diff --git a/egs/librispeech/ASR/conformer_mmi/train-with-attention.py b/egs/librispeech/ASR/conformer_mmi/train-with-attention.py
new file mode 100755
index 000000000..a66f77676
--- /dev/null
+++ b/egs/librispeech/ASR/conformer_mmi/train-with-attention.py
@@ -0,0 +1,836 @@
+#!/usr/bin/env python3
+# 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 argparse
+import logging
+from pathlib import Path
+from shutil import copyfile
+from typing import Dict, Optional
+
+import k2
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
+from conformer import Conformer
+from lhotse.utils import fix_random_seed
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.nn.utils import clip_grad_norm_
+from torch.utils.tensorboard import SummaryWriter
+from transformer import Noam
+
+from icefall.ali import (
+    convert_alignments_to_tensor,
+    load_alignments,
+    lookup_alignments,
+)
+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.lexicon import Lexicon
+from icefall.mmi import LFMMILoss
+from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler
+from icefall.utils import (
+    AttributeDict,
+    encode_supervisions,
+    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=50,
+        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
+        conformer_mmi/exp/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--ali-dir",
+        type=str,
+        default="data/ali_500",
+        help="""This folder is expected to contain
+        two files, train-960.pt and valid.pt, which
+        contain framewise alignment information for
+        the training set and validation set.
+        """,
+    )
+
+    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`:
+
+        - exp_dir: It specifies the directory where all training related
+                   files, e.g., checkpoints, log, etc, are saved
+
+        - lang_dir: It contains language related input files such as
+                    "lexicon.txt"
+
+        - 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.
+
+        - head: Number of heads of multi-head attention model.
+
+        - num_decoder_layers: Number of decoder layer of transformer decoder.
+
+        - weight_decay:  The weight_decay for the optimizer.
+
+        - lr_factor: The lr_factor for Noam optimizer.
+
+        - warm_step: The warm_step for Noam optimizer.
+    """
+    params = AttributeDict(
+        {
+            "exp_dir": Path("conformer_mmi/exp_500_with_attention"),
+            "lang_dir": Path("data/lang_bpe_500"),
+            "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,
+            # parameters for conformer
+            "feature_dim": 80,
+            "subsampling_factor": 4,
+            "use_feat_batchnorm": True,
+            "attention_dim": 512,
+            "nhead": 8,
+            # parameters for loss
+            "beam_size": 6,  # will change it to 8 after some batches (see code)
+            "reduction": "sum",
+            "use_double_scores": True,
+            #  "att_rate": 0.0,
+            #  "num_decoder_layers": 0,
+            "att_rate": 0.7,
+            "num_decoder_layers": 6,
+            # parameters for Noam
+            "weight_decay": 1e-6,
+            "lr_factor": 5.0,
+            "warm_step": 80000,
+            "use_pruned_intersect": False,
+            "den_scale": 1.0,
+            "use_ali_until": 13000,  # use alignments before this number of batches
+        }
+    )
+
+    return params
+
+
+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,
+    batch: dict,
+    graph_compiler: MmiTrainingGraphCompiler,
+    is_training: bool,
+    ali: Optional[Dict[str, torch.Tensor]],
+):
+    """
+    Compute LF-MMI 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.
+      graph_compiler:
+        It is used to build a decoding graph from a ctc topo and training
+        transcript. The training transcript is contained in the given `batch`,
+        while the ctc topo is built when this compiler is instantiated.
+      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.
+      ali:
+        Precomputed alignments.
+    """
+    device = graph_compiler.device
+    feature = batch["inputs"]
+    # at entry, feature is (N, T, C)
+    assert feature.ndim == 3
+    feature = feature.to(device)
+
+    supervisions = batch["supervisions"]
+    with torch.set_grad_enabled(is_training):
+        nnet_output, encoder_memory, memory_mask = model(feature, supervisions)
+        # nnet_output is (N, T, C)
+
+        # NOTE: We need `encode_supervisions` to sort sequences with
+        # different duration in decreasing order, required by
+        # `k2.intersect_dense` called in `LFMMILoss.forward()`
+        supervision_segments, texts = encode_supervisions(
+            supervisions, subsampling_factor=params.subsampling_factor
+        )
+
+        if ali is not None and params.batch_idx_train < params.use_ali_until:
+            cut_ids = [cut.id for cut in supervisions["cut"]]
+
+            # As encode_supervisions reorders cuts, we need
+            # also to reorder cut IDs here
+            new2old = supervision_segments[:, 0].tolist()
+            cut_ids = [cut_ids[i] for i in new2old]
+
+            # Check that new2old is just a permutation,
+            # i.e., each cut contains only one utterance
+            new2old.sort()
+            assert new2old == torch.arange(len(new2old)).tolist()
+            mask = lookup_alignments(
+                cut_ids=cut_ids,
+                alignments=ali,
+                num_classes=nnet_output.shape[2],
+            ).to(nnet_output)
+
+            min_len = min(nnet_output.shape[1], mask.shape[1])
+            ali_scale = 500.0 / (params.batch_idx_train + 500)
+
+            nnet_output = nnet_output.clone()
+            nnet_output[:, :min_len, :] += ali_scale * mask[:, :min_len, :]
+
+        if (
+            params.batch_idx_train > params.use_ali_until
+            and params.beam_size < 8
+        ):
+            logging.info("Change beam size to 8")
+            params.beam_size = 8
+        else:
+            params.beam_size = 6
+
+        loss_fn = LFMMILoss(
+            graph_compiler=graph_compiler,
+            use_pruned_intersect=params.use_pruned_intersect,
+            den_scale=params.den_scale,
+            beam_size=params.beam_size,
+        )
+
+        dense_fsa_vec = k2.DenseFsaVec(
+            nnet_output,
+            supervision_segments,
+            allow_truncate=params.subsampling_factor - 1,
+        )
+        mmi_loss = loss_fn(dense_fsa_vec=dense_fsa_vec, texts=texts)
+
+    if params.att_rate != 0.0:
+        token_ids = graph_compiler.texts_to_ids(texts)
+        with torch.set_grad_enabled(is_training):
+            if hasattr(model, "module"):
+                att_loss = model.module.decoder_forward(
+                    encoder_memory,
+                    memory_mask,
+                    token_ids=token_ids,
+                    sos_id=graph_compiler.sos_id,
+                    eos_id=graph_compiler.eos_id,
+                )
+            else:
+                att_loss = model.decoder_forward(
+                    encoder_memory,
+                    memory_mask,
+                    token_ids=token_ids,
+                    sos_id=graph_compiler.sos_id,
+                    eos_id=graph_compiler.eos_id,
+                )
+        loss = (1.0 - params.att_rate) * mmi_loss + params.att_rate * att_loss
+    else:
+        loss = mmi_loss
+        att_loss = torch.tensor([0])
+
+    # train_frames and valid_frames are used for printing.
+    if is_training:
+        params.train_frames = supervision_segments[:, 2].sum().item()
+    else:
+        params.valid_frames = supervision_segments[:, 2].sum().item()
+
+    assert loss.requires_grad == is_training
+
+    return loss, mmi_loss.detach(), att_loss.detach()
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    graph_compiler: MmiTrainingGraphCompiler,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+    ali: Optional[Dict[str, torch.Tensor]] = None,
+) -> None:
+    """Run the validation process. The validation loss
+    is saved in `params.valid_loss`.
+    """
+    model.eval()
+
+    tot_loss = 0.0
+    tot_mmi_loss = 0.0
+    tot_att_loss = 0.0
+    tot_frames = 0.0
+    for batch_idx, batch in enumerate(valid_dl):
+        loss, mmi_loss, att_loss = compute_loss(
+            params=params,
+            model=model,
+            batch=batch,
+            graph_compiler=graph_compiler,
+            is_training=False,
+            ali=ali,
+        )
+        assert loss.requires_grad is False
+        assert mmi_loss.requires_grad is False
+        assert att_loss.requires_grad is False
+
+        loss_cpu = loss.detach().cpu().item()
+        tot_loss += loss_cpu
+
+        tot_mmi_loss += mmi_loss.detach().cpu().item()
+        tot_att_loss += att_loss.detach().cpu().item()
+
+        tot_frames += params.valid_frames
+
+    if world_size > 1:
+        s = torch.tensor(
+            [tot_loss, tot_mmi_loss, tot_att_loss, tot_frames],
+            device=loss.device,
+        )
+        dist.all_reduce(s, op=dist.ReduceOp.SUM)
+        s = s.cpu().tolist()
+        tot_loss = s[0]
+        tot_mmi_loss = s[1]
+        tot_att_loss = s[2]
+        tot_frames = s[3]
+
+    params.valid_loss = tot_loss / tot_frames
+    params.valid_mmi_loss = tot_mmi_loss / tot_frames
+    params.valid_att_loss = tot_att_loss / tot_frames
+
+    if params.valid_loss < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = params.valid_loss
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: torch.optim.Optimizer,
+    graph_compiler: MmiTrainingGraphCompiler,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    train_ali: Optional[Dict[str, torch.Tensor]],
+    valid_ali: Optional[Dict[str, torch.Tensor]],
+    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.
+      graph_compiler:
+        It is used to convert transcripts to FSAs.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      train_ali:
+        Precomputed alignments for the training set.
+      valid_ali:
+        Precomputed alignments for the validation set.
+      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 = 0.0  # sum of losses over all batches
+    tot_mmi_loss = 0.0
+    tot_att_loss = 0.0
+
+    tot_frames = 0.0  # sum of frames over all batches
+    params.tot_loss = 0.0
+    params.tot_frames = 0.0
+    for batch_idx, batch in enumerate(train_dl):
+        params.batch_idx_train += 1
+        batch_size = len(batch["supervisions"]["text"])
+
+        loss, mmi_loss, att_loss = compute_loss(
+            params=params,
+            model=model,
+            batch=batch,
+            graph_compiler=graph_compiler,
+            is_training=True,
+            ali=train_ali,
+        )
+
+        # 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()
+
+        loss_cpu = loss.detach().cpu().item()
+        mmi_loss_cpu = mmi_loss.detach().cpu().item()
+        att_loss_cpu = att_loss.detach().cpu().item()
+
+        tot_frames += params.train_frames
+        tot_loss += loss_cpu
+        tot_mmi_loss += mmi_loss_cpu
+        tot_att_loss += att_loss_cpu
+
+        params.tot_frames += params.train_frames
+        params.tot_loss += loss_cpu
+
+        tot_avg_loss = tot_loss / tot_frames
+        tot_avg_mmi_loss = tot_mmi_loss / tot_frames
+        tot_avg_att_loss = tot_att_loss / tot_frames
+
+        if batch_idx % params.log_interval == 0:
+            logging.info(
+                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
+                f"batch avg mmi loss {mmi_loss_cpu/params.train_frames:.4f}, "
+                f"batch avg att loss {att_loss_cpu/params.train_frames:.4f}, "
+                f"batch avg loss {loss_cpu/params.train_frames:.4f}, "
+                f"total avg mmiloss: {tot_avg_mmi_loss:.4f}, "
+                f"total avg att loss: {tot_avg_att_loss:.4f}, "
+                f"total avg loss: {tot_avg_loss:.4f}, "
+                f"batch size: {batch_size}"
+            )
+
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/current_mmi_loss",
+                    mmi_loss_cpu / params.train_frames,
+                    params.batch_idx_train,
+                )
+                tb_writer.add_scalar(
+                    "train/current_att_loss",
+                    att_loss_cpu / params.train_frames,
+                    params.batch_idx_train,
+                )
+                tb_writer.add_scalar(
+                    "train/current_loss",
+                    loss_cpu / params.train_frames,
+                    params.batch_idx_train,
+                )
+                tb_writer.add_scalar(
+                    "train/tot_avg_mmi_loss",
+                    tot_avg_mmi_loss,
+                    params.batch_idx_train,
+                )
+
+                tb_writer.add_scalar(
+                    "train/tot_avg_att_loss",
+                    tot_avg_att_loss,
+                    params.batch_idx_train,
+                )
+                tb_writer.add_scalar(
+                    "train/tot_avg_loss",
+                    tot_avg_loss,
+                    params.batch_idx_train,
+                )
+        if batch_idx > 0 and batch_idx % params.reset_interval == 0:
+            tot_loss = 0.0  # sum of losses over all batches
+            tot_mmi_loss = 0.0
+            tot_att_loss = 0.0
+
+            tot_frames = 0.0  # sum of frames over all batches
+
+        if batch_idx > 0 and batch_idx % params.valid_interval == 0:
+            compute_validation_loss(
+                params=params,
+                model=model,
+                graph_compiler=graph_compiler,
+                valid_dl=valid_dl,
+                world_size=world_size,
+                ali=valid_ali,
+            )
+            model.train()
+            logging.info(
+                f"Epoch {params.cur_epoch}, "
+                f"valid mmi loss {params.valid_mmi_loss:.4f},"
+                f"valid att loss {params.valid_att_loss:.4f},"
+                f"valid loss {params.valid_loss:.4f},"
+                f" best valid loss: {params.best_valid_loss:.4f} "
+                f"best valid epoch: {params.best_valid_epoch}"
+            )
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/valid_mmi_loss",
+                    params.valid_mmi_loss,
+                    params.batch_idx_train,
+                )
+                tb_writer.add_scalar(
+                    "train/valid_att_loss",
+                    params.valid_att_loss,
+                    params.batch_idx_train,
+                )
+                tb_writer.add_scalar(
+                    "train/valid_loss",
+                    params.valid_loss,
+                    params.batch_idx_train,
+                )
+
+    params.train_loss = params.tot_loss / params.tot_frames
+
+    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))
+
+    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")
+    logging.info(params)
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    lexicon = Lexicon(params.lang_dir)
+    max_token_id = max(lexicon.tokens)
+    num_classes = max_token_id + 1  # +1 for the blank
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+
+    graph_compiler = MmiTrainingGraphCompiler(
+        params.lang_dir,
+        uniq_filename="lexicon.txt",
+        device=device,
+        oov="<UNK>",
+        sos_id=1,
+        eos_id=1,
+    )
+
+    logging.info("About to create model")
+    if params.att_rate == 0:
+        assert params.num_decoder_layers == 0, f"{params.num_decoder_layers}"
+
+    model = Conformer(
+        num_features=params.feature_dim,
+        nhead=params.nhead,
+        d_model=params.attention_dim,
+        num_classes=num_classes,
+        subsampling_factor=params.subsampling_factor,
+        num_decoder_layers=params.num_decoder_layers,
+        vgg_frontend=False,
+        use_feat_batchnorm=params.use_feat_batchnorm,
+    )
+
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    model.to(device)
+    if world_size > 1:
+        model = DDP(model, device_ids=[rank])
+
+    optimizer = Noam(
+        model.parameters(),
+        model_size=params.attention_dim,
+        factor=params.lr_factor,
+        warm_step=params.warm_step,
+        weight_decay=params.weight_decay,
+    )
+
+    if checkpoints:
+        optimizer.load_state_dict(checkpoints["optimizer"])
+
+    train_960_ali_filename = Path(params.ali_dir) / "train-960.pt"
+    if (
+        params.batch_idx_train < params.use_ali_until
+        and train_960_ali_filename.is_file()
+    ):
+        logging.info("Use pre-computed alignments")
+        subsampling_factor, train_ali = load_alignments(train_960_ali_filename)
+        assert subsampling_factor == params.subsampling_factor
+        assert len(train_ali) == 843723, f"{len(train_ali)} vs 843723"
+
+        valid_ali_filename = Path(params.ali_dir) / "valid.pt"
+        subsampling_factor, valid_ali = load_alignments(valid_ali_filename)
+        assert subsampling_factor == params.subsampling_factor
+
+        train_ali = convert_alignments_to_tensor(train_ali, device=device)
+        valid_ali = convert_alignments_to_tensor(valid_ali, device=device)
+    else:
+        logging.info("Not using alignments")
+        train_ali = None
+        valid_ali = None
+
+    librispeech = LibriSpeechAsrDataModule(args)
+    train_dl = librispeech.train_dataloaders()
+    valid_dl = librispeech.valid_dataloaders()
+
+    for epoch in range(params.start_epoch, params.num_epochs):
+        train_dl.sampler.set_epoch(epoch)
+        if (
+            params.batch_idx_train >= params.use_ali_until
+            and train_ali is not None
+        ):
+            # Delete the alignments to save memory
+            train_ali = None
+            valid_ali = None
+
+        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,
+            graph_compiler=graph_compiler,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            train_ali=train_ali,
+            valid_ali=valid_ali,
+            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 main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+
+    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()
diff --git a/egs/librispeech/ASR/conformer_mmi/train.py b/egs/librispeech/ASR/conformer_mmi/train.py
index 900d109a8..18cc80a9a 100755
--- a/egs/librispeech/ASR/conformer_mmi/train.py
+++ b/egs/librispeech/ASR/conformer_mmi/train.py
@@ -189,18 +189,21 @@ def get_params() -> AttributeDict:
             "use_feat_batchnorm": True,
             "attention_dim": 512,
             "nhead": 8,
-            "num_decoder_layers": 6,
             # parameters for loss
-            "beam_size": 10,
+            "beam_size": 6,  # will change it to 8 after some batches (see code)
             "reduction": "sum",
             "use_double_scores": True,
-            "att_rate": 0.7,
+            "att_rate": 0.0,
+            "num_decoder_layers": 0,
+            #  "att_rate": 0.7,
+            #  "num_decoder_layers": 6,
             # parameters for Noam
             "weight_decay": 1e-6,
             "lr_factor": 5.0,
             "warm_step": 80000,
             "use_pruned_intersect": False,
             "den_scale": 1.0,
+            "use_ali_until": 13000,  # use alignments before this number of batches
         }
     )
 
@@ -342,7 +345,7 @@ def compute_loss(
             supervisions, subsampling_factor=params.subsampling_factor
         )
 
-        if ali is not None and params.batch_idx_train < 4000:
+        if ali is not None and params.batch_idx_train < params.use_ali_until:
             cut_ids = [cut.id for cut in supervisions["cut"]]
 
             # As encode_supervisions reorders cuts, we need
@@ -366,10 +369,20 @@ def compute_loss(
             nnet_output = nnet_output.clone()
             nnet_output[:, :min_len, :] += ali_scale * mask[:, :min_len, :]
 
+        if (
+            params.batch_idx_train > params.use_ali_until
+            and params.beam_size < 8
+        ):
+            logging.info("Change beam size to 8")
+            params.beam_size = 8
+        else:
+            params.beam_size = 6
+
         loss_fn = LFMMILoss(
             graph_compiler=graph_compiler,
             use_pruned_intersect=params.use_pruned_intersect,
             den_scale=params.den_scale,
+            beam_size=params.beam_size,
         )
 
         dense_fsa_vec = k2.DenseFsaVec(
@@ -698,6 +711,9 @@ def run(rank, world_size, args):
     )
 
     logging.info("About to create model")
+    if params.att_rate == 0:
+        assert params.num_decoder_layers == 0, f"{params.num_decoder_layers}"
+
     model = Conformer(
         num_features=params.feature_dim,
         nhead=params.nhead,
@@ -727,7 +743,10 @@ def run(rank, world_size, args):
         optimizer.load_state_dict(checkpoints["optimizer"])
 
     train_960_ali_filename = Path(params.ali_dir) / "train-960.pt"
-    if params.batch_idx_train < 4000 and train_960_ali_filename.is_file():
+    if (
+        params.batch_idx_train < params.use_ali_until
+        and train_960_ali_filename.is_file()
+    ):
         logging.info("Use pre-computed alignments")
         subsampling_factor, train_ali = load_alignments(train_960_ali_filename)
         assert subsampling_factor == params.subsampling_factor
@@ -750,7 +769,10 @@ def run(rank, world_size, args):
 
     for epoch in range(params.start_epoch, params.num_epochs):
         train_dl.sampler.set_epoch(epoch)
-        if params.batch_idx_train > 4000 and train_ali is not None:
+        if (
+            params.batch_idx_train >= params.use_ali_until
+            and train_ali is not None
+        ):
             # Delete the alignments to save memory
             train_ali = None
             valid_ali = None
diff --git a/icefall/mmi.py b/icefall/mmi.py
index f9ba46df9..2c479fc2c 100644
--- a/icefall/mmi.py
+++ b/icefall/mmi.py
@@ -12,6 +12,7 @@ def _compute_mmi_loss_exact_optimized(
     texts: List[str],
     graph_compiler: MmiTrainingGraphCompiler,
     den_scale: float = 1.0,
+    beam_size: float = 8.0,
 ) -> torch.Tensor:
     """
     The function name contains `exact`, which means it uses a version of
@@ -79,7 +80,7 @@ def _compute_mmi_loss_exact_optimized(
     num_den_lats = k2.intersect_dense(
         num_den_reordered_graphs,
         dense_fsa_vec,
-        output_beam=10.0,
+        output_beam=beam_size,
         a_to_b_map=a_to_b_map,
     )
 
@@ -100,6 +101,7 @@ def _compute_mmi_loss_exact_non_optimized(
     texts: List[str],
     graph_compiler: MmiTrainingGraphCompiler,
     den_scale: float = 1.0,
+    beam_size: float = 8.0,
 ) -> torch.Tensor:
     """
     See :func:`_compute_mmi_loss_exact_optimized` for the meaning
@@ -113,8 +115,12 @@ def _compute_mmi_loss_exact_non_optimized(
     num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=True)
 
     # TODO: pass output_beam as function argument
-    num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=10.0)
-    den_lats = k2.intersect_dense(den_graphs, dense_fsa_vec, output_beam=10.0)
+    num_lats = k2.intersect_dense(
+        num_graphs, dense_fsa_vec, output_beam=beam_size
+    )
+    den_lats = k2.intersect_dense(
+        den_graphs, dense_fsa_vec, output_beam=beam_size
+    )
 
     num_tot_scores = num_lats.get_tot_scores(
         log_semiring=True, use_double_scores=True
@@ -135,6 +141,7 @@ def _compute_mmi_loss_pruned(
     texts: List[str],
     graph_compiler: MmiTrainingGraphCompiler,
     den_scale: float = 1.0,
+    beam_size: float = 8.0,
 ) -> torch.Tensor:
     """
     See :func:`_compute_mmi_loss_exact_optimized` for the meaning
@@ -156,7 +163,7 @@ def _compute_mmi_loss_pruned(
         den_graphs,
         dense_fsa_vec,
         search_beam=20.0,
-        output_beam=8.0,
+        output_beam=beam_size,
         min_active_states=30,
         max_active_states=10000,
     )
@@ -187,11 +194,13 @@ class LFMMILoss(nn.Module):
         graph_compiler: MmiTrainingGraphCompiler,
         use_pruned_intersect: bool = False,
         den_scale: float = 1.0,
+        beam_size: float = 8.0,
     ):
         super().__init__()
         self.graph_compiler = graph_compiler
         self.den_scale = den_scale
         self.use_pruned_intersect = use_pruned_intersect
+        self.beam_size = beam_size
 
     def forward(
         self,
@@ -219,4 +228,5 @@ class LFMMILoss(nn.Module):
             texts=texts,
             graph_compiler=self.graph_compiler,
             den_scale=self.den_scale,
+            beam_size=self.beam_size,
         )