Merge branch 'conformer_ctc2' of github.com:WayneWiser/icefall into conformer_ctc2

.github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh

@@ -27,7 +27,7 @@ soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp
-ln -s pretrained-epoch-29-avg-5-torch-1.10.pt pretrained.pt
+ln -s pretrained-epoch-29-avg-5-torch-1.10.0.pt pretrained.pt
 popd
 
 for sym in 1 2 3; do

.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh

@@ -37,7 +37,7 @@ for sym in 1 2 3; do
     --nhead 8 \
     --encoder-dim 512 \
     --decoder-dim 512 \
-    --joiner-dim 512
+    --joiner-dim 512 \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav
@@ -82,6 +82,7 @@ if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" ==
 
     ./pruned_transducer_stateless5/decode.py \
       --decoding-method $method \
+      --use-averaged-model 0 \
       --epoch 999 \
       --avg 1 \
       --max-duration $max_duration \

egs/aishell2/ASR/README.md

@@ -0,0 +1,19 @@
+
+# Introduction
+
+This recipe includes some different ASR models trained with Aishell2.
+
+[./RESULTS.md](./RESULTS.md) contains the latest results.
+
+# Transducers
+
+There are various folders containing the name `transducer` in this folder.
+The following table lists the differences among them.
+
+|                                       | Encoder             | Decoder            | Comment                     |
+|---------------------------------------|---------------------|--------------------|-----------------------------|
+| `pruned_transducer_stateless5`        | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless5 in librispeech recipe  |
+
+The decoder in `transducer_stateless` is modified from the paper
+[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).
+We place an additional Conv1d layer right after the input embedding layer.

egs/aishell2/ASR/RESULTS.md

@@ -0,0 +1,89 @@
+## Results
+
+### Aishell2 char-based training results (Pruned Transducer 5)
+
+#### 2022-07-11
+
+Using the codes from this commit https://github.com/k2-fsa/icefall/pull/465.
+
+When training with context size equals to 1, the WERs are
+
+|                                    |  dev-ios  | test-ios | comment                      |
+|------------------------------------|-------|----------|----------------------------------|
+|          greedy search             | 5.57  | 5.89     | --epoch 25, --avg 5, --max-duration 600  |
+| modified beam search (beam size 4) | 5.32  | 5.56     | --epoch 25, --avg 5, --max-duration 600  |
+| fast beam search (set as default)  | 5.5   |  5.78    | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search nbest             | 5.46  |  5.74    | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search oracle            | 1.92  |  2.2     | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search nbest LG          | 5.59  |  5.93    | --epoch 25, --avg 5, --max-duration 600 |
+
+The training command for reproducing is given below:
+
+```bash
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./pruned_transducer_stateless5/train.py \
+  --world-size 4 \
+  --lang-dir data/lang_char \
+  --num-epochs 40 \
+  --start-epoch 1 \
+  --exp-dir /result \
+  --max-duration 300 \
+  --use-fp16 0 \
+  --num-encoder-layers 24 \
+  --dim-feedforward 1536 \
+  --nhead 8 \
+  --encoder-dim 384 \
+  --decoder-dim 512 \
+  --joiner-dim 512 \
+  --context-size 1
+```
+
+The decoding command is:
+```bash
+for method in greedy_search modified_beam_search \
+              fast_beam_search fast_beam_search_nbest \
+              fast_beam_search_nbest_oracle fast_beam_search_nbest_LG; do
+  ./pruned_transducer_stateless5/decode.py \
+    --epoch 25 \
+    --avg 5 \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --max-duration 600 \
+    --decoding-method $method \
+    --max-sym-per-frame 1 \
+    --num-encoder-layers 24 \
+    --dim-feedforward 1536 \
+    --nhead 8 \
+    --encoder-dim 384 \
+    --decoder-dim 512 \
+    --joiner-dim 512 \
+    --context-size 1 \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5 \
+    --context-size 1 \
+    --use-averaged-model True
+done
+```
+The tensorboard training log can be found at
+https://tensorboard.dev/experiment/RXyX4QjQQVKjBS2eQ2Qajg/#scalars
+
+A pre-trained model and decoding logs can be found at <https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12>
+
+When training with context size equals to 2, the WERs are
+
+|                                    |  dev-ios  | test-ios | comment                      |
+|------------------------------------|-------|----------|----------------------------------|
+|          greedy search             | 5.47  |  5.81    | --epoch 25, --avg 5, --max-duration 600  |
+| modified beam search (beam size 4) | 5.38  |  5.61    | --epoch 25, --avg 5, --max-duration 600  |
+| fast beam search (set as default)  | 5.36  |  5.61    | --epoch 25, --avg 5, --max-duration 600  |
+| fast beam search nbest             | 5.37  |  5.6     | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search oracle            | 2.04  |  2.2     | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search nbest LG          | 5.59  |  5.82     | --epoch 25, --avg 5, --max-duration 600 |
+
+The tensorboard training log can be found at
+https://tensorboard.dev/experiment/5AxJ8LHoSre8kDAuLp4L7Q/#scalars
+
+A pre-trained model and decoding logs can be found at <https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12>

egs/aishell2/ASR/local/compute_fbank_aishell2.py

@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+This file computes fbank features of the aishell2 dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import argparse
+import logging
+import os
+from pathlib import Path
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
+from lhotse.recipes.utils import read_manifests_if_cached
+
+from icefall.utils import get_executor
+
+# 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)
+
+
+def compute_fbank_aishell2(num_mel_bins: int = 80):
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+    num_jobs = min(15, os.cpu_count())
+
+    dataset_parts = (
+        "train",
+        "dev",
+        "test",
+    )
+    prefix = "aishell2"
+    suffix = "jsonl.gz"
+    manifests = read_manifests_if_cached(
+        dataset_parts=dataset_parts,
+        output_dir=src_dir,
+        prefix=prefix,
+        suffix=suffix,
+    )
+    assert manifests is not None
+
+    extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        for partition, m in manifests.items():
+            if (output_dir / f"{prefix}_cuts_{partition}.{suffix}").is_file():
+                logging.info(f"{partition} already exists - skipping.")
+                continue
+            logging.info(f"Processing {partition}")
+            cut_set = CutSet.from_manifests(
+                recordings=m["recordings"],
+                supervisions=m["supervisions"],
+            )
+            if "train" in partition:
+                cut_set = (
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
+                )
+            cut_set = cut_set.compute_and_store_features(
+                extractor=extractor,
+                storage_path=f"{output_dir}/{prefix}_feats_{partition}",
+                # when an executor is specified, make more partitions
+                num_jobs=num_jobs if ex is None else 80,
+                executor=ex,
+                storage_type=LilcomChunkyWriter,
+            )
+            cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}")
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--num-mel-bins",
+        type=int,
+        default=80,
+        help="""The number of mel bins for Fbank""",
+    )
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_args()
+    compute_fbank_aishell2(num_mel_bins=args.num_mel_bins)

egs/aishell2/ASR/local/compute_fbank_musan.py

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

egs/aishell2/ASR/local/display_manifest_statistics.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This file 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 transducer_stateless/train.py
+for usage.
+"""
+
+
+from lhotse import load_manifest_lazy
+
+
+def main():
+    paths = [
+        "./data/fbank/aishell2_cuts_train.jsonl.gz",
+        "./data/fbank/aishell2_cuts_dev.jsonl.gz",
+        "./data/fbank/aishell2_cuts_test.jsonl.gz",
+    ]
+
+    for path in paths:
+        print(f"Starting display the statistics for {path}")
+        cuts = load_manifest_lazy(path)
+        cuts.describe()
+
+
+if __name__ == "__main__":
+    main()
+
+"""
+Starting display the statistics for ./data/fbank/aishell2_cuts_train.jsonl.gz
+Cuts count: 3026106
+Total duration (hours): 3021.2
+Speech duration (hours): 3021.2 (100.0%)
+***
+Duration statistics (seconds):
+mean	3.6
+std	1.5
+min	0.3
+25%	2.4
+50%	3.3
+75%	4.4
+99%	8.2
+99.5%	8.9
+99.9%	10.6
+max	21.5
+Starting display the statistics for ./data/fbank/aishell2_cuts_dev.jsonl.gz
+Cuts count: 2500
+Total duration (hours): 2.0
+Speech duration (hours): 2.0 (100.0%)
+***
+Duration statistics (seconds):
+mean	2.9
+std	1.0
+min	1.1
+25%	2.2
+50%	2.7
+75%	3.4
+99%	6.3
+99.5%	6.7
+99.9%	7.8
+max	9.4
+Starting display the statistics for ./data/fbank/aishell2_cuts_test.jsonl.gz
+Cuts count: 5000
+Total duration (hours): 4.0
+Speech duration (hours): 4.0 (100.0%)
+***
+Duration statistics (seconds):
+mean	2.9
+std	1.0
+min	1.1
+25%	2.2
+50%	2.7
+75%	3.3
+99%	6.2
+99.5%	6.6
+99.9%	7.7
+max	8.5
+"""

egs/aishell2/ASR/local/prepare_char.py

@@ -0,0 +1 @@
+../../../aidatatang_200zh/ASR/local/prepare_char.py

egs/aishell2/ASR/local/prepare_lang.py

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

egs/aishell2/ASR/local/prepare_words.py

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

egs/aishell2/ASR/local/text2segments.py

@@ -0,0 +1 @@
+../../../wenetspeech/ASR/local/text2segments.py

egs/aishell2/ASR/local/text2token.py

@@ -0,0 +1 @@
+../../../aidatatang_200zh/ASR/local/text2token.py

egs/aishell2/ASR/prepare.sh

@@ -0,0 +1,181 @@
+#!/usr/bin/env bash
+
+set -eou pipefail
+
+nj=30
+stage=0
+stop_stage=5
+
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, you need to apply aishell2 through
+# their official website.
+# https://www.aishelltech.com/aishell_2
+#
+#  - $dl_dir/aishell2
+#
+#
+#  - $dl_dir/musan
+#      This directory contains the following directories downloaded from
+#       http://www.openslr.org/17/
+#
+#     - music
+#     - noise
+#     - speech
+
+dl_dir=$PWD/download
+
+. shared/parse_options.sh || exit 1
+
+# 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"
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "stage 0: Download data"
+
+  # If you have pre-downloaded it to /path/to/aishell2,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/aishell2 $dl_dir/aishell2
+  #
+  # The directory structure is
+  # aishell2/
+  # |-- AISHELL-2
+  # |   |-- iOS
+  #         |-- data
+  #             |-- wav
+  #             |-- trans.txt
+  #         |-- dev
+  #             |-- wav
+  #             |-- trans.txt
+  #         |-- test
+  #             |-- wav
+  #             |-- trans.txt
+
+
+  # If you have pre-downloaded it to /path/to/musan,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/musan $dl_dir/musan
+  #
+  if [ ! -d $dl_dir/musan ]; then
+    lhotse download musan $dl_dir
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare aishell2 manifest"
+  # We assume that you have downloaded and unzip the aishell2 corpus
+  # to $dl_dir/aishell2
+  if [ ! -f data/manifests/.aishell2_manifests.done ]; then
+    mkdir -p data/manifests
+    lhotse prepare aishell2 $dl_dir/aishell2 data/manifests -j $nj
+    touch data/manifests/.aishell2_manifests.done
+  fi
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Prepare musan manifest"
+  # We assume that you have downloaded the musan corpus
+  # to data/musan
+  if [ ! -f data/manifests/.musan_manifests.done ]; then
+    log "It may take 6 minutes"
+    mkdir -p data/manifests
+    lhotse prepare musan $dl_dir/musan data/manifests
+    touch data/manifests/.musan_manifests.done
+  fi
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Compute fbank for aishell2"
+  if [ ! -f data/fbank/.aishell2.done ]; then
+    mkdir -p data/fbank
+    ./local/compute_fbank_aishell2.py
+    touch data/fbank/.aishell2.done
+  fi
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Compute fbank for musan"
+  if [ ! -f data/fbank/.msuan.done ]; then
+    mkdir -p data/fbank
+    ./local/compute_fbank_musan.py
+    touch data/fbank/.msuan.done
+  fi
+fi
+
+lang_char_dir=data/lang_char
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Prepare char based lang"
+  mkdir -p $lang_char_dir
+
+  # Prepare text.
+  # Note: in Linux, you can install jq with the following command:
+  # 1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
+  # 2. chmod +x ./jq
+  # 3. cp jq /usr/bin
+  if [ ! -f $lang_char_dir/text ]; then
+    gunzip -c data/manifests/aishell2_supervisions_train.jsonl.gz \
+      | jq '.text' | sed 's/"//g' \
+      | ./local/text2token.py -t "char" > $lang_char_dir/text
+  fi
+
+  # The implementation of chinese word segmentation for text,
+  # and it will take about 15 minutes.
+  # If you can't install paddle-tiny with python 3.8, please refer to
+  # https://github.com/fxsjy/jieba/issues/920
+  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
+  fi
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Prepare G"
+  # We assume you have install kaldilm, if not, please install
+  # it using: pip install kaldilm
+
+  if [ ! -f ${lang_char_dir}/3-gram.unpruned.arpa ]; then
+    ./shared/make_kn_lm.py \
+      -ngram-order 3 \
+      -text $lang_char_dir/text_words_segmentation \
+      -lm $lang_char_dir/3-gram.unpruned.arpa
+  fi
+
+  mkdir -p data/lm
+  if [ ! -f data/lm/G_3_gram.fst.txt ]; then
+    # It is used in building LG
+    python3 -m kaldilm \
+      --read-symbol-table="$lang_char_dir/words.txt" \
+      --disambig-symbol='#0' \
+      --max-order=3 \
+      $lang_char_dir/3-gram.unpruned.arpa > data/lm/G_3_gram.fst.txt
+  fi
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 7: Compile LG"
+  ./local/compile_lg.py --lang-dir $lang_char_dir
+fi

egs/aishell2/ASR/pruned_transducer_stateless5/asr_datamodule.py

@@ -0,0 +1,418 @@
+# 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, Optional
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SingleCutSampler,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
+    AudioSamples,
+    OnTheFlyFeatures,
+)
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+class AiShell2AsrDataModule:
+    """
+    DataModule for k2 ASR experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. ios, android, mic).
+
+    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/valid/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=True,
+            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=True,
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures",
+        )
+
+    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 = []
+        if self.args.enable_musan:
+            logging.info("Enable MUSAN")
+            logging.info("About to get Musan cuts")
+            cuts_musan = load_manifest(
+                self.args.manifest_dir / "musan_cuts.jsonl.gz"
+            )
+            transforms.append(
+                CutMix(
+                    cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
+                )
+            )
+        else:
+            logging.info("Disable MUSAN")
+
+        if self.args.concatenate_cuts:
+            logging.info(
+                f"Using cut concatenation with duration factor "
+                f"{self.args.duration_factor} and gap {self.args.gap}."
+            )
+            # Cut concatenation should be the first transform in the list,
+            # so that if we e.g. mix noise in, it will fill the gaps between
+            # different utterances.
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + 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(
+            input_strategy=eval(self.args.input_strategy)(),
+            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 SingleCutSampler.")
+            train_sampler = SingleCutSampler(
+                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)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        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.debug("About to create test dataset")
+        test = K2SpeechRecognitionDataset(
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            if self.args.on_the_fly_feats
+            else eval(self.args.input_strategy)(),
+            return_cuts=self.args.return_cuts,
+        )
+        sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.debug("About to create test dataloader")
+        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 gen cuts from aishell2_cuts_train.jsonl.gz")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aishell2_cuts_train.jsonl.gz"
+        )
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to gen cuts from aishell2_cuts_dev.jsonl.gz")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aishell2_cuts_dev.jsonl.gz"
+        )
+
+    @lru_cache()
+    def test_cuts(self) -> CutSet:
+        logging.info("About to gen cuts from aishell2_cuts_test.jsonl.gz")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aishell2_cuts_test.jsonl.gz"
+        )

egs/aishell2/ASR/pruned_transducer_stateless5/beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/beam_search.py

egs/aishell2/ASR/pruned_transducer_stateless5/conformer.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless5/conformer.py

egs/aishell2/ASR/pruned_transducer_stateless5/decode.py

@@ -0,0 +1,791 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 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
+./pruned_transducer_stateless5/decode.py \
+        --epoch 25 \
+        --avg 5 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --lang-dir data/lang_char \
+        --max-duration 600 \
+        --decoding-method greedy_search
+
+(2) beam search (not recommended)
+./pruned_transducer_stateless5/decode.py \
+    --epoch 25 \
+    --avg 5 \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --lang-dir data/lang_char \
+    --max-duration 600 \
+    --decoding-method beam_search \
+    --beam-size 4
+
+(3) modified beam search
+./pruned_transducer_stateless5/decode.py \
+        --epoch 25 \
+        --avg 5 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --lang-dir data/lang_char \
+        --max-duration 600 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(4) fast beam search (one best)
+./pruned_transducer_stateless5/decode.py \
+        --epoch 25 \
+        --avg 5 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --lang-dir data/lang_char \
+        --max-duration 600 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+
+(5) fast beam search (nbest)
+./pruned_transducer_stateless5/decode.py \
+    --epoch 25 \
+    --avg 5 \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --lang-dir data/lang_char \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(6) fast beam search (nbest oracle WER)
+./pruned_transducer_stateless5/decode.py \
+    --epoch 25 \
+    --avg 5 \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --lang-dir data/lang_char \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_oracle \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(7) fast beam search (with LG)
+./pruned_transducer_stateless5/decode.py \
+    --epoch 25 \
+    --avg 5 \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --lang-dir data/lang_char \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_LG \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
+"""
+
+
+import argparse
+import logging
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from asr_datamodule import AiShell2AsrDataModule
+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 train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
+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,
+    write_error_stats,
+)
+
+
+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="pruned_transducer_stateless5/exp",
+        help="The experiment dir",
+    )
+
+    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="""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,
+    lexicon: Lexicon,
+    graph_compiler: CharCtcTrainingGraphCompiler,
+    batch: dict,
+    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.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      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)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    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:
+            sentence = "".join([lexicon.word_table[i] for i in hyp])
+            hyps.append(list(sentence))
+    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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    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=graph_compiler.texts_to_ids(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    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([lexicon.token_table[idx] for idx in hyp])
+
+    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,
+    lexicon: Lexicon,
+    graph_compiler: CharCtcTrainingGraphCompiler,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[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.
+      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"]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            graph_compiler=graph_compiler,
+            decoding_graph=decoding_graph,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for hyp_words, ref_text in zip(hyps, texts):
+                this_batch.append((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[List[int], List[int]]]],
+):
+    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"
+        )
+        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()
+    AiShell2AsrDataModule.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 "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}")
+
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.unk_id = lexicon.token_table["<unk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    graph_compiler = CharCtcTrainingGraphCompiler(
+        lexicon=lexicon,
+        device=device,
+    )
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_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)
+            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:
+            decoding_graph = k2.trivial_graph(
+                params.vocab_size - 1, device=device
+            )
+    else:
+        decoding_graph = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    aishell2 = AiShell2AsrDataModule(args)
+
+    valid_cuts = aishell2.valid_cuts()
+    test_cuts = aishell2.test_cuts()
+
+    # use ios sets for dev and test
+    dev_dl = aishell2.valid_dataloaders(valid_cuts)
+    test_dl = aishell2.test_dataloaders(test_cuts)
+
+    test_sets = ["dev", "test"]
+    test_dl = [dev_dl, test_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            graph_compiler=graph_compiler,
+            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/aishell2/ASR/pruned_transducer_stateless5/decoder.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/decoder.py

egs/aishell2/ASR/pruned_transducer_stateless5/encoder_interface.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/encoder_interface.py

egs/aishell2/ASR/pruned_transducer_stateless5/export.py

@@ -0,0 +1,274 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: 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 converts several saved checkpoints
+# to a single one using model averaging.
+"""
+Usage:
+./pruned_transducer_stateless5/export.py \
+  --exp-dir ./pruned_transducer_stateless5/exp \
+  --lang-dir data/lang_char
+  --epoch 25 \
+  --avg 5
+
+It will generate a file exp_dir/pretrained.pt
+
+To use the generated file with `pruned_transducer_stateless5/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/aishell2/ASR
+    ./pruned_transducer_stateless5/decode.py \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 600 \
+        --decoding-method greedy_search \
+        --lang-dir data/lang_char
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import torch
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        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=False,
+        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="pruned_transducer_stateless5/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_char",
+        help="The lang dir",
+    )
+
+    parser.add_argument(
+        "--jit",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.script.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.unk_id = lexicon.token_table["<unk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_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("cpu")
+    model.eval()
+
+    if params.jit:
+        # We won't use the forward() method of the model in C++, so just ignore
+        # it here.
+        # Otherwise, one of its arguments is a ragged tensor and is not
+        # torch scriptabe.
+        model.__class__.forward = torch.jit.ignore(model.__class__.forward)
+        logging.info("Using torch.jit.script")
+        model = torch.jit.script(model)
+        filename = params.exp_dir / "cpu_jit.pt"
+        model.save(str(filename))
+        logging.info(f"Saved to {filename}")
+    else:
+        logging.info("Not using torch.jit.script")
+        # Save it using a format so that it can be loaded
+        # by :func:`load_checkpoint`
+        filename = params.exp_dir / "pretrained.pt"
+        torch.save({"model": model.state_dict()}, str(filename))
+        logging.info(f"Saved to {filename}")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/aishell2/ASR/pruned_transducer_stateless5/joiner.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/joiner.py

egs/aishell2/ASR/pruned_transducer_stateless5/model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/model.py

egs/aishell2/ASR/pruned_transducer_stateless5/optim.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/optim.py

egs/aishell2/ASR/pruned_transducer_stateless5/pretrained.py

@@ -0,0 +1,342 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+
+(1) greedy search
+./pruned_transducer_stateless5/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
+    --lang-dir ./data/lang_char \
+    --method greedy_search \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+(2) modified beam search
+./pruned_transducer_stateless5/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
+    --lang-dir ./data/lang_char \
+    --method modified_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+(3) fast beam search
+./pruned_transducer_stateless5/pretrained.py \
+    --checkpoint ./pruned_transducer_stateless5/exp/pretrained.pt \
+    --lang-dir ./data/lang_char \
+    --method fast_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+You can also use `./pruned_transducer_stateless5/exp/epoch-xx.pt`.
+
+Note: ./pruned_transducer_stateless5/exp/pretrained.pt is generated by
+./pruned_transducer_stateless5/export.py
+"""
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from beam_search import (
+    beam_search,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.lexicon import Lexicon
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Path to lang.
+        """,
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="The sample rate of the input sound file",
+    )
+
+    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 --method is beam_search or
+        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 --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --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(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame. Used only when
+        --method is greedy_search.
+        """,
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+
+    params.update(vars(args))
+
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.unk_id = lexicon.token_table["<unk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = get_transducer_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"], strict=False)
+    model.to(device)
+    model.eval()
+    model.device = device
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
+
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=features, x_lens=feature_lengths
+    )
+
+    num_waves = encoder_out.size(0)
+    hyps = []
+    msg = f"Using {params.method}"
+    if params.method == "beam_search":
+        msg += f" with beam size {params.beam_size}"
+    logging.info(msg)
+
+    if params.method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    elif params.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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    elif params.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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    else:
+        for i in range(num_waves):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(f"Unsupported method: {params.method}")
+
+            hyps.append([lexicon.token_table[idx] for idx in hyp])
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        words = "".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/aishell2/ASR/pruned_transducer_stateless5/scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/scaling.py

egs/aishell2/ASR/shared

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

egs/librispeech/ASR/README.md

@@ -23,8 +23,8 @@ The following table lists the differences among them.
 | `pruned_transducer_stateless5`        | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + more layers + random combiner|
 | `pruned_transducer_stateless6`        | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + distillation with hubert|
 | `pruned_stateless_emformer_rnnt2`     | Emformer(from torchaudio) | Embedding + Conv1d | Using Emformer from torchaudio for streaming ASR|
-| `conv_emformer_transducer_stateless`  | Emformer | Embedding + Conv1d | Using Emformer augmented with convolution for streaming ASR + mechanisms in reworked model |
+| `conv_emformer_transducer_stateless`  | ConvEmformer | Embedding + Conv1d | Using ConvEmformer for streaming ASR + mechanisms in reworked model |
-
+| `conv_emformer_transducer_stateless2` | ConvEmformer | Embedding + Conv1d | Using ConvEmformer with simplified memory for streaming ASR + mechanisms in reworked model |
 
 The decoder in `transducer_stateless` is modified from the paper
 [Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).

egs/librispeech/ASR/RESULTS.md

@@ -1,5 +1,317 @@
 ## Results
 
+### LibriSpeech BPE training results (Pruned Stateless Conv-Emformer RNN-T 2)
+
+[conv_emformer_transducer_stateless2](./conv_emformer_transducer_stateless2)
+
+It implements [Emformer](https://arxiv.org/abs/2010.10759) augmented with convolution module and simplified memory bank for streaming ASR.
+It is modified from [torchaudio](https://github.com/pytorch/audio).
+
+See <https://github.com/k2-fsa/icefall/pull/440> for more details.
+
+#### With lower latency setup, training on full librispeech
+
+In this model, the lengths of chunk and right context are 32 frames (i.e., 0.32s) and 8 frames (i.e., 0.08s), respectively.
+
+The WERs are:
+
+|                                     | test-clean | test-other | comment              | decoding mode        |
+|-------------------------------------|------------|------------|----------------------|----------------------|
+| greedy search (max sym per frame 1) | 3.5        | 9.09       | --epoch 30 --avg 10  | simulated streaming  |
+| greedy search (max sym per frame 1) | 3.57       | 9.1        | --epoch 30 --avg 10  | streaming            |
+| fast beam search                    | 3.5        | 8.91       | --epoch 30 --avg 10  | simulated streaming  |
+| fast beam search                    | 3.54       | 8.91       | --epoch 30 --avg 10  | streaming            |
+| modified beam search                | 3.43       | 8.86       | --epoch 30 --avg 10  | simulated streaming  |
+| modified beam search                | 3.48       | 8.88       | --epoch 30 --avg 10  | streaming            |
+
+The training command is:
+
+```bash
+./conv_emformer_transducer_stateless2/train.py \
+  --world-size 6 \
+  --num-epochs 30 \
+  --start-epoch 1 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --full-libri 1 \
+  --max-duration 280 \
+  --master-port 12321 \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32
+```
+
+The tensorboard log can be found at
+<https://tensorboard.dev/experiment/W5MpxekiQLSPyM4fe5hbKg/>
+
+The simulated streaming decoding command using greedy search is:
+```bash
+./conv_emformer_transducer_stateless2/decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --max-duration 300 \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32 \
+  --decoding-method greedy_search \
+  --use-averaged-model True
+```
+
+The simulated streaming decoding command using fast beam search is:
+```bash
+./conv_emformer_transducer_stateless2/decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --max-duration 300 \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32 \
+  --decoding-method fast_beam_search \
+  --use-averaged-model True \
+  --beam 4 \
+  --max-contexts 4 \
+  --max-states 8
+```
+
+The simulated streaming decoding command using modified beam search is:
+```bash
+./conv_emformer_transducer_stateless2/decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --max-duration 300 \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32 \
+  --decoding-method modified_beam_search \
+  --use-averaged-model True \
+  --beam-size 4
+```
+
+The streaming decoding command using greedy search is:
+```bash
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --num-decode-streams 2000 \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32 \
+  --decoding-method greedy_search \
+  --use-averaged-model True
+```
+
+The streaming decoding command using fast beam search is:
+```bash
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --num-decode-streams 2000 \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32 \
+  --decoding-method fast_beam_search \
+  --use-averaged-model True \
+  --beam 4 \
+  --max-contexts 4 \
+  --max-states 8
+```
+
+The streaming decoding command using modified beam search is:
+```bash
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --num-decode-streams 2000 \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32 \
+  --decoding-method modified_beam_search \
+  --use-averaged-model True \
+  --beam-size 4
+```
+
+Pretrained models, training logs, decoding logs, and decoding results
+are available at
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05>
+
+#### With higher latency setup, training on full librispeech
+
+In this model, the lengths of chunk and right context are 64 frames (i.e., 0.64s) and 16 frames (i.e., 0.16s), respectively.
+
+The WERs are:
+
+|                                     | test-clean | test-other | comment              | decoding mode        |
+|-------------------------------------|------------|------------|----------------------|----------------------|
+| greedy search (max sym per frame 1) | 3.3        | 8.71       | --epoch 30 --avg 10  | simulated streaming  |
+| greedy search (max sym per frame 1) | 3.35       | 8.65       | --epoch 30 --avg 10  | streaming            |
+| fast beam search                    | 3.27       | 8.58       | --epoch 30 --avg 10  | simulated streaming  |
+| fast beam search                    | 3.31       | 8.48       | --epoch 30 --avg 10  | streaming            |
+| modified beam search                | 3.26       | 8.56       | --epoch 30 --avg 10  | simulated streaming  |
+| modified beam search                | 3.29       | 8.47       | --epoch 30 --avg 10  | streaming            |
+
+The training command is:
+
+```bash
+./conv_emformer_transducer_stateless2/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 1 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --full-libri 1 \
+  --max-duration 280 \
+  --master-port 12321 \
+  --num-encoder-layers 12 \
+  --chunk-length 64 \
+  --cnn-module-kernel 31 \
+  --left-context-length 64 \
+  --right-context-length 16 \
+  --memory-size 32
+```
+
+The tensorboard log can be found at
+<https://tensorboard.dev/experiment/eRx6XwbOQhGlywgD8lWBjw/>
+
+The simulated streaming decoding command using greedy search is:
+```bash
+./conv_emformer_transducer_stateless2/decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --max-duration 300 \
+  --num-encoder-layers 12 \
+  --chunk-length 64 \
+  --cnn-module-kernel 31 \
+  --left-context-length 64 \
+  --right-context-length 16 \
+  --memory-size 32 \
+  --decoding-method greedy_search \
+  --use-averaged-model True
+```
+
+The simulated streaming decoding command using fast beam search is:
+```bash
+./conv_emformer_transducer_stateless2/decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --max-duration 300 \
+  --num-encoder-layers 12 \
+  --chunk-length 64 \
+  --cnn-module-kernel 31 \
+  --left-context-length 64 \
+  --right-context-length 16 \
+  --memory-size 32 \
+  --decoding-method fast_beam_search \
+  --use-averaged-model True \
+  --beam 4 \
+  --max-contexts 4 \
+  --max-states 8
+```
+
+The simulated streaming decoding command using modified beam search is:
+```bash
+./conv_emformer_transducer_stateless2/decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --max-duration 300 \
+  --num-encoder-layers 12 \
+  --chunk-length 64 \
+  --cnn-module-kernel 31 \
+  --left-context-length 64 \
+  --right-context-length 16 \
+  --memory-size 32 \
+  --decoding-method modified_beam_search \
+  --use-averaged-model True \
+  --beam-size 4
+```
+
+The streaming decoding command using greedy search is:
+```bash
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --num-decode-streams 2000 \
+  --num-encoder-layers 12 \
+  --chunk-length 64 \
+  --cnn-module-kernel 31 \
+  --left-context-length 64 \
+  --right-context-length 16 \
+  --memory-size 32 \
+  --decoding-method greedy_search \
+  --use-averaged-model True
+```
+
+The streaming decoding command using fast beam search is:
+```bash
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --num-decode-streams 2000 \
+  --num-encoder-layers 12 \
+  --chunk-length 64 \
+  --cnn-module-kernel 31 \
+  --left-context-length 64 \
+  --right-context-length 16 \
+  --memory-size 32 \
+  --decoding-method fast_beam_search \
+  --use-averaged-model True \
+  --beam 4 \
+  --max-contexts 4 \
+  --max-states 8
+```
+
+The streaming decoding command using modified beam search is:
+```bash
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+  --epoch 30 \
+  --avg 10 \
+  --exp-dir conv_emformer_transducer_stateless2/exp \
+  --num-decode-streams 2000 \
+  --num-encoder-layers 12 \
+  --chunk-length 64 \
+  --cnn-module-kernel 31 \
+  --left-context-length 64 \
+  --right-context-length 16 \
+  --memory-size 32 \
+  --decoding-method modified_beam_search \
+  --use-averaged-model True \
+  --beam-size 4
+```
+
+Pretrained models, training logs, decoding logs, and decoding results
+are available at
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-larger-latency-2022-07-06>
+
+
 ### LibriSpeech BPE training results (Pruned Stateless Streaming Conformer RNN-T)
 
 #### [pruned_transducer_stateless](./pruned_transducer_stateless)
@@ -556,9 +868,9 @@ Number of model parameters 118129516 (i.e, 118.13 M).
 
 |                                     | test-clean | test-other | comment                                |
 |-------------------------------------|------------|------------|----------------------------------------|
-| greedy search (max sym per frame 1) | 2.39       | 5.57       | --epoch 39 --avg 7  --max-duration 600 |
+| greedy search (max sym per frame 1) | 2.43       | 5.72       | --epoch 30 --avg 10 --max-duration 600 |
-| modified beam search                | 2.35       | 5.50       | --epoch 39 --avg 7  --max-duration 600 |
+| modified beam search                | 2.43       | 5.69       | --epoch 30 --avg 10 --max-duration 600 |
-| fast beam search                    | 2.38       | 5.50       | --epoch 39 --avg 7 --max-duration 600  |
+| fast beam search                    | 2.43       | 5.67       | --epoch 30 --avg 10 --max-duration 600 |
 
 The training commands are:
 
@@ -567,8 +879,8 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 
 ./pruned_transducer_stateless5/train.py \
   --world-size 8 \
-  --num-epochs 40 \
+  --num-epochs 30 \
-  --start-epoch 0 \
+  --start-epoch 1 \
   --full-libri 1 \
   --exp-dir pruned_transducer_stateless5/exp-L \
   --max-duration 300 \
@@ -582,15 +894,15 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 ```
 
 The tensorboard log can be found at
-<https://tensorboard.dev/experiment/Zq0h3KpnQ2igWbeR4U82Pw/>
+<https://tensorboard.dev/experiment/aWzDj5swSE2VmcOYgoe3vQ>
 
 The decoding commands are:
 
 ```bash
 for method in greedy_search modified_beam_search fast_beam_search; do
   ./pruned_transducer_stateless5/decode.py \
-    --epoch 39 \
+    --epoch 30 \
-    --avg 7 \
+    --avg 10 \
     --exp-dir ./pruned_transducer_stateless5/exp-L \
     --max-duration 600 \
     --decoding-method $method \
@@ -600,13 +912,14 @@ for method in greedy_search modified_beam_search fast_beam_search; do
     --nhead 8 \
     --encoder-dim 512 \
     --decoder-dim 512 \
-    --joiner-dim 512
+    --joiner-dim 512 \
+    --use-averaged-model True
 done
 ```
 
 You can find a pretrained model, training logs, decoding logs, and decoding
 results at:
-<https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-2022-05-13>
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-2022-07-07>
 
 
 #### Medium
@@ -615,9 +928,9 @@ Number of model parameters 30896748 (i.e, 30.9 M).
 
 |                                     | test-clean | test-other | comment                                 |
 |-------------------------------------|------------|------------|-----------------------------------------|
-| greedy search (max sym per frame 1) | 2.88       | 6.69       | --epoch 39 --avg 17  --max-duration 600 |
+| greedy search (max sym per frame 1) | 2.87       | 6.92       | --epoch 30 --avg 10  --max-duration 600 |
-| modified beam search                | 2.83       | 6.59       | --epoch 39 --avg 17  --max-duration 600 |
+| modified beam search                | 2.83       | 6.75       | --epoch 30 --avg 10  --max-duration 600 |
-| fast beam search                    | 2.83       | 6.61       | --epoch 39 --avg 17 --max-duration 600  |
+| fast beam search                    | 2.81       | 6.76       | --epoch 30 --avg 10  --max-duration 600  |
 
 The training commands are:
 
@@ -626,8 +939,8 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 
 ./pruned_transducer_stateless5/train.py \
   --world-size 8 \
-  --num-epochs 40 \
+  --num-epochs 30 \
-  --start-epoch 0 \
+  --start-epoch 1 \
   --full-libri 1 \
   --exp-dir pruned_transducer_stateless5/exp-M \
   --max-duration 300 \
@@ -641,15 +954,15 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 ```
 
 The tensorboard log can be found at
-<https://tensorboard.dev/experiment/bOQvULPsQ1iL7xpdI0VbXw/>
+<https://tensorboard.dev/experiment/04xtWUKPRmebSnpzN1GMHQ>
 
 The decoding commands are:
 
 ```bash
 for method in greedy_search modified_beam_search fast_beam_search; do
   ./pruned_transducer_stateless5/decode.py \
-    --epoch 39 \
+    --epoch 30 \
-    --avg 17 \
+    --avg 10 \
     --exp-dir ./pruned_transducer_stateless5/exp-M \
     --max-duration 600 \
     --decoding-method $method \
@@ -659,13 +972,14 @@ for method in greedy_search modified_beam_search fast_beam_search; do
     --nhead 4 \
     --encoder-dim 256 \
     --decoder-dim 512 \
-    --joiner-dim 512
+    --joiner-dim 512 \
+    --use-averaged-model True
 done
 ```
 
 You can find a pretrained model, training logs, decoding logs, and decoding
 results at:
-<https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-M-2022-05-13>
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-M-2022-07-07>
 
 
 #### Baseline-2
@@ -675,19 +989,19 @@ layers (24 v.s 12) but a narrower model (1536 feedforward dim and 384 encoder di
 
 |                                     | test-clean | test-other | comment                                 |
 |-------------------------------------|------------|------------|-----------------------------------------|
-| greedy search (max sym per frame 1) | 2.41       | 5.70       | --epoch 31 --avg 17  --max-duration 600 |
+| greedy search (max sym per frame 1) | 2.54       | 5.72       | --epoch 30 --avg 10  --max-duration 600 |
-| modified beam search                | 2.41       | 5.69       | --epoch 31 --avg 17  --max-duration 600 |
+| modified beam search                | 2.47       | 5.71       | --epoch 30 --avg 10  --max-duration 600 |
-| fast beam search                    | 2.41       | 5.69       | --epoch 31 --avg 17 --max-duration 600  |
+| fast beam search                    | 2.5        | 5.72       | --epoch 30 --avg 10  --max-duration 600 |
 
 ```bash
 export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 
 ./pruned_transducer_stateless5/train.py \
   --world-size 8 \
-  --num-epochs 40 \
+  --num-epochs 30 \
-  --start-epoch 0 \
+  --start-epoch 1 \
   --full-libri 1 \
-  --exp-dir pruned_transducer_stateless5/exp \
+  --exp-dir pruned_transducer_stateless5/exp-B \
   --max-duration 300 \
   --use-fp16 0 \
   --num-encoder-layers 24 \
@@ -699,19 +1013,16 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 ```
 
 The tensorboard log can be found at
-<https://tensorboard.dev/experiment/73oY9U1mQiq0tbbcovZplw/>
+<https://tensorboard.dev/experiment/foVHNyqiRi2LhybmRUOAyg>
-
-**Caution**: The training script is updated so that epochs are counted from 1
-after the training.
 
 The decoding commands are:
 
 ```bash
 for method in greedy_search modified_beam_search fast_beam_search; do
   ./pruned_transducer_stateless5/decode.py \
-    --epoch 31 \
+    --epoch 30 \
-    --avg 17 \
+    --avg 10 \
-    --exp-dir ./pruned_transducer_stateless5/exp-M \
+    --exp-dir ./pruned_transducer_stateless5/exp-B \
     --max-duration 600 \
     --decoding-method $method \
     --max-sym-per-frame 1 \
@@ -720,13 +1031,14 @@ for method in greedy_search modified_beam_search fast_beam_search; do
     --nhead 8 \
     --encoder-dim 384 \
     --decoder-dim 512 \
-    --joiner-dim 512
+    --joiner-dim 512 \
+    --use-averaged-model True
 done
 ```
 
 You can find a pretrained model, training logs, decoding logs, and decoding
 results at:
-<https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-narrower-2022-05-13>
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-B-2022-07-07>
 
 
 ### LibriSpeech BPE training results (Pruned Stateless Transducer 4)

egs/librispeech/ASR/conv_emformer_transducer_stateless/decode.py

@@ -277,10 +277,10 @@ def decode_one_batch(
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
-    feature_lens += params.right_context_length
+    feature_lens += params.chunk_length
     feature = torch.nn.functional.pad(
         feature,
-        pad=(0, 0, 0, params.right_context_length),
+        pad=(0, 0, 0, params.chunk_length),
         value=LOG_EPS,
     )
 

egs/librispeech/ASR/conv_emformer_transducer_stateless/emformer.py

@@ -1141,8 +1141,8 @@ class EmformerEncoderLayer(nn.Module):
              - output utterance, with shape (U, B, D);
              - output right_context, with shape (R, B, D);
              - output memory, with shape (1, B, D) or (0, B, D).
-             - output state.
+             - updated attention cache.
-             - updated conv_cache.
+             - updated convolution cache.
         """
         R = right_context.size(0)
         src = torch.cat([right_context, utterance])
@@ -1252,6 +1252,11 @@ class EmformerEncoder(nn.Module):
     ):
         super().__init__()
 
+        assert (
+            chunk_length - 1
+        ) & chunk_length == 0, "chunk_length should be a power of 2."
+        self.shift = int(math.log(chunk_length, 2))
+
         self.use_memory = memory_size > 0
         self.init_memory_op = nn.AvgPool1d(
             kernel_size=chunk_length,
@@ -1525,7 +1530,6 @@ class EmformerEncoder(nn.Module):
             right_context at the end.
           states (List[torch.Tensor, List[List[torch.Tensor]], List[torch.Tensor]]: # noqa
             Cached states containing:
-            - past_lens: number of past frames for each sample in batch
             - attn_caches: attention states from preceding chunk's computation,
               where each element corresponds to each emformer layer
             - conv_caches: left context for causal convolution, where each
@@ -1580,13 +1584,15 @@ class EmformerEncoder(nn.Module):
         # calcualte padding mask to mask out initial zero caches
         chunk_mask = make_pad_mask(output_lengths).to(x.device)
         memory_mask = (
-            torch.div(
+            (
-                num_processed_frames, self.chunk_length, rounding_mode="floor"
+                (num_processed_frames >> self.shift).view(x.size(1), 1)
-            ).view(x.size(1), 1)
                 <= torch.arange(self.memory_size, device=x.device).expand(
                     x.size(1), self.memory_size
                 )
             ).flip(1)
+            if self.use_memory
+            else torch.empty(0).to(dtype=torch.bool, device=x.device)
+        )
         left_context_mask = (
             num_processed_frames.view(x.size(1), 1)
             <= torch.arange(self.left_context_length, device=x.device).expand(
@@ -1631,6 +1637,31 @@ class EmformerEncoder(nn.Module):
         )
         return output, output_lengths, output_states
 
+    @torch.jit.export
+    def init_states(self, device: torch.device = torch.device("cpu")):
+        """Create initial states."""
+        attn_caches = [
+            [
+                torch.zeros(self.memory_size, self.d_model, device=device),
+                torch.zeros(
+                    self.left_context_length, self.d_model, device=device
+                ),
+                torch.zeros(
+                    self.left_context_length, self.d_model, device=device
+                ),
+            ]
+            for _ in range(self.num_encoder_layers)
+        ]
+        conv_caches = [
+            torch.zeros(self.d_model, self.cnn_module_kernel - 1, device=device)
+            for _ in range(self.num_encoder_layers)
+        ]
+        states: Tuple[List[List[torch.Tensor]], List[torch.Tensor]] = (
+            attn_caches,
+            conv_caches,
+        )
+        return states
+
 
 class Emformer(EncoderInterface):
     def __init__(
@@ -1655,6 +1686,7 @@ class Emformer(EncoderInterface):
 
         self.subsampling_factor = subsampling_factor
         self.right_context_length = right_context_length
+        self.chunk_length = chunk_length
         if subsampling_factor != 4:
             raise NotImplementedError("Support only 'subsampling_factor=4'.")
         if chunk_length % subsampling_factor != 0:
@@ -1803,6 +1835,11 @@ class Emformer(EncoderInterface):
 
         return output, output_lengths, output_states
 
+    @torch.jit.export
+    def init_states(self, device: torch.device = torch.device("cpu")):
+        """Create initial states."""
+        return self.encoder.init_states(device)
+
 
 class Conv2dSubsampling(nn.Module):
     """Convolutional 2D subsampling (to 1/4 length).

egs/librispeech/ASR/conv_emformer_transducer_stateless/stream.py

@@ -43,15 +43,12 @@ class Stream(object):
           device:
             The device to run this stream.
         """
-        self.device = device
         self.LOG_EPS = LOG_EPS
 
         # Containing attention caches and convolution caches
         self.states: Optional[
             Tuple[List[List[torch.Tensor]], List[torch.Tensor]]
         ] = None
-        # Initailize zero states.
-        self.init_states(params)
 
         # It uses different attributes for different decoding methods.
         self.context_size = params.context_size
@@ -107,34 +104,11 @@ class Stream(object):
     def set_ground_truth(self, ground_truth: str) -> None:
         self.ground_truth = ground_truth
 
-    def init_states(self, params: AttributeDict) -> None:
+    def set_states(
-        attn_caches = [
+        self, states: Tuple[List[List[torch.Tensor]], List[torch.Tensor]]
-            [
+    ) -> None:
-                torch.zeros(
+        """Set states."""
-                    params.memory_size, params.encoder_dim, device=self.device
+        self.states = states
-                ),
-                torch.zeros(
-                    params.left_context_length // params.subsampling_factor,
-                    params.encoder_dim,
-                    device=self.device,
-                ),
-                torch.zeros(
-                    params.left_context_length // params.subsampling_factor,
-                    params.encoder_dim,
-                    device=self.device,
-                ),
-            ]
-            for _ in range(params.num_encoder_layers)
-        ]
-        conv_caches = [
-            torch.zeros(
-                params.encoder_dim,
-                params.cnn_module_kernel - 1,
-                device=self.device,
-            )
-            for _ in range(params.num_encoder_layers)
-        ]
-        self.states = (attn_caches, conv_caches)
 
     def get_feature_chunk(self) -> torch.Tensor:
         """Get a chunk of feature frames.

egs/librispeech/ASR/conv_emformer_transducer_stateless/streaming_decode.py

@@ -683,6 +683,8 @@ def decode_dataset(
             LOG_EPS=LOG_EPSILON,
         )
 
+        stream.set_states(model.encoder.init_states(device))
+
         audio: np.ndarray = cut.load_audio()
         # audio.shape: (1, num_samples)
         assert len(audio.shape) == 2

egs/librispeech/ASR/conv_emformer_transducer_stateless/train.py

@@ -28,7 +28,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
   --start-epoch 1 \
   --exp-dir conv_emformer_transducer_stateless/exp \
   --full-libri 1 \
-  --max-duration 300 \
+  --max-duration 280 \
   --master-port 12321 \
   --num-encoder-layers 12 \
   --chunk-length 32 \

egs/librispeech/ASR/conv_emformer_transducer_stateless2/asr_datamodule.py

@@ -0,0 +1 @@
+../conv_emformer_transducer_stateless/asr_datamodule.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/beam_search.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/beam_search.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/decode.py

@@ -0,0 +1,657 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 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
+./conv_emformer_transducer_stateless2/decode.py \
+      --epoch 30 \
+      --avg 10 \
+      --exp-dir conv_emformer_transducer_stateless2/exp \
+      --max-duration 300 \
+      --num-encoder-layers 12 \
+      --chunk-length 32 \
+      --cnn-module-kernel 31 \
+      --left-context-length 32 \
+      --right-context-length 8 \
+      --memory-size 32 \
+      --decoding-method greedy_search \
+      --use-averaged-model True
+
+(2) modified beam search
+./conv_emformer_transducer_stateless2/decode.py \
+      --epoch 30 \
+      --avg 10 \
+      --exp-dir conv_emformer_transducer_stateless2/exp \
+      --max-duration 300 \
+      --num-encoder-layers 12 \
+      --chunk-length 32 \
+      --cnn-module-kernel 31 \
+      --left-context-length 32 \
+      --right-context-length 8 \
+      --memory-size 32 \
+      --decoding-method modified_beam_search \
+      --use-averaged-model True \
+      --beam-size 4
+
+(3) fast beam search
+./conv_emformer_transducer_stateless2/decode.py \
+      --epoch 30 \
+      --avg 10 \
+      --exp-dir conv_emformer_transducer_stateless2/exp \
+      --max-duration 300 \
+      --num-encoder-layers 12 \
+      --chunk-length 32 \
+      --cnn-module-kernel 31 \
+      --left-context-length 32 \
+      --right-context-length 8 \
+      --memory-size 32 \
+      --decoding-method fast_beam_search \
+      --use-averaged-model True \
+      --beam 4 \
+      --max-contexts 4 \
+      --max-states 8
+"""
+
+
+import argparse
+import logging
+import math
+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 LibriSpeechAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import (
+    AttributeDict,
+    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(
+        "--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=10,
+        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="pruned_transducer_stateless4/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(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    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=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=8,
+        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(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    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`.
+      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 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)
+
+    feature_lens += params.chunk_length
+    feature = torch.nn.functional.pad(
+        feature,
+        pad=(0, 0, 0, params.chunk_length),
+        value=LOG_EPS,
+    )
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=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(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(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(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(sp.decode(hyp).split())
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": hyps}
+    elif params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): 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,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[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.
+      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.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 100
+    else:
+        log_interval = 2
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for hyp_words, ref_text in zip(hyps, texts):
+                ref_words = ref_text.split()
+                this_batch.append((ref_words, 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[List[int], List[int]]]],
+):
+    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"
+        )
+        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()
+    LibriSpeechAsrDataModule.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",
+        "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 "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}"
+    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> 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_transducer_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
+            start = params.epoch - params.avg
+            assert start >= 1
+            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 params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+
+    test_sets = ["test-clean", "test-other"]
+    test_dl = [test_clean_dl, test_other_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            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/librispeech/ASR/conv_emformer_transducer_stateless2/decoder.py

@@ -0,0 +1 @@
+../conv_emformer_transducer_stateless/decoder.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/encoder_interface.py

@@ -0,0 +1 @@
+../conv_emformer_transducer_stateless/encoder_interface.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/export.py

@@ -0,0 +1,287 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: 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 converts several saved checkpoints
+# to a single one using model averaging.
+"""
+Usage:
+./conv_emformer_transducer_stateless2/export.py \
+  --exp-dir ./conv_emformer_transducer_stateless2/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 30 \
+  --avg 10 \
+  --use-averaged-model=True \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32 \
+  --jit False
+
+It will generate a file exp_dir/pretrained.pt
+
+To use the generated file with `conv_emformer_transducer_stateless2/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+    ./conv_emformer_transducer_stateless2/decode.py \
+        --exp-dir ./conv_emformer_transducer_stateless2/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 100 \
+        --bpe-model data/lang_bpe_500/bpe.model \
+        --use-averaged-model=False \
+        --num-encoder-layers 12 \
+        --chunk-length 32 \
+        --cnn-module-kernel 31 \
+        --left-context-length 32 \
+        --right-context-length 8 \
+        --memory-size 32
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import sentencepiece as spm
+import torch
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        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(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless2/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--jit",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.script.
+        """,
+    )
+
+    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(
+        "--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. ",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_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.eval()
+
+    if params.jit:
+        # We won't use the forward() method of the model in C++, so just ignore
+        # it here.
+        # Otherwise, one of its arguments is a ragged tensor and is not
+        # torch scriptabe.
+        model.__class__.forward = torch.jit.ignore(model.__class__.forward)
+        logging.info("Using torch.jit.script")
+        model = torch.jit.script(model)
+        filename = params.exp_dir / "cpu_jit.pt"
+        model.save(str(filename))
+        logging.info(f"Saved to {filename}")
+    else:
+        logging.info("Not using torch.jit.script")
+        # Save it using a format so that it can be loaded
+        # by :func:`load_checkpoint`
+        filename = params.exp_dir / "pretrained.pt"
+        torch.save({"model": model.state_dict()}, str(filename))
+        logging.info(f"Saved to {filename}")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conv_emformer_transducer_stateless2/joiner.py

@@ -0,0 +1 @@
+../conv_emformer_transducer_stateless/joiner.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/model.py

@@ -0,0 +1 @@
+../conv_emformer_transducer_stateless/model.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/optim.py

@@ -0,0 +1 @@
+../conv_emformer_transducer_stateless/optim.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/scaling.py

@@ -0,0 +1 @@
+../conv_emformer_transducer_stateless/scaling.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/stream.py

@@ -0,0 +1 @@
+../conv_emformer_transducer_stateless/stream.py

egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming_decode.py

@@ -0,0 +1,980 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 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
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+      --epoch 30 \
+      --avg 10 \
+      --exp-dir conv_emformer_transducer_stateless2/exp \
+      --num-decode-streams 2000 \
+      --num-encoder-layers 12 \
+      --chunk-length 32 \
+      --cnn-module-kernel 31 \
+      --left-context-length 32 \
+      --right-context-length 8 \
+      --memory-size 32 \
+      --decoding-method greedy_search \
+      --use-averaged-model True
+
+(2) modified beam search
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+      --epoch 30 \
+      --avg 10 \
+      --exp-dir conv_emformer_transducer_stateless2/exp \
+      --num-decode-streams 2000 \
+      --num-encoder-layers 12 \
+      --chunk-length 32 \
+      --cnn-module-kernel 31 \
+      --left-context-length 32 \
+      --right-context-length 8 \
+      --memory-size 32 \
+      --decoding-method modified_beam_search \
+      --use-averaged-model True \
+      --beam-size 4
+
+(3) fast beam search
+./conv_emformer_transducer_stateless2/streaming_decode.py \
+      --epoch 30 \
+      --avg 10 \
+      --exp-dir conv_emformer_transducer_stateless2/exp \
+      --num-decode-streams 2000 \
+      --num-encoder-layers 12 \
+      --chunk-length 32 \
+      --cnn-module-kernel 31 \
+      --left-context-length 32 \
+      --right-context-length 8 \
+      --memory-size 32 \
+      --decoding-method fast_beam_search \
+      --use-averaged-model True \
+      --beam 4 \
+      --max-contexts 4 \
+      --max-states 8
+"""
+import argparse
+import logging
+import warnings
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+from lhotse import CutSet
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
+from beam_search import Hypothesis, HypothesisList, get_hyps_shape
+from emformer import LOG_EPSILON, stack_states, unstack_states
+from kaldifeat import Fbank, FbankOptions
+from stream import Stream
+from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.decode import one_best_decoding
+from icefall.utils import (
+    AttributeDict,
+    get_texts,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+
+    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'. ",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=False,
+        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="transducer_emformer/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(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An interger 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=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=8,
+        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(
+        "--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(
+        "--sampling-rate",
+        type=float,
+        default=16000,
+        help="Sample rate of the audio",
+    )
+
+    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 greedy_search(
+    model: nn.Module,
+    encoder_out: torch.Tensor,
+    streams: List[Stream],
+) -> None:
+    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
+
+    Args:
+      model:
+        The transducer model.
+      encoder_out:
+        Output from the encoder. Its shape is (N, T, C), where N >= 1.
+      streams:
+        A list of Stream objects.
+    """
+    assert len(streams) == encoder_out.size(0)
+    assert encoder_out.ndim == 3
+
+    blank_id = model.decoder.blank_id
+    context_size = model.decoder.context_size
+    device = next(model.parameters()).device
+    T = encoder_out.size(1)
+
+    encoder_out = model.joiner.encoder_proj(encoder_out)
+
+    decoder_input = torch.tensor(
+        [stream.hyp[-context_size:] for stream in streams],
+        device=device,
+        dtype=torch.int64,
+    )
+    # decoder_out is of shape (batch_size, 1, decoder_out_dim)
+    decoder_out = model.decoder(decoder_input, need_pad=False)
+    decoder_out = model.joiner.decoder_proj(decoder_out)
+
+    for t in range(T):
+        # current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
+        current_encoder_out = encoder_out[:, t : t + 1, :]  # noqa
+
+        logits = model.joiner(
+            current_encoder_out.unsqueeze(2),
+            decoder_out.unsqueeze(1),
+            project_input=False,
+        )
+        # logits'shape (batch_size,  vocab_size)
+        logits = logits.squeeze(1).squeeze(1)
+
+        assert logits.ndim == 2, logits.shape
+        y = logits.argmax(dim=1).tolist()
+        emitted = False
+        for i, v in enumerate(y):
+            if v != blank_id:
+                streams[i].hyp.append(v)
+                emitted = True
+        if emitted:
+            # update decoder output
+            decoder_input = torch.tensor(
+                [stream.hyp[-context_size:] for stream in streams],
+                device=device,
+                dtype=torch.int64,
+            )
+            decoder_out = model.decoder(
+                decoder_input,
+                need_pad=False,
+            )
+            decoder_out = model.joiner.decoder_proj(decoder_out)
+
+
+def modified_beam_search(
+    model: nn.Module,
+    encoder_out: torch.Tensor,
+    streams: List[Stream],
+    beam: int = 4,
+):
+    """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded.
+
+    Args:
+      model:
+        The RNN-T model.
+      encoder_out:
+        A 3-D tensor of shape (N, T, encoder_out_dim) containing the output of
+        the encoder model.
+      streams:
+        A list of stream objects.
+      beam:
+        Number of active paths during the beam search.
+    """
+    assert encoder_out.ndim == 3, encoder_out.shape
+    assert len(streams) == encoder_out.size(0)
+
+    blank_id = model.decoder.blank_id
+    context_size = model.decoder.context_size
+    device = next(model.parameters()).device
+    batch_size = len(streams)
+    T = encoder_out.size(1)
+
+    B = [stream.hyps for stream in streams]
+
+    encoder_out = model.joiner.encoder_proj(encoder_out)
+
+    for t in range(T):
+        current_encoder_out = encoder_out[:, t].unsqueeze(1).unsqueeze(1)
+        # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
+
+        hyps_shape = get_hyps_shape(B).to(device)
+
+        A = [list(b) for b in B]
+        B = [HypothesisList() for _ in range(batch_size)]
+
+        ys_log_probs = torch.stack(
+            [hyp.log_prob.reshape(1) for hyps in A for hyp in hyps], dim=0
+        )  # (num_hyps, 1)
+
+        decoder_input = torch.tensor(
+            [hyp.ys[-context_size:] for hyps in A for hyp in hyps],
+            device=device,
+            dtype=torch.int64,
+        )  # (num_hyps, context_size)
+
+        decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1)
+        decoder_out = model.joiner.decoder_proj(decoder_out)
+        # decoder_out is of shape (num_hyps, 1, 1, decoder_output_dim)
+
+        # Note: For torch 1.7.1 and below, it requires a torch.int64 tensor
+        # as index, so we use `to(torch.int64)` below.
+        current_encoder_out = torch.index_select(
+            current_encoder_out,
+            dim=0,
+            index=hyps_shape.row_ids(1).to(torch.int64),
+        )  # (num_hyps, encoder_out_dim)
+
+        logits = model.joiner(
+            current_encoder_out, decoder_out, project_input=False
+        )
+        # logits is of shape (num_hyps, 1, 1, vocab_size)
+
+        logits = logits.squeeze(1).squeeze(1)
+
+        log_probs = logits.log_softmax(dim=-1)  # (num_hyps, vocab_size)
+
+        log_probs.add_(ys_log_probs)
+
+        vocab_size = log_probs.size(-1)
+
+        log_probs = log_probs.reshape(-1)
+
+        row_splits = hyps_shape.row_splits(1) * vocab_size
+        log_probs_shape = k2.ragged.create_ragged_shape2(
+            row_splits=row_splits, cached_tot_size=log_probs.numel()
+        )
+        ragged_log_probs = k2.RaggedTensor(
+            shape=log_probs_shape, value=log_probs
+        )
+
+        for i in range(batch_size):
+            topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
+                topk_token_indexes = (topk_indexes % vocab_size).tolist()
+
+            for k in range(len(topk_hyp_indexes)):
+                hyp_idx = topk_hyp_indexes[k]
+                hyp = A[i][hyp_idx]
+
+                new_ys = hyp.ys[:]
+                new_token = topk_token_indexes[k]
+                if new_token != blank_id:
+                    new_ys.append(new_token)
+
+                new_log_prob = topk_log_probs[k]
+                new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
+                B[i].add(new_hyp)
+
+    for i in range(batch_size):
+        streams[i].hyps = B[i]
+
+
+def fast_beam_search_one_best(
+    model: nn.Module,
+    streams: List[Stream],
+    encoder_out: torch.Tensor,
+    processed_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+) -> None:
+    """It limits the maximum number of symbols per frame to 1.
+
+    A lattice is first obtained using modified beam search, and then
+    the shortest path within the lattice is used as the final output.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      streams:
+        A list of stream objects.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      processed_lens:
+        A tensor of shape (N,) containing the number of processed frames
+        in `encoder_out` before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+    """
+    assert encoder_out.ndim == 3
+
+    context_size = model.decoder.context_size
+    vocab_size = model.decoder.vocab_size
+
+    B, T, C = encoder_out.shape
+    assert B == len(streams)
+
+    config = k2.RnntDecodingConfig(
+        vocab_size=vocab_size,
+        decoder_history_len=context_size,
+        beam=beam,
+        max_contexts=max_contexts,
+        max_states=max_states,
+    )
+    individual_streams = []
+    for i in range(B):
+        individual_streams.append(streams[i].rnnt_decoding_stream)
+    decoding_streams = k2.RnntDecodingStreams(individual_streams, config)
+
+    encoder_out = model.joiner.encoder_proj(encoder_out)
+
+    for t in range(T):
+        # shape is a RaggedShape of shape (B, context)
+        # contexts is a Tensor of shape (shape.NumElements(), context_size)
+        shape, contexts = decoding_streams.get_contexts()
+        # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
+        contexts = contexts.to(torch.int64)
+        # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
+        decoder_out = model.decoder(contexts, need_pad=False)
+        decoder_out = model.joiner.decoder_proj(decoder_out)
+        # current_encoder_out is of shape
+        # (shape.NumElements(), 1, joiner_dim)
+        # fmt: off
+        current_encoder_out = torch.index_select(
+            encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
+        )
+        # fmt: on
+        logits = model.joiner(
+            current_encoder_out.unsqueeze(2),
+            decoder_out.unsqueeze(1),
+            project_input=False,
+        )
+        logits = logits.squeeze(1).squeeze(1)
+        log_probs = logits.log_softmax(dim=-1)
+        decoding_streams.advance(log_probs)
+
+    decoding_streams.terminate_and_flush_to_streams()
+
+    lattice = decoding_streams.format_output(processed_lens.tolist())
+
+    best_path = one_best_decoding(lattice)
+    hyps = get_texts(best_path)
+
+    for i in range(B):
+        streams[i].hyp = hyps[i]
+
+
+def decode_one_chunk(
+    model: nn.Module,
+    streams: List[Stream],
+    params: AttributeDict,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> List[int]:
+    """
+    Args:
+      model:
+        The Transducer model.
+      streams:
+        A list of Stream objects.
+      params:
+        It is returned by :func:`get_params`.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+
+    Returns:
+       A list of indexes indicating the finished streams.
+    """
+    device = next(model.parameters()).device
+
+    feature_list = []
+    feature_len_list = []
+    state_list = []
+    num_processed_frames_list = []
+
+    for stream in streams:
+        # We should first get `stream.num_processed_frames`
+        # before calling `stream.get_feature_chunk()`
+        # since `stream.num_processed_frames` would be updated
+        num_processed_frames_list.append(stream.num_processed_frames)
+        feature = stream.get_feature_chunk()
+        feature_len = feature.size(0)
+        feature_list.append(feature)
+        feature_len_list.append(feature_len)
+        state_list.append(stream.states)
+
+    features = pad_sequence(
+        feature_list, batch_first=True, padding_value=LOG_EPSILON
+    ).to(device)
+    feature_lens = torch.tensor(feature_len_list, device=device)
+    num_processed_frames = torch.tensor(
+        num_processed_frames_list, device=device
+    )
+
+    # Make sure it has at least 1 frame after subsampling, first-and-last-frame cutting, and right context cutting  # noqa
+    tail_length = (
+        3 * params.subsampling_factor + params.right_context_length + 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_EPSILON,
+        )
+
+    # Stack states of all streams
+    states = stack_states(state_list)
+
+    encoder_out, encoder_out_lens, states = model.encoder.infer(
+        x=features,
+        x_lens=feature_lens,
+        states=states,
+        num_processed_frames=num_processed_frames,
+    )
+
+    if params.decoding_method == "greedy_search":
+        greedy_search(
+            model=model,
+            streams=streams,
+            encoder_out=encoder_out,
+        )
+    elif params.decoding_method == "modified_beam_search":
+        modified_beam_search(
+            model=model,
+            streams=streams,
+            encoder_out=encoder_out,
+            beam=params.beam_size,
+        )
+    elif params.decoding_method == "fast_beam_search":
+        # feature_len is needed to get partial results.
+        # The rnnt_decoding_stream for fast_beam_search.
+        fast_beam_search_one_best(
+            model=model,
+            streams=streams,
+            encoder_out=encoder_out,
+            processed_lens=(num_processed_frames >> 2) + encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+    else:
+        raise ValueError(
+            f"Unsupported decoding method: {params.decoding_method}"
+        )
+
+    # Update cached states of each stream
+    state_list = unstack_states(states)
+    for i, s in enumerate(state_list):
+        streams[i].states = s
+
+    finished_streams = [i for i, stream in enumerate(streams) if stream.done]
+    return finished_streams
+
+
+def create_streaming_feature_extractor() -> Fbank:
+    """Create a CPU streaming feature extractor.
+
+    At present, we assume it returns a fbank feature extractor with
+    fixed options. In the future, we will support passing in the options
+    from outside.
+
+    Returns:
+      Return a CPU streaming feature extractor.
+    """
+    opts = FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+    return Fbank(opts)
+
+
+def decode_dataset(
+    cuts: CutSet,
+    model: nn.Module,
+    params: AttributeDict,
+    sp: spm.SentencePieceProcessor,
+    decoding_graph: Optional[k2.Fsa] = None,
+):
+    """Decode dataset.
+
+    Args:
+      cuts:
+        Lhotse Cutset containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The Transducer 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 = next(model.parameters()).device
+
+    log_interval = 300
+
+    fbank = create_streaming_feature_extractor()
+
+    decode_results = []
+    streams = []
+    for num, cut in enumerate(cuts):
+        # Each utterance has a Stream.
+        stream = Stream(
+            params=params,
+            decoding_graph=decoding_graph,
+            device=device,
+            LOG_EPS=LOG_EPSILON,
+        )
+
+        stream.set_states(model.encoder.init_states(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
+        assert audio.max() <= 1, "Should be normalized to [-1, 1])"
+
+        samples = torch.from_numpy(audio).squeeze(0)
+        feature = fbank(samples)
+        stream.set_feature(feature)
+        stream.set_ground_truth(cut.supervisions[0].text)
+
+        streams.append(stream)
+
+        while len(streams) >= params.num_decode_streams:
+            finished_streams = decode_one_chunk(
+                model=model,
+                streams=streams,
+                params=params,
+                decoding_graph=decoding_graph,
+            )
+
+            for i in sorted(finished_streams, reverse=True):
+                decode_results.append(
+                    (
+                        streams[i].ground_truth.split(),
+                        sp.decode(streams[i].decoding_result()).split(),
+                    )
+                )
+                del streams[i]
+
+        if num % log_interval == 0:
+            logging.info(f"Cuts processed until now is {num}.")
+
+    while len(streams) > 0:
+        finished_streams = decode_one_chunk(
+            model=model,
+            streams=streams,
+            params=params,
+            decoding_graph=decoding_graph,
+        )
+
+        for i in sorted(finished_streams, reverse=True):
+            decode_results.append(
+                (
+                    streams[i].ground_truth.split(),
+                    sp.decode(streams[i].decoding_result()).split(),
+                )
+            )
+            del streams[i]
+
+    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}"
+        )
+    else:
+        key = f"beam_size_{params.beam_size}"
+
+    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"
+        )
+        store_transcripts(filename=recog_path, texts=sorted(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()
+    LibriSpeechAsrDataModule.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",
+        "fast_beam_search",
+        "modified_beam_search",
+    )
+    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
+    params.suffix += f"-streaming-chunk-length-{params.chunk_length}"
+    params.suffix += f"-left-context-length-{params.left_context_length}"
+    params.suffix += f"-right-context-length-{params.right_context_length}"
+    params.suffix += f"-memory-size-{params.memory_size}"
+
+    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}"
+    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-streaming-decode")
+    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()
+
+    params.device = device
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_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.eval()
+
+    if params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_sets = ["test-clean", "test-other"]
+    test_cuts = [test_clean_cuts, test_other_cuts]
+
+    for test_set, test_cut in zip(test_sets, test_cuts):
+        results_dict = decode_dataset(
+            cuts=test_cut,
+            model=model,
+            params=params,
+            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__":
+    torch.manual_seed(20220410)
+    main()

egs/librispeech/ASR/conv_emformer_transducer_stateless2/test_emformer.py

@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+#
+# Copyright 2022 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.
+
+
+import torch
+from emformer import ConvolutionModule, Emformer, stack_states, unstack_states
+
+
+def test_convolution_module_forward():
+    B, D = 2, 256
+    chunk_length = 4
+    right_context_length = 2
+    num_chunks = 3
+    U = num_chunks * chunk_length
+    R = num_chunks * right_context_length
+    kernel_size = 31
+    conv_module = ConvolutionModule(
+        chunk_length,
+        right_context_length,
+        D,
+        kernel_size,
+    )
+
+    utterance = torch.randn(U, B, D)
+    right_context = torch.randn(R, B, D)
+
+    utterance, right_context = conv_module(utterance, right_context)
+    assert utterance.shape == (U, B, D), utterance.shape
+    assert right_context.shape == (R, B, D), right_context.shape
+
+
+def test_convolution_module_infer():
+    from emformer import ConvolutionModule
+
+    B, D = 2, 256
+    chunk_length = 4
+    right_context_length = 2
+    num_chunks = 1
+    U = num_chunks * chunk_length
+    R = num_chunks * right_context_length
+    kernel_size = 31
+    conv_module = ConvolutionModule(
+        chunk_length,
+        right_context_length,
+        D,
+        kernel_size,
+    )
+
+    utterance = torch.randn(U, B, D)
+    right_context = torch.randn(R, B, D)
+    cache = torch.randn(B, D, kernel_size - 1)
+
+    utterance, right_context, new_cache = conv_module.infer(
+        utterance, right_context, cache
+    )
+    assert utterance.shape == (U, B, D), utterance.shape
+    assert right_context.shape == (R, B, D), right_context.shape
+    assert new_cache.shape == (B, D, kernel_size - 1), new_cache.shape
+
+
+def test_state_stack_unstack():
+    num_features = 80
+    chunk_length = 32
+    encoder_dim = 512
+    num_encoder_layers = 2
+    kernel_size = 31
+    left_context_length = 32
+    right_context_length = 8
+    memory_size = 32
+
+    model = Emformer(
+        num_features=num_features,
+        chunk_length=chunk_length,
+        subsampling_factor=4,
+        d_model=encoder_dim,
+        num_encoder_layers=num_encoder_layers,
+        cnn_module_kernel=kernel_size,
+        left_context_length=left_context_length,
+        right_context_length=right_context_length,
+        memory_size=memory_size,
+    )
+
+    for batch_size in [1, 2]:
+        attn_caches = [
+            [
+                torch.zeros(memory_size, batch_size, encoder_dim),
+                torch.zeros(left_context_length // 4, batch_size, encoder_dim),
+                torch.zeros(
+                    left_context_length // 4,
+                    batch_size,
+                    encoder_dim,
+                ),
+            ]
+            for _ in range(num_encoder_layers)
+        ]
+        conv_caches = [
+            torch.zeros(batch_size, encoder_dim, kernel_size - 1)
+            for _ in range(num_encoder_layers)
+        ]
+        states = [attn_caches, conv_caches]
+        x = torch.randn(batch_size, 23, num_features)
+        x_lens = torch.full((batch_size,), 23)
+        num_processed_frames = torch.full((batch_size,), 0)
+        y, y_lens, states = model.infer(
+            x, x_lens, num_processed_frames=num_processed_frames, states=states
+        )
+
+        state_list = unstack_states(states)
+        states2 = stack_states(state_list)
+
+        for ss, ss2 in zip(states[0], states2[0]):
+            for s, s2 in zip(ss, ss2):
+                assert torch.allclose(s, s2), f"{s.sum()}, {s2.sum()}"
+
+        for s, s2 in zip(states[1], states2[1]):
+            assert torch.allclose(s, s2), f"{s.sum()}, {s2.sum()}"
+
+
+def test_torchscript_consistency_infer():
+    r"""Verify that scripting Emformer does not change the behavior of method `infer`."""  # noqa
+    num_features = 80
+    chunk_length = 32
+    encoder_dim = 512
+    num_encoder_layers = 2
+    kernel_size = 31
+    left_context_length = 32
+    right_context_length = 8
+    memory_size = 32
+    batch_size = 2
+
+    model = Emformer(
+        num_features=num_features,
+        chunk_length=chunk_length,
+        subsampling_factor=4,
+        d_model=encoder_dim,
+        num_encoder_layers=num_encoder_layers,
+        cnn_module_kernel=kernel_size,
+        left_context_length=left_context_length,
+        right_context_length=right_context_length,
+        memory_size=memory_size,
+    ).eval()
+    attn_caches = [
+        [
+            torch.zeros(memory_size, batch_size, encoder_dim),
+            torch.zeros(left_context_length // 4, batch_size, encoder_dim),
+            torch.zeros(
+                left_context_length // 4,
+                batch_size,
+                encoder_dim,
+            ),
+        ]
+        for _ in range(num_encoder_layers)
+    ]
+    conv_caches = [
+        torch.zeros(batch_size, encoder_dim, kernel_size - 1)
+        for _ in range(num_encoder_layers)
+    ]
+    states = [attn_caches, conv_caches]
+    x = torch.randn(batch_size, 23, num_features)
+    x_lens = torch.full((batch_size,), 23)
+    num_processed_frames = torch.full((batch_size,), 0)
+    y, y_lens, out_states = model.infer(
+        x, x_lens, num_processed_frames=num_processed_frames, states=states
+    )
+
+    sc_model = torch.jit.script(model).eval()
+    sc_y, sc_y_lens, sc_out_states = sc_model.infer(
+        x, x_lens, num_processed_frames=num_processed_frames, states=states
+    )
+
+    assert torch.allclose(y, sc_y)
+
+
+if __name__ == "__main__":
+    test_convolution_module_forward()
+    test_convolution_module_infer()
+    test_state_stack_unstack()
+    test_torchscript_consistency_infer()

egs/librispeech/ASR/distillation_with_hubert.sh

@@ -77,9 +77,9 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ] && [ ! "$use_extracted_codebook" ==
   fi
 
   # Install quantization toolkit:
-  # pip install git+https://github.com/danpovey/quantization.git@master
+  # pip install git+https://github.com/k2-fsa/multi_quantization.git
-  # when testing this code:
+  # or
-  # commit c17ffe67aa2e6ca6b6855c50fde812f2eed7870b is used.
+  # pip install multi_quantization
 
   has_quantization=$(python3 -c "import importlib; print(importlib.util.find_spec('quantization') is not None)")
   if [ $has_quantization == 'False' ]; then

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -19,6 +19,7 @@ from dataclasses import dataclass
 from typing import Dict, List, Optional
 
 import k2
+import sentencepiece as spm
 import torch
 from model import Transducer
 
@@ -34,6 +35,7 @@ def fast_beam_search_one_best(
     beam: float,
     max_states: int,
     max_contexts: int,
+    temperature: float = 1.0,
 ) -> List[List[int]]:
     """It limits the maximum number of symbols per frame to 1.
 
@@ -56,6 +58,8 @@ def fast_beam_search_one_best(
         Max states per stream per frame.
       max_contexts:
         Max contexts pre stream per frame.
+      temperature:
+        Softmax temperature.
     Returns:
       Return the decoded result.
     """
@@ -67,6 +71,7 @@ def fast_beam_search_one_best(
         beam=beam,
         max_states=max_states,
         max_contexts=max_contexts,
+        temperature=temperature,
     )
 
     best_path = one_best_decoding(lattice)
@@ -85,6 +90,7 @@ def fast_beam_search_nbest_LG(
     num_paths: int,
     nbest_scale: float = 0.5,
     use_double_scores: bool = True,
+    temperature: float = 1.0,
 ) -> List[List[int]]:
     """It limits the maximum number of symbols per frame to 1.
 
@@ -120,6 +126,8 @@ def fast_beam_search_nbest_LG(
       use_double_scores:
         True to use double precision for computation. False to use
         single precision.
+      temperature:
+        Softmax temperature.
     Returns:
       Return the decoded result.
     """
@@ -131,6 +139,7 @@ def fast_beam_search_nbest_LG(
         beam=beam,
         max_states=max_states,
         max_contexts=max_contexts,
+        temperature=temperature,
     )
 
     nbest = Nbest.from_lattice(
@@ -201,6 +210,7 @@ def fast_beam_search_nbest(
     num_paths: int,
     nbest_scale: float = 0.5,
     use_double_scores: bool = True,
+    temperature: float = 1.0,
 ) -> List[List[int]]:
     """It limits the maximum number of symbols per frame to 1.
 
@@ -236,6 +246,8 @@ def fast_beam_search_nbest(
       use_double_scores:
         True to use double precision for computation. False to use
         single precision.
+      temperature:
+        Softmax temperature.
     Returns:
       Return the decoded result.
     """
@@ -247,6 +259,7 @@ def fast_beam_search_nbest(
         beam=beam,
         max_states=max_states,
         max_contexts=max_contexts,
+        temperature=temperature,
     )
 
     nbest = Nbest.from_lattice(
@@ -282,6 +295,7 @@ def fast_beam_search_nbest_oracle(
     ref_texts: List[List[int]],
     use_double_scores: bool = True,
     nbest_scale: float = 0.5,
+    temperature: float = 1.0,
 ) -> List[List[int]]:
     """It limits the maximum number of symbols per frame to 1.
 
@@ -321,7 +335,8 @@ def fast_beam_search_nbest_oracle(
       nbest_scale:
         It's the scale applied to the lattice.scores. A smaller value
         yields more unique paths.
-
+      temperature:
+        Softmax temperature.
     Returns:
       Return the decoded result.
     """
@@ -333,6 +348,7 @@ def fast_beam_search_nbest_oracle(
         beam=beam,
         max_states=max_states,
         max_contexts=max_contexts,
+        temperature=temperature,
     )
 
     nbest = Nbest.from_lattice(
@@ -373,6 +389,7 @@ def fast_beam_search(
     beam: float,
     max_states: int,
     max_contexts: int,
+    temperature: float = 1.0,
 ) -> k2.Fsa:
     """It limits the maximum number of symbols per frame to 1.
 
@@ -392,6 +409,8 @@ def fast_beam_search(
         Max states per stream per frame.
       max_contexts:
         Max contexts pre stream per frame.
+      temperature:
+        Softmax temperature.
     Returns:
       Return an FsaVec with axes [utt][state][arc] containing the decoded
       lattice. Note: When the input graph is a TrivialGraph, the returned
@@ -440,7 +459,7 @@ def fast_beam_search(
             project_input=False,
         )
         logits = logits.squeeze(1).squeeze(1)
-        log_probs = logits.log_softmax(dim=-1)
+        log_probs = (logits / temperature).log_softmax(dim=-1)
         decoding_streams.advance(log_probs)
     decoding_streams.terminate_and_flush_to_streams()
     lattice = decoding_streams.format_output(encoder_out_lens.tolist())
@@ -783,6 +802,7 @@ def modified_beam_search(
     encoder_out: torch.Tensor,
     encoder_out_lens: torch.Tensor,
     beam: int = 4,
+    temperature: float = 1.0,
 ) -> List[List[int]]:
     """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded.
 
@@ -796,6 +816,8 @@ def modified_beam_search(
         encoder_out before padding.
       beam:
         Number of active paths during the beam search.
+      temperature:
+        Softmax temperature.
     Returns:
       Return a list-of-list of token IDs. ans[i] is the decoding results
       for the i-th utterance.
@@ -879,7 +901,9 @@ def modified_beam_search(
 
         logits = logits.squeeze(1).squeeze(1)  # (num_hyps, vocab_size)
 
-        log_probs = logits.log_softmax(dim=-1)  # (num_hyps, vocab_size)
+        log_probs = (logits / temperature).log_softmax(
+            dim=-1
+        )  # (num_hyps, vocab_size)
 
         log_probs.add_(ys_log_probs)
 
@@ -1043,6 +1067,7 @@ def beam_search(
     model: Transducer,
     encoder_out: torch.Tensor,
     beam: int = 4,
+    temperature: float = 1.0,
 ) -> List[int]:
     """
     It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf
@@ -1056,6 +1081,8 @@ def beam_search(
         A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
       beam:
         Beam size.
+      temperature:
+        Softmax temperature.
     Returns:
       Return the decoded result.
     """
@@ -1132,7 +1159,7 @@ def beam_search(
                 )
 
                 # TODO(fangjun): Scale the blank posterior
-                log_prob = logits.log_softmax(dim=-1)
+                log_prob = (logits / temperature).log_softmax(dim=-1)
                 # log_prob is (1, 1, 1, vocab_size)
                 log_prob = log_prob.squeeze()
                 # Now log_prob is (vocab_size,)
@@ -1171,3 +1198,155 @@ def beam_search(
     best_hyp = B.get_most_probable(length_norm=True)
     ys = best_hyp.ys[context_size:]  # [context_size:] to remove blanks
     return ys
+
+
+def fast_beam_search_with_nbest_rescoring(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+    ngram_lm_scale_list: List[float],
+    num_paths: int,
+    G: k2.Fsa,
+    sp: spm.SentencePieceProcessor,
+    word_table: k2.SymbolTable,
+    oov_word: str = "<UNK>",
+    use_double_scores: bool = True,
+    nbest_scale: float = 0.5,
+    temperature: float = 1.0,
+) -> Dict[str, List[List[int]]]:
+    """It limits the maximum number of symbols per frame to 1.
+    A lattice is first obtained using modified beam search, and then
+    the shortest path within the lattice is used as the final output.
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi.
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+      ngram_lm_scale_list:
+        A list of floats representing LM score scales.
+      num_paths:
+        Number of paths to extract from the decoded lattice.
+      G:
+        An FsaVec containing only a single FSA. It is an n-gram LM.
+      sp:
+        The BPE model.
+      word_table:
+        The word symbol table.
+      oov_word:
+        OOV words are replaced with this word.
+      use_double_scores:
+        True to use double precision for computation. False to use
+        single precision.
+      nbest_scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+      temperature:
+        Softmax temperature.
+    Returns:
+      Return the decoded result in a dict, where the key has the form
+      'ngram_lm_scale_xx' and the value is the decoded results. `xx` is the
+      ngram LM scale value used during decoding, i.e., 0.1.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+        temperature=temperature,
+    )
+
+    nbest = Nbest.from_lattice(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=use_double_scores,
+        nbest_scale=nbest_scale,
+    )
+    # at this point, nbest.fsa.scores are all zeros.
+
+    nbest = nbest.intersect(lattice)
+    # Now nbest.fsa.scores contains acoustic scores
+
+    am_scores = nbest.tot_scores()
+
+    # Now we need to compute the LM scores of each path.
+    # (1) Get the token IDs of each Path. We assume the decoding_graph
+    # is an acceptor, i.e., lattice is also an acceptor
+    tokens_shape = nbest.fsa.arcs.shape().remove_axis(1)  # [path][arc]
+
+    tokens = k2.RaggedTensor(tokens_shape, nbest.fsa.labels.contiguous())
+    tokens = tokens.remove_values_leq(0)  # remove -1 and 0
+
+    token_list: List[List[int]] = tokens.tolist()
+    word_list: List[List[str]] = sp.decode(token_list)
+
+    assert isinstance(oov_word, str), oov_word
+    assert oov_word in word_table, oov_word
+    oov_word_id = word_table[oov_word]
+
+    word_ids_list: List[List[int]] = []
+
+    for words in word_list:
+        this_word_ids = []
+        for w in words.split():
+            if w in word_table:
+                this_word_ids.append(word_table[w])
+            else:
+                this_word_ids.append(oov_word_id)
+        word_ids_list.append(this_word_ids)
+
+    word_fsas = k2.linear_fsa(word_ids_list, device=lattice.device)
+    word_fsas_with_self_loops = k2.add_epsilon_self_loops(word_fsas)
+
+    num_unique_paths = len(word_ids_list)
+
+    b_to_a_map = torch.zeros(
+        num_unique_paths,
+        dtype=torch.int32,
+        device=lattice.device,
+    )
+
+    rescored_word_fsas = k2.intersect_device(
+        a_fsas=G,
+        b_fsas=word_fsas_with_self_loops,
+        b_to_a_map=b_to_a_map,
+        sorted_match_a=True,
+        ret_arc_maps=False,
+    )
+
+    rescored_word_fsas = k2.remove_epsilon_self_loops(rescored_word_fsas)
+    rescored_word_fsas = k2.top_sort(k2.connect(rescored_word_fsas))
+    ngram_lm_scores = rescored_word_fsas.get_tot_scores(
+        use_double_scores=True,
+        log_semiring=False,
+    )
+
+    ans: Dict[str, List[List[int]]] = {}
+    for s in ngram_lm_scale_list:
+        key = f"ngram_lm_scale_{s}"
+        tot_scores = am_scores.values + s * ngram_lm_scores
+        ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
+        max_indexes = ragged_tot_scores.argmax()
+        best_path = k2.index_fsa(nbest.fsa, max_indexes)
+        hyps = get_texts(best_path)
+
+        ans[key] = hyps
+
+    return ans

egs/librispeech/ASR/pruned_transducer_stateless3/decode.py

@@ -111,6 +111,7 @@ from beam_search import (
     fast_beam_search_nbest_LG,
     fast_beam_search_nbest_oracle,
     fast_beam_search_one_best,
+    fast_beam_search_with_nbest_rescoring,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -312,6 +313,35 @@ def get_parser():
         help="left context can be seen during decoding (in frames after subsampling)",
     )
 
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=1.0,
+        help="""Softmax temperature.
+         The output of the model is (logits / temperature).log_softmax().
+         """,
+    )
+
+    parser.add_argument(
+        "--lm-dir",
+        type=Path,
+        default=Path("./data/lm"),
+        help="""Used only when --decoding-method is
+         fast_beam_search_with_nbest_rescoring.
+         It should contain either G_4_gram.pt or G_4_gram.fst.txt
+         """,
+    )
+
+    parser.add_argument(
+        "--words-txt",
+        type=Path,
+        default=Path("./data/lang_bpe_500/words.txt"),
+        help="""Used only when --decoding-method is
+         fast_beam_search_with_nbest_rescoring.
+         It is the word table.
+         """,
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -324,6 +354,7 @@ def decode_one_batch(
     batch: dict,
     word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
+    G: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
     following format:
@@ -352,6 +383,11 @@ def decode_one_batch(
         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.
+      G:
+        Optional. Used only when decoding method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_oracle,
+        or fast_beam_search_with_nbest_rescoring.
+        It an FsaVec containing an acceptor.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -397,6 +433,7 @@ def decode_one_batch(
             beam=params.beam,
             max_contexts=params.max_contexts,
             max_states=params.max_states,
+            temperature=params.temperature,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -411,6 +448,7 @@ def decode_one_batch(
             max_states=params.max_states,
             num_paths=params.num_paths,
             nbest_scale=params.nbest_scale,
+            temperature=params.temperature,
         )
         for hyp in hyp_tokens:
             hyps.append([word_table[i] for i in hyp])
@@ -425,6 +463,7 @@ def decode_one_batch(
             max_states=params.max_states,
             num_paths=params.num_paths,
             nbest_scale=params.nbest_scale,
+            temperature=params.temperature,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -440,6 +479,7 @@ def decode_one_batch(
             num_paths=params.num_paths,
             ref_texts=sp.encode(supervisions["text"]),
             nbest_scale=params.nbest_scale,
+            temperature=params.temperature,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -460,9 +500,32 @@ def decode_one_batch(
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
+            temperature=params.temperature,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_with_nbest_rescoring":
+        ngram_lm_scale_list = [-0.5, -0.2, -0.1, -0.05, -0.02, 0]
+        ngram_lm_scale_list += [0.01, 0.02, 0.05]
+        ngram_lm_scale_list += [0.1, 0.3, 0.5, 0.8]
+        ngram_lm_scale_list += [1.0, 1.5, 2.5, 3]
+        hyp_tokens = fast_beam_search_with_nbest_rescoring(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_states=params.max_states,
+            max_contexts=params.max_contexts,
+            ngram_lm_scale_list=ngram_lm_scale_list,
+            num_paths=params.num_paths,
+            G=G,
+            sp=sp,
+            word_table=word_table,
+            use_double_scores=True,
+            nbest_scale=params.nbest_scale,
+            temperature=params.temperature,
+        )
     else:
         batch_size = encoder_out.size(0)
 
@@ -496,6 +559,7 @@ def decode_one_batch(
                 f"beam_{params.beam}_"
                 f"max_contexts_{params.max_contexts}_"
                 f"max_states_{params.max_states}"
+                f"temperature_{params.temperature}"
             ): hyps
         }
     elif params.decoding_method == "fast_beam_search":
@@ -504,8 +568,23 @@ def decode_one_batch(
                 f"beam_{params.beam}_"
                 f"max_contexts_{params.max_contexts}_"
                 f"max_states_{params.max_states}"
+                f"temperature_{params.temperature}"
             ): hyps
         }
+    elif params.decoding_method == "fast_beam_search_with_nbest_rescoring":
+        prefix = (
+            f"beam_{params.beam}_"
+            f"max_contexts_{params.max_contexts}_"
+            f"max_states_{params.max_states}_"
+            f"num_paths_{params.num_paths}_"
+            f"nbest_scale_{params.nbest_scale}_"
+            f"temperature_{params.temperature}_"
+        )
+        ans: Dict[str, List[List[str]]] = {}
+        for key, hyp in hyp_tokens.items():
+            t: List[str] = sp.decode(hyp)
+            ans[prefix + key] = [s.split() for s in t]
+        return ans
     elif "fast_beam_search" in params.decoding_method:
         key = f"beam_{params.beam}_"
         key += f"max_contexts_{params.max_contexts}_"
@@ -515,10 +594,14 @@ def decode_one_batch(
             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}
+        return {
+            (
+                f"beam_size_{params.beam_size}_"
+                f"temperature_{params.temperature}"
+            ): hyps
+        }
 
 
 def decode_dataset(
@@ -528,6 +611,7 @@ def decode_dataset(
     sp: spm.SentencePieceProcessor,
     word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
+    G: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
@@ -546,6 +630,11 @@ def decode_dataset(
         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.
+      G:
+        Optional. Used only when decoding method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_oracle,
+        or fast_beam_search_with_nbest_rescoring.
+        It's an FsaVec containing an acceptor.
     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.
@@ -576,6 +665,7 @@ def decode_dataset(
             word_table=word_table,
             decoding_graph=decoding_graph,
             batch=batch,
+            G=G,
         )
 
         for name, hyps in hyps_dict.items():
@@ -642,6 +732,71 @@ def save_results(
     logging.info(s)
 
 
+def load_ngram_LM(
+    lm_dir: Path, word_table: k2.SymbolTable, device: torch.device
+) -> k2.Fsa:
+    """Read a ngram model from the given directory.
+    Args:
+      lm_dir:
+        It should contain either G_4_gram.pt or G_4_gram.fst.txt
+      word_table:
+        The word table mapping words to IDs and vice versa.
+      device:
+        The resulting FSA will be moved to this device.
+    Returns:
+      Return an FsaVec containing a single acceptor.
+    """
+    lm_dir = Path(lm_dir)
+    assert lm_dir.is_dir(), f"{lm_dir} does not exist"
+
+    pt_file = lm_dir / "G_4_gram.pt"
+
+    if pt_file.is_file():
+        logging.info(f"Loading pre-compiled {pt_file}")
+        d = torch.load(pt_file, map_location=device)
+        G = k2.Fsa.from_dict(d)
+        G = k2.add_epsilon_self_loops(G)
+        G = k2.arc_sort(G)
+        return G
+
+    txt_file = lm_dir / "G_4_gram.fst.txt"
+
+    assert txt_file.is_file(), f"{txt_file} does not exist"
+    logging.info(f"Loading {txt_file}")
+    logging.warning("It may take 8 minutes (Will be cached for later use).")
+    with open(txt_file) as f:
+        G = k2.Fsa.from_openfst(f.read(), acceptor=False)
+
+        # G.aux_labels is not needed in later computations, so
+        # remove it here.
+        del G.aux_labels
+        # Now G is an acceptor
+
+        first_word_disambig_id = word_table["#0"]
+        # CAUTION: The following line is crucial.
+        # Arcs entering the back-off state have label equal to #0.
+        # We have to change it to 0 here.
+        G.labels[G.labels >= first_word_disambig_id] = 0
+
+        # See https://github.com/k2-fsa/k2/issues/874
+        # for why we need to set G.properties to None
+        G.__dict__["_properties"] = None
+
+        G = k2.Fsa.from_fsas([G]).to(device)
+
+        # Save a dummy value so that it can be loaded in C++.
+        # See https://github.com/pytorch/pytorch/issues/67902
+        # for why we need to do this.
+        G.dummy = 1
+
+        logging.info(f"Saving to {pt_file} for later use")
+        torch.save(G.as_dict(), pt_file)
+
+        G = k2.add_epsilon_self_loops(G)
+        G = k2.arc_sort(G)
+        return G
+
+
 @torch.no_grad()
 def main():
     parser = get_parser()
@@ -660,6 +815,7 @@ def main():
         "fast_beam_search_nbest_LG",
         "fast_beam_search_nbest_oracle",
         "modified_beam_search",
+        "fast_beam_search_with_nbest_rescoring",
     )
     params.res_dir = params.exp_dir / params.decoding_method
 
@@ -676,6 +832,7 @@ def main():
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
+        params.suffix += f"-temperature-{params.temperature}"
         if "nbest" in params.decoding_method:
             params.suffix += f"-nbest-scale-{params.nbest_scale}"
             params.suffix += f"-num-paths-{params.num_paths}"
@@ -685,9 +842,11 @@ def main():
         params.suffix += (
             f"-{params.decoding_method}-beam-size-{params.beam_size}"
         )
+        params.suffix += f"-temperature-{params.temperature}"
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+        params.suffix += f"-temperature-{params.temperature}"
 
     setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
     logging.info("Decoding started")
@@ -760,6 +919,19 @@ def main():
                 torch.load(lg_filename, map_location=device)
             )
             decoding_graph.scores *= params.ngram_lm_scale
+        elif params.decoding_method == "fast_beam_search_with_nbest_rescoring":
+            logging.info(f"Loading word symbol table from {params.words_txt}")
+            word_table = k2.SymbolTable.from_file(params.words_txt)
+
+            G = load_ngram_LM(
+                lm_dir=params.lm_dir,
+                word_table=word_table,
+                device=device,
+            )
+            decoding_graph = k2.trivial_graph(
+                params.vocab_size - 1, device=device
+            )
+            logging.info(f"G properties_str: {G.properties_str}")
         else:
             word_table = None
             decoding_graph = k2.trivial_graph(
@@ -792,6 +964,7 @@ def main():
             sp=sp,
             word_table=word_table,
             decoding_graph=decoding_graph,
+            G=G,
         )
 
         save_results(

egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py

@@ -1064,10 +1064,6 @@ class RandomCombine(nn.Module):
     is a random combination of all the inputs; but which in test time
     will be just the last input.
 
-    All but the last input will have a linear transform before we
-    randomly combine them; these linear transforms will be initialized
-    to the identity transform.
-
     The idea is that the list of Tensors will be a list of outputs of multiple
     conformer layers.  This has a similar effect as iterated loss. (See:
     DEJA-VU: DOUBLE FEATURE PRESENTATION AND ITERATED LOSS IN DEEP TRANSFORMER
@@ -1267,7 +1263,6 @@ def _test_random_combine(final_weight: float, pure_prob: float, stddev: float):
     num_channels = 50
     m = RandomCombine(
         num_inputs=num_inputs,
-        num_channels=num_channels,
         final_weight=final_weight,
         pure_prob=pure_prob,
         stddev=stddev,
@@ -1289,9 +1284,7 @@ def _test_random_combine_main():
     _test_random_combine(0.5, 0.5, 0.3)
 
     feature_dim = 50
-    c = Conformer(
+    c = Conformer(num_features=feature_dim, d_model=128, nhead=4)
-        num_features=feature_dim, output_dim=256, d_model=128, nhead=4
-    )
     batch_size = 5
     seq_len = 20
     # Just make sure the forward pass runs.

egs/librispeech/ASR/pruned_transducer_stateless6/model.py

@@ -23,7 +23,7 @@ from scaling import ScaledLinear
 
 from icefall.utils import add_sos
 
-from quantization.prediction import JointCodebookLoss
+from multi_quantization.prediction import JointCodebookLoss
 
 
 class Transducer(nn.Module):
@@ -75,7 +75,9 @@ class Transducer(nn.Module):
         self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size)
         if num_codebooks > 0:
             self.codebook_loss_net = JointCodebookLoss(
-                predictor_channels=encoder_dim, num_codebooks=num_codebooks
+                predictor_channels=encoder_dim,
+                num_codebooks=num_codebooks,
+                is_joint=False,
             )
 
     def forward(

egs/librispeech/ASR/pruned_transducer_stateless6/train.py

@@ -879,6 +879,11 @@ def run(rank, world_size, args):
         The return value of get_parser().parse_args()
     """
     params = get_params()
+
+    # Note: it's better to set --spec-aug-time-warpi-factor=-1
+    # when doing distillation with vq.
+    assert args.spec_aug_time_warp_factor < 1
+
     params.update(vars(args))
     if params.full_libri is False:
         params.valid_interval = 1600

egs/wenetspeech/ASR/local/prepare_words.py

@@ -75,6 +75,16 @@ def main():
 
     logging.info("Starting writing the words.txt")
     f_out = open(output_file, "w", encoding="utf-8")
+
+    # LG decoding needs below symbols.
+    id1, id2, id3 = (
+        str(len(new_lines)),
+        str(len(new_lines) + 1),
+        str(len(new_lines) + 2),
+    )
+    add_words = ["#0 " + id1, "<s> " + id2, "</s> " + id3]
+    new_lines.extend(add_words)
+
     for line in new_lines:
         f_out.write(line)
         f_out.write("\n")

icefall/env.py

@@ -29,6 +29,7 @@ import torch
 
 
 def get_git_sha1():
+    try:
         git_commit = (
             subprocess.run(
                 ["git", "rev-parse", "--short", "HEAD"],
@@ -55,10 +56,14 @@ def get_git_sha1():
         git_commit = (
             git_commit + "-dirty" if dirty_commit else git_commit + "-clean"
         )
+    except:  # noqa
+        return None
+
     return git_commit
 
 
 def get_git_date():
+    try:
         git_date = (
             subprocess.run(
                 ["git", "log", "-1", "--format=%ad", "--date=local"],
@@ -69,10 +74,14 @@ def get_git_date():
             .rstrip("\n")
             .strip()
         )
+    except:  # noqa
+        return None
+
     return git_date
 
 
 def get_git_branch_name():
+    try:
         git_date = (
             subprocess.run(
                 ["git", "rev-parse", "--abbrev-ref", "HEAD"],
@@ -83,6 +92,9 @@ def get_git_branch_name():
             .rstrip("\n")
             .strip()
         )
+    except:  # noqa
+        return None
+
     return git_date
 
 

icefall/utils.py

@@ -96,8 +96,6 @@ def str2bool(v):
 def setup_logger(
     log_filename: Pathlike,
     log_level: str = "info",
-    rank: int = 0,
-    world_size: int = 1,
     use_console: bool = True,
 ) -> None:
     """Setup log level.
@@ -108,16 +106,14 @@ def setup_logger(
       log_level:
         The log level to use, e.g., "debug", "info", "warning", "error",
         "critical"
-      rank:
-        Rank of this node in DDP training.
-      world_size:
-        Number of nodes in DDP training.
       use_console:
         True to also print logs to console.
     """
     now = datetime.now()
     date_time = now.strftime("%Y-%m-%d-%H-%M-%S")
-    if world_size > 1:
+    if dist.is_available() and dist.is_initialized():
+        world_size = dist.get_world_size()
+        rank = dist.get_rank()
         formatter = f"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] ({rank}/{world_size}) %(message)s"  # noqa
         log_filename = f"{log_filename}-{date_time}-{rank}"
     else:

requirements-ci.txt

@@ -19,3 +19,4 @@ kaldialign==0.2
 sentencepiece==0.1.96
 tensorboard==2.8.0
 typeguard==2.13.3
+multi_quantization

requirements.txt

@@ -3,3 +3,4 @@ kaldialign
 sentencepiece>=0.1.96
 tensorboard
 typeguard
+multi_quantization