training libricss surt model

egs/libricss/SURT/dprnn_zipformer/asr_datamodule.py

@@ -0,0 +1,372 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022  Xiaomi Corporation     (Author: Mingshuang Luo)
+# Copyright      2023  Johns Hopkins Univrtsity (Author: 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.
+
+
+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 LibriCssAsrDataModule:
+    """
+    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,
+    - 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,
+        return_sources: bool = True,
+        strict: bool = True,
+    ) -> 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=return_sources,
+            strict=strict,
+        )
+
+        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,
+            max_cuts=self.args.max_cuts,
+            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 = 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,
+        )
+        logging.debug("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def lsmix_cuts(
+        self,
+        rvb_affix: str = "clean",
+        type_affix: str = "full",
+        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}_{type_affix}{source_affix}.jsonl.gz"
+        )
+        cs = cs.filter(lambda c: c.duration >= 1.0 and c.duration <= 30.0)
+        return cs
+
+    @lru_cache()
+    def libricss_cuts(self, split="dev", type="sdm") -> CutSet:
+        logging.info(f"About to get LibriCSS {split} {type} cuts")
+        cs = load_manifest_lazy(
+            self.args.manifest_dir / f"cuts_{split}_libricss-{type}.jsonl.gz"
+        )
+        return cs

egs/libricss/SURT/dprnn_zipformer/decode.py

@@ -0,0 +1,871 @@
+#!/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 30 \
+    --avg 9 \
+    --use-averaged-model true \
+    --exp-dir ./dprnn_zipformer/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
+
+(2) modified beam search
+./dprnn_zipformer/decode.py \
+    --epoch 30 \
+    --avg 9 \
+    --use-averaged-model true \
+    --exp-dir ./dprnn_zipformer/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+"""
+
+
+import argparse
+import logging
+from collections import defaultdict
+from itertools import chain, groupby, repeat
+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 LibriCssAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+    modified_beam_search_LODR,
+)
+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,
+)
+
+OVERLAP_RATIOS = ["0L", "0S", "OV10", "OV20", "OV30", "OV40"]
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=9,
+        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(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        help="The lang dir containing word table and LG graph",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - fast_beam_search
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=20.0,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=64,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--save-masks",
+        type=str2bool,
+        default=False,
+        help="""If true, save masks generated by unmixing module.""",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+    ngram_lm: Optional[NgramLm] = None,
+    ngram_lm_scale: float = 1.0,
+    LM: Optional[LmScorer] = None,
+) -> Dict[str, List[List[str]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_7"
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    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]
+
+    # To save the masks, we split them by batch and trim each mask to the length of
+    # the corresponding feature. We save them in a dict, where the key is the
+    # cut ID and the value is the mask.
+    masks_dict = {}
+    for i in range(B):
+        mask = torch.cat(
+            [x_masked[j][i, : feature_lens[i]] for j in range(params.num_channels)],
+            dim=-1,
+        )
+        mask = mask.cpu().numpy()
+        masks_dict[batch["cuts"][i].id] = mask
+
+    # 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 == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp)
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp)
+    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp)
+    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)
+    elif params.decoding_method == "modified_beam_search_LODR":
+        hyp_tokens = modified_beam_search_LODR(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+            LODR_lm=ngram_lm,
+            LODR_lm_scale=ngram_lm_scale,
+            LM=LM,
+        )
+        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)}, masks_dict
+    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)}, masks_dict
+    else:
+        return {f"beam_size_{params.beam_size}": _group_channels(hyps)}, masks_dict
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+    ngram_lm: Optional[NgramLm] = None,
+    ngram_lm_scale: float = 1.0,
+    LM: Optional[LmScorer] = None,
+) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 is used.
+      Its value is a list of tuples. Each tuple contains two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 50
+    else:
+        log_interval = 20
+
+    results = defaultdict(list)
+    masks = {}
+    for batch_idx, batch in enumerate(dl):
+        cut_ids = [cut.id for cut in batch["cuts"]]
+        cuts_batch = batch["cuts"]
+
+        hyps_dict, masks_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            word_table=word_table,
+            batch=batch,
+            ngram_lm=ngram_lm,
+            ngram_lm_scale=ngram_lm_scale,
+            LM=LM,
+        )
+        masks.update(masks_dict)
+
+        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, masks_dict
+
+
+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)
+
+
+def save_masks(
+    params: AttributeDict,
+    test_set_name: str,
+    masks: List[torch.Tensor],
+):
+    masks_path = params.res_dir / f"masks-{test_set_name}.txt"
+    torch.save(masks, masks_path)
+    logging.info(f"The masks are stored in {masks_path}")
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LmScorer.add_arguments(parser)
+    LibriCssAsrDataModule.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",
+        "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
+        "modified_beam_search",
+        "modified_beam_search_LODR",
+    ), 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 "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    if "LODR" in params.decoding_method:
+        params.suffix += (
+            f"-LODR-{params.tokens_ngram}gram-scale-{params.ngram_lm_scale}"
+        )
+
+    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()
+
+    if "fast_beam_search" in params.decoding_method:
+        if params.decoding_method == "fast_beam_search_nbest_LG":
+            lexicon = Lexicon(params.lang_dir)
+            word_table = lexicon.word_table
+            lg_filename = params.lang_dir / "LG.pt"
+            logging.info(f"Loading {lg_filename}")
+            decoding_graph = k2.Fsa.from_dict(
+                torch.load(lg_filename, map_location=device)
+            )
+            decoding_graph.scores *= params.ngram_lm_scale
+        else:
+            word_table = None
+            decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+        word_table = None
+
+    # only load N-gram LM when needed
+    if "LODR" in params.decoding_method:
+        lm_filename = params.lang_dir / f"{params.tokens_ngram}gram.fst.txt"
+        logging.info(f"lm filename: {lm_filename}")
+        ngram_lm = NgramLm(
+            lm_filename,
+            backoff_id=params.backoff_id,
+            is_binary=False,
+        )
+        logging.info(f"num states: {ngram_lm.lm.num_states}")
+        ngram_lm_scale = params.ngram_lm_scale
+    else:
+        ngram_lm = None
+        ngram_lm_scale = None
+
+    # only load the neural network LM if doing shallow fusion
+    if params.use_shallow_fusion:
+        LM = LmScorer(
+            lm_type=params.lm_type,
+            params=params,
+            device=device,
+            lm_scale=params.lm_scale,
+        )
+        LM.to(device)
+        LM.eval()
+
+    else:
+        LM = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    libricss = LibriCssAsrDataModule(args)
+
+    dev_cuts = libricss.libricss_cuts(split="dev", type="ihm-mix").to_eager()
+    dev_cuts_grouped = [dev_cuts.filter(lambda x: ol in x.id) for ol in OVERLAP_RATIOS]
+    test_cuts = libricss.libricss_cuts(split="test", type="ihm-mix").to_eager()
+    test_cuts_grouped = [
+        test_cuts.filter(lambda x: ol in x.id) for ol in OVERLAP_RATIOS
+    ]
+
+    for dev_set, ol in zip(dev_cuts_grouped, OVERLAP_RATIOS):
+        dev_dl = libricss.test_dataloaders(dev_set)
+        results_dict, masks = decode_dataset(
+            dl=dev_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+            ngram_lm=ngram_lm,
+            ngram_lm_scale=ngram_lm_scale,
+            LM=LM,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=f"dev_{ol}",
+            results_dict=results_dict,
+        )
+
+        if params.save_masks:
+            save_masks(
+                params=params,
+                test_set_name=f"dev_{ol}",
+                masks=masks,
+            )
+
+    for test_set, ol in zip(test_cuts_grouped, OVERLAP_RATIOS):
+        test_dl = libricss.test_dataloaders(test_set)
+        results_dict, masks = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+            ngram_lm=ngram_lm,
+            ngram_lm_scale=ngram_lm_scale,
+            LM=LM,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=f"test_{ol}",
+            results_dict=results_dict,
+        )
+
+        if params.save_masks:
+            save_masks(
+                params=params,
+                test_set_name=f"test_{ol}",
+                masks=masks,
+            )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/libricss/SURT/dprnn_zipformer/decoder.py

@@ -0,0 +1,102 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Decoder(nn.Module):
+    """This class modifies the stateless decoder from the following paper:
+
+        RNN-transducer with stateless prediction network
+        https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419
+
+    It removes the recurrent connection from the decoder, i.e., the prediction
+    network. Different from the above paper, it adds an extra Conv1d
+    right after the embedding layer.
+
+    TODO: Implement https://arxiv.org/pdf/2109.07513.pdf
+    """
+
+    def __init__(
+        self,
+        vocab_size: int,
+        decoder_dim: int,
+        blank_id: int,
+        context_size: int,
+    ):
+        """
+        Args:
+          vocab_size:
+            Number of tokens of the modeling unit including blank.
+          decoder_dim:
+            Dimension of the input embedding, and of the decoder output.
+          blank_id:
+            The ID of the blank symbol.
+          context_size:
+            Number of previous words to use to predict the next word.
+            1 means bigram; 2 means trigram. n means (n+1)-gram.
+        """
+        super().__init__()
+
+        self.embedding = nn.Embedding(
+            num_embeddings=vocab_size,
+            embedding_dim=decoder_dim,
+            padding_idx=blank_id,
+        )
+        self.blank_id = blank_id
+
+        assert context_size >= 1, context_size
+        self.context_size = context_size
+        self.vocab_size = vocab_size
+        if context_size > 1:
+            self.conv = nn.Conv1d(
+                in_channels=decoder_dim,
+                out_channels=decoder_dim,
+                kernel_size=context_size,
+                padding=0,
+                groups=decoder_dim // 4,  # group size == 4
+                bias=False,
+            )
+
+    def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
+        """
+        Args:
+          y:
+            A 2-D tensor of shape (N, U).
+          need_pad:
+            True to left pad the input. Should be True during training.
+            False to not pad the input. Should be False during inference.
+        Returns:
+          Return a tensor of shape (N, U, decoder_dim).
+        """
+        y = y.to(torch.int64)
+        # this stuff about clamp() is a temporary fix for a mismatch
+        # at utterance start, we use negative ids in beam_search.py
+        embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(-1)
+        if self.context_size > 1:
+            embedding_out = embedding_out.permute(0, 2, 1)
+            if need_pad is True:
+                embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
+            else:
+                # During inference time, there is no need to do extra padding
+                # as we only need one output
+                assert embedding_out.size(-1) == self.context_size
+            embedding_out = self.conv(embedding_out)
+            embedding_out = embedding_out.permute(0, 2, 1)
+        embedding_out = F.relu(embedding_out)
+        return embedding_out

egs/libricss/SURT/dprnn_zipformer/dprnn.py

@@ -0,0 +1,305 @@
+import random
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from scaling import ActivationBalancer, BasicNorm, DoubleSwish, ScaledLinear, ScaledLSTM
+from torch.autograd import Variable
+
+EPS = torch.finfo(torch.get_default_dtype()).eps
+
+
+def _pad_segment(input, segment_size):
+    # Source: https://github.com/espnet/espnet/blob/master/espnet2/enh/layers/dprnn.py#L342
+    # input is the features: (B, N, T)
+    batch_size, dim, seq_len = input.shape
+    segment_stride = segment_size // 2
+
+    rest = segment_size - (segment_stride + seq_len % segment_size) % segment_size
+    if rest > 0:
+        pad = Variable(torch.zeros(batch_size, dim, rest)).type(input.type())
+        input = torch.cat([input, pad], 2)
+
+    pad_aux = Variable(torch.zeros(batch_size, dim, segment_stride)).type(input.type())
+    input = torch.cat([pad_aux, input, pad_aux], 2)
+
+    return input, rest
+
+
+def split_feature(input, segment_size):
+    # Source: https://github.com/espnet/espnet/blob/master/espnet2/enh/layers/dprnn.py#L358
+    # split the feature into chunks of segment size
+    # input is the features: (B, N, T)
+
+    input, rest = _pad_segment(input, segment_size)
+    batch_size, dim, seq_len = input.shape
+    segment_stride = segment_size // 2
+
+    segments1 = (
+        input[:, :, :-segment_stride]
+        .contiguous()
+        .view(batch_size, dim, -1, segment_size)
+    )
+    segments2 = (
+        input[:, :, segment_stride:]
+        .contiguous()
+        .view(batch_size, dim, -1, segment_size)
+    )
+    segments = (
+        torch.cat([segments1, segments2], 3)
+        .view(batch_size, dim, -1, segment_size)
+        .transpose(2, 3)
+    )
+
+    return segments.contiguous(), rest
+
+
+def merge_feature(input, rest):
+    # Source: https://github.com/espnet/espnet/blob/master/espnet2/enh/layers/dprnn.py#L385
+    # merge the splitted features into full utterance
+    # input is the features: (B, N, L, K)
+
+    batch_size, dim, segment_size, _ = input.shape
+    segment_stride = segment_size // 2
+    input = (
+        input.transpose(2, 3).contiguous().view(batch_size, dim, -1, segment_size * 2)
+    )  # B, N, K, L
+
+    input1 = (
+        input[:, :, :, :segment_size]
+        .contiguous()
+        .view(batch_size, dim, -1)[:, :, segment_stride:]
+    )
+    input2 = (
+        input[:, :, :, segment_size:]
+        .contiguous()
+        .view(batch_size, dim, -1)[:, :, :-segment_stride]
+    )
+
+    output = input1 + input2
+    if rest > 0:
+        output = output[:, :, :-rest]
+
+    return output.contiguous()  # B, N, T
+
+
+class RNNEncoderLayer(nn.Module):
+    """
+    RNNEncoderLayer is made up of lstm and feedforward networks.
+    Args:
+      input_size:
+        The number of expected features in the input (required).
+      hidden_size:
+        The hidden dimension of rnn layer.
+      dropout:
+        The dropout value (default=0.1).
+      layer_dropout:
+        The dropout value for model-level warmup (default=0.075).
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        dropout: float = 0.1,
+        bidirectional: bool = False,
+    ) -> None:
+        super(RNNEncoderLayer, self).__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+
+        assert hidden_size >= input_size, (hidden_size, input_size)
+        self.lstm = ScaledLSTM(
+            input_size=input_size,
+            hidden_size=hidden_size // 2 if bidirectional else hidden_size,
+            proj_size=0,
+            num_layers=1,
+            dropout=0.0,
+            batch_first=True,
+            bidirectional=bidirectional,
+        )
+        self.norm_final = BasicNorm(input_size)
+
+        # try to ensure the output is close to zero-mean (or at least, zero-median).  # noqa
+        self.balancer = ActivationBalancer(
+            num_channels=input_size,
+            channel_dim=-1,
+            min_positive=0.45,
+            max_positive=0.55,
+            max_abs=6.0,
+        )
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(
+        self,
+        src: torch.Tensor,
+        states: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        warmup: float = 1.0,
+    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Pass the input through the encoder layer.
+        Args:
+          src:
+            The sequence to the encoder layer (required).
+            Its shape is (S, N, E), where S is the sequence length,
+            N is the batch size, and E is the feature number.
+          states:
+            A tuple of 2 tensors (optional). It is for streaming inference.
+            states[0] is the hidden states of all layers,
+              with shape of (1, N, input_size);
+            states[1] is the cell states of all layers,
+              with shape of (1, N, hidden_size).
+        """
+        src_orig = src
+
+        # alpha = 1.0 means fully use this encoder layer, 0.0 would mean
+        # completely bypass it.
+        alpha = warmup if self.training else 1.0
+
+        # lstm module
+        src_lstm, new_states = self.lstm(src, states)
+        src = self.dropout(src_lstm) + src
+        src = self.norm_final(self.balancer(src))
+
+        if alpha != 1.0:
+            src = alpha * src + (1 - alpha) * src_orig
+
+        return src
+
+
+# dual-path RNN
+class DPRNN(nn.Module):
+    """Deep dual-path RNN.
+    Source: https://github.com/espnet/espnet/blob/master/espnet2/enh/layers/dprnn.py
+
+    args:
+        input_size: int, dimension of the input feature. The input should have shape
+                    (batch, seq_len, input_size).
+        hidden_size: int, dimension of the hidden state.
+        output_size: int, dimension of the output size.
+        dropout: float, dropout ratio. Default is 0.
+        num_blocks: int, number of stacked RNN layers. Default is 1.
+    """
+
+    def __init__(
+        self,
+        feature_dim,
+        input_size,
+        hidden_size,
+        output_size,
+        dropout=0.1,
+        num_blocks=1,
+        segment_size=50,
+        chunk_width_randomization=False,
+    ):
+        super().__init__()
+
+        self.input_size = input_size
+        self.output_size = output_size
+        self.hidden_size = hidden_size
+
+        self.segment_size = segment_size
+        self.chunk_width_randomization = chunk_width_randomization
+
+        self.input_embed = nn.Sequential(
+            ScaledLinear(feature_dim, input_size),
+            BasicNorm(input_size),
+            ActivationBalancer(
+                num_channels=input_size,
+                channel_dim=-1,
+                min_positive=0.45,
+                max_positive=0.55,
+            ),
+        )
+
+        # dual-path RNN
+        self.row_rnn = nn.ModuleList([])
+        self.col_rnn = nn.ModuleList([])
+        for _ in range(num_blocks):
+            # intra-RNN is non-causal
+            self.row_rnn.append(
+                RNNEncoderLayer(
+                    input_size, hidden_size, dropout=dropout, bidirectional=True
+                )
+            )
+            self.col_rnn.append(
+                RNNEncoderLayer(
+                    input_size, hidden_size, dropout=dropout, bidirectional=False
+                )
+            )
+
+        # output layer
+        self.out_embed = nn.Sequential(
+            ScaledLinear(input_size, output_size),
+            BasicNorm(output_size),
+            ActivationBalancer(
+                num_channels=output_size,
+                channel_dim=-1,
+                min_positive=0.45,
+                max_positive=0.55,
+            ),
+        )
+
+    def forward(self, input):
+        # input shape: B, T, F
+        input = self.input_embed(input)
+        B, T, D = input.shape
+
+        if self.chunk_width_randomization and self.training:
+            segment_size = random.randint(self.segment_size // 2, self.segment_size)
+        else:
+            segment_size = self.segment_size
+        input, rest = split_feature(input.transpose(1, 2), segment_size)
+        # input shape: batch, N, dim1, dim2
+        # apply RNN on dim1 first and then dim2
+        # output shape: B, output_size, dim1, dim2
+        # input = input.to(device)
+        batch_size, _, dim1, dim2 = input.shape
+        output = input
+        for i in range(len(self.row_rnn)):
+            row_input = (
+                output.permute(0, 3, 2, 1)
+                .contiguous()
+                .view(batch_size * dim2, dim1, -1)
+            )  # B*dim2, dim1, N
+            output = self.row_rnn[i](row_input)  # B*dim2, dim1, H
+            output = (
+                output.view(batch_size, dim2, dim1, -1).permute(0, 3, 2, 1).contiguous()
+            )  # B, N, dim1, dim2
+
+            col_input = (
+                output.permute(0, 2, 3, 1)
+                .contiguous()
+                .view(batch_size * dim1, dim2, -1)
+            )  # B*dim1, dim2, N
+            output = self.col_rnn[i](col_input)  # B*dim1, dim2, H
+            output = (
+                output.view(batch_size, dim1, dim2, -1).permute(0, 3, 1, 2).contiguous()
+            )  # B, N, dim1, dim2
+
+        output = merge_feature(output, rest)
+        output = output.transpose(1, 2)
+        output = self.out_embed(output)
+
+        # Apply ReLU to the output
+        output = torch.relu(output)
+
+        return output
+
+
+if __name__ == "__main__":
+
+    model = DPRNN(
+        80,
+        256,
+        256,
+        160,
+        dropout=0.1,
+        num_blocks=4,
+        segment_size=32,
+        chunk_width_randomization=True,
+    )
+    input = torch.randn(2, 1002, 80)
+    print(sum(p.numel() for p in model.parameters()))
+    print(model(input).shape)

egs/libricss/SURT/dprnn_zipformer/encoder_interface.py

@@ -0,0 +1,43 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+
+
+class EncoderInterface(nn.Module):
+    def forward(
+        self, x: torch.Tensor, x_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A tensor of shape (batch_size, input_seq_len, num_features)
+            containing the input features.
+          x_lens:
+            A tensor of shape (batch_size,) containing the number of frames
+            in `x` before padding.
+        Returns:
+          Return a tuple containing two tensors:
+            - encoder_out, a tensor of (batch_size, out_seq_len, output_dim)
+              containing unnormalized probabilities, i.e., the output of a
+              linear layer.
+            - encoder_out_lens, a tensor of shape (batch_size,) containing
+              the number of frames in `encoder_out` before padding.
+        """
+        raise NotImplementedError("Please implement it in a subclass")

egs/libricss/SURT/dprnn_zipformer/joiner.py

@@ -0,0 +1,65 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.nn as nn
+
+
+class Joiner(nn.Module):
+    def __init__(
+        self,
+        encoder_dim: int,
+        decoder_dim: int,
+        joiner_dim: int,
+        vocab_size: int,
+    ):
+        super().__init__()
+
+        self.encoder_proj = nn.Linear(encoder_dim, joiner_dim)
+        self.decoder_proj = nn.Linear(decoder_dim, joiner_dim)
+        self.output_linear = nn.Linear(joiner_dim, vocab_size)
+
+    def forward(
+        self,
+        encoder_out: torch.Tensor,
+        decoder_out: torch.Tensor,
+        project_input: bool = True,
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            Output from the encoder. Its shape is (N, T, s_range, C).
+          decoder_out:
+            Output from the decoder. Its shape is (N, T, s_range, C).
+           project_input:
+            If true, apply input projections encoder_proj and decoder_proj.
+            If this is false, it is the user's responsibility to do this
+            manually.
+        Returns:
+          Return a tensor of shape (N, T, s_range, C).
+        """
+        assert encoder_out.ndim == decoder_out.ndim
+        assert encoder_out.ndim in (2, 4)
+        assert encoder_out.shape[:-1] == decoder_out.shape[:-1]
+
+        if project_input:
+            logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
+        else:
+            logit = encoder_out + decoder_out
+
+        logit = self.output_linear(torch.tanh(logit))
+
+        return logit

egs/libricss/SURT/dprnn_zipformer/model.py

@@ -0,0 +1,316 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang, Wei Kang)
+# Copyright    2023  Johns Hopkins University (author: 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.
+
+from typing import List, Optional, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+
+from icefall.utils import add_sos
+
+
+class SURT(nn.Module):
+    """It implements Streaming Unmixing and Recognition Transducer (SURT).
+    https://arxiv.org/abs/2011.13148
+    """
+
+    def __init__(
+        self,
+        mask_encoder: nn.Module,
+        encoder: EncoderInterface,
+        joint_encoder_layer: Optional[nn.Module],
+        decoder: nn.Module,
+        joiner: nn.Module,
+        num_channels: int,
+        encoder_dim: int,
+        decoder_dim: int,
+        joiner_dim: int,
+        vocab_size: int,
+    ):
+        """
+        Args:
+          mask_encoder:
+            It is the masking network. It generates a mask for each channel of the
+            encoder. These masks are applied to the input features, and then passed
+            to the transcription network.
+          encoder:
+            It is the transcription network in the paper. Its accepts
+            two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
+            It returns two tensors: `logits` of shape (N, T, encoder_dm) and
+            `logit_lens` of shape (N,).
+          decoder:
+            It is the prediction network in the paper. Its input shape
+            is (N, U) and its output shape is (N, U, decoder_dim).
+            It should contain one attribute: `blank_id`.
+          joiner:
+            It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim).
+            Its output shape is (N, T, U, vocab_size). Note that its output contains
+            unnormalized probs, i.e., not processed by log-softmax.
+          num_channels:
+            It is the number of channels that the input features will be split into.
+            In general, it should be equal to the maximum number of simultaneously
+            active speakers. For most real scenarios, using 2 channels is sufficient.
+        """
+        super().__init__()
+        assert isinstance(encoder, EncoderInterface), type(encoder)
+        assert hasattr(decoder, "blank_id")
+
+        self.mask_encoder = mask_encoder
+        self.encoder = encoder
+        self.joint_encoder_layer = joint_encoder_layer
+        self.decoder = decoder
+        self.joiner = joiner
+        self.num_channels = num_channels
+
+        self.simple_am_proj = nn.Linear(
+            encoder_dim,
+            vocab_size,
+        )
+        self.simple_lm_proj = nn.Linear(decoder_dim, vocab_size)
+
+        self.ctc_output = nn.Sequential(
+            nn.Dropout(p=0.1),
+            nn.Linear(encoder_dim, vocab_size),
+            nn.LogSoftmax(dim=-1),
+        )
+
+    def forward_helper(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: k2.RaggedTensor,
+        prune_range: int = 5,
+        am_scale: float = 0.0,
+        lm_scale: float = 0.0,
+        reduction: str = "sum",
+        beam_size: int = 10,
+        use_double_scores: bool = False,
+        subsampling_factor: int = 1,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Compute transducer loss for one branch of the SURT model.
+        """
+        encoder_out, x_lens = self.encoder(x, x_lens)
+        assert torch.all(x_lens > 0)
+
+        if self.joint_encoder_layer is not None:
+            encoder_out = self.joint_encoder_layer(encoder_out)
+
+        # compute ctc log-probs
+        ctc_output = self.ctc_output(encoder_out)
+
+        # For the decoder, i.e., the prediction network
+        row_splits = y.shape.row_splits(1)
+        y_lens = row_splits[1:] - row_splits[:-1]
+
+        blank_id = self.decoder.blank_id
+        sos_y = add_sos(y, sos_id=blank_id)
+
+        # sos_y_padded: [B, S + 1], start with SOS.
+        sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
+
+        # decoder_out: [B, S + 1, decoder_dim]
+        decoder_out = self.decoder(sos_y_padded)
+
+        # Note: y does not start with SOS
+        # y_padded : [B, S]
+        y_padded = y.pad(mode="constant", padding_value=0)
+
+        y_padded = y_padded.to(torch.int64)
+        boundary = torch.zeros((x.size(0), 4), dtype=torch.int64, device=x.device)
+        boundary[:, 2] = y_lens
+        boundary[:, 3] = x_lens
+
+        lm = self.simple_lm_proj(decoder_out)
+        am = self.simple_am_proj(encoder_out)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
+                lm=lm.float(),
+                am=am.float(),
+                symbols=y_padded,
+                termination_symbol=blank_id,
+                lm_only_scale=lm_scale,
+                am_only_scale=am_scale,
+                boundary=boundary,
+                reduction=reduction,
+                return_grad=True,
+            )
+
+        # ranges : [B, T, prune_range]
+        ranges = k2.get_rnnt_prune_ranges(
+            px_grad=px_grad,
+            py_grad=py_grad,
+            boundary=boundary,
+            s_range=prune_range,
+        )
+
+        # am_pruned : [B, T, prune_range, encoder_dim]
+        # lm_pruned : [B, T, prune_range, decoder_dim]
+        am_pruned, lm_pruned = k2.do_rnnt_pruning(
+            am=self.joiner.encoder_proj(encoder_out),
+            lm=self.joiner.decoder_proj(decoder_out),
+            ranges=ranges,
+        )
+
+        # logits : [B, T, prune_range, vocab_size]
+
+        # project_input=False since we applied the decoder's input projections
+        # prior to do_rnnt_pruning (this is an optimization for speed).
+        logits = self.joiner(am_pruned, lm_pruned, project_input=False)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            pruned_loss = k2.rnnt_loss_pruned(
+                logits=logits.float(),
+                symbols=y_padded,
+                ranges=ranges,
+                termination_symbol=blank_id,
+                boundary=boundary,
+                reduction=reduction,
+            )
+
+        # Compute ctc loss
+        supervision_segments = torch.stack(
+            (
+                torch.arange(len(x_lens), device="cpu"),
+                torch.zeros_like(x_lens, device="cpu"),
+                torch.clone(x_lens).detach().cpu(),
+            ),
+            dim=1,
+        ).to(torch.int32)
+        # We need to sort supervision_segments in decreasing order of num_frames
+        indices = torch.argsort(supervision_segments[:, 2], descending=True)
+        supervision_segments = supervision_segments[indices]
+
+        # Works with a BPE model
+        decoding_graph = k2.ctc_graph(y, modified=False, device=x.device)
+        dense_fsa_vec = k2.DenseFsaVec(
+            ctc_output,
+            supervision_segments,
+            allow_truncate=subsampling_factor - 1,
+        )
+        ctc_loss = k2.ctc_loss(
+            decoding_graph=decoding_graph,
+            dense_fsa_vec=dense_fsa_vec,
+            output_beam=beam_size,
+            reduction="none",
+            use_double_scores=use_double_scores,
+        )
+
+        return (simple_loss, pruned_loss, ctc_loss)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: k2.RaggedTensor,
+        prune_range: int = 5,
+        am_scale: float = 0.0,
+        lm_scale: float = 0.0,
+        reduction: str = "sum",
+        beam_size: int = 10,
+        use_double_scores: bool = False,
+        subsampling_factor: int = 1,
+        return_masks: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C).
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of frames in `x`
+            before padding.
+          y:
+            A ragged tensor of shape (N*num_channels, S). It contains the labels
+            of the N utterances. The labels are in the range [0, vocab_size). All
+            the channels are concatenated together one after another.
+          prune_range:
+            The prune range for rnnt loss, it means how many symbols(context)
+            we are considering for each frame to compute the loss.
+          am_scale:
+            The scale to smooth the loss with am (output of encoder network)
+            part
+          lm_scale:
+            The scale to smooth the loss with lm (output of predictor network)
+            part
+          reduction:
+            "sum" to sum the losses over all utterances in the batch.
+            "none" to return the loss in a 1-D tensor for each utterance
+            in the batch.
+          beam_size:
+            The beam size used in CTC decoding.
+          use_double_scores:
+            If True, use double precision for CTC decoding.
+          subsampling_factor:
+            The subsampling factor of the model. It is used to compute the
+            supervision segments for CTC loss.
+          return_masks:
+            If True, return the masks as well as masked features.
+        Returns:
+          Return the transducer loss.
+
+        Note:
+           Regarding am_scale & lm_scale, it will make the loss-function one of
+           the form:
+              lm_scale * lm_probs + am_scale * am_probs +
+              (1-lm_scale-am_scale) * combined_probs
+        """
+        assert x.ndim == 3, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+        assert y.num_axes == 2, y.num_axes
+
+        assert x.size(0) == x_lens.size(0), (x.size(), x_lens.size())
+
+        # Apply the mask encoder
+        B, T, F = x.shape
+        processed = self.mask_encoder(x)  # B,T,F*num_channels
+        masks = processed.view(B, T, F, self.num_channels).unbind(dim=-1)
+        x_masked = [x * m for m in masks]
+
+        # Recognition
+        # Stack the inputs along the batch axis
+        h = torch.cat(x_masked, dim=0)
+        h_lens = torch.cat([x_lens for _ in range(self.num_channels)], dim=0)
+
+        simple_loss, pruned_loss, ctc_loss = self.forward_helper(
+            h,
+            h_lens,
+            y,
+            prune_range,
+            am_scale,
+            lm_scale,
+            reduction=reduction,
+            beam_size=beam_size,
+            use_double_scores=use_double_scores,
+            subsampling_factor=subsampling_factor,
+        )
+
+        # Chunks the outputs into 2 parts along batch axis and then stack them along a new axis.
+        simple_loss = torch.stack(
+            torch.chunk(simple_loss, self.num_channels, dim=0), dim=0
+        )
+        pruned_loss = torch.stack(
+            torch.chunk(pruned_loss, self.num_channels, dim=0), dim=0
+        )
+        ctc_loss = torch.stack(torch.chunk(ctc_loss, self.num_channels, dim=0), dim=0)
+
+        if return_masks:
+            return (simple_loss, pruned_loss, ctc_loss, x_masked, masks)
+        else:
+            return (simple_loss, pruned_loss, ctc_loss, x_masked)

egs/libricss/SURT/dprnn_zipformer/scaling_converter.py

@@ -0,0 +1,114 @@
+# Copyright    2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This file replaces various modules in a model.
+Specifically, ActivationBalancer is replaced with an identity operator;
+Whiten is also replaced with an identity operator;
+BasicNorm is replaced by a module with `exp` removed.
+"""
+
+import copy
+from typing import List
+
+import torch
+import torch.nn as nn
+from scaling import ActivationBalancer, BasicNorm, Whiten
+
+
+class NonScaledNorm(nn.Module):
+    """See BasicNorm for doc"""
+
+    def __init__(
+        self,
+        num_channels: int,
+        eps_exp: float,
+        channel_dim: int = -1,  # CAUTION: see documentation.
+    ):
+        super().__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        self.eps_exp = eps_exp
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if not torch.jit.is_tracing():
+            assert x.shape[self.channel_dim] == self.num_channels
+        scales = (
+            torch.mean(x * x, dim=self.channel_dim, keepdim=True) + self.eps_exp
+        ).pow(-0.5)
+        return x * scales
+
+
+def convert_basic_norm(basic_norm: BasicNorm) -> NonScaledNorm:
+    assert isinstance(basic_norm, BasicNorm), type(BasicNorm)
+    norm = NonScaledNorm(
+        num_channels=basic_norm.num_channels,
+        eps_exp=basic_norm.eps.data.exp().item(),
+        channel_dim=basic_norm.channel_dim,
+    )
+    return norm
+
+
+# Copied from https://pytorch.org/docs/1.9.0/_modules/torch/nn/modules/module.html#Module.get_submodule  # noqa
+# get_submodule was added to nn.Module at v1.9.0
+def get_submodule(model, target):
+    if target == "":
+        return model
+    atoms: List[str] = target.split(".")
+    mod: torch.nn.Module = model
+    for item in atoms:
+        if not hasattr(mod, item):
+            raise AttributeError(
+                mod._get_name() + " has no " "attribute `" + item + "`"
+            )
+        mod = getattr(mod, item)
+        if not isinstance(mod, torch.nn.Module):
+            raise AttributeError("`" + item + "` is not " "an nn.Module")
+    return mod
+
+
+def convert_scaled_to_non_scaled(
+    model: nn.Module,
+    inplace: bool = False,
+):
+    """
+    Args:
+      model:
+        The model to be converted.
+      inplace:
+        If True, the input model is modified inplace.
+        If False, the input model is copied and we modify the copied version.
+    Return:
+      Return a model without scaled layers.
+    """
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    d = {}
+    for name, m in model.named_modules():
+        if isinstance(m, BasicNorm):
+            d[name] = convert_basic_norm(m)
+        elif isinstance(m, (ActivationBalancer, Whiten)):
+            d[name] = nn.Identity()
+
+    for k, v in d.items():
+        if "." in k:
+            parent, child = k.rsplit(".", maxsplit=1)
+            setattr(get_submodule(model, parent), child, v)
+        else:
+            setattr(model, k, v)
+
+    return model

egs/libricss/SURT/local/add_source_feats.py

@@ -0,0 +1,85 @@
+#!/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(num_jobs=1):
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+
+    for type_affix in ["full", "ov40"]:
+        logging.info(f"Adding source features for {type_affix}")
+        mixed_name_clean = f"train_clean_{type_affix}"
+        mixed_name_rvb = f"train_rvb_{type_affix}"
+
+        logging.info("Reading mixed cuts")
+        mixed_cuts_clean = load_manifest_lazy(
+            src_dir / f"cuts_{mixed_name_clean}.jsonl.gz"
+        )
+        mixed_cuts_rvb = load_manifest_lazy(src_dir / f"cuts_{mixed_name_rvb}.jsonl.gz")
+
+        logging.info("Reading source cuts")
+        source_cuts = load_manifest(src_dir / "librispeech_cuts_train_trimmed.jsonl.gz")
+
+        logging.info("Adding source features to the mixed cuts")
+        with tqdm() as pbar, CutSet.open_writer(
+            src_dir / f"cuts_{mixed_name_clean}_sources.jsonl.gz"
+        ) as cut_writer_clean, CutSet.open_writer(
+            src_dir / f"cuts_{mixed_name_rvb}_sources.jsonl.gz"
+        ) as cut_writer_rvb, LilcomChunkyWriter(
+            output_dir / f"feats_train_{type_affix}_sources"
+        ) as source_feat_writer:
+            for cut_clean, cut_rvb in zip(mixed_cuts_clean, mixed_cuts_rvb):
+                assert cut_rvb.id == cut_clean.id + "_rvb"
+                # Create source_feats and source_feat_offsets
+                # (See `lhotse.datasets.K2SurtDataset` for details)
+                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)
+                cut_rvb.source_feats = cut_clean.source_feats
+                cut_rvb.source_feat_offsets = cut_clean.source_feat_offsets
+                cut_writer_rvb.write(cut_rvb)
+                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/libricss/SURT/prepare.sh

@@ -185,7 +185,7 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
   for type in full ov40; do
     cat <(gunzip -c data/manifests/cuts_train_clean_${type}_sources.jsonl.gz) \
       <(gunzip -c data/manifests/cuts_train_rvb_${type}_sources.jsonl.gz) |\
-      shuf | gzip -c > data/manifests/cuts_train_${type}_sources.jsonl.gz
+      shuf | gzip -c > data/manifests/cuts_train_comb_${type}_sources.jsonl.gz
   done
 fi
 

icefall/utils.py

@@ -887,8 +887,7 @@ def write_error_stats_with_timestamps(
         hyp_count = corr + hyp_sub + ins
 
         print(f"{word}   {corr} {tot_errs} {ref_count} {hyp_count}", file=f)
-
+    return float(tot_err_rate), float(mean_delay), float(var_delay)
-    return tot_err_rate, mean_delay, var_delay
 
 
 def write_surt_error_stats(
@@ -1282,10 +1281,10 @@ def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
     assert lengths.ndim == 1, lengths.ndim
     max_len = max(max_len, lengths.max())
     n = lengths.size(0)
-    seq_range = torch.arange(0, max_len, device=lengths.device)
-    expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
 
-    return expaned_lengths >= lengths.unsqueeze(-1)
+    expaned_lengths = torch.arange(max_len).expand(n, max_len).to(lengths)
+
+    return expaned_lengths >= lengths.unsqueeze(1)
 
 
 # Copied and modified from https://github.com/wenet-e2e/wenet/blob/main/wenet/utils/mask.py
@@ -1648,7 +1647,7 @@ def parse_timestamp(tokens: List[str], timestamp: List[float]) -> List[float]:
       List of timestamp of each word.
     """
     start_token = b"\xe2\x96\x81".decode()  # '_'
-    assert len(tokens) == len(timestamp), (len(tokens), len(timestamp))
+    assert len(tokens) == len(timestamp)
     ans = []
     for i in range(len(tokens)):
         flag = False