Add subformer into zipformer

egs/librispeech/ASR/subformer/asr_datamodule.py

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+../pruned_transducer_stateless2/asr_datamodule.py

egs/librispeech/ASR/subformer/beam_search.py

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+../pruned_transducer_stateless2/beam_search.py

egs/librispeech/ASR/subformer/decode.py

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+#!/usr/bin/env python3
+#
+# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+(1) greedy search
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
+
+(2) beam search (not recommended)
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method beam_search \
+    --beam-size 4
+
+(3) modified beam search
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+
+(4) fast beam search (one best)
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
+
+(5) fast beam search (nbest)
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(6) fast beam search (nbest oracle WER)
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_oracle \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(7) fast beam search (with LG)
+./zipformer/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./zipformer/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_LG \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
+"""
+
+
+import argparse
+import logging
+import math
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    make_pad_mask,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="zipformer/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_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
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=20.0,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=64,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[List[str]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_7"
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    if params.causal:
+        # this seems to cause insertions at the end of the utterance if used with zipformer.
+        pad_len = int(params.chunk_size)
+        feature_lens += pad_len
+        feature = torch.nn.functional.pad(
+            feature,
+            pad=(0, 0, 0, pad_len),
+            value=LOG_EPS,
+        )
+
+    x, x_lens = model.encoder_embed(feature, feature_lens)
+
+    src_key_padding_mask = make_pad_mask(x_lens)
+    x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x, x_lens, src_key_padding_mask
+    )
+    encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in hyp_tokens:
+            hyps.append([word_table[i] for i in hyp])
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append(sp.decode(hyp).split())
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": hyps}
+    elif "fast_beam_search" in params.decoding_method:
+        key = f"beam_{params.beam}_"
+        key += f"max_contexts_{params.max_contexts}_"
+        key += f"max_states_{params.max_states}"
+        if "nbest" in params.decoding_method:
+            key += f"_num_paths_{params.num_paths}_"
+            key += f"nbest_scale_{params.nbest_scale}"
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
+    else:
+        return {f"beam_size_{params.beam_size}": hyps}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 is used.
+      Its value is a list of tuples. Each tuple contains two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 50
+    else:
+        log_interval = 20
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            word_table=word_table,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
+                ref_words = ref_text.split()
+                this_batch.append((cut_id, ref_words, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % log_interval == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if params.causal:
+        assert (
+            "," not in params.chunk_size
+        ), "chunk_size should be one value in decoding."
+        assert (
+            "," not in params.left_context_frames
+        ), "left_context_frames should be one value in decoding."
+        params.suffix += f"-chunk-{params.chunk_size}"
+        params.suffix += f"-left-context-{params.left_context_frames}"
+
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to(device)
+    model.eval()
+
+    if "fast_beam_search" in params.decoding_method:
+        if params.decoding_method == "fast_beam_search_nbest_LG":
+            lexicon = Lexicon(params.lang_dir)
+            word_table = lexicon.word_table
+            lg_filename = params.lang_dir / "LG.pt"
+            logging.info(f"Loading {lg_filename}")
+            decoding_graph = k2.Fsa.from_dict(
+                torch.load(lg_filename, map_location=device)
+            )
+            decoding_graph.scores *= params.ngram_lm_scale
+        else:
+            word_table = None
+            decoding_graph = k2.trivial_graph(
+                params.vocab_size - 1, device=device
+            )
+    else:
+        decoding_graph = None
+        word_table = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+
+    test_sets = ["test-clean", "test-other"]
+    test_dl = [test_clean_dl, test_other_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/librispeech/ASR/subformer/decoder.py

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

egs/librispeech/ASR/subformer/encoder_interface.py

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

egs/librispeech/ASR/subformer/joiner.py

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

egs/librispeech/ASR/subformer/mixformer.py

@@ -0,0 +1,484 @@
+#!/usr/bin/env python3
+# Copyright (c)  2023 Xiaomi Corp.           (author: Wei Kang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+import logging
+import torch
+import random
+from encoder_interface import EncoderInterface
+from scaling import (
+    Balancer,
+    BiasNorm,
+    Dropout2,
+    ChunkCausalDepthwiseConv1d,
+    ActivationDropoutAndLinear,
+    ScaledLinear,  # not as in other dirs.. just scales down initial parameter values.
+    Whiten,
+    Identity,  # more friendly to backward hooks than nn.Identity(), for diagnostic reasons.
+    penalize_abs_values_gt,
+    softmax,
+    ScheduledFloat,
+    FloatLike,
+    limit_param_value,
+    convert_num_channels,
+)
+from subformer import (
+    BypassModule,
+    CompactRelPositionalEncoding,
+    LearnedDownsamplingModule,
+    SubformerEncoder,
+    SubformerEncoderLayer,
+)
+from zipformer import (
+    DownsampledZipformer2Encoder,
+    SimpleDownsample,
+    SimpleUpsample,
+    Zipformer2Encoder,
+    Zipformer2EncoderLayer,
+)
+from torch import Tensor, nn
+
+
+class Mixformer(EncoderInterface):
+    def __init__(
+        self,
+        structure: str = "ZZS(S(S)S)SZ",
+        output_downsampling_factor: int = 2,
+        downsampling_factor: Tuple[int] = (1, 1, 2, 2, 1),
+        encoder_dim: Union[int, Tuple[int]] = (
+            192, 
+            192,
+            256,
+            384,
+            512,
+            384,
+            256,
+            192,
+        ),
+        num_encoder_layers: Union[int, Tuple[int]] = (
+            2,
+            2,
+            2,
+            2,
+            2,
+            2,
+            2,
+            2,
+        ),
+        encoder_unmasked_dim: Union[int, Tuple[int]] = (192, 192, 192),
+        query_head_dim: Union[int, Tuple[int]] = (32,),
+        value_head_dim: Union[int, Tuple[int]] = (12,),
+        pos_head_dim: Union[int, Tuple[int]] = (4,),
+        pos_dim: int = (48,),
+        num_heads: Union[int, Tuple[int]] = (4,),
+        feedforward_dim: Union[int, Tuple[int]] = (
+            512,
+            768,
+            1024,
+            1536,
+            2048,
+            1536,
+            1024,
+            768,
+        ),
+        cnn_module_kernel: Union[int, Tuple[int]] = (15, 31, 31),
+        encoder_chunk_sizes: Tuple[Tuple[int, ...]] = ((128, 1024),),
+        memory_dim: int = -1,
+        dropout: Optional[FloatLike] = None,  # see code below for default
+        warmup_batches: float = 4000.0,
+        causal: bool = False,
+    ) -> None:
+        super(Mixformer, self).__init__()
+
+        if dropout is None:
+            dropout = ScheduledFloat((0.0, 0.3), (20000.0, 0.1))
+
+        num_zip_encoders = len([s for s in structure if s == 'Z'])
+        num_sub_encoders = len([s for s in structure if s == 'S'])
+        num_encoders = num_zip_encoders + num_sub_encoders
+        num_downsamplers = len([s for s in structure if s == '('])
+
+        def _to_tuple(x, length):
+            """Converts a single int or a 1-tuple of an int to a tuple with the same length
+            as downsampling_factor"""
+            assert isinstance(x, tuple)
+            if len(x) == 1:
+                x = x * length
+            else:
+                assert len(x) == length and isinstance(
+                    x[0], int
+                )
+            return x
+
+        self.output_downsampling_factor = output_downsampling_factor  # int
+        self.downsampling_factor = (
+            downsampling_factor
+        ) = _to_tuple(downsampling_factor, num_zip_encoders + num_downsamplers)  # tuple
+        self.encoder_dim = encoder_dim = _to_tuple(encoder_dim, num_encoders)  # tuple
+        self.encoder_unmasked_dim = encoder_unmasked_dim = _to_tuple(
+            encoder_unmasked_dim, num_zip_encoders
+        )  # tuple
+        num_encoder_layers = _to_tuple(num_encoder_layers, num_encoders)
+        self.query_head_dim = query_head_dim = _to_tuple(query_head_dim, num_encoders)
+        self.value_head_dim = value_head_dim = _to_tuple(value_head_dim, num_encoders)
+        pos_head_dim = _to_tuple(pos_head_dim, num_encoders)
+        pos_dim = _to_tuple(pos_dim, num_encoders)
+        self.num_heads = num_heads = _to_tuple(num_heads, num_encoders)
+        feedforward_dim = _to_tuple(feedforward_dim, num_encoders)
+        self.cnn_module_kernel = cnn_module_kernel = _to_tuple(
+            cnn_module_kernel, num_zip_encoders
+        )
+        encoder_chunk_sizes = _to_tuple(encoder_chunk_sizes, num_sub_encoders)
+
+        self.causal = causal
+
+        # for u, d in zip(encoder_unmasked_dim, encoder_dim):
+            # assert u <= d
+
+        # each one will be Zipformer2Encoder, DownsampledZipformer2Encoder,
+        # SubformerEncoder or DownsampledSubformerEncoder
+        zip_encoders = []
+        sub_encoders = []
+        downsamplers = []
+        bypasses = []
+
+        layer_indexes = []
+
+        cur_max_dim = 0
+
+        downsampling_factors_list = []
+        def cur_downsampling_factor():
+            c = 1
+            for d in downsampling_factors_list: c *= d
+            return c
+
+        zip_encoder_dim = []
+        zip_downsampling_factor = []
+        for s in structure:
+            if s == "Z":
+                i = len(zip_encoders) + len(sub_encoders)
+                j = len(zip_encoders)
+                k = len(downsamplers) + len(zip_encoders)
+                assert encoder_unmasked_dim[j] <= encoder_dim[i]
+                zip_encoder_dim.append(encoder_dim[i])
+                encoder_layer = Zipformer2EncoderLayer(
+                    embed_dim=encoder_dim[i],
+                    pos_dim=pos_dim[i],
+                    num_heads=num_heads[i],
+                    query_head_dim=query_head_dim[i],
+                    pos_head_dim=pos_head_dim[i],
+                    value_head_dim=value_head_dim[i],
+                    feedforward_dim=feedforward_dim[i],
+                    dropout=dropout,
+                    cnn_module_kernel=cnn_module_kernel[j],
+                    causal=causal,
+                )
+
+                # For the segment of the warmup period, we let the Conv2dSubsampling
+                # layer learn something.  Then we start to warm up the other encoders.
+                encoder = Zipformer2Encoder(
+                    encoder_layer,
+                    num_encoder_layers[i],
+                    pos_dim=pos_dim[i],
+                    dropout=dropout,
+                    warmup_begin=warmup_batches * (i + 1) / (num_encoders + 1),
+                    warmup_end=warmup_batches * (i + 2) / (num_encoders + 1),
+                    final_layerdrop_rate=0.035 * (downsampling_factor[k] ** 0.5),
+                )
+
+                if downsampling_factor[k] != 1:
+                    encoder = DownsampledZipformer2Encoder(
+                        encoder,
+                        dim=encoder_dim[i],
+                        downsample=downsampling_factor[k],
+                        dropout=dropout,
+                    )
+                zip_downsampling_factor.append(downsampling_factor[k])
+                layer_indexes.append(len(zip_encoders))
+                zip_encoders.append(encoder)
+            elif s == 'S':
+                i = len(zip_encoders) + len(sub_encoders)
+                j = len(sub_encoders)
+                if len(sub_encoders) == 0:
+                    cur_max_dim = encoder_dim[i]
+                encoder_layer = SubformerEncoderLayer(
+                    embed_dim=encoder_dim[i],
+                    pos_dim=pos_head_dim[i],
+                    num_heads=num_heads[i],
+                    query_head_dim=query_head_dim[i],
+                    value_head_dim=value_head_dim[i],
+                    feedforward_dim=feedforward_dim[i],
+                    memory_dim=memory_dim,
+                    dropout=dropout,
+                    causal=causal,
+                )
+                cur_max_dim = max(cur_max_dim, encoder_dim[i])
+                encoder = SubformerEncoder(
+                    encoder_layer,
+                    num_encoder_layers[i],
+                    embed_dim=cur_max_dim,
+                    dropout=dropout,
+                    chunk_sizes=encoder_chunk_sizes[j],
+                    warmup_begin=warmup_batches * (i + 1) / (num_encoders + 1),
+                    warmup_end=warmup_batches * (i + 2) / (num_encoders + 1),
+                    final_layerdrop_rate=0.035 * (cur_downsampling_factor() ** 0.5),
+                )
+                layer_indexes.append(len(sub_encoders))
+                sub_encoders.append(encoder)
+            elif s =='(':
+                i = len(zip_encoders) + len(downsamplers)
+                downsampler = LearnedDownsamplingModule(cur_max_dim,
+                                                        downsampling_factor[i])
+                downsampling_factors_list.append(downsampling_factor[i])
+                layer_indexes.append(len(downsamplers))
+                downsamplers.append(downsampler)
+            else:
+                assert s == ')'
+                bypass = BypassModule(cur_max_dim, straight_through_rate=0.0)
+                layer_indexes.append(len(bypasses))
+                bypasses.append(bypass)
+                downsampling_factors_list.pop()
+
+        logging.info(f"cur_downsampling_factor={cur_downsampling_factor()}")
+
+        self.zip_encoder_dim = zip_encoder_dim
+        self.zip_downsampling_factor = zip_downsampling_factor
+        self.layer_indexes = layer_indexes
+        self.structure = structure
+        self.zip_encoders = nn.ModuleList(zip_encoders)
+        self.sub_encoders = nn.ModuleList(sub_encoders)
+        self.downsamplers = nn.ModuleList(downsamplers)
+        self.bypasses = nn.ModuleList(bypasses)
+
+        self.encoder_pos = CompactRelPositionalEncoding(64, pos_head_dim[0],
+                                                        dropout_rate=0.15,
+                                                        length_factor=1.0)
+
+        self.downsample_output = SimpleDownsample(
+            max(encoder_dim),
+            downsample=output_downsampling_factor,
+            dropout=dropout,
+        )
+
+    def _get_full_dim_output(self, outputs: List[Tensor]):
+        num_encoders = len(self.zip_encoders) + 1
+        assert len(outputs) == num_encoders
+        output_dim = max(self.encoder_dim)
+        output_pieces = [outputs[-1]]
+        cur_dim = self.encoder_dim[-1]
+        for i in range(num_encoders - 2, -1, -1):
+            d = list(outputs[i].shape)[-1]
+            if d > cur_dim:
+                this_output = outputs[i]
+                output_pieces.append(this_output[..., cur_dim:d])
+                cur_dim = d
+        assert cur_dim == output_dim, (cur_dim, output_dim)
+        return torch.cat(output_pieces, dim=-1)
+
+    def get_feature_masks(self, x: Tensor) -> Union[List[float], List[Tensor]]:
+        """
+        In eval mode, returns [1.0] * num_encoders; in training mode, returns a number of
+        randomized feature masks, one per encoder.
+        On e.g. 15% of frames, these masks will zero out all enocder dims larger than
+        some supplied number, e.g. >256, so in effect on those frames we are using
+        a smaller encoer dim.
+
+        We generate the random masks at this level because we want the 2 masks to 'agree'
+        all the way up the encoder stack. This will mean that the 1st mask will have
+        mask values repeated self.zipformer_subsampling_factor times.
+
+        Args:
+           x: the embeddings (needed for the shape and dtype and device), of shape
+             (1, batch_size, encoder_dims0)
+        """
+        num_encoders = len(self.zip_encoders)
+        if not self.training:
+            return [1.0] * num_encoders
+
+        (num_frames0, batch_size, _encoder_dims0) = x.shape
+
+        assert self.encoder_dim[0] == _encoder_dims0
+
+        feature_mask_dropout_prob = 0.125
+
+        # mask1 shape: (1, batch_size, 1)
+        mask1 = (
+            torch.rand(1, batch_size, 1, device=x.device)
+            > feature_mask_dropout_prob
+        ).to(x.dtype)
+
+        # mask2 has additional sequences masked, about twice the number.
+        mask2 = torch.logical_and(
+            mask1,
+            (
+                torch.rand(1, batch_size, 1, device=x.device)
+                > feature_mask_dropout_prob
+            ).to(x.dtype),
+        )
+
+        # dim: (1, batch_size, 2)
+        mask = torch.cat((mask1, mask2), dim=-1)
+
+        feature_masks = []
+        for i in range(num_encoders):
+            channels = self.zip_encoder_dim[i]
+            feature_mask = torch.ones(
+                1, batch_size, channels, dtype=x.dtype, device=x.device
+            )
+            u1 = self.encoder_unmasked_dim[i]
+            u2 = u1 + (channels - u1) // 2
+
+            feature_mask[:, :, u1:u2] *= mask[..., 0:1]
+            feature_mask[:, :, u2:] *= mask[..., 1:2]
+
+            feature_masks.append(feature_mask)
+
+        return feature_masks
+
+    def _get_attn_offset(self, x: Tensor, src_key_padding_mask: Optional[Tensor]) -> Optional[Tensor]:
+        """
+        Return attention offset of shape (1 or batch_size, seq_len, seq_len), interpreted as (1 or batch_size, tgt_seq_len,
+            src_seq_len); this reflects masking, if causal == True, otherwise will be all zeros.
+
+        Args:
+           x: embeddings after self.encoder_embed(), of shape (seq_len, batch_size, embed_dim).
+         src_key_padding_mask: optional key-padding mask of shape (batch_size, seq_len) with True in masked positions.
+        """
+        seq_len, batch_size, _num_channels = x.shape
+
+        ans = torch.zeros(batch_size, seq_len, seq_len, device=x.device)
+
+        if self.causal:
+            # t is frame index, shape (seq_len,)
+            t = torch.arange(seq_len, dtype=torch.int32, device=x.device)
+            src_t = t
+            tgt_t = t.unsqueeze(-1)
+            attn_mask = (src_t > tgt_t)
+            ans.masked_fill_(attn_mask, float('-inf'))
+
+        if src_key_padding_mask is not None:
+            ans.masked_fill_(src_key_padding_mask.unsqueeze(1), float('-inf'))
+            # now ans: (batch_size, seq_len, seq_len).
+        return ans
+
+
+    def forward(
+        self,
+        x: Tensor,
+        x_lens: Tensor,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+          x:
+            The input tensor. Its shape is (seq_len, batch_size, feature_dim).
+          x_lens:
+            A tensor of shape (batch_size,) containing the number of frames in
+            `x` before padding.
+          src_key_padding_mask:
+            The mask for padding, of shape (batch_size, seq_len); True means
+            masked position. May be None.
+        Returns:
+          Return a tuple containing 2 tensors:
+            - embeddings: its shape is (output_seq_len, batch_size, max(encoder_dim))
+            - lengths, a tensor of shape (batch_size,) containing the number
+              of frames in `embeddings` before padding.
+        """
+        outputs = []
+
+        attn_offsets = [ self._get_attn_offset(x, src_key_padding_mask) ]
+        pos_embs = [ self.encoder_pos(x) ]
+        downsample_info = []
+
+        if torch.jit.is_scripting():
+            feature_masks = [1.0] * len(self.zip_encoders)
+        else:
+            feature_masks = self.get_feature_masks(x)
+
+        for s, i in zip(self.structure, self.layer_indexes):
+            if s == 'Z':
+                encoder = self.zip_encoders[i]
+                ds = self.zip_downsampling_factor[i]
+                x = convert_num_channels(x, self.zip_encoder_dim[i])
+                x = encoder(
+                    x,
+                    feature_mask=feature_masks[i],
+                    src_key_padding_mask=(
+                        None
+                        if src_key_padding_mask is None
+                        else src_key_padding_mask[..., ::ds]
+                    ),
+                )
+                outputs.append(x)
+            elif s == 'S':
+                encoder = self.sub_encoders[i]  # one encoder stack
+                x = encoder(x,
+                            pos_embs[-1],
+                            attn_offset=attn_offsets[-1])
+
+                # only the last output of subformer will be used to combine the
+                # final output.
+                if i == len(self.sub_encoders) - 1:
+                    outputs.append(x)
+                # x will have the maximum dimension up till now, even if
+                # `encoder` uses lower dim in its layers.
+            elif s == '(':
+                downsampler = self.downsamplers[i]
+
+                indexes, weights, x_new = downsampler(x)
+                downsample_info.append((indexes, weights, x))
+                x = x_new
+
+                pos_embs.append(downsampler.downsample_pos_emb(pos_embs[-1], indexes))
+
+                attn_offsets.append(downsampler.downsample_attn_offset(attn_offsets[-1],
+                                                                       indexes,
+                                                                       weights))
+            else:
+                assert s == ')'  # upsample and bypass
+                indexes, weights, x_orig = downsample_info.pop()
+                _attn_offset = attn_offsets.pop()
+                _pos_emb = pos_embs.pop()
+                x_orig = convert_num_channels(x_orig, x.shape[-1])
+
+                x = LearnedDownsamplingModule.upsample(x_orig, x, indexes, weights)
+
+                bypass = self.bypasses[i]
+                x = bypass(x_orig, x)
+
+        # Only "balanced" structure is supported now
+        assert len(downsample_info) == 0, len(downsample_info)
+
+        # if the last output has the largest dimension, x will be unchanged,
+        # it will be the same as outputs[-1].  Otherwise it will be concatenated
+        # from different pieces of 'outputs', taking each dimension from the
+        # most recent output that has it present.
+        x = self._get_full_dim_output(outputs)
+        x = self.downsample_output(x)
+        # class Downsample has this rounding behavior..
+        assert self.output_downsampling_factor == 2
+        if torch.jit.is_scripting():
+            lengths = (x_lens + 1) // 2
+        else:
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                lengths = (x_lens + 1) // 2
+
+        return x, lengths

egs/librispeech/ASR/subformer/model.py

@@ -0,0 +1,217 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang, Wei Kang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import k2
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+
+from icefall.utils import add_sos, make_pad_mask
+from scaling import ScaledLinear
+
+
+class Transducer(nn.Module):
+    """It implements https://arxiv.org/pdf/1211.3711.pdf
+    "Sequence Transduction with Recurrent Neural Networks"
+    """
+
+    def __init__(
+        self,
+        encoder_embed: nn.Module,
+        encoder: EncoderInterface,
+        decoder: nn.Module,
+        joiner: nn.Module,
+        encoder_dim: int,
+        decoder_dim: int,
+        joiner_dim: int,
+        vocab_size: int,
+    ):
+        """
+        Args:
+          encoder_embed:
+            It is a Convolutional 2D subsampling module. It converts
+            an input of shape (N, T, idim) to an output of of shape
+            (N, T', odim), where T' = (T-3)//2-2 = (T-7)//2.
+          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.
+        """
+        super().__init__()
+        assert isinstance(encoder, EncoderInterface), type(encoder)
+        assert hasattr(decoder, "blank_id")
+
+        self.encoder_embed = encoder_embed
+        self.encoder = encoder
+        self.decoder = decoder
+        self.joiner = joiner
+
+        self.simple_am_proj = ScaledLinear(
+            encoder_dim,
+            vocab_size,
+            initial_scale=0.25,
+        )
+        self.simple_lm_proj = ScaledLinear(
+            decoder_dim,
+            vocab_size,
+            initial_scale=0.25,
+        )
+
+    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,
+    ) -> 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 with 2 axes [utt][label]. It contains labels of each
+            utterance.
+          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
+        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) == y.dim0
+
+        # logging.info(f"Memory allocated at entry: {torch.cuda.memory_allocated() // 1000000}M")
+        x, x_lens = self.encoder_embed(x, x_lens)
+        # logging.info(f"Memory allocated after encoder_embed: {torch.cuda.memory_allocated() // 1000000}M")
+
+        src_key_padding_mask = make_pad_mask(x_lens)
+        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
+
+        encoder_out, x_lens = self.encoder(x, x_lens, src_key_padding_mask)
+        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+
+        assert torch.all(x_lens > 0)
+
+        # Now 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(
+            (encoder_out.size(0), 4),
+            dtype=torch.int64,
+            device=encoder_out.device,
+        )
+        boundary[:, 2] = y_lens
+        boundary[:, 3] = x_lens
+
+        lm = self.simple_lm_proj(decoder_out)
+        am = self.simple_am_proj(encoder_out)
+
+        # if self.training and random.random() < 0.25:
+        #    lm = penalize_abs_values_gt(lm, 100.0, 1.0e-04)
+        # if self.training and random.random() < 0.25:
+        #    am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
+
+        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="sum",
+                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="sum",
+            )
+
+        return (simple_loss, pruned_loss)

egs/librispeech/ASR/subformer/optim.py

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

egs/librispeech/ASR/subformer/scaling.py

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

egs/librispeech/ASR/subformer/subsampling.py

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

egs/librispeech/ASR/subformer/zipformer.py

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

egs/librispeech/ASR/zipformer/scaling.py

@@ -274,6 +274,24 @@ def softmax(x: Tensor, dim: int):
     return SoftmaxFunction.apply(x, dim)
 
 
+class ClipGradFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+            ctx,
+            x: Tensor,
+            limit: float):
+        ctx.limit = limit
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad, *args):
+        return x_grad.clamp(-ctx.limit, ctx.limit), None
+
+
+def clip_grad(x: Tensor, limit: float):
+    return ClipGradFunction.apply(x, limit)
+
+
 class MaxEigLimiterFunction(torch.autograd.Function):
     @staticmethod
     def forward(
@@ -875,6 +893,40 @@ def penalize_abs_values_gt(x: Tensor, limit: float, penalty: float,
     return x
 
 
+class AbsValuePenalizer(nn.Module):
+    """
+    This module adds a penalty to the loss function when ever the absolute value of
+    any element of the input tensor exceeds a certain limit.
+    """
+    def __init__(self,
+                 limit: float,
+                 prob: float = 0.1,
+                 penalty: float = 1.0e-04):
+        super().__init__()
+        self.limit = limit
+        self.penalty = penalty
+
+        self.prob = prob
+        self.name = None  # will be set in training loop
+
+        # 20% of the time we will return and do nothing because memory usage is
+        # too high.
+        self.mem_cutoff = CutoffEstimator(0.2)
+
+    def forward(self, x: Tensor) -> Tensor:
+        if (torch.jit.is_scripting() or not x.requires_grad
+            or not self.training
+            or random.random() > self.prob):
+            # or (x.is_cuda and self.mem_cutoff(torch.cuda.memory_allocated()))
+            return _no_op(x)  # the _no_op op is to make our diagnostics code work.
+
+        x = penalize_abs_values_gt(x,
+                                   limit=self.limit,
+                                   penalty=self.penalty,
+                                   name=self.name)
+        return x
+
+
 def _diag(x: Tensor):  # like .diag(), but works for tensors with 3 dims.
     if x.ndim == 2:
         return x.diag()