Use LSTM layers for the encoder.

Need more tunings.

egs/librispeech/ASR/transducer/README.md

@@ -0,0 +1,21 @@
+## Introduction
+
+The encoder consists of Conformer layers in this folder. You can use the
+following command to start the training:
+
+```bash
+cd egs/librispeech/ASR
+
+export CUDA_VISIBLE_DEVICES="0,1,2"
+
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./transducer/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir transducer/exp \
+  --full-libri 1 \
+  --max-duration 250 \
+  --lr-factor 2.5 \
+```

egs/librispeech/ASR/transducer/beam_search.py

@@ -70,7 +70,7 @@ def greedy_search(model: Transducer, encoder_out: torch.Tensor) -> List[int]:
 
 @dataclass
 class Hypothesis:
-    ys: List[int]  # the predicated sequences so far
+    ys: List[int]  # the predicted sequences so far
     log_prob: float  # The log prob of ys
 
     # Optional decoder state. We assume it is LSTM for now,

egs/librispeech/ASR/transducer/rnn.py

@@ -212,6 +212,24 @@ class LayerNormLSTMCell(nn.Module):
             if "layernorm" not in name:
                 nn.init.uniform_(weight, -stdv, stdv)
 
+            if "bias_ih" in name or "bias_hh" in name:
+                # See the paper
+                # An Empirical Exploration of Recurrent Network Architectures
+                # https://proceedings.mlr.press/v37/jozefowicz15.pdf
+                #
+                # It recommends initializing the bias of the forget gate to
+                # a large value, such as 1 or 2. In PyTorch, there are two
+                # biases for the forget gate, we set both of them to 1 here.
+                #
+                # See also https://arxiv.org/pdf/1804.04849.pdf
+                assert weight.ndim == 1
+                # Layout of the bias:
+                # | in_gate | forget_gate | cell_gate | output_gate |
+                start = weight.numel() // 4
+                end = weight.numel() // 2
+                with torch.no_grad():
+                    weight[start:end].fill_(1.0)
+
 
 class LayerNormLSTMLayer(nn.Module):
     """

egs/librispeech/ASR/transducer/test_rnn.py

@@ -23,7 +23,7 @@ To run this file, do:
 """
 import torch
 import torch.nn as nn
-from transducer.rnn import (
+from rnn import (
     LayerNormGRU,
     LayerNormGRUCell,
     LayerNormGRULayer,
@@ -505,6 +505,28 @@ def test_layernorm_lstm_with_projection_forward(device="cpu"):
     assert_allclose(x.grad, x_clone.grad)
 
 
+def test_lstm_forget_gate_bias(device):
+    input_size = 2
+    hidden_size = 3
+    num_layers = 4
+    bias = True
+
+    lstm = LayerNormLSTM(
+        input_size=input_size,
+        hidden_size=hidden_size,
+        num_layers=num_layers,
+        bias=bias,
+        ln=nn.Identity,
+        device=device,
+    )
+    for name, weight in lstm.named_parameters():
+        if "bias_hh" in name or "bias_ih" in name:
+            start = weight.numel() // 4
+            end = weight.numel() // 2
+            expected = torch.ones(hidden_size).to(weight)
+            assert torch.all(torch.eq(weight[start:end], expected))
+
+
 def test_layernorm_gru_cell_jit(device="cpu"):
     input_size = 10
     hidden_size = 20
@@ -741,6 +763,8 @@ def _test_lstm(device):
     test_layernorm_lstm_with_projection_jit(device)
     test_layernorm_lstm_forward(device)
     test_layernorm_lstm_with_projection_forward(device)
+    #
+    test_lstm_forget_gate_bias(device)
 
 
 def _test_gru(device):

egs/librispeech/ASR/transducer/train.py

@@ -16,6 +16,20 @@
 # 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:
+
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./transducer/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir transducer/exp \
+  --full-libri 1 \
+  --max-duration 250 \
+  --lr-factor 2.5
+"""
 
 
 import argparse
@@ -88,7 +102,7 @@ def get_parser():
         default=0,
         help="""Resume training from from this epoch.
         If it is positive, it will load checkpoint from
-        conformer_ctc/exp/epoch-{start_epoch-1}.pt
+        transducer/exp/epoch-{start_epoch-1}.pt
         """,
     )
 

egs/librispeech/ASR/transducer_lstm/README.md

@@ -0,0 +1,19 @@
+## Introduction
+
+The encoder consists of LSTM layers in this folder. You can use the
+following command to start the training:
+
+```bash
+cd egs/librispeech/ASR
+
+export CUDA_VISIBLE_DEVICES="0,1,2"
+
+./transducer_lstm/train.py \
+  --world-size 3 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir transducer_lstm/exp \
+  --full-libri 1 \
+  --max-duration 300 \
+  --lr-factor 3
+```

egs/librispeech/ASR/transducer_lstm/asr_datamodule.py

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

egs/librispeech/ASR/transducer_lstm/beam_search.py

@@ -0,0 +1,212 @@
+# 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 dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
+
+import torch
+from model import Transducer
+
+
+def greedy_search(model: Transducer, encoder_out: torch.Tensor) -> List[int]:
+    """
+    Args:
+      model:
+        An instance of `Transducer`.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
+    Returns:
+      Return the decoded result.
+    """
+    assert encoder_out.ndim == 3
+
+    # support only batch_size == 1 for now
+    assert encoder_out.size(0) == 1, encoder_out.size(0)
+    blank_id = model.decoder.blank_id
+    device = model.device
+
+    sos = torch.tensor([blank_id], device=device).reshape(1, 1)
+    decoder_out, (h, c) = model.decoder(sos)
+    T = encoder_out.size(1)
+    t = 0
+    hyp = []
+    max_u = 1000  # terminate after this number of steps
+    u = 0
+
+    while t < T and u < max_u:
+        # fmt: off
+        current_encoder_out = encoder_out[:, t:t+1, :]
+        # fmt: on
+        logits = model.joiner(current_encoder_out, decoder_out)
+        # logits is (1, 1, 1, vocab_size)
+
+        log_prob = logits.log_softmax(dim=-1)
+        # log_prob is (1, 1, 1, vocab_size)
+        # TODO: Use logits.argmax()
+        y = log_prob.argmax()
+        if y != blank_id:
+            hyp.append(y.item())
+            y = y.reshape(1, 1)
+            decoder_out, (h, c) = model.decoder(y, (h, c))
+            u += 1
+        else:
+            t += 1
+
+    return hyp
+
+
+@dataclass
+class Hypothesis:
+    ys: List[int]  # the predicted sequences so far
+    log_prob: float  # The log prob of ys
+
+    # Optional decoder state. We assume it is LSTM for now,
+    # so the state is a tuple (h, c)
+    decoder_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
+
+
+def beam_search(
+    model: Transducer,
+    encoder_out: torch.Tensor,
+    beam: int = 5,
+) -> List[int]:
+    """
+    It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf
+
+    espnet/nets/beam_search_transducer.py#L247 is used as a reference.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
+      beam:
+        Beam size.
+    Returns:
+      Return the decoded result.
+    """
+    assert encoder_out.ndim == 3
+
+    # support only batch_size == 1 for now
+    assert encoder_out.size(0) == 1, encoder_out.size(0)
+    blank_id = model.decoder.blank_id
+    sos_id = model.decoder.sos_id
+    device = model.device
+
+    sos = torch.tensor([blank_id], device=device).reshape(1, 1)
+    decoder_out, (h, c) = model.decoder(sos)
+    T = encoder_out.size(1)
+    t = 0
+    B = [Hypothesis(ys=[blank_id], log_prob=0.0, decoder_state=None)]
+    max_u = 20000  # terminate after this number of steps
+    u = 0
+
+    cache: Dict[
+        str, Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]
+    ] = {}
+
+    while t < T and u < max_u:
+        # fmt: off
+        current_encoder_out = encoder_out[:, t:t+1, :]
+        # fmt: on
+        A = B
+        B = []
+        #  for hyp in A:
+        #      for h in A:
+        #          if h.ys == hyp.ys[:-1]:
+        #              # update the score of hyp
+        #              decoder_input = torch.tensor(
+        #                  [h.ys[-1]], device=device
+        #              ).reshape(1, 1)
+        #              decoder_out, _ = model.decoder(
+        #                  decoder_input, h.decoder_state
+        #              )
+        #              logits = model.joiner(current_encoder_out, decoder_out)
+        #              log_prob = logits.log_softmax(dim=-1)
+        #              log_prob = log_prob.squeeze()
+        #              hyp.log_prob += h.log_prob + log_prob[hyp.ys[-1]].item()
+
+        while u < max_u:
+            y_star = max(A, key=lambda hyp: hyp.log_prob)
+            A.remove(y_star)
+
+            # Note: y_star.ys is unhashable, i.e., cannot be used
+            # as a key into a dict
+            cached_key = "_".join(map(str, y_star.ys))
+
+            if cached_key not in cache:
+                decoder_input = torch.tensor(
+                    [y_star.ys[-1]], device=device
+                ).reshape(1, 1)
+
+                decoder_out, decoder_state = model.decoder(
+                    decoder_input,
+                    y_star.decoder_state,
+                )
+                cache[cached_key] = (decoder_out, decoder_state)
+            else:
+                decoder_out, decoder_state = cache[cached_key]
+
+            logits = model.joiner(current_encoder_out, decoder_out)
+            log_prob = logits.log_softmax(dim=-1)
+            # log_prob is (1, 1, 1, vocab_size)
+            log_prob = log_prob.squeeze()
+            # Now log_prob is (vocab_size,)
+
+            # If we choose blank here, add the new hypothesis to B.
+            # Otherwise, add the new hypothesis to A
+
+            # First, choose blank
+            skip_log_prob = log_prob[blank_id]
+            new_y_star_log_prob = y_star.log_prob + skip_log_prob.item()
+
+            # ys[:] returns a copy of ys
+            new_y_star = Hypothesis(
+                ys=y_star.ys[:],
+                log_prob=new_y_star_log_prob,
+                # Caution: Use y_star.decoder_state here
+                decoder_state=y_star.decoder_state,
+            )
+            B.append(new_y_star)
+
+            # Second, choose other labels
+            for i, v in enumerate(log_prob.tolist()):
+                if i in (blank_id, sos_id):
+                    continue
+                new_ys = y_star.ys + [i]
+                new_log_prob = y_star.log_prob + v
+                new_hyp = Hypothesis(
+                    ys=new_ys,
+                    log_prob=new_log_prob,
+                    decoder_state=decoder_state,
+                )
+                A.append(new_hyp)
+            u += 1
+            # check whether B contains more than "beam" elements more probable
+            # than the most probable in A
+            A_most_probable = max(A, key=lambda hyp: hyp.log_prob)
+            B = sorted(
+                [hyp for hyp in B if hyp.log_prob > A_most_probable.log_prob],
+                key=lambda hyp: hyp.log_prob,
+                reverse=True,
+            )
+            if len(B) >= beam:
+                B = B[:beam]
+                break
+        t += 1
+    best_hyp = max(B, key=lambda hyp: hyp.log_prob / len(hyp.ys[1:]))
+    ys = best_hyp.ys[1:]  # [1:] to remove the blank
+    return ys

egs/librispeech/ASR/transducer_lstm/decode.py

@@ -0,0 +1,457 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+(1) greedy search
+./transducer_lstm/decode.py \
+        --epoch 14 \
+        --avg 7 \
+        --exp-dir ./transducer_lstm/exp \
+        --max-duration 100 \
+        --decoding-method greedy_search
+(2) beam search
+
+./transducer_lstm/decode.py \
+        --epoch 14 \
+        --avg 7 \
+        --exp-dir ./transducer_lstm/exp \
+        --max-duration 100 \
+        --decoding-method beam_search \
+        --beam-size 8
+"""
+
+
+import argparse
+import logging
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
+from beam_search import beam_search, greedy_search
+from decoder import Decoder
+from encoder import LstmEncoder
+from joiner import Joiner
+from model import Transducer
+
+from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.env import get_env_info
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=77,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=55,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="transducer_lstm/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=5,
+        help="Used only when --decoding-method is beam_search",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            # parameters for conformer
+            "feature_dim": 80,
+            "encoder_out_dim": 512,
+            "subsampling_factor": 4,
+            "encoder_hidden_size": 1024,
+            "num_encoder_layers": 4,
+            "proj_size": 512,
+            "vgg_frontend": False,
+            # decoder params
+            "decoder_embedding_dim": 1024,
+            "num_decoder_layers": 4,
+            "decoder_hidden_dim": 512,
+            "env_info": get_env_info(),
+        }
+    )
+    return params
+
+
+def get_encoder_model(params: AttributeDict):
+    encoder = LstmEncoder(
+        num_features=params.feature_dim,
+        hidden_size=params.encoder_hidden_size,
+        output_dim=params.encoder_out_dim,
+        subsampling_factor=params.subsampling_factor,
+        num_encoder_layers=params.num_encoder_layers,
+        vgg_frontend=params.vgg_frontend,
+    )
+    return encoder
+
+
+def get_decoder_model(params: AttributeDict):
+    decoder = Decoder(
+        vocab_size=params.vocab_size,
+        embedding_dim=params.decoder_embedding_dim,
+        blank_id=params.blank_id,
+        sos_id=params.sos_id,
+        num_layers=params.num_decoder_layers,
+        hidden_dim=params.decoder_hidden_dim,
+        output_dim=params.encoder_out_dim,
+    )
+    return decoder
+
+
+def get_joiner_model(params: AttributeDict):
+    joiner = Joiner(
+        input_dim=params.encoder_out_dim,
+        output_dim=params.vocab_size,
+    )
+    return joiner
+
+
+def get_transducer_model(params: AttributeDict):
+    encoder = get_encoder_model(params)
+    decoder = get_decoder_model(params)
+    joiner = get_joiner_model(params)
+
+    model = Transducer(
+        encoder=encoder,
+        decoder=decoder,
+        joiner=joiner,
+    )
+    return model
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+) -> Dict[str, List[List[str]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_7"
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = model.device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
+    hyps = []
+    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)
+        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}
+    else:
+        return {f"beam_{params.beam_size}": hyps}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+) -> Dict[str, List[Tuple[List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 is used.
+      Its value is a list of tuples. Each tuple contains two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 100
+    else:
+        log_interval = 2
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for hyp_words, ref_text in zip(hyps, texts):
+                ref_words = ref_text.split()
+                this_batch.append((ref_words, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % log_interval == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in ("greedy_search", "beam_search")
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+    if params.decoding_method == "beam_search":
+        params.suffix += f"-beam-{params.beam_size}"
+
+    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 <sos/eos> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.sos_id = sp.piece_to_id("<sos/eos>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if 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 start >= 0:
+                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))
+
+    model.to(device)
+    model.eval()
+    model.device = device
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+
+    test_sets = ["test-clean", "test-other"]
+    test_dl = [test_clean_dl, test_other_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()

egs/librispeech/ASR/transducer_lstm/decoder.py

@@ -0,0 +1,101 @@
+# 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 Optional, Tuple
+
+import torch
+import torch.nn as nn
+
+
+# TODO(fangjun): Support switching between LSTM and GRU
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        embedding_dim: int,
+        blank_id: int,
+        sos_id: int,
+        num_layers: int,
+        hidden_dim: int,
+        output_dim: int,
+        embedding_dropout: float = 0.0,
+        rnn_dropout: float = 0.0,
+    ):
+        """
+        Args:
+          vocab_size:
+            Number of tokens of the modeling unit including blank.
+          embedding_dim:
+            Dimension of the input embedding.
+          blank_id:
+            The ID of the blank symbol.
+          sos_id:
+            The ID of the SOS symbol.
+          num_layers:
+            Number of LSTM layers.
+          hidden_dim:
+            Hidden dimension of LSTM layers.
+          output_dim:
+            Output dimension of the decoder.
+          embedding_dropout:
+            Dropout rate for the embedding layer.
+          rnn_dropout:
+            Dropout for LSTM layers.
+        """
+        super().__init__()
+        self.embedding = nn.Embedding(
+            num_embeddings=vocab_size,
+            embedding_dim=embedding_dim,
+            padding_idx=blank_id,
+        )
+        self.embedding_dropout = nn.Dropout(embedding_dropout)
+        # TODO(fangjun): Use layer normalized LSTM
+        self.rnn = nn.LSTM(
+            input_size=embedding_dim,
+            hidden_size=hidden_dim,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=rnn_dropout,
+        )
+        self.blank_id = blank_id
+        self.sos_id = sos_id
+        self.output_linear = nn.Linear(hidden_dim, output_dim)
+
+    def forward(
+        self,
+        y: torch.Tensor,
+        states: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Args:
+          y:
+            A 2-D tensor of shape (N, U) with BOS prepended.
+          states:
+            A tuple of two tensors containing the states information of
+            LSTM layers in this decoder.
+        Returns:
+          Return a tuple containing:
+
+            - rnn_output, a tensor of shape (N, U, C)
+            - (h, c), containing the state information for LSTM layers.
+              Both are of shape (num_layers, N, C)
+        """
+        embeding_out = self.embedding(y)
+        embeding_out = self.embedding_dropout(embeding_out)
+        rnn_out, (h, c) = self.rnn(embeding_out, states)
+        out = self.output_linear(rnn_out)
+
+        return out, (h, c)

egs/librispeech/ASR/transducer_lstm/encoder.py

@@ -0,0 +1,115 @@
+# 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
+from encoder_interface import EncoderInterface
+from subsampling import Conv2dSubsampling, VggSubsampling
+from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
+
+
+class LstmEncoder(EncoderInterface):
+    def __init__(
+        self,
+        num_features: int,
+        hidden_size: int,
+        output_dim: int,
+        subsampling_factor: int = 4,
+        num_encoder_layers: int = 12,
+        dropout: float = 0.1,
+        vgg_frontend: bool = False,
+        proj_size: int = 0,
+    ):
+        super().__init__()
+        real_hidden_size = proj_size if proj_size > 0 else hidden_size
+        assert (
+            subsampling_factor == 4
+        ), "Only subsampling_factor==4 is supported at present"
+
+        # self.encoder_embed converts the input of shape (N, T, num_features)
+        # to the shape (N, T//subsampling_factor, d_model).
+        # That is, it does two things simultaneously:
+        #   (1) subsampling: T -> T//subsampling_factor
+        #   (2) embedding: num_features -> d_model
+        if vgg_frontend:
+            self.encoder_embed = VggSubsampling(num_features, real_hidden_size)
+        else:
+            self.encoder_embed = Conv2dSubsampling(
+                num_features, real_hidden_size
+            )
+
+        self.rnn = nn.LSTM(
+            input_size=hidden_size,
+            hidden_size=hidden_size,
+            num_layers=num_encoder_layers,
+            bias=True,
+            proj_size=proj_size,
+            batch_first=True,
+            dropout=dropout,
+            bidirectional=False,
+        )
+
+        self.encoder_output_layer = nn.Sequential(
+            nn.Dropout(p=dropout),
+            nn.Linear(real_hidden_size, output_dim),
+        )
+
+    def forward(
+        self, x: torch.Tensor, x_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            The input tensor. Its shape is (batch_size, seq_len, feature_dim).
+          x_lens:
+            A tensor of shape (batch_size,) containing the number of frames in
+            `x` before padding.
+        Returns:
+          Return a tuple containing 2 tensors:
+            - logits, its shape is (batch_size, output_seq_len, output_dim)
+            - logit_lens, a tensor of shape (batch_size,) containing the number
+              of frames in `logits` before padding.
+        """
+        x = self.encoder_embed(x)
+
+        # Caution: We assume the subsampling factor is 4!
+        lengths = ((x_lens - 1) // 2 - 1) // 2
+        assert x.size(1) == lengths.max().item(), (
+            x.size(1),
+            lengths.max(),
+        )
+
+        if False:
+            # It is commented out as DPP complains that not all parameters are
+            # used. Need more checks later for the reason.
+            #
+            # Caution: We assume the dataloader returns utterances with
+            # duration being sorted in decreasing order
+            packed_x = pack_padded_sequence(
+                input=x,
+                lengths=lengths.cpu(),
+                batch_first=True,
+                enforce_sorted=True,
+            )
+
+            packed_rnn_out, _ = self.rnn(packed_x)
+            rnn_out, _ = pad_packed_sequence(packed_x, batch_first=True)
+        else:
+            rnn_out, _ = self.rnn(x)
+
+        logits = self.encoder_output_layer(rnn_out)
+        return logits, lengths

egs/librispeech/ASR/transducer_lstm/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/librispeech/ASR/transducer_lstm/joiner.py

@@ -0,0 +1,55 @@
+# 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 Joiner(nn.Module):
+    def __init__(self, input_dim: int, output_dim: int):
+        super().__init__()
+
+        self.output_linear = nn.Linear(input_dim, output_dim)
+
+    def forward(
+        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            Output from the encoder. Its shape is (N, T, C).
+          decoder_out:
+            Output from the decoder. Its shape is (N, U, C).
+        Returns:
+          Return a tensor of shape (N, T, U, C).
+        """
+        assert encoder_out.ndim == decoder_out.ndim == 3
+        assert encoder_out.size(0) == decoder_out.size(0)
+        assert encoder_out.size(2) == decoder_out.size(2)
+
+        encoder_out = encoder_out.unsqueeze(2)
+        # Now encoder_out is (N, T, 1, C)
+
+        decoder_out = decoder_out.unsqueeze(1)
+        # Now decoder_out is (N, 1, U, C)
+
+        logit = encoder_out + decoder_out
+        logit = F.relu(logit)
+
+        output = self.output_linear(logit)
+
+        return output

egs/librispeech/ASR/transducer_lstm/model.py

@@ -0,0 +1,127 @@
+# 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.
+
+"""
+Note we use `rnnt_loss` from torchaudio, which exists only in
+torchaudio >= v0.10.0. It also means you have to use torch >= v1.10.0
+"""
+import k2
+import torch
+import torch.nn as nn
+import torchaudio
+import torchaudio.functional
+from encoder_interface import EncoderInterface
+
+from icefall.utils import add_sos
+
+assert hasattr(torchaudio.functional, "rnnt_loss"), (
+    f"Current torchaudio version: {torchaudio.__version__}\n"
+    "Please install a version >= 0.10.0"
+)
+
+
+class Transducer(nn.Module):
+    """It implements https://arxiv.org/pdf/1211.3711.pdf
+    "Sequence Transduction with Recurrent Neural Networks"
+    """
+
+    def __init__(
+        self,
+        encoder: EncoderInterface,
+        decoder: nn.Module,
+        joiner: nn.Module,
+    ):
+        """
+        Args:
+          encoder:
+            It is the transcription network in the paper. Its accepts
+            two inputs: `x` of (N, T, C) and `x_lens` of shape (N,).
+            It returns two tensors: `logits` of shape (N, T, C) 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, C). It should contain
+            two attributes: `blank_id` and `sos_id`.
+          joiner:
+            It has two inputs with shapes: (N, T, C) and (N, U, C). Its
+            output shape is (N, T, U, C). Note that its output contains
+            unnormalized probs, i.e., not processed by log-softmax.
+        """
+        super().__init__()
+        assert isinstance(encoder, EncoderInterface)
+        assert hasattr(decoder, "blank_id")
+        assert hasattr(decoder, "sos_id")
+
+        self.encoder = encoder
+        self.decoder = decoder
+        self.joiner = joiner
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: k2.RaggedTensor,
+    ) -> 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.
+        Returns:
+          Return the transducer loss.
+        """
+        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
+
+        encoder_out, x_lens = self.encoder(x, x_lens)
+        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_id = self.decoder.sos_id
+        sos_y = add_sos(y, sos_id=sos_id)
+
+        sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
+
+        decoder_out, _ = self.decoder(sos_y_padded)
+
+        logits = self.joiner(encoder_out, decoder_out)
+
+        # rnnt_loss requires 0 padded targets
+        # Note: y does not start with SOS
+        y_padded = y.pad(mode="constant", padding_value=0)
+
+        loss = torchaudio.functional.rnnt_loss(
+            logits=logits,
+            targets=y_padded,
+            logit_lengths=x_lens,
+            target_lengths=y_lens,
+            blank=blank_id,
+            reduction="sum",
+        )
+
+        return loss

egs/librispeech/ASR/transducer_lstm/noam.py

@@ -0,0 +1,104 @@
+# Copyright    2021 University of Chinese Academy of Sciences (author: Han Zhu)
+#
+# 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
+
+
+class Noam(object):
+    """
+    Implements Noam optimizer.
+
+    Proposed in
+    "Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf
+
+    Modified from
+    https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py  # noqa
+
+    Args:
+      params:
+        iterable of parameters to optimize or dicts defining parameter groups
+      model_size:
+        attention dimension of the transformer model
+      factor:
+        learning rate factor
+      warm_step:
+        warmup steps
+    """
+
+    def __init__(
+        self,
+        params,
+        model_size: int = 256,
+        factor: float = 10.0,
+        warm_step: int = 25000,
+        weight_decay=0,
+    ) -> None:
+        """Construct an Noam object."""
+        self.optimizer = torch.optim.Adam(
+            params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay
+        )
+        self._step = 0
+        self.warmup = warm_step
+        self.factor = factor
+        self.model_size = model_size
+        self._rate = 0
+
+    @property
+    def param_groups(self):
+        """Return param_groups."""
+        return self.optimizer.param_groups
+
+    def step(self):
+        """Update parameters and rate."""
+        self._step += 1
+        rate = self.rate()
+        for p in self.optimizer.param_groups:
+            p["lr"] = rate
+        self._rate = rate
+        self.optimizer.step()
+
+    def rate(self, step=None):
+        """Implement `lrate` above."""
+        if step is None:
+            step = self._step
+        return (
+            self.factor
+            * self.model_size ** (-0.5)
+            * min(step ** (-0.5), step * self.warmup ** (-1.5))
+        )
+
+    def zero_grad(self):
+        """Reset gradient."""
+        self.optimizer.zero_grad()
+
+    def state_dict(self):
+        """Return state_dict."""
+        return {
+            "_step": self._step,
+            "warmup": self.warmup,
+            "factor": self.factor,
+            "model_size": self.model_size,
+            "_rate": self._rate,
+            "optimizer": self.optimizer.state_dict(),
+        }
+
+    def load_state_dict(self, state_dict):
+        """Load state_dict."""
+        for key, value in state_dict.items():
+            if key == "optimizer":
+                self.optimizer.load_state_dict(state_dict["optimizer"])
+            else:
+                setattr(self, key, value)

egs/librispeech/ASR/transducer_lstm/subsampling.py

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

egs/librispeech/ASR/transducer_lstm/test_encoder.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+#
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+To run this file, do:
+
+    cd icefall/egs/librispeech/ASR
+    python ./transducer_lstm/test_encoder.py
+"""
+
+from encoder import LstmEncoder
+
+
+def test_encoder():
+    encoder = LstmEncoder(
+        num_features=80,
+        hidden_size=1024,
+        proj_size=512,
+        output_dim=512,
+        subsampling_factor=4,
+        num_encoder_layers=12,
+    )
+    num_params = sum(p.numel() for p in encoder.parameters() if p.requires_grad)
+    print(num_params)
+    # 93979284
+    # 66427392
+
+
+def main():
+    test_encoder()
+
+
+if __name__ == "__main__":
+    main()

egs/librispeech/ASR/transducer_lstm/train.py

@@ -0,0 +1,738 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                  Wei Kang
+#                                                  Mingshuang Luo)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Usage:
+
+export CUDA_VISIBLE_DEVICES="0,1,2"
+
+./transducer_lstm/train.py \
+  --world-size 3 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir transducer_lstm/exp \
+  --full-libri 1 \
+  --max-duration 400 \
+  --lr-factor 3
+"""
+
+
+import argparse
+import logging
+from pathlib import Path
+from shutil import copyfile
+from typing import Optional, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
+from decoder import Decoder
+from encoder import LstmEncoder
+from joiner import Joiner
+from lhotse.cut import Cut
+from lhotse.utils import fix_random_seed
+from model import Transducer
+from noam import Noam
+from torch import Tensor
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.nn.utils import clip_grad_norm_
+from torch.utils.tensorboard import SummaryWriter
+
+from icefall.checkpoint import load_checkpoint
+from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.env import get_env_info
+from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12354,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=78,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=0,
+        help="""Resume training from from this epoch.
+        If it is positive, it will load checkpoint from
+        transducer_lstm/exp/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="transducer_lstm/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--lr-factor",
+        type=float,
+        default=5.0,
+        help="The lr_factor for Noam optimizer",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    """Return a dict containing training parameters.
+
+    All training related parameters that are not passed from the commandline
+    are saved in the variable `params`.
+
+    Commandline options are merged into `params` after they are parsed, so
+    you can also access them via `params`.
+
+    Explanation of options saved in `params`:
+
+        - best_train_loss: Best training loss so far. It is used to select
+                           the model that has the lowest training loss. It is
+                           updated during the training.
+
+        - best_valid_loss: Best validation loss so far. It is used to select
+                           the model that has the lowest validation loss. It is
+                           updated during the training.
+
+        - best_train_epoch: It is the epoch that has the best training loss.
+
+        - best_valid_epoch: It is the epoch that has the best validation loss.
+
+        - batch_idx_train: Used to writing statistics to tensorboard. It
+                           contains number of batches trained so far across
+                           epochs.
+
+        - log_interval:  Print training loss if batch_idx % log_interval` is 0
+
+        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+
+        - valid_interval:  Run validation if batch_idx % valid_interval is 0
+
+        - feature_dim: The model input dim. It has to match the one used
+                       in computing features.
+
+        - subsampling_factor:  The subsampling factor for the model.
+
+        - use_feat_batchnorm: Whether to do batch normalization for the
+                              input features.
+
+        - attention_dim: Hidden dim for multi-head attention model.
+
+        - num_decoder_layers: Number of decoder layer of transformer decoder.
+
+        - weight_decay:  The weight_decay for the optimizer.
+
+        - warm_step: The warm_step for Noam optimizer.
+    """
+    params = AttributeDict(
+        {
+            "best_train_loss": float("inf"),
+            "best_valid_loss": float("inf"),
+            "best_train_epoch": -1,
+            "best_valid_epoch": -1,
+            "batch_idx_train": 0,
+            "log_interval": 50,
+            "reset_interval": 200,
+            "valid_interval": 3000,  # For the 100h subset, use 800
+            # parameters for conformer
+            "feature_dim": 80,
+            "encoder_out_dim": 512,
+            "subsampling_factor": 4,
+            "encoder_hidden_size": 1024,
+            "num_encoder_layers": 4,
+            "proj_size": 512,
+            "vgg_frontend": False,
+            # decoder params
+            "decoder_embedding_dim": 1024,
+            "num_decoder_layers": 4,
+            "decoder_hidden_dim": 512,
+            # parameters for Noam
+            "weight_decay": 1e-6,
+            "warm_step": 80000,  # For the 100h subset, use 8k
+            "env_info": get_env_info(),
+        }
+    )
+
+    return params
+
+
+def get_encoder_model(params: AttributeDict):
+    encoder = LstmEncoder(
+        num_features=params.feature_dim,
+        hidden_size=params.encoder_hidden_size,
+        output_dim=params.encoder_out_dim,
+        subsampling_factor=params.subsampling_factor,
+        num_encoder_layers=params.num_encoder_layers,
+        vgg_frontend=params.vgg_frontend,
+    )
+    return encoder
+
+
+def get_decoder_model(params: AttributeDict):
+    decoder = Decoder(
+        vocab_size=params.vocab_size,
+        embedding_dim=params.decoder_embedding_dim,
+        blank_id=params.blank_id,
+        sos_id=params.sos_id,
+        num_layers=params.num_decoder_layers,
+        hidden_dim=params.decoder_hidden_dim,
+        output_dim=params.encoder_out_dim,
+    )
+    return decoder
+
+
+def get_joiner_model(params: AttributeDict):
+    joiner = Joiner(
+        input_dim=params.encoder_out_dim,
+        output_dim=params.vocab_size,
+    )
+    return joiner
+
+
+def get_transducer_model(params: AttributeDict):
+    encoder = get_encoder_model(params)
+    decoder = get_decoder_model(params)
+    joiner = get_joiner_model(params)
+
+    model = Transducer(
+        encoder=encoder,
+        decoder=decoder,
+        joiner=joiner,
+    )
+    return model
+
+
+def load_checkpoint_if_available(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
+) -> None:
+    """Load checkpoint from file.
+
+    If params.start_epoch is positive, it will load the checkpoint from
+    `params.start_epoch - 1`. Otherwise, this function does nothing.
+
+    Apart from loading state dict for `model`, `optimizer` and `scheduler`,
+    it also updates `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+      optimizer:
+        The optimizer that we are using.
+      scheduler:
+        The learning rate scheduler we are using.
+    Returns:
+      Return None.
+    """
+    if params.start_epoch <= 0:
+        return
+
+    filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    saved_params = load_checkpoint(
+        filename,
+        model=model,
+        optimizer=optimizer,
+        scheduler=scheduler,
+    )
+
+    keys = [
+        "best_train_epoch",
+        "best_valid_epoch",
+        "batch_idx_train",
+        "best_train_loss",
+        "best_valid_loss",
+    ]
+    for k in keys:
+        params[k] = saved_params[k]
+
+    return saved_params
+
+
+def save_checkpoint(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
+    rank: int = 0,
+) -> None:
+    """Save model, optimizer, scheduler and training stats to file.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The training model.
+    """
+    if rank != 0:
+        return
+    filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+    save_checkpoint_impl(
+        filename=filename,
+        model=model,
+        params=params,
+        optimizer=optimizer,
+        scheduler=scheduler,
+        rank=rank,
+    )
+
+    if params.best_train_epoch == params.cur_epoch:
+        best_train_filename = params.exp_dir / "best-train-loss.pt"
+        copyfile(src=filename, dst=best_train_filename)
+
+    if params.best_valid_epoch == params.cur_epoch:
+        best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+        copyfile(src=filename, dst=best_valid_filename)
+
+
+def compute_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    is_training: bool,
+) -> Tuple[Tensor, MetricsTracker]:
+    """
+    Compute CTC loss given the model and its inputs.
+
+    Args:
+      params:
+        Parameters for training. See :func:`get_params`.
+      model:
+        The model for training. It is an instance of Conformer in our case.
+      batch:
+        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
+        for the content in it.
+      is_training:
+        True for training. False for validation. When it is True, this
+        function enables autograd during computation; when it is False, it
+        disables autograd.
+    """
+    device = model.device
+    feature = batch["inputs"]
+    # at entry, feature is (N, T, C)
+    assert feature.ndim == 3
+    feature = feature.to(device)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    texts = batch["supervisions"]["text"]
+    y = sp.encode(texts, out_type=int)
+    y = k2.RaggedTensor(y).to(device)
+
+    with torch.set_grad_enabled(is_training):
+        loss = model(x=feature, x_lens=feature_lens, y=y)
+
+    assert loss.requires_grad == is_training
+
+    info = MetricsTracker()
+    info["frames"] = (feature_lens // params.subsampling_factor).sum().item()
+
+    # Note: We use reduction=sum while computing the loss.
+    info["loss"] = loss.detach().cpu().item()
+
+    return loss, info
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+) -> MetricsTracker:
+    """Run the validation process."""
+    model.eval()
+
+    tot_loss = MetricsTracker()
+
+    for batch_idx, batch in enumerate(valid_dl):
+        loss, loss_info = compute_loss(
+            params=params,
+            model=model,
+            sp=sp,
+            batch=batch,
+            is_training=False,
+        )
+        assert loss.requires_grad is False
+        tot_loss = tot_loss + loss_info
+
+    if world_size > 1:
+        tot_loss.reduce(loss.device)
+
+    loss_value = tot_loss["loss"] / tot_loss["frames"]
+    if loss_value < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = loss_value
+
+    return tot_loss
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: torch.optim.Optimizer,
+    sp: spm.SentencePieceProcessor,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      optimizer:
+        The optimizer we are using.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+      world_size:
+        Number of nodes in DDP training. If it is 1, DDP is disabled.
+    """
+    model.train()
+
+    tot_loss = MetricsTracker()
+
+    for batch_idx, batch in enumerate(train_dl):
+        params.batch_idx_train += 1
+        batch_size = len(batch["supervisions"]["text"])
+
+        loss, loss_info = compute_loss(
+            params=params,
+            model=model,
+            sp=sp,
+            batch=batch,
+            is_training=True,
+        )
+        # summary stats
+        tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
+
+        # NOTE: We use reduction==sum and loss is computed over utterances
+        # in the batch and there is no normalization to it so far.
+
+        optimizer.zero_grad()
+        loss.backward()
+        clip_grad_norm_(model.parameters(), 5.0, 2.0)
+        optimizer.step()
+
+        if batch_idx % params.log_interval == 0:
+            logging.info(
+                f"Epoch {params.cur_epoch}, "
+                f"batch {batch_idx}, loss[{loss_info}], "
+                f"tot_loss[{tot_loss}], batch size: {batch_size}"
+            )
+
+        if batch_idx % params.log_interval == 0:
+
+            if tb_writer is not None:
+                loss_info.write_summary(
+                    tb_writer, "train/current_", params.batch_idx_train
+                )
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
+
+        if batch_idx > 0 and batch_idx % params.valid_interval == 0:
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                model=model,
+                sp=sp,
+                valid_dl=valid_dl,
+                world_size=world_size,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
+
+    loss_value = tot_loss["loss"] / tot_loss["frames"]
+    params.train_loss = loss_value
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def run(rank, world_size, args):
+    """
+    Args:
+      rank:
+        It is a value between 0 and `world_size-1`, which is
+        passed automatically by `mp.spawn()` in :func:`main`.
+        The node with rank 0 is responsible for saving checkpoint.
+      world_size:
+        Number of GPUs for DDP training.
+      args:
+        The return value of get_parser().parse_args()
+    """
+    params = get_params()
+    params.update(vars(args))
+    if params.full_libri is False:
+        params.valid_interval = 800
+        params.warm_step = 8000
+
+    fix_random_seed(42)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    logging.info("Training started")
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <sos/eos> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.sos_id = sp.piece_to_id("<sos/eos>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    num_param = sum([p.numel() for p in model.parameters() if p.requires_grad])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    model.to(device)
+    if world_size > 1:
+        logging.info("Using DDP")
+        model = DDP(model, device_ids=[rank])
+    model.device = device
+
+    optimizer = Noam(
+        model.parameters(),
+        model_size=params.encoder_hidden_size,
+        factor=params.lr_factor,
+        warm_step=params.warm_step,
+        weight_decay=params.weight_decay,
+    )
+
+    if checkpoints and "optimizer" in checkpoints:
+        logging.info("Loading optimizer state dict")
+        optimizer.load_state_dict(checkpoints["optimizer"])
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    train_cuts = librispeech.train_clean_100_cuts()
+    if params.full_libri:
+        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts += librispeech.train_other_500_cuts()
+
+    def remove_short_and_long_utt(c: Cut):
+        # Keep only utterances with duration between 1 second and 20 seconds
+        return 1.0 <= c.duration <= 20.0
+
+    num_in_total = len(train_cuts)
+
+    train_cuts = train_cuts.filter(remove_short_and_long_utt)
+
+    num_left = len(train_cuts)
+    num_removed = num_in_total - num_left
+    removed_percent = num_removed / num_in_total * 100
+
+    logging.info(f"Before removing short and long utterances: {num_in_total}")
+    logging.info(f"After removing short and long utterances: {num_left}")
+    logging.info(f"Removed {num_removed} utterances ({removed_percent:.5f}%)")
+
+    train_dl = librispeech.train_dataloaders(train_cuts)
+
+    valid_cuts = librispeech.dev_clean_cuts()
+    valid_cuts += librispeech.dev_other_cuts()
+    valid_dl = librispeech.valid_dataloaders(valid_cuts)
+
+    scan_pessimistic_batches_for_oom(
+        model=model,
+        train_dl=train_dl,
+        optimizer=optimizer,
+        sp=sp,
+        params=params,
+    )
+
+    for epoch in range(params.start_epoch, params.num_epochs):
+        train_dl.sampler.set_epoch(epoch)
+
+        cur_lr = optimizer._rate
+        if tb_writer is not None:
+            tb_writer.add_scalar(
+                "train/learning_rate", cur_lr, params.batch_idx_train
+            )
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        if rank == 0:
+            logging.info("epoch {}, learning rate {}".format(epoch, cur_lr))
+
+        params.cur_epoch = epoch
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            optimizer=optimizer,
+            sp=sp,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            tb_writer=tb_writer,
+            world_size=world_size,
+        )
+
+        save_checkpoint(
+            params=params,
+            model=model,
+            optimizer=optimizer,
+            rank=rank,
+        )
+
+    logging.info("Done!")
+
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def scan_pessimistic_batches_for_oom(
+    model: nn.Module,
+    train_dl: torch.utils.data.DataLoader,
+    optimizer: torch.optim.Optimizer,
+    sp: spm.SentencePieceProcessor,
+    params: AttributeDict,
+):
+    from lhotse.dataset import find_pessimistic_batches
+
+    logging.info(
+        "Sanity check -- see if any of the batches in epoch 0 would cause OOM."
+    )
+    batches, crit_values = find_pessimistic_batches(train_dl.sampler)
+    for criterion, cuts in batches.items():
+        batch = train_dl.dataset[cuts]
+        try:
+            optimizer.zero_grad()
+            loss, _ = compute_loss(
+                params=params,
+                model=model,
+                sp=sp,
+                batch=batch,
+                is_training=True,
+            )
+            loss.backward()
+            clip_grad_norm_(model.parameters(), 5.0, 2.0)
+            optimizer.step()
+        except RuntimeError as e:
+            if "CUDA out of memory" in str(e):
+                logging.error(
+                    "Your GPU ran out of memory with the current "
+                    "max_duration setting. We recommend decreasing "
+                    "max_duration and trying again.\n"
+                    f"Failing criterion: {criterion} "
+                    f"(={crit_values[criterion]}) ..."
+                )
+            raise
+
+
+def main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()