SURT recipe for AMI and ICSI (#1133)

* merge upstream

* add SURT model and training

* add libricss decoding

* add chunk width randomization

* decode SURT with libricss

* initial commit for zipformer_ctc

* remove unwanted changes

* remove changes to other recipe

* fix zipformer softlink

* fix for JIT export

* add missing file

* fix symbolic links

* update results

* clean commit for SURT recipe

* training libricss surt model

* remove unwanted files

* remove unwanted changes

* remove changes in librispeech

* change some files to symlinks

* remove unwanted changes in utils

* add export script

* add README

* minor fix in README

* add assets for README

* replace some files with symlinks

* remove unused decoding methods

* initial commit for SURT AMI recipe

* fix symlink

* add train + decode scripts

* add missing symlink

* change files to symlink

* change file type

egs/ami/SURT/README.md

@@ -0,0 +1,156 @@
+# Introduction
+
+This is a multi-talker ASR recipe for the AMI and ICSI datasets. We train a Streaming
+Unmixing and Recognition Transducer (SURT) model for the task.
+
+Please refer to the `egs/libricss/SURT` recipe README for details about the task and the
+model.
+
+## Description of the recipe
+
+### Pre-requisites
+
+The recipes in this directory need the following packages to be installed:
+
+- [meeteval](https://github.com/fgnt/meeteval)
+- [einops](https://github.com/arogozhnikov/einops)
+
+Additionally, we initialize the model with the pre-trained model from the LibriCSS recipe.
+Please download this checkpoint (see below) or train the LibriCSS recipe first.
+
+### Training
+
+To train the model, run the following from within `egs/ami/SURT`:
+
+```bash
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+python dprnn_zipformer/train.py \
+    --use-fp16 True \
+    --exp-dir dprnn_zipformer/exp/surt_base \
+    --world-size 4 \
+    --max-duration 500 \
+    --max-duration-valid 250 \
+    --max-cuts 200 \
+    --num-buckets 50 \
+    --num-epochs 30 \
+    --enable-spec-aug True \
+    --enable-musan False \
+    --ctc-loss-scale 0.2 \
+    --heat-loss-scale 0.2 \
+    --base-lr 0.004 \
+    --model-init-ckpt exp/libricss_base.pt \
+    --chunk-width-randomization True \
+    --num-mask-encoder-layers 4 \
+    --num-encoder-layers 2,2,2,2,2
+```
+
+The above is for SURT-base (~26M). For SURT-large (~38M), use:
+
+```bash
+    --model-init-ckpt exp/libricss_large.pt \
+    --num-mask-encoder-layers 6 \
+    --num-encoder-layers 2,4,3,2,4 \
+    --model-init-ckpt exp/zipformer_large.pt \
+```
+
+**NOTE:** You may need to decrease the `--max-duration` for SURT-large to avoid OOM.
+
+### Adaptation
+
+The training step above only trains on simulated mixtures. For best results, we also
+adapt the final model on the AMI+ICSI train set. For this, run the following from within
+`egs/ami/SURT`:
+
+```bash
+export CUDA_VISIBLE_DEVICES="0"
+
+python dprnn_zipformer/train_adapt.py \
+    --use-fp16 True \
+    --exp-dir dprnn_zipformer/exp/surt_base_adapt \
+    --world-size 4 \
+    --max-duration 500 \
+    --max-duration-valid 250 \
+    --max-cuts 200 \
+    --num-buckets 50 \
+    --num-epochs 8 \
+    --lr-epochs 2 \
+    --enable-spec-aug True \
+    --enable-musan False \
+    --ctc-loss-scale 0.2 \
+    --base-lr 0.0004 \
+    --model-init-ckpt dprnn_zipformer/exp/surt_base/epoch-30.pt \
+    --chunk-width-randomization True \
+    --num-mask-encoder-layers 4 \
+    --num-encoder-layers 2,2,2,2,2
+```
+
+For SURT-large, use the following config:
+
+```bash
+    --num-mask-encoder-layers 6 \
+    --num-encoder-layers 2,4,3,2,4 \
+    --model-init-ckpt dprnn_zipformer/exp/surt_large/epoch-30.pt \
+    --num-epochs 15 \
+    --lr-epochs 4 \
+```
+
+
+### Decoding
+
+To decode the model, run the following from within `egs/ami/SURT`:
+
+#### Greedy search
+
+```bash
+export CUDA_VISIBLE_DEVICES="0"
+
+python dprnn_zipformer/decode.py \
+    --epoch 20 --avg 1 --use-averaged-model False \
+    --exp-dir dprnn_zipformer/exp/surt_base_adapt \
+    --max-duration 250 \
+    --decoding-method greedy_search
+```
+
+#### Beam search
+
+```bash
+python dprnn_zipformer/decode.py \
+    --epoch 20 --avg 1 --use-averaged-model False \
+    --exp-dir dprnn_zipformer/exp/surt_base_adapt \
+    --max-duration 250 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+```
+
+## Results (using beam search)
+
+**AMI**
+
+| Model      | IHM-Mix |  SDM |  MDM |
+|------------|:-------:|:----:|:----:|
+| SURT-base  |   39.8  | 65.4 | 46.6 |
+|   + adapt  |   37.4  | 46.9 | 43.7 |
+| SURT-large |   36.8  | 62.5 | 44.4 |
+|    + adapt |   **35.1**  | **44.6** | **41.4** |
+
+**ICSI**
+
+| Model      | IHM-Mix |  SDM |
+|------------|:-------:|:----:|
+| SURT-base  |   28.3  | 60.0 |
+|   + adapt  |   26.3  | 33.9 |
+| SURT-large |   27.8  | 59.7 |
+|    + adapt |   **24.4**  | **32.3** |
+
+## Pre-trained models and logs
+
+* LibriCSS pre-trained model (for initialization): [base](https://huggingface.co/desh2608/icefall-surt-libricss-dprnn-zipformer/tree/main/exp/surt_base) [large](https://huggingface.co/desh2608/icefall-surt-libricss-dprnn-zipformer/tree/main/exp/surt_large)
+
+* Pre-trained models: <https://huggingface.co/desh2608/icefall-surt-ami-dprnn-zipformer>
+
+* Training logs:
+    - surt_base: <https://tensorboard.dev/experiment/8awy98VZSWegLmH4l2JWSA/>
+    - surt_base_adapt: <https://tensorboard.dev/experiment/aGVgXVzYRDKbGUbPekcNjg/>
+    - surt_large: <https://tensorboard.dev/experiment/ZXMkez0VSYKbPLqRk4clOQ/>
+    - surt_large_adapt: <https://tensorboard.dev/experiment/WLKL1e7bTVyEjSonYSNYwg/>

egs/ami/SURT/dprnn_zipformer/asr_datamodule.py

@@ -0,0 +1,399 @@
+# 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, Callable, Dict, List, Optional
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    CutMix,
+    DynamicBucketingSampler,
+    K2SurtDataset,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import 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 AmiAsrDataModule:
+    """
+    DataModule for k2 SURT 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,
+    - 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(
+            "--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(
+            "--max-duration-valid",
+            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(
+            "--max-cuts",
+            type=int,
+            default=100,
+            help="Maximum number of cuts 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(
+            "--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. ",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+        sources: bool = False,
+    ) -> 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")
+
+        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 = K2SurtDataset(
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            if self.args.on_the_fly_feats
+            else PrecomputedFeatures(),
+            cut_transforms=transforms,
+            input_transforms=input_transforms,
+            return_cuts=self.args.return_cuts,
+            return_sources=sources,
+            strict=False,
+        )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                quadratic_duration=30.0,
+                max_cuts=self.args.max_cuts,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info("Using SingleCutSampler.")
+            train_sampler = SimpleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                max_cuts=self.args.max_cuts,
+                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 = []
+
+        logging.info("About to create dev dataset")
+        validate = K2SurtDataset(
+            input_strategy=OnTheFlyFeatures(
+                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            )
+            if self.args.on_the_fly_feats
+            else PrecomputedFeatures(),
+            cut_transforms=transforms,
+            return_cuts=self.args.return_cuts,
+            return_sources=False,
+            strict=False,
+        )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration_valid,
+            quadratic_duration=30.0,
+            max_cuts=self.args.max_cuts,
+            shuffle=False,
+        )
+        logging.info("About to create dev dataloader")
+
+        # '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)
+
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.debug("About to create test dataset")
+        test = K2SurtDataset(
+            input_strategy=OnTheFlyFeatures(
+                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            )
+            if self.args.on_the_fly_feats
+            else PrecomputedFeatures(),
+            return_cuts=self.args.return_cuts,
+            return_sources=False,
+            strict=False,
+        )
+        sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration_valid,
+            max_cuts=self.args.max_cuts,
+            shuffle=False,
+        )
+
+        # '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)
+
+        logging.debug("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=sampler,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+        return test_dl
+
+    @lru_cache()
+    def aimix_train_cuts(
+        self,
+        rvb_affix: str = "clean",
+        sources: bool = True,
+    ) -> CutSet:
+        logging.info("About to get train cuts")
+        source_affix = "_sources" if sources else ""
+        cs = load_manifest_lazy(
+            self.args.manifest_dir / f"cuts_train_{rvb_affix}{source_affix}.jsonl.gz"
+        )
+        cs = cs.filter(lambda c: c.duration >= 1.0 and c.duration <= 30.0)
+        return cs
+
+    @lru_cache()
+    def train_cuts(
+        self,
+    ) -> CutSet:
+        logging.info("About to get train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "cuts_train_ami_icsi.jsonl.gz"
+        )
+
+    @lru_cache()
+    def ami_cuts(self, split: str = "dev", type: str = "sdm") -> CutSet:
+        logging.info(f"About to get AMI {split} {type} cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / f"cuts_ami-{type}_{split}.jsonl.gz"
+        )
+
+    @lru_cache()
+    def icsi_cuts(self, split: str = "dev", type: str = "sdm") -> CutSet:
+        logging.info(f"About to get ICSI {split} {type} cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / f"cuts_icsi-{type}_{split}.jsonl.gz"
+        )

egs/ami/SURT/dprnn_zipformer/beam_search.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/beam_search.py

egs/ami/SURT/dprnn_zipformer/decode.py

@@ -0,0 +1,622 @@
+#!/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
+./dprnn_zipformer/decode.py \
+    --epoch 20 \
+    --avg 1 \
+    --use-averaged-model false \
+    --exp-dir ./dprnn_zipformer/exp_adapt \
+    --max-duration 600 \
+    --decoding-method greedy_search
+
+(2) beam search (not recommended)
+./dprnn_zipformer/decode.py \
+    --epoch 20 \
+    --avg 1 \
+    --use-averaged-model false \
+    --exp-dir ./dprnn_zipformer/exp_adapt \
+    --max-duration 600 \
+    --decoding-method beam_search \
+    --beam-size 4
+
+(3) modified beam search
+./dprnn_zipformer/decode.py \
+    --epoch 20 \
+    --avg 1 \
+    --use-averaged-model false \
+    --exp-dir ./dprnn_zipformer/exp_adapt \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+"""
+
+
+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 AmiAsrDataModule
+from beam_search import (
+    beam_search,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from lhotse.utils import EPSILON
+from train import add_model_arguments, get_params, get_surt_model
+
+from icefall import LmScorer, 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_surt_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=20,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=1,
+        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="dprnn_zipformer/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_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(
+        "--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,
+) -> 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`.
+    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)
+    feature_lens = batch["input_lens"].to(device)
+
+    # Apply the mask encoder
+    B, T, F = feature.shape
+    processed = model.mask_encoder(feature)  # B,T,F*num_channels
+    masks = processed.view(B, T, F, params.num_channels).unbind(dim=-1)
+    x_masked = [feature * m for m in masks]
+
+    # Recognition
+    # Stack the inputs along the batch axis
+    h = torch.cat(x_masked, dim=0)
+    h_lens = torch.cat([feature_lens for _ in range(params.num_channels)], dim=0)
+    encoder_out, encoder_out_lens = model.encoder(x=h, x_lens=h_lens)
+
+    if model.joint_encoder_layer is not None:
+        encoder_out = model.joint_encoder_layer(encoder_out)
+
+    def _group_channels(hyps: List[str]) -> List[List[str]]:
+        """
+        Currently we have a batch of size M*B, where M is the number of
+        channels and B is the batch size. We need to group the hypotheses
+        into B groups, each of which contains M hypotheses.
+
+        Example:
+            hyps = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2']
+            _group_channels(hyps) = [['a1', 'a2'], ['b1', 'b2'], ['c1', 'c2']]
+        """
+        assert len(hyps) == B * params.num_channels
+        out_hyps = []
+        for i in range(B):
+            out_hyps.append(hyps[i::B])
+        return out_hyps
+
+    hyps = []
+    if 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)
+    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)
+    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))
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": _group_channels(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: _group_channels(hyps)}
+    else:
+        return {f"beam_size_{params.beam_size}": _group_channels(hyps)}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+    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):
+        cut_ids = [cut.id for cut in batch["cuts"]]
+        cuts_batch = batch["cuts"]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            for cut_id, hyp_words in zip(cut_ids, hyps):
+                # Reference is a list of supervision texts sorted by start time.
+                ref_words = [
+                    s.text.strip()
+                    for s in sorted(
+                        cuts_batch[cut_id].supervisions, key=lambda s: s.start
+                    )
+                ]
+                this_batch.append((cut_id, ref_words, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(cut_ids)
+
+        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_surt_error_stats(
+                f,
+                f"{test_set_name}-{key}",
+                results,
+                enable_log=True,
+                num_channels=params.num_channels,
+            )
+            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()
+    LmScorer.add_arguments(parser)
+    AmiAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+    args.lang_dir = Path(args.lang_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "modified_beam_search",
+    ), f"Decoding method {params.decoding_method} is not supported."
+    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 "beam_search" in params.decoding_method:
+        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_surt_model(params)
+    assert model.encoder.decode_chunk_size == params.decode_chunk_len // 2, (
+        model.encoder.decode_chunk_size,
+        params.decode_chunk_len,
+    )
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg + 1
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to(device)
+    model.eval()
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    ami = AmiAsrDataModule(args)
+
+    # NOTE(@desh2608): we filter segments longer than 120s to avoid OOM errors in decoding.
+    # However, 99.9% of the segments are shorter than 120s, so this should not
+    # substantially affect the results. In future, we will implement an overlapped
+    # inference method to avoid OOM errors.
+
+    test_sets = {}
+    for split in ["dev", "test"]:
+        for type in ["ihm-mix", "sdm", "mdm8-bf"]:
+            test_sets[f"ami-{split}_{type}"] = (
+                ami.ami_cuts(split=split, type=type)
+                .trim_to_supervision_groups(max_pause=0.0)
+                .filter(lambda c: 0.1 < c.duration < 120.0)
+                .to_eager()
+            )
+
+    for split in ["dev", "test"]:
+        for type in ["ihm-mix", "sdm"]:
+            test_sets[f"icsi-{split}_{type}"] = (
+                ami.icsi_cuts(split=split, type=type)
+                .trim_to_supervision_groups(max_pause=0.0)
+                .filter(lambda c: 0.1 < c.duration < 120.0)
+                .to_eager()
+            )
+
+    for test_set, test_cuts in test_sets.items():
+        test_dl = ami.test_dataloaders(test_cuts)
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/ami/SURT/dprnn_zipformer/decoder.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/decoder.py

egs/ami/SURT/dprnn_zipformer/dprnn.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/dprnn.py

egs/ami/SURT/dprnn_zipformer/encoder_interface.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/encoder_interface.py

egs/ami/SURT/dprnn_zipformer/export.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/export.py

egs/ami/SURT/dprnn_zipformer/joiner.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/joiner.py

egs/ami/SURT/dprnn_zipformer/model.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/model.py

egs/ami/SURT/dprnn_zipformer/optim.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/optim.py

egs/ami/SURT/dprnn_zipformer/scaling.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/scaling.py

egs/ami/SURT/dprnn_zipformer/scaling_converter.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/scaling_converter.py

egs/ami/SURT/dprnn_zipformer/test_model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7_streaming/test_model.py

egs/ami/SURT/dprnn_zipformer/zipformer.py

@@ -0,0 +1 @@
+../../../libricss/SURT/dprnn_zipformer/zipformer.py

egs/ami/SURT/local/add_source_feats.py

@@ -0,0 +1,78 @@
+#!/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 adds source features as temporal arrays to the mixture manifests.
+It looks for manifests in the directory data/manifests.
+"""
+import logging
+from pathlib import Path
+
+import numpy as np
+from lhotse import CutSet, LilcomChunkyWriter, load_manifest, load_manifest_lazy
+from tqdm import tqdm
+
+
+def add_source_feats():
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+
+    logging.info("Reading mixed cuts")
+    mixed_cuts_clean = load_manifest_lazy(src_dir / "cuts_train_clean.jsonl.gz")
+    mixed_cuts_reverb = load_manifest_lazy(src_dir / "cuts_train_reverb.jsonl.gz")
+
+    logging.info("Reading source cuts")
+    source_cuts = load_manifest(src_dir / "ihm_cuts_train_trimmed.jsonl.gz")
+
+    logging.info("Adding source features to the mixed cuts")
+    pbar = tqdm(total=len(mixed_cuts_clean), desc="Adding source features")
+    with CutSet.open_writer(
+        src_dir / "cuts_train_clean_sources.jsonl.gz"
+    ) as cut_writer_clean, CutSet.open_writer(
+        src_dir / "cuts_train_reverb_sources.jsonl.gz"
+    ) as cut_writer_reverb, LilcomChunkyWriter(
+        output_dir / "feats_train_clean_sources"
+    ) as source_feat_writer:
+        for cut_clean, cut_reverb in zip(mixed_cuts_clean, mixed_cuts_reverb):
+            assert cut_reverb.id == cut_clean.id + "_rvb"
+            source_feats = []
+            source_feat_offsets = []
+            cur_offset = 0
+            for sup in sorted(
+                cut_clean.supervisions, key=lambda s: (s.start, s.speaker)
+            ):
+                source_cut = source_cuts[sup.id]
+                source_feats.append(source_cut.load_features())
+                source_feat_offsets.append(cur_offset)
+                cur_offset += source_cut.num_frames
+            cut_clean.source_feats = source_feat_writer.store_array(
+                cut_clean.id, np.concatenate(source_feats, axis=0)
+            )
+            cut_clean.source_feat_offsets = source_feat_offsets
+            cut_writer_clean.write(cut_clean)
+            # Also write the reverb cut
+            cut_reverb.source_feats = cut_clean.source_feats
+            cut_reverb.source_feat_offsets = cut_clean.source_feat_offsets
+            cut_writer_reverb.write(cut_reverb)
+            pbar.update(1)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    add_source_feats()

egs/ami/SURT/local/compute_fbank_aimix.py

@@ -0,0 +1,185 @@
+#!/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 synthetically mixed AMI and ICSI
+train set.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+import logging
+import random
+import warnings
+from pathlib import Path
+
+import torch
+import torch.multiprocessing
+import torchaudio
+from lhotse import (
+    AudioSource,
+    LilcomChunkyWriter,
+    Recording,
+    load_manifest,
+    load_manifest_lazy,
+)
+from lhotse.audio import set_ffmpeg_torchaudio_info_enabled
+from lhotse.cut import MixedCut, MixTrack, MultiCut
+from lhotse.features.kaldifeat import (
+    KaldifeatFbank,
+    KaldifeatFbankConfig,
+    KaldifeatFrameOptions,
+    KaldifeatMelOptions,
+)
+from lhotse.utils import fix_random_seed, uuid4
+from tqdm import tqdm
+
+# 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")
+torchaudio.set_audio_backend("soundfile")
+set_ffmpeg_torchaudio_info_enabled(False)
+
+
+def compute_fbank_aimix():
+    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")
+    train_cuts = load_manifest_lazy(src_dir / "ai-mix_cuts_clean_full.jsonl.gz")
+
+    # only uses RIRs and noises from REVERB challenge
+    real_rirs = load_manifest(src_dir / "real-rir_recordings_all.jsonl.gz").filter(
+        lambda r: "RVB2014" in r.id
+    )
+    noises = load_manifest(src_dir / "iso-noise_recordings_all.jsonl.gz").filter(
+        lambda r: "RVB2014" in r.id
+    )
+
+    # Apply perturbation to the training cuts
+    logging.info("Applying perturbation to the training cuts")
+    train_cuts_rvb = train_cuts.map(
+        lambda c: augment(
+            c, perturb_snr=True, rirs=real_rirs, noises=noises, perturb_loudness=True
+        )
+    )
+
+    logging.info("Extracting fbank features for training cuts")
+    _ = train_cuts.compute_and_store_features_batch(
+        extractor=extractor,
+        storage_path=output_dir / "ai-mix_feats_clean",
+        manifest_path=src_dir / "cuts_train_clean.jsonl.gz",
+        batch_duration=5000,
+        num_workers=4,
+        storage_type=LilcomChunkyWriter,
+        overwrite=True,
+    )
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        _ = train_cuts_rvb.compute_and_store_features_batch(
+            extractor=extractor,
+            storage_path=output_dir / "ai-mix_feats_reverb",
+            manifest_path=src_dir / "cuts_train_reverb.jsonl.gz",
+            batch_duration=5000,
+            num_workers=4,
+            storage_type=LilcomChunkyWriter,
+            overwrite=True,
+        )
+
+
+def augment(cut, perturb_snr=False, rirs=None, noises=None, perturb_loudness=False):
+    """
+    Given a mixed cut, this function optionally applies the following augmentations:
+    - Perturbing the SNRs of the tracks (in range [-5, 5] dB)
+    - Reverberation using a randomly selected RIR
+    - Adding noise
+    - Perturbing the loudness (in range [-20, -25] dB)
+    """
+    out_cut = cut.drop_features()
+
+    # Perturb the SNRs (optional)
+    if perturb_snr:
+        snrs = [random.uniform(-5, 5) for _ in range(len(cut.tracks))]
+        for i, (track, snr) in enumerate(zip(out_cut.tracks, snrs)):
+            if i == 0:
+                # Skip the first track since it is the reference
+                continue
+            track.snr = snr
+
+    # Reverberate the cut (optional)
+    if rirs is not None:
+        # Select an RIR at random
+        rir = random.choice(rirs)
+        # Select a channel at random
+        rir_channel = random.choice(list(range(rir.num_channels)))
+        # Reverberate the cut
+        out_cut = out_cut.reverb_rir(rir_recording=rir, rir_channels=[rir_channel])
+
+    # Add noise (optional)
+    if noises is not None:
+        # Select a noise recording at random
+        noise = random.choice(noises).to_cut()
+        if isinstance(noise, MultiCut):
+            noise = noise.to_mono()[0]
+        # Select an SNR at random
+        snr = random.uniform(10, 30)
+        # Repeat the noise to match the duration of the cut
+        noise = repeat_cut(noise, out_cut.duration)
+        out_cut = MixedCut(
+            id=out_cut.id,
+            tracks=[
+                MixTrack(cut=out_cut, type="MixedCut"),
+                MixTrack(cut=noise, type="DataCut", snr=snr),
+            ],
+        )
+
+    # Perturb the loudness (optional)
+    if perturb_loudness:
+        target_loudness = random.uniform(-20, -25)
+        out_cut = out_cut.normalize_loudness(target_loudness, mix_first=True)
+    return out_cut
+
+
+def repeat_cut(cut, duration):
+    while cut.duration < duration:
+        cut = cut.mix(cut, offset_other_by=cut.duration)
+    return cut.truncate(duration=duration)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    fix_random_seed(42)
+    compute_fbank_aimix()

egs/ami/SURT/local/compute_fbank_ami.py

@@ -0,0 +1,94 @@
+#!/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 AMI dataset.
+We compute features for full recordings (i.e., without trimming to supervisions).
+This way we can create arbitrary segmentations later.
+
+The generated fbank features are saved in data/fbank.
+"""
+import logging
+import math
+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 = {}
+    for part in ["ihm-mix", "sdm", "mdm8-bf"]:
+        manifests[part] = read_manifests_if_cached(
+            dataset_parts=["train", "dev", "test"],
+            output_dir=src_dir,
+            prefix=f"ami-{part}",
+            suffix="jsonl.gz",
+        )
+
+    for part in ["ihm-mix", "sdm", "mdm8-bf"]:
+        for split in ["train", "dev", "test"]:
+            logging.info(f"Processing {part} {split}")
+            cuts = CutSet.from_manifests(
+                **manifests[part][split]
+            ).compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=output_dir / f"ami-{part}_{split}_feats",
+                manifest_path=src_dir / f"cuts_ami-{part}_{split}.jsonl.gz",
+                batch_duration=5000,
+                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/ami/SURT/local/compute_fbank_icsi.py

@@ -0,0 +1,95 @@
+#!/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 ICSI dataset.
+We compute features for full recordings (i.e., without trimming to supervisions).
+This way we can create arbitrary segmentations later.
+
+The generated fbank features are saved in data/fbank.
+"""
+import logging
+import math
+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_icsi():
+    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 = {}
+    for part in ["ihm-mix", "sdm"]:
+        manifests[part] = read_manifests_if_cached(
+            dataset_parts=["train"],
+            output_dir=src_dir,
+            prefix=f"icsi-{part}",
+            suffix="jsonl.gz",
+        )
+
+    for part in ["ihm-mix", "sdm"]:
+        for split in ["train"]:
+            logging.info(f"Processing {part} {split}")
+            cuts = CutSet.from_manifests(
+                **manifests[part][split]
+            ).compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=output_dir / f"icsi-{part}_{split}_feats",
+                manifest_path=src_dir / f"cuts_icsi-{part}_{split}.jsonl.gz",
+                batch_duration=5000,
+                num_workers=4,
+                storage_type=LilcomChunkyWriter,
+                overwrite=True,
+            )
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    compute_fbank_icsi()

egs/ami/SURT/local/compute_fbank_ihm.py

@@ -0,0 +1,101 @@
+#!/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 trimmed sub-segments which will be
+used for simulating the training mixtures.
+
+The generated fbank features are saved in data/fbank.
+"""
+import logging
+import math
+from pathlib import Path
+
+import torch
+import torch.multiprocessing
+import torchaudio
+from lhotse import CutSet, LilcomChunkyWriter, load_manifest
+from lhotse.audio import set_ffmpeg_torchaudio_info_enabled
+from lhotse.features.kaldifeat import (
+    KaldifeatFbank,
+    KaldifeatFbankConfig,
+    KaldifeatFrameOptions,
+    KaldifeatMelOptions,
+)
+from lhotse.recipes.utils import read_manifests_if_cached
+from tqdm import tqdm
+
+# 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")
+torchaudio.set_audio_backend("soundfile")
+set_ffmpeg_torchaudio_info_enabled(False)
+
+
+def compute_fbank_ihm():
+    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 = {}
+    for data in ["ami", "icsi"]:
+        manifests[data] = read_manifests_if_cached(
+            dataset_parts=["train"],
+            output_dir=src_dir,
+            types=["recordings", "supervisions"],
+            prefix=f"{data}-ihm",
+            suffix="jsonl.gz",
+        )
+
+    logging.info("Computing features")
+    for data in ["ami", "icsi"]:
+        cs = CutSet.from_manifests(**manifests[data]["train"])
+        cs = cs.trim_to_supervisions(keep_overlapping=False)
+        cs = cs.normalize_loudness(target=-23.0, affix_id=False)
+        cs = cs + cs.perturb_speed(0.9) + cs.perturb_speed(1.1)
+        _ = cs.compute_and_store_features_batch(
+            extractor=extractor,
+            storage_path=output_dir / f"{data}-ihm_train_feats",
+            manifest_path=src_dir / f"{data}-ihm_cuts_train.jsonl.gz",
+            batch_duration=5000,
+            num_workers=4,
+            storage_type=LilcomChunkyWriter,
+            overwrite=True,
+        )
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    compute_fbank_ihm()

egs/ami/SURT/local/prepare_ami_train_cuts.py

@@ -0,0 +1,146 @@
+#!/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 creates AMI train segments.
+"""
+import logging
+import math
+from pathlib import Path
+
+import torch
+import torch.multiprocessing
+from lhotse import LilcomChunkyWriter, load_manifest_lazy
+from lhotse.cut import Cut, CutSet
+from lhotse.utils import EPSILON, add_durations
+from tqdm import tqdm
+
+
+def cut_into_windows(cuts: CutSet, duration: float):
+    """
+    This function takes a CutSet and cuts each cut into windows of roughly
+    `duration` seconds. By roughly, we mean that we try to adjust for the last supervision
+    that exceeds the duration, or is shorter than the duration.
+    """
+    res = []
+    with tqdm() as pbar:
+        for cut in cuts:
+            pbar.update(1)
+            sups = cut.index_supervisions()[cut.id]
+            sr = cut.sampling_rate
+            start = 0.0
+            end = duration
+            num_tries = 0
+            while start < cut.duration and num_tries < 2:
+                # Find the supervision that are cut by the window endpoint
+                hitlist = [iv for iv in sups.at(end) if iv.begin < end]
+                # If there are no supervisions, we are done
+                if not hitlist:
+                    res.append(
+                        cut.truncate(
+                            offset=start,
+                            duration=add_durations(end, -start, sampling_rate=sr),
+                            keep_excessive_supervisions=False,
+                        )
+                    )
+                    # Update the start and end for the next window
+                    start = end
+                    end = add_durations(end, duration, sampling_rate=sr)
+                else:
+                    # find ratio of durations cut by the window endpoint
+                    ratios = [
+                        add_durations(end, -iv.end, sampling_rate=sr) / iv.length()
+                        for iv in hitlist
+                    ]
+                    # we retain the supervisions that have >50% of their duration
+                    # in the window, and discard the others
+                    retained = []
+                    discarded = []
+                    for iv, ratio in zip(hitlist, ratios):
+                        if ratio > 0.5:
+                            retained.append(iv)
+                        else:
+                            discarded.append(iv)
+                    cur_end = max(iv.end for iv in retained) if retained else end
+                    res.append(
+                        cut.truncate(
+                            offset=start,
+                            duration=add_durations(cur_end, -start, sampling_rate=sr),
+                            keep_excessive_supervisions=False,
+                        )
+                    )
+                    # For the next window, we start at the earliest discarded supervision
+                    next_start = min(iv.begin for iv in discarded) if discarded else end
+                    next_end = add_durations(next_start, duration, sampling_rate=sr)
+                    # It may happen that next_start is the same as start, in which case
+                    # we will advance the window anyway
+                    if next_start == start:
+                        logging.warning(
+                            f"Next start is the same as start: {next_start} == {start} for cut {cut.id}"
+                        )
+                        start = end + EPSILON
+                        end = add_durations(start, duration, sampling_rate=sr)
+                        num_tries += 1
+                    else:
+                        start = next_start
+                        end = next_end
+    return CutSet.from_cuts(res)
+
+
+def prepare_train_cuts():
+    src_dir = Path("data/manifests")
+
+    logging.info("Loading the manifests")
+    train_cuts_ihm = load_manifest_lazy(
+        src_dir / "cuts_ami-ihm-mix_train.jsonl.gz"
+    ).map(lambda c: c.with_id(f"{c.id}_ihm-mix"))
+    train_cuts_sdm = load_manifest_lazy(src_dir / "cuts_ami-sdm_train.jsonl.gz").map(
+        lambda c: c.with_id(f"{c.id}_sdm")
+    )
+    train_cuts_mdm = load_manifest_lazy(
+        src_dir / "cuts_ami-mdm8-bf_train.jsonl.gz"
+    ).map(lambda c: c.with_id(f"{c.id}_mdm8-bf"))
+
+    # Combine all cuts into one CutSet
+    train_cuts = train_cuts_ihm + train_cuts_sdm + train_cuts_mdm
+
+    train_cuts_1 = train_cuts.trim_to_supervision_groups(max_pause=0.5)
+    train_cuts_2 = train_cuts.trim_to_supervision_groups(max_pause=0.0)
+
+    # Combine the two segmentations
+    train_all = train_cuts_1 + train_cuts_2
+
+    # At this point, some of the cuts may be very long. We will cut them into windows of
+    # roughly 30 seconds.
+    logging.info("Cutting the segments into windows of 30 seconds")
+    train_all_30 = cut_into_windows(train_all, duration=30.0)
+    logging.info(f"Number of cuts after cutting into windows: {len(train_all_30)}")
+
+    # Show statistics
+    train_all.describe(full=True)
+
+    # Save the cuts
+    logging.info("Saving the cuts")
+    train_all.to_file(src_dir / "cuts_train_ami.jsonl.gz")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    prepare_train_cuts()

egs/ami/SURT/local/prepare_icsi_train_cuts.py

@@ -0,0 +1,67 @@
+#!/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 creates ICSI train segments.
+"""
+import logging
+from pathlib import Path
+
+from lhotse import load_manifest_lazy
+from prepare_ami_train_cuts import cut_into_windows
+
+
+def prepare_train_cuts():
+    src_dir = Path("data/manifests")
+
+    logging.info("Loading the manifests")
+    train_cuts_ihm = load_manifest_lazy(
+        src_dir / "cuts_icsi-ihm-mix_train.jsonl.gz"
+    ).map(lambda c: c.with_id(f"{c.id}_ihm-mix"))
+    train_cuts_sdm = load_manifest_lazy(src_dir / "cuts_icsi-sdm_train.jsonl.gz").map(
+        lambda c: c.with_id(f"{c.id}_sdm")
+    )
+
+    # Combine all cuts into one CutSet
+    train_cuts = train_cuts_ihm + train_cuts_sdm
+
+    train_cuts_1 = train_cuts.trim_to_supervision_groups(max_pause=0.5)
+    train_cuts_2 = train_cuts.trim_to_supervision_groups(max_pause=0.0)
+
+    # Combine the two segmentations
+    train_all = train_cuts_1 + train_cuts_2
+
+    # At this point, some of the cuts may be very long. We will cut them into windows of
+    # roughly 30 seconds.
+    logging.info("Cutting the segments into windows of 30 seconds")
+    train_all_30 = cut_into_windows(train_all, duration=30.0)
+    logging.info(f"Number of cuts after cutting into windows: {len(train_all_30)}")
+
+    # Show statistics
+    train_all.describe(full=True)
+
+    # Save the cuts
+    logging.info("Saving the cuts")
+    train_all.to_file(src_dir / "cuts_train_icsi.jsonl.gz")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    prepare_train_cuts()

egs/ami/SURT/local/prepare_lang_bpe.py

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

egs/ami/SURT/local/train_bpe_model.py

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

egs/ami/SURT/prepare.sh

@@ -0,0 +1,195 @@
+#!/usr/bin/env bash
+
+set -eou pipefail
+
+stage=-1
+stop_stage=100
+
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, they will be downloaded
+# by this script automatically.
+#
+#  - $dl_dir/ami
+#      You can find audio and transcripts for AMI in this path.
+#
+#  - $dl_dir/icsi
+#      You can find audio and transcripts for ICSI in this path.
+#
+#  - $dl_dir/rirs_noises
+#      This directory contains the RIRS_NOISES corpus downloaded from https://openslr.org/28/.
+#
+dl_dir=$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
+vocab_size=500
+
+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/amicorpus,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/amicorpus $dl_dir/amicorpus
+  #
+  if [ ! -d $dl_dir/amicorpus ]; then
+    for mic in ihm ihm-mix sdm mdm8-bf; do
+      lhotse download ami --mic $mic $dl_dir/amicorpus
+    done
+  fi
+
+  # If you have pre-downloaded it to /path/to/icsi,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/icsi $dl_dir/icsi
+  #
+  if [ ! -d $dl_dir/icsi ]; then
+    lhotse download icsi $dl_dir/icsi
+  fi
+
+  # If you have pre-downloaded it to /path/to/rirs_noises,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/rirs_noises $dl_dir/
+  #
+  if [ ! -d $dl_dir/rirs_noises ]; then
+    lhotse download rirs_noises $dl_dir
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare AMI manifests"
+  # We assume that you have downloaded the AMI corpus
+  # to $dl_dir/amicorpus. We perform text normalization for the transcripts.
+  mkdir -p data/manifests
+  for mic in ihm ihm-mix sdm mdm8-bf; do
+    log "Preparing AMI manifest for $mic"
+    lhotse prepare ami --mic $mic --max-words-per-segment 30 --merge-consecutive $dl_dir/amicorpus data/manifests/
+  done
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Prepare ICSI manifests"
+  # We assume that you have downloaded the ICSI corpus
+  # to $dl_dir/icsi. We perform text normalization for the transcripts.
+  mkdir -p data/manifests
+  log "Preparing ICSI manifest"
+  for mic in ihm ihm-mix sdm; do
+    lhotse prepare icsi --mic $mic $dl_dir/icsi data/manifests/
+  done
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Prepare RIRs"
+  # We assume that you have downloaded the RIRS_NOISES corpus
+  # to $dl_dir/rirs_noises
+  lhotse prepare rir-noise -p real_rir -p iso_noise $dl_dir/rirs_noises data/manifests
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 3: Extract features for AMI and ICSI recordings"
+  python local/compute_fbank_ami.py
+  python local/compute_fbank_icsi.py
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Create sources for simulating mixtures"
+  # In the following script, we speed-perturb the IHM recordings and extract features.
+  python local/compute_fbank_ihm.py
+  lhotse combine data/manifests/ami-ihm_cuts_train.jsonl.gz \
+    data/manifests/icsi-ihm_cuts_train.jsonl.gz - |\
+    lhotse cut trim-to-alignments --type word --max-pause 0.5 - - |\
+    lhotse filter 'duration<=12.0' - - |\
+    shuf | gzip -c > data/manifests/ihm_cuts_train_trimmed.jsonl.gz
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Create training mixtures"
+  lhotse workflows simulate-meetings \
+    --method conversational \
+    --same-spk-pause 0.5 \
+    --diff-spk-pause 0.5 \
+    --diff-spk-overlap 1.0 \
+    --prob-diff-spk-overlap 0.8 \
+    --num-meetings 200000 \
+    --num-speakers-per-meeting 2,3 \
+    --max-duration-per-speaker 15.0 \
+    --max-utterances-per-speaker 3 \
+    --seed 1234 \
+    --num-jobs 2 \
+    data/manifests/ihm_cuts_train_trimmed.jsonl.gz \
+    data/manifests/ai-mix_cuts_clean.jsonl.gz
+
+  python local/compute_fbank_aimix.py
+
+  # Add source features to the manifest (will be used for masking loss)
+  # This may take ~2 hours.
+  python local/add_source_feats.py
+
+  # Combine clean and reverb
+  cat <(gunzip -c data/manifests/cuts_train_clean_sources.jsonl.gz) \
+    <(gunzip -c data/manifests/cuts_train_reverb_sources.jsonl.gz) |\
+    shuf | gzip -c > data/manifests/cuts_train_comb_sources.jsonl.gz
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 7: Create training mixtures from real sessions"
+  python local/prepare_ami_train_cuts.py
+  python local/prepare_icsi_train_cuts.py
+
+  # Combine AMI and ICSI
+  cat <(gunzip -c data/manifests/cuts_train_ami.jsonl.gz) \
+    <(gunzip -c data/manifests/cuts_train_icsi.jsonl.gz) |\
+    shuf | gzip -c > data/manifests/cuts_train_ami_icsi.jsonl.gz
+fi
+
+if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
+  log "Stage 8: Dump transcripts for BPE model training (using AMI and ICSI)."
+  mkdir -p data/lm
+  cat <(gunzip -c data/manifests/ami-sdm_supervisions_train.jsonl.gz | jq '.text' | sed 's:"::g') \
+      <(gunzip -c data/manifests/icsi-sdm_supervisions_train.jsonl.gz | jq '.text' | sed 's:"::g') \
+  > data/lm/transcript_words.txt
+fi
+
+if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
+  log "Stage 9: Prepare BPE based lang (combining AMI and ICSI)"
+
+  lang_dir=data/lang_bpe_${vocab_size}
+  mkdir -p $lang_dir
+
+  # Add special words to words.txt
+  echo "<eps> 0" > $lang_dir/words.txt
+  echo "!SIL 1" >> $lang_dir/words.txt
+  echo "<UNK> 2" >> $lang_dir/words.txt
+
+  # Add regular words to words.txt
+  cat data/lm/transcript_words.txt | grep -o -E '\w+' | sort -u | awk '{print $0,NR+2}' >> $lang_dir/words.txt
+
+  # Add remaining special word symbols expected by LM scripts.
+  num_words=$(cat $lang_dir/words.txt | wc -l)
+  echo "<s> ${num_words}" >> $lang_dir/words.txt
+  num_words=$(cat $lang_dir/words.txt | wc -l)
+  echo "</s> ${num_words}" >> $lang_dir/words.txt
+  num_words=$(cat $lang_dir/words.txt | wc -l)
+  echo "#0 ${num_words}" >> $lang_dir/words.txt
+
+  ./local/train_bpe_model.py \
+    --lang-dir $lang_dir \
+    --vocab-size $vocab_size \
+    --transcript data/lm/transcript_words.txt
+
+  if [ ! -f $lang_dir/L_disambig.pt ]; then
+    ./local/prepare_lang_bpe.py --lang-dir $lang_dir
+  fi
+fi

egs/ami/SURT/shared

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