max() <= 10
+        ), "Should be normalized to [-1, 1], 10 for tolerance..."
 
         samples = torch.from_numpy(audio).squeeze(0)
 
         fbank = Fbank(opts)
         feature = fbank(samples.to(device))
-        decode_stream.set_features(feature, tail_pad_len=params.decode_chunk_len)
+        decode_stream.set_features(feature, tail_pad_len=30)
-        decode_stream.ground_truth = cut.supervisions[0].custom[params.transcript_mode]
+        decode_stream.ground_truth = cut.supervisions[0].text
-
         decode_streams.append(decode_stream)
 
         while len(decode_streams) >= params.num_decode_streams:
@@ -380,8 +606,8 @@ def decode_dataset(
                 decode_results.append(
                     (
                         decode_streams[i].id,
-                        sp.text2word(decode_streams[i].ground_truth),
+                        decode_streams[i].ground_truth.split(),
-                        sp.text2word(sp.decode(decode_streams[i].decoding_result())),
+                        tokenizer.decode(decode_streams[i].decoding_result()).split(),
                     )
                 )
                 del decode_streams[i]
@@ -391,18 +617,37 @@ def decode_dataset(
 
     # decode final chunks of last sequences
     while len(decode_streams):
+        # print("INSIDE LEN DECODE STREAMS")
+        # pdb.set_trace()
+        # print(model.device)
+        # test_device = model.device
+        # print("done")
         finished_streams = decode_one_chunk(
             params=params, model=model, decode_streams=decode_streams
         )
+        # print('INSIDE FOR LOOP ')
+        # print(finished_streams)
+
+        if not finished_streams:
+            print("No finished streams, breaking the loop")
+            break
+
         for i in sorted(finished_streams, reverse=True):
-            decode_results.append(
+            try:
-                (
+                decode_results.append(
-                    decode_streams[i].id,
+                    (
-                    sp.text2word(decode_streams[i].ground_truth),
+                        decode_streams[i].id,
-                    sp.text2word(sp.decode(decode_streams[i].decoding_result())),
+                        decode_streams[i].ground_truth.split(),
+                        tokenizer.decode(decode_streams[i].decoding_result()).split(),
+                    )
                 )
-            )
+                del decode_streams[i]
-            del decode_streams[i]
+            except IndexError as e:
+                print(f"IndexError: {e}")
+                print(f"decode_streams length: {len(decode_streams)}")
+                print(f"finished_streams: {finished_streams}")
+                print(f"i: {i}")
+                continue
 
     if params.decoding_method == "greedy_search":
         key = "greedy_search"
@@ -416,9 +661,54 @@ def decode_dataset(
         key = f"num_active_paths_{params.num_active_paths}"
     else:
         raise ValueError(f"Unsupported decoding method: {params.decoding_method}")
+    torch.cuda.synchronize()
     return {key: decode_results}
 
 
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[List[str], List[str]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        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.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        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.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.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()
@@ -430,16 +720,20 @@ def main():
     params = get_params()
     params.update(vars(args))
 
-    if not params.res_dir:
+    params.res_dir = params.exp_dir / "streaming" / params.decoding_method
-        params.res_dir = params.exp_dir / "streaming" / params.decoding_method
 
     if params.iter > 0:
         params.suffix = f"iter-{params.iter}-avg-{params.avg}"
     else:
         params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
 
-    # for streaming
+    assert params.causal, params.causal
-    params.suffix += f"-streaming-chunk-size-{params.decode_chunk_len}"
+    assert "," not in params.chunk_size, "chunk_size should be one value in decoding."
+    assert (
+        "," not in params.left_context_frames
+    ), "left_context_frames should be one value in decoding."
+    params.suffix += f"-chunk-{params.chunk_size}"
+    params.suffix += f"-left-context-{params.left_context_frames}"
 
     # for fast_beam_search
     if params.decoding_method == "fast_beam_search":
@@ -455,21 +749,21 @@ def main():
 
     device = torch.device("cpu")
     if torch.cuda.is_available():
-        device = torch.device("cuda", params.gpu)
+        device = torch.device("cuda", 0)
 
     logging.info(f"Device: {device}")
 
-    sp = Tokenizer.load(params.lang, params.lang_type)
+    sp_token = Tokenizer.load(params.lang, params.lang_type)
 
-    # <blk> and <unk> is defined in local/prepare_lang_char.py
+    # <blk> and <unk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
+    params.blank_id = sp_token.piece_to_id("<blk>")
-    params.unk_id = sp.piece_to_id("<unk>")
+    params.unk_id = sp_token.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
+    params.vocab_size = sp_token.get_piece_size()
 
     logging.info(params)
 
     logging.info("About to create model")
-    model = get_transducer_model(params)
+    model = get_model(params)
 
     if not params.use_averaged_model:
         if params.iter > 0:
@@ -553,42 +847,51 @@ def main():
     model.device = device
 
     decoding_graph = None
-    if params.decoding_graph:
+    if params.decoding_method == "fast_beam_search":
-        decoding_graph = k2.Fsa.from_dict(
-            torch.load(params.decoding_graph, map_location=device)
-        )
-    elif params.decoding_method == "fast_beam_search":
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
 
     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
     reazonspeech_corpus = ReazonSpeechAsrDataModule(args)
 
-    for subdir in ["valid"]:
+    valid_cuts = reazonspeech_corpus.valid_cuts()
+    test_cuts = reazonspeech_corpus.test_cuts()
+
+    test_sets = ["valid", "test"]
+    test_cuts = [valid_cuts, test_cuts]
+
+    for test_set, test_cut in zip(test_sets, test_cuts):
         results_dict = decode_dataset(
-            cuts=getattr(reazonspeech_corpus, f"{subdir}_cuts")(),
+            cuts=test_cut,
             params=params,
             model=model,
-            sp=sp,
+            tokenizer=sp_token,
             decoding_graph=decoding_graph,
         )
-        tot_err = save_results(
+        save_results(
-            params=params, test_set_name=subdir, results_dict=results_dict
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
         )
 
-        with (
+    # valid_cuts = reazonspeech_corpus.valid_cuts()
-            params.res_dir
+
-            / (
+    # for valid_cut in valid_cuts:
-                f"{subdir}-{params.decode_chunk_len}"
+    #     results_dict = decode_dataset(
-                f"_{params.avg}_{params.epoch}.cer"
+    #         cuts=valid_cut,
-            )
+    #         params=params,
-        ).open("w") as fout:
+    #         model=model,
-            if len(tot_err) == 1:
+    #         sp=sp,
-                fout.write(f"{tot_err[0][1]}")
+    #         decoding_graph=decoding_graph,
-            else:
+    #     )
-                fout.write("\n".join(f"{k}\t{v}") for k, v in tot_err)
+    #     save_results(
+    #         params=params,
+    #         test_set_name="valid",
+    #         results_dict=results_dict,
+    #     )
 
     logging.info("Done!")
 

icefall/__init__.py

@@ -68,6 +68,7 @@ from .utils import (
     str2bool,
     subsequent_chunk_mask,
     tokenize_by_CJK_char,
+    tokenize_by_ja_char,
     write_error_stats,
 )
 

icefall/utils.py

@@ -1758,6 +1758,30 @@ def tokenize_by_CJK_char(line: str) -> str:
     return " ".join([w.strip() for w in chars if w.strip()])
 
 
+def tokenize_by_ja_char(line: str) -> str:
+    """
+    Tokenize a line of text with Japanese characters.
+
+    Note: All non-Japanese characters will be upper case.
+
+    Example:
+      input = "こんにちは世界は hello world の日本語"
+      output = "こ ん に ち は 世 界 は HELLO WORLD の 日 本 語"
+
+    Args:
+      line:
+        The input text.
+
+    Return:
+      A new string tokenized by Japanese characters.
+    """
+    pattern = re.compile(r"([\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF])")
+    chars = pattern.split(line.strip())
+    return " ".join(
+        [w.strip().upper() if not pattern.match(w) else w for w in chars if w.strip()]
+    )
+
+
 def display_and_save_batch(
     batch: dict,
     params: AttributeDict,