Add AliMeeting multi-condition training recipe (#751)

* add AliMeeting multi-domain recipe

* convert scripts to symbolic links

egs/alimeeting/ASR_v2/README.md

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+
+# Introduction
+
+This recipe trains multi-domain ASR models for AliMeeting. By multi-domain, we mean that
+we train a single model on close-talk and far-field conditions. This recipe optionally
+uses [GSS]-based enhancement for far-field array microphone.
+We pool data in the following 4 ways and train a single model on the pooled data:
+
+(i) individual headset microphone (IHM)
+(ii) IHM with simulated reverb
+(iii) Single distant microphone (SDM)
+(iv) GSS-enhanced array microphones
+
+This is different from `alimeeting/ASR` since that recipe trains a model only on the
+far-field audio. Additionally, we use text normalization here similar to the original
+M2MeT challenge, so the results should be more comparable to those from Table 4 of
+the [paper](https://arxiv.org/abs/2110.07393).
+
+The following additional packages need to be installed to run this recipe:
+* `pip install jieba`
+* `pip install paddlepaddle`
+* `pip install git+https://github.com/desh2608/gss.git`
+
+[./RESULTS.md](./RESULTS.md) contains the latest results.
+
+## Performance Record
+
+### pruned_transducer_stateless7
+
+The following are decoded using `modified_beam_search`:
+
+| Evaluation set           | eval WER    | test WER |
+|--------------------------|------------|---------|
+| IHM                      |  9.58  | 11.53 |
+| SDM                      |  23.37  | 25.85 |
+| MDM (GSS-enhanced)       |  11.82  | 14.22 |
+
+See [RESULTS](/egs/alimeeting/ASR_v2/RESULTS.md) for details.

egs/alimeeting/ASR_v2/RESULTS.md

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+## Results (CER)
+
+#### 2022-12-09
+
+#### Zipformer (pruned_transducer_stateless7)
+
+Zipformer encoder + non-current decoder. The decoder
+contains only an embedding layer, a Conv1d (with kernel size 2) and a linear
+layer (to transform tensor dim).
+
+All the results below are using a single model that is trained by combining the following
+data: IHM, IHM+reverb, SDM, and GSS-enhanced MDM. Speed perturbation and MUSAN noise
+augmentation are applied on top of the pooled data.
+
+**WERs for IHM:**
+
+|                           | eval | test | comment                                  |
+|---------------------------|------------|------------|------------------------------------------|
+| greedy search             |  10.13  |  12.21  | --epoch 15 --avg 8 --max-duration 500 |
+| modified beam search      |  9.58  |  11.53  | --epoch 15 --avg 8 --max-duration 500 --beam-size 4 |
+| fast beam search          |  9.92  |  12.07  | --epoch 15 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 |
+
+**WERs for SDM:**
+
+|                           | eval | test | comment                                  |
+|---------------------------|------------|------------|------------------------------------------|
+| greedy search             |  23.70  |  26.41  | --epoch 15 --avg 8 --max-duration 500 |
+| modified beam search      |  23.37  |  25.85  | --epoch 15 --avg 8 --max-duration 500 --beam-size 4 |
+| fast beam search          |  23.60  |  26.38  | --epoch 15 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 |
+
+**WERs for GSS-enhanced MDM:**
+
+|                           | eval | test | comment                                  |
+|---------------------------|------------|------------|------------------------------------------|
+| greedy search             |  12.24  |  14.99  | --epoch 15 --avg 8 --max-duration 500 |
+| modified beam search      |  11.82  |  14.22  | --epoch 15 --avg 8 --max-duration 500 --beam-size 4 |
+| fast beam search          |  12.30  |  14.98  | --epoch 15 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 |
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./pruned_transducer_stateless7/train.py \
+  --world-size 4 \
+  --num-epochs 15 \
+  --exp-dir pruned_transducer_stateless7/exp \
+  --max-duration 300 \
+  --max-cuts 100 \
+  --prune-range 5 \
+  --lr-factor 5 \
+  --lm-scale 0.25 \
+  --use-fp16 True
+```
+
+The decoding command is:
+```
+# greedy search
+./pruned_transducer_stateless7/decode.py \
+        --epoch 15 \
+        --avg 8 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method greedy_search
+
+# modified beam search
+./pruned_transducer_stateless7/decode.py \
+        --epoch 15 \
+        --avg 8 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+# fast beam search
+./pruned_transducer_stateless7/decode.py \
+        --epoch 15 \
+        --avg 8 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --max-duration 500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+```
+
+Pretrained model is available at <https://huggingface.co/desh2608/icefall-asr-alimeeting-pruned-transducer-stateless7>
+
+The tensorboard training log can be found at
+<https://tensorboard.dev/experiment/EzmVahMMTb2YfKWXwQ2dyQ/#scalars>

egs/alimeeting/ASR_v2/local/compute_fbank_alimeeting.py

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+#!/usr/bin/env python3
+# Copyright    2022  Johns Hopkins University        (authors: Desh Raj)
+#
+# 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 AliMeeting dataset.
+For the training data, we prepare IHM, reverberated IHM, SDM, and GSS-enhanced
+audios. For the test data, we separately prepare IHM, SDM, and GSS-enhanced
+parts (which are the 3 evaluation settings).
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+import logging
+from pathlib import Path
+
+import torch
+import torch.multiprocessing
+from lhotse import CutSet, LilcomChunkyWriter
+from lhotse.features.kaldifeat import (
+    KaldifeatFbank,
+    KaldifeatFbankConfig,
+    KaldifeatFrameOptions,
+    KaldifeatMelOptions,
+)
+from lhotse.recipes.utils import read_manifests_if_cached
+
+# 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)
+torch.multiprocessing.set_sharing_strategy("file_system")
+
+
+def compute_fbank_ami():
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+
+    sampling_rate = 16000
+    num_mel_bins = 80
+
+    extractor = KaldifeatFbank(
+        KaldifeatFbankConfig(
+            frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate),
+            mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins),
+            device="cuda",
+        )
+    )
+
+    logging.info("Reading manifests")
+    manifests_ihm = read_manifests_if_cached(
+        dataset_parts=["train", "eval", "test"],
+        output_dir=src_dir,
+        prefix="alimeeting-ihm",
+        suffix="jsonl.gz",
+    )
+    manifests_sdm = read_manifests_if_cached(
+        dataset_parts=["train", "eval", "test"],
+        output_dir=src_dir,
+        prefix="alimeeting-sdm",
+        suffix="jsonl.gz",
+    )
+    # For GSS we already have cuts so we read them directly.
+    manifests_gss = read_manifests_if_cached(
+        dataset_parts=["train", "eval", "test"],
+        output_dir=src_dir,
+        prefix="alimeeting-gss",
+        suffix="jsonl.gz",
+    )
+
+    def _extract_feats(cuts: CutSet, storage_path: Path, manifest_path: Path) -> None:
+        cuts = cuts + cuts.perturb_speed(0.9) + cuts.perturb_speed(1.1)
+        _ = cuts.compute_and_store_features_batch(
+            extractor=extractor,
+            storage_path=storage_path,
+            manifest_path=manifest_path,
+            batch_duration=5000,
+            num_workers=8,
+            storage_type=LilcomChunkyWriter,
+        )
+
+    logging.info(
+        "Preparing training cuts: IHM + reverberated IHM + SDM + GSS (optional)"
+    )
+
+    logging.info("Processing train split IHM")
+    cuts_ihm = (
+        CutSet.from_manifests(**manifests_ihm["train"])
+        .trim_to_supervisions(keep_overlapping=False, keep_all_channels=False)
+        .modify_ids(lambda x: x + "-ihm")
+    )
+    _extract_feats(
+        cuts_ihm,
+        output_dir / "feats_train_ihm",
+        src_dir / "cuts_train_ihm.jsonl.gz",
+    )
+
+    logging.info("Processing train split IHM + reverberated IHM")
+    cuts_ihm_rvb = cuts_ihm.reverb_rir()
+    _extract_feats(
+        cuts_ihm_rvb,
+        output_dir / "feats_train_ihm_rvb",
+        src_dir / "cuts_train_ihm_rvb.jsonl.gz",
+    )
+
+    logging.info("Processing train split SDM")
+    cuts_sdm = (
+        CutSet.from_manifests(**manifests_sdm["train"])
+        .trim_to_supervisions(keep_overlapping=False)
+        .modify_ids(lambda x: x + "-sdm")
+    )
+    _extract_feats(
+        cuts_sdm,
+        output_dir / "feats_train_sdm",
+        src_dir / "cuts_train_sdm.jsonl.gz",
+    )
+
+    logging.info("Processing train split GSS")
+    cuts_gss = (
+        CutSet.from_manifests(**manifests_gss["train"])
+        .trim_to_supervisions(keep_overlapping=False)
+        .modify_ids(lambda x: x + "-gss")
+    )
+    _extract_feats(
+        cuts_gss,
+        output_dir / "feats_train_gss",
+        src_dir / "cuts_train_gss.jsonl.gz",
+    )
+
+    logging.info("Preparing test cuts: IHM, SDM, GSS (optional)")
+    for split in ["eval", "test"]:
+        logging.info(f"Processing {split} IHM")
+        cuts_ihm = (
+            CutSet.from_manifests(**manifests_ihm[split])
+            .trim_to_supervisions(keep_overlapping=False, keep_all_channels=False)
+            .compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=output_dir / f"feats_{split}_ihm",
+                manifest_path=src_dir / f"cuts_{split}_ihm.jsonl.gz",
+                batch_duration=500,
+                num_workers=4,
+                storage_type=LilcomChunkyWriter,
+            )
+        )
+        logging.info(f"Processing {split} SDM")
+        cuts_sdm = (
+            CutSet.from_manifests(**manifests_sdm[split])
+            .trim_to_supervisions(keep_overlapping=False)
+            .compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=output_dir / f"feats_{split}_sdm",
+                manifest_path=src_dir / f"cuts_{split}_sdm.jsonl.gz",
+                batch_duration=500,
+                num_workers=4,
+                storage_type=LilcomChunkyWriter,
+            )
+        )
+        logging.info(f"Processing {split} GSS")
+        cuts_gss = (
+            CutSet.from_manifests(**manifests_gss[split])
+            .trim_to_supervisions(keep_overlapping=False)
+            .compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=output_dir / f"feats_{split}_gss",
+                manifest_path=src_dir / f"cuts_{split}_gss.jsonl.gz",
+                batch_duration=500,
+                num_workers=4,
+                storage_type=LilcomChunkyWriter,
+            )
+        )
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    compute_fbank_ami()

egs/alimeeting/ASR_v2/local/compute_fbank_musan.py

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+../../../librispeech/ASR/local/compute_fbank_musan.py

egs/alimeeting/ASR_v2/local/prepare_alimeeting_enhanced.py

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+#!/usr/local/bin/python
+# -*- coding: utf-8 -*-
+# Data preparation for AliMeeting GSS-enhanced dataset.
+
+import logging
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+from lhotse import Recording, RecordingSet, SupervisionSet
+from lhotse.qa import fix_manifests
+from lhotse.recipes.utils import read_manifests_if_cached
+from lhotse.utils import fastcopy
+from tqdm import tqdm
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+def get_args():
+    import argparse
+
+    parser = argparse.ArgumentParser(description="AMI enhanced dataset preparation.")
+    parser.add_argument(
+        "manifests_dir",
+        type=Path,
+        help="Path to directory containing AliMeeting manifests.",
+    )
+    parser.add_argument(
+        "enhanced_dir",
+        type=Path,
+        help="Path to enhanced data directory.",
+    )
+    parser.add_argument(
+        "--num-jobs",
+        "-j",
+        type=int,
+        default=1,
+        help="Number of parallel jobs to run.",
+    )
+    parser.add_argument(
+        "--min-segment-duration",
+        "-d",
+        type=float,
+        default=0.0,
+        help="Minimum duration of a segment in seconds.",
+    )
+    return parser.parse_args()
+
+
+def find_recording_and_create_new_supervision(enhanced_dir, supervision):
+    """
+    Given a supervision (corresponding to original AMI recording), this function finds the
+    enhanced recording correspoding to the supervision, and returns this recording and
+    a new supervision whose start and end times are adjusted to match the enhanced recording.
+    """
+    file_name = Path(
+        f"{supervision.recording_id}-{supervision.speaker}-{int(100*supervision.start):06d}_{int(100*supervision.end):06d}.flac"
+    )
+    save_path = enhanced_dir / f"{supervision.recording_id}" / file_name
+    if save_path.exists():
+        recording = Recording.from_file(save_path)
+        if recording.duration == 0:
+            logging.warning(f"Skipping {save_path} which has duration 0 seconds.")
+            return None
+
+        # Old supervision is wrt to the original recording, we create new supervision
+        # wrt to the enhanced segment
+        new_supervision = fastcopy(
+            supervision,
+            recording_id=recording.id,
+            start=0,
+            duration=recording.duration,
+        )
+        return recording, new_supervision
+    else:
+        logging.warning(f"{save_path} does not exist.")
+        return None
+
+
+def main(args):
+    # Get arguments
+    manifests_dir = args.manifests_dir
+    enhanced_dir = args.enhanced_dir
+
+    # Load manifests from cache if they exist (saves time)
+    manifests = read_manifests_if_cached(
+        dataset_parts=["train", "eval", "test"],
+        output_dir=manifests_dir,
+        prefix="alimeeting-sdm",
+        suffix="jsonl.gz",
+    )
+    if not manifests:
+        raise ValueError(
+            "AliMeeting SDM manifests not found in {}".format(manifests_dir)
+        )
+
+    with ThreadPoolExecutor(args.num_jobs) as ex:
+        for part in ["train", "eval", "test"]:
+            logging.info(f"Processing {part}...")
+            supervisions_orig = manifests[part]["supervisions"].filter(
+                lambda s: s.duration >= args.min_segment_duration
+            )
+            futures = []
+
+            for supervision in tqdm(
+                supervisions_orig,
+                desc="Distributing tasks",
+            ):
+                futures.append(
+                    ex.submit(
+                        find_recording_and_create_new_supervision,
+                        enhanced_dir,
+                        supervision,
+                    )
+                )
+
+            recordings = []
+            supervisions = []
+            for future in tqdm(
+                futures,
+                total=len(futures),
+                desc="Processing tasks",
+            ):
+                result = future.result()
+                if result is not None:
+                    recording, new_supervision = result
+                    recordings.append(recording)
+                    supervisions.append(new_supervision)
+
+            # Remove duplicates from the recordings
+            recordings_nodup = {}
+            for recording in recordings:
+                if recording.id not in recordings_nodup:
+                    recordings_nodup[recording.id] = recording
+                else:
+                    logging.warning("Recording {} is duplicated.".format(recording.id))
+            recordings = RecordingSet.from_recordings(recordings_nodup.values())
+            supervisions = SupervisionSet.from_segments(supervisions)
+
+            recordings, supervisions = fix_manifests(
+                recordings=recordings, supervisions=supervisions
+            )
+
+            logging.info(f"Writing {part} enhanced manifests")
+            recordings.to_file(
+                manifests_dir / f"alimeeting-gss_recordings_{part}.jsonl.gz"
+            )
+            supervisions.to_file(
+                manifests_dir / f"alimeeting-gss_supervisions_{part}.jsonl.gz"
+            )
+
+
+if __name__ == "__main__":
+    args = get_args()
+    main(args)

egs/alimeeting/ASR_v2/local/prepare_alimeeting_gss.sh

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+#!/bin/bash
+# This script is used to run GSS-based enhancement on AMI data.
+set -euo pipefail
+nj=4
+stage=0
+
+. shared/parse_options.sh || exit 1
+
+if [ $# != 2 ]; then
+   echo "Wrong #arguments ($#, expected 2)"
+   echo "Usage: local/prepare_alimeeting_gss.sh [options] <data-dir> <exp-dir>"
+   echo "e.g. local/prepare_alimeeting_gss.sh data/manifests exp/ami_gss"
+   echo "main options (for others, see top of script file)"
+   echo "  --nj <nj>                                # number of parallel jobs"
+   echo "  --stage <stage>                          # stage to start running from"
+   exit 1;
+fi
+
+DATA_DIR=$1
+EXP_DIR=$2
+
+mkdir -p $EXP_DIR
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+if [ $stage -le 1 ]; then
+  log "Stage 1: Prepare cut sets"
+  for part in train eval test; do
+    lhotse cut simple \
+      -r $DATA_DIR/alimeeting-mdm_recordings_${part}.jsonl.gz \
+      -s $DATA_DIR/alimeeting-mdm_supervisions_${part}.jsonl.gz \
+      $EXP_DIR/cuts_${part}.jsonl.gz
+  done
+fi
+
+if [ $stage -le 2 ]; then
+  log "Stage 2: Trim cuts to supervisions (1 cut per supervision segment)"
+  for part in train eval test; do
+    lhotse cut trim-to-supervisions --discard-overlapping \
+        $EXP_DIR/cuts_${part}.jsonl.gz $EXP_DIR/cuts_per_segment_${part}.jsonl.gz
+  done
+fi
+
+if [ $stage -le 3 ]; then
+  log "Stage 3: Split manifests for multi-GPU processing (optional)"
+  for part in train eval test; do
+    gss utils split $nj $EXP_DIR/cuts_per_segment_${part}.jsonl.gz \
+      $EXP_DIR/cuts_per_segment_${part}_split$nj
+  done
+fi
+
+if [ $stage -le 4 ]; then
+  log "Stage 4: Enhance train segments using GSS (requires GPU)"
+  # for train, we use smaller context and larger batches to speed-up processing
+  for JOB in $(seq $nj); do
+    gss enhance cuts $EXP_DIR/cuts_train.jsonl.gz \
+      $EXP_DIR/cuts_per_segment_train_split$nj/cuts_per_segment_train.JOB.jsonl.gz $EXP_DIR/enhanced \
+      --bss-iterations 10 \
+      --context-duration 5.0 \
+      --use-garbage-class \
+      --channels 0,1,2,3,4,5,6,7 \
+      --min-segment-length 0.05 \
+      --max-segment-length 25.0 \
+      --max-batch-duration 60.0 \
+      --num-buckets 4 \
+      --num-workers 4
+  done
+fi
+
+if [ $stage -le 5 ]; then
+  log "Stage 5: Enhance eval/test segments using GSS (using GPU)"
+  # for eval/test, we use larger context and smaller batches to get better quality
+  for part in eval test; do
+    for JOB in $(seq $nj); do
+      gss enhance cuts $EXP_DIR/cuts_${part}.jsonl.gz \
+      $EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.JOB.jsonl.gz \
+      $EXP_DIR/enhanced \
+      --bss-iterations 10 \
+      --context-duration 15.0 \
+      --use-garbage-class \
+      --channels 0,1,2,3,4,5,6,7 \
+      --min-segment-length 0.05 \
+      --max-segment-length 16.0 \
+      --max-batch-duration 45.0 \
+      --num-buckets 4 \
+      --num-workers 4
+    done
+  done
+fi
+
+if [ $stage -le 6 ]; then
+  log "Stage 6: Prepare manifests for GSS-enhanced data"
+  python local/prepare_alimeeting_enhanced.py $DATA_DIR $EXP_DIR/enhanced -j $nj --min-segment-duration 0.05
+fi

egs/alimeeting/ASR_v2/local/prepare_char.py

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+../../ASR/local/prepare_char.py

egs/alimeeting/ASR_v2/local/prepare_words.py

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+../../ASR/local/prepare_words.py

egs/alimeeting/ASR_v2/local/text2segments.py

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+../../ASR/local/text2segments.py

egs/alimeeting/ASR_v2/local/text2token.py

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+../../ASR/local/text2token.py

egs/alimeeting/ASR_v2/prepare.sh

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+#!/usr/bin/env bash
+
+set -eou pipefail
+
+stage=-1
+stop_stage=100
+use_gss=true  # Use GSS-based enhancement with MDM setting
+
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, they will be downloaded
+# by this script automatically.
+#
+#  - $dl_dir/alimeeting
+#     This directory contains the following files downloaded from
+#       https://openslr.org/62/
+#
+#     - Train_Ali_far.tar.gz
+#     - Train_Ali_near.tar.gz
+#     - Test_Ali.tar.gz
+#     - Eval_Ali.tar.gz
+#
+#  - $dl_dir/musan
+#      This directory contains the following directories downloaded from
+#       http://www.openslr.org/17/
+#
+#     - music
+#     - noise
+#     - speech
+
+dl_dir=$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 [ ! -f $dl_dir/alimeeting/Train_Ali_far.tar.gz ]; then
+    lhotse download ali-meeting $dl_dir/alimeeting
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare alimeeting manifest"
+  # We assume that you have downloaded the alimeeting corpus
+  # to $dl_dir/alimeeting
+  for part in ihm sdm mdm; do
+    mkdir -p data/manifests/alimeeting
+    lhotse prepare ali-meeting --mic $part --save-mono --normalize-text m2met \
+      $dl_dir/alimeeting data/manifests
+  done
+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
+  mkdir -p data/manifests
+  lhotse prepare musan $dl_dir/musan data/manifests
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ] && [ $use_gss = true ]; then
+  log "Stage 3: Apply GSS enhancement on MDM data (this stage requires a GPU)"
+  # We assume that you have installed the GSS package: https://github.com/desh2608/gss
+  local/prepare_alimeeting_gss.sh data/manifests exp/alimeeting_gss
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Compute fbank for musan"
+  mkdir -p data/fbank
+  python local/compute_fbank_musan.py
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Compute fbank for alimeeting"
+  mkdir -p data/fbank
+  python local/compute_fbank_alimeeting.py
+  log "Combine features from train splits"
+  lhotse combine data/manifests/cuts_train_{ihm,ihm_rvb,sdm,gss}.jsonl.gz - | shuf |\
+    gzip -c > data/manifests/cuts_train_all.jsonl.gz
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Prepare char based lang"
+  lang_char_dir=data/lang_char
+  mkdir -p $lang_char_dir
+
+  # Prepare text.
+  # Note: in Linux, you can install jq with the  following command:
+  # wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
+  gunzip -c data/manifests/alimeeting-sdm_supervisions_train.jsonl.gz \
+    | jq ".text" | sed 's/"//g' \
+    | ./local/text2token.py -t "char" > $lang_char_dir/text
+
+  # Prepare words segments
+  python ./local/text2segments.py \
+    --input $lang_char_dir/text \
+    --output $lang_char_dir/text_words_segmentation
+
+  cat $lang_char_dir/text_words_segmentation | sed "s/ /\n/g" \
+    | sort -u | sed "/^$/d" \
+    | uniq > $lang_char_dir/words_no_ids.txt
+
+  # Prepare words.txt
+  if [ ! -f $lang_char_dir/words.txt ]; then
+    ./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
+    ./local/prepare_char.py
+  fi
+fi

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/asr_datamodule.py

@@ -0,0 +1,419 @@
+# Copyright      2021  Piotr Żelasko
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+import re
+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.cut import Cut
+from lhotse.dataset import (
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import OnTheFlyFeatures
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+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 AlimeetingAsrDataModule:
+    """
+    DataModule for k2 ASR experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - augmentation,
+    - on-the-fly feature extraction
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="ASR data related options",
+            description=(
+                "These options are used for the preparation of "
+                "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+                "effective batch sizes, sampling strategies, applied data "
+                "augmentations, etc."
+            ),
+        )
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/manifests"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        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(
+            "--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(
+            "--max-duration",
+            type=int,
+            default=100.0,
+            help=(
+                "Maximum pooled recordings duration (seconds) in a "
+                "single batch. You can reduce it if it causes CUDA OOM."
+            ),
+        )
+        group.add_argument(
+            "--max-cuts", type=int, default=None, help="Maximum cuts in a single batch."
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=50,
+            help=(
+                "The number of buckets for the BucketingSampler"
+                "(you might want to increase it for larger datasets)."
+            ),
+        )
+        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(
+            "--num-workers",
+            type=int,
+            default=8,
+            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."
+            ),
+        )
+
+    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.
+        """
+        logging.info("About to get Musan cuts")
+
+        transforms = []
+        if self.args.enable_musan:
+            logging.info("Enable MUSAN")
+            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(
+                "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}")
+            input_transforms.append(
+                SpecAugment(
+                    time_warp_factor=self.args.spec_aug_time_warp_factor,
+                    num_frame_masks=2,
+                    features_mask_size=27,
+                    num_feature_masks=2,
+                    frames_mask_size=100,
+                )
+            )
+        else:
+            logging.info("Disable SpecAugment")
+
+        logging.info("About to create train dataset")
+        if self.args.on_the_fly_feats:
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_transforms=input_transforms,
+            )
+        else:
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_transforms=input_transforms,
+            )
+
+        logging.info("Using DynamicBucketingSampler.")
+        train_sampler = DynamicBucketingSampler(
+            cuts_train,
+            max_duration=self.args.max_duration,
+            max_cuts=self.args.max_cuts,
+            shuffle=False,
+            num_buckets=self.args.num_buckets,
+            drop_last=True,
+        )
+        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))),
+            )
+        else:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+            )
+        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 PrecomputedFeatures(),
+            return_cuts=True,
+        )
+        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
+
+    def remove_short_cuts(self, cut: Cut) -> bool:
+        """
+        See: https://github.com/k2-fsa/icefall/issues/500
+        Basically, the zipformer model subsamples the input using the following formula:
+        num_out_frames = ((num_in_frames - 7)//2 + 1)//2
+        For num_out_frames to be at least 1, num_in_frames must be at least 9.
+        """
+        return cut.duration >= 0.09
+
+    @lru_cache()
+    def train_cuts(self, sp: Optional[Any] = None) -> CutSet:
+        logging.info("About to get AMI train cuts")
+
+        def _remove_short_and_long_utt(c: Cut):
+            if c.duration < 0.1 or c.duration > 25.0:
+                return False
+
+            # In pruned RNN-T, we require that T >= S
+            # where T is the number of feature frames after subsampling
+            # and S is the number of tokens in the utterance
+
+            # In ./zipformer.py, the conv module uses the following expression
+            # for subsampling
+            T = ((c.num_frames - 7) // 2 + 1) // 2
+            tokens = c.supervisions[0].text
+            return T >= len(tokens)
+
+        cuts_train = load_manifest_lazy(
+            self.args.manifest_dir / "cuts_train_all.jsonl.gz"
+        )
+
+        return cuts_train.filter(_remove_short_and_long_utt)
+
+    @lru_cache()
+    def eval_ihm_cuts(self) -> CutSet:
+        logging.info("About to get AliMeeting IHM eval cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_eval_ihm.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def eval_sdm_cuts(self) -> CutSet:
+        logging.info("About to get AliMeeting SDM eval cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_eval_sdm.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def eval_gss_cuts(self) -> CutSet:
+        if not (self.args.manifest_dir / "cuts_eval_gss.jsonl.gz").exists():
+            logging.info("No GSS dev cuts found")
+            return None
+        logging.info("About to get AliMeeting GSS-enhanced eval cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_eval_gss.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def test_ihm_cuts(self) -> CutSet:
+        logging.info("About to get AliMeeting IHM test cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_ihm.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def test_sdm_cuts(self) -> CutSet:
+        logging.info("About to get AliMeeting SDM test cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_sdm.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def test_gss_cuts(self) -> CutSet:
+        if not (self.args.manifest_dir / "cuts_test_gss.jsonl.gz").exists():
+            logging.info("No GSS test cuts found")
+            return None
+        logging.info("About to get AliMeeting GSS-enhanced test cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_gss.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/beam_search.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/decode.py

@@ -0,0 +1,698 @@
+#!/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.
+"""
+Usage:
+(1) greedy search
+./pruned_transducer_stateless7/decode.py \
+        --epoch 15 \
+        --avg 8 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method greedy_search
+
+(2) modified beam search
+./pruned_transducer_stateless7/decode.py \
+        --epoch 15 \
+        --avg 8 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(3) fast beam search
+./pruned_transducer_stateless7/decode.py \
+        --epoch 15 \
+        --avg 8 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+"""
+
+
+import argparse
+import logging
+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 AlimeetingAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_nbest_LG,
+    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 import NgramLm
+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 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=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_stateless2/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_char",
+        help="""The lang dir
+        It contains language related input files such as
+        "lexicon.txt"
+        """,
+    )
+
+    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 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(
+        "--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,
+    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.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = model.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 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 params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): 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,
+    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.
+    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"]
+        texts = [list(str(text).replace(" ", "")) for text in texts]
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            decoding_graph=decoding_graph,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
+                this_batch.append((cut_id, ref_text, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % log_interval == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    AlimeetingAsrDataModule.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_LG",
+        "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}"
+
+    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.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(device)
+    model.eval()
+    model.device = device
+
+    if "fast_beam_search" in params.decoding_method:
+        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}")
+
+    alimeeting = AlimeetingAsrDataModule(args)
+
+    eval_ihm_cuts = alimeeting.eval_ihm_cuts()
+    test_ihm_cuts = alimeeting.test_ihm_cuts()
+    eval_sdm_cuts = alimeeting.eval_sdm_cuts()
+    test_sdm_cuts = alimeeting.test_sdm_cuts()
+    eval_gss_cuts = alimeeting.eval_gss_cuts()
+    test_gss_cuts = alimeeting.test_gss_cuts()
+
+    eval_ihm_dl = alimeeting.test_dataloaders(eval_ihm_cuts)
+    test_ihm_dl = alimeeting.test_dataloaders(test_ihm_cuts)
+    eval_sdm_dl = alimeeting.test_dataloaders(eval_sdm_cuts)
+    test_sdm_dl = alimeeting.test_dataloaders(test_sdm_cuts)
+    if eval_gss_cuts is not None:
+        eval_gss_dl = alimeeting.test_dataloaders(eval_gss_cuts)
+    if test_gss_cuts is not None:
+        test_gss_dl = alimeeting.test_dataloaders(test_gss_cuts)
+
+    test_sets = {
+        "eval_ihm": (eval_ihm_dl, eval_ihm_cuts),
+        "test_ihm": (test_ihm_dl, test_ihm_cuts),
+        "eval_sdm": (eval_sdm_dl, eval_sdm_cuts),
+        "test_sdm": (test_sdm_dl, test_sdm_cuts),
+    }
+    if eval_gss_cuts is not None:
+        test_sets["eval_gss"] = (eval_gss_dl, eval_gss_cuts)
+    if test_gss_cuts is not None:
+        test_sets["test_gss"] = (test_gss_dl, test_gss_cuts)
+
+    for test_set in test_sets:
+        logging.info(f"Decoding {test_set}")
+        dl, cuts = test_sets[test_set]
+        results_dict = decode_dataset(
+            dl=dl,
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            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/alimeeting/ASR_v2/pruned_transducer_stateless7/decoder.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/decoder.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/encoder_interface.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/encoder_interface.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/export.py

@@ -0,0 +1,320 @@
+#!/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:
+
+(1) Export to torchscript model using torch.jit.script()
+
+./pruned_transducer_stateless7/export.py \
+  --exp-dir ./pruned_transducer_stateless7/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 30 \
+  --avg 9 \
+  --jit 1
+
+It will generate a file `cpu_jit.pt` in the given `exp_dir`. You can later
+load it by `torch.jit.load("cpu_jit.pt")`.
+
+Note `cpu` in the name `cpu_jit.pt` means the parameters when loaded into Python
+are on CPU. You can use `to("cuda")` to move them to a CUDA device.
+
+Check
+https://github.com/k2-fsa/sherpa
+for how to use the exported models outside of icefall.
+
+(2) Export `model.state_dict()`
+
+./pruned_transducer_stateless7/export.py \
+  --exp-dir ./pruned_transducer_stateless7/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 20 \
+  --avg 10
+
+It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
+load it by `icefall.checkpoint.load_checkpoint()`.
+
+To use the generated file with `pruned_transducer_stateless7/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+    ./pruned_transducer_stateless7/decode.py \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 600 \
+        --decoding-method greedy_search \
+        --bpe-model data/lang_bpe_500/bpe.model
+
+Check ./pretrained.py for its usage.
+
+Note: If you don't want to train a model from scratch, we have
+provided one for you. You can get it at
+
+https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
+
+with the following commands:
+
+    sudo apt-get install git-lfs
+    git lfs install
+    git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
+    # You will find the pre-trained model in icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11/exp
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from scaling_converter import convert_scaled_to_non_scaled
+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=15,
+        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=8,
+        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_stateless7/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.
+        It will generate a file named cpu_jit.pt
+
+        Check ./jit_pretrained.py for how to use it.
+        """,
+    )
+
+    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
+
+
+@torch.no_grad()
+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 = 0
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    model.to(device)
+
+    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 is True:
+        convert_scaled_to_non_scaled(model, inplace=True)
+        logging.info("Using torch.jit.script()")
+        # 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 torchscript. Export model.state_dict()")
+        # 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/alimeeting/ASR_v2/pruned_transducer_stateless7/jit_pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/jit_pretrained.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/joiner.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/joiner.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/model.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/optim.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/optim.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/pretrained.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/scaling.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/scaling_converter.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/test_model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/test_model.py

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/zipformer.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/zipformer.py

egs/alimeeting/ASR_v2/shared

@@ -0,0 +1 @@
+../../../egs/aishell/ASR/shared