train.py

#!/usr/bin/env python3
# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang,
#                                                  Wei Kang,
#                                                  Mingshuang Luo,)
#                                                  Zengwei Yao)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Usage:

export CUDA_VISIBLE_DEVICES="0,1,2,3"

./pruned_stateless_emformer_rnnt/train.py \
  --world-size 4 \
  --num-epochs 30 \
  --start-epoch 1 \
  --exp-dir pruned_stateless_emformer_rnnt/exp \
  --full-libri 1 \
  --max-duration 300
"""


import argparse
import copy
import logging
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, Optional, Tuple, Union

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 emformer import Emformer
from joiner import Joiner
from lhotse.cut import Cut
from lhotse.dataset.sampling.base import CutSampler
from lhotse.utils import fix_random_seed
from model import Transducer
from noam import Noam
from torch import Tensor
from torch.cuda.amp import GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter

from icefall import diagnostics
from icefall.checkpoint import load_checkpoint, remove_checkpoints
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.checkpoint import (
    save_checkpoint_with_global_batch_idx,
    update_averaged_model,
)
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 add_model_arguments(parser: argparse.ArgumentParser):
    parser.add_argument(
        "--attention-dim",
        type=int,
        default=512,
        help="Attention dim for the Emformer",
    )

    parser.add_argument(
        "--nhead",
        type=int,
        default=8,
        help="Number of attention heads for the Emformer",
    )

    parser.add_argument(
        "--dim-feedforward",
        type=int,
        default=2048,
        help="Feed-forward dimension for the Emformer",
    )

    parser.add_argument(
        "--num-encoder-layers",
        type=int,
        default=18,
        help="Number of encoder layers for the Emformer",
    )

    parser.add_argument(
        "--left-context-length",
        type=int,
        default=128,
        help="Number of frames for the left context in the Emformer",
    )

    parser.add_argument(
        "--segment-length",
        type=int,
        default=8,
        help="Number of frames for each segment in the Emformer",
    )

    parser.add_argument(
        "--right-context-length",
        type=int,
        default=4,
        help="Number of frames for right context in the Emformer",
    )

    parser.add_argument(
        "--memory-size",
        type=int,
        default=0,
        help="Number of entries in the memory for the Emformer",
    )


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=30,
        help="Number of epochs to train.",
    )

    parser.add_argument(
        "--start-epoch",
        type=int,
        default=1,
        help="""Resume training from this epoch. It should be positive.
        If larger than 1, it will load checkpoint from
        exp-dir/epoch-{start_epoch-1}.pt
        """,
    )

    parser.add_argument(
        "--start-batch",
        type=int,
        default=0,
        help="""If positive, --start-epoch is ignored and
        it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt
        """,
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="pruned_stateless_emformer_rnnt/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",
    )

    parser.add_argument(
        "--context-size",
        type=int,
        default=2,
        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
    )

    parser.add_argument(
        "--prune-range",
        type=int,
        default=5,
        help="The prune range for rnnt loss, it means how many symbols(context)"
        "we are using to compute the loss",
    )

    parser.add_argument(
        "--lm-scale",
        type=float,
        default=0.25,
        help="The scale to smooth the loss with lm "
        "(output of prediction network) part.",
    )

    parser.add_argument(
        "--am-scale",
        type=float,
        default=0.0,
        help="The scale to smooth the loss with am (output of encoder network) part.",
    )

    parser.add_argument(
        "--simple-loss-scale",
        type=float,
        default=0.5,
        help="To get pruning ranges, we will calculate a simple version"
        "loss(joiner is just addition), this simple loss also uses for"
        "training (as a regularization item). We will scale the simple loss"
        "with this parameter before adding to the final loss.",
    )

    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="The seed for random generators intended for reproducibility",
    )

    parser.add_argument(
        "--print-diagnostics",
        type=str2bool,
        default=False,
        help="Accumulate stats on activations, print them and exit.",
    )

    parser.add_argument(
        "--save-every-n",
        type=int,
        default=8000,
        help="""Save checkpoint after processing this number of batches"
        periodically. We save checkpoint to exp-dir/ whenever
        params.batch_idx_train % save_every_n == 0. The checkpoint filename
        has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
        Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
        end of each epoch where `xxx` is the epoch number counting from 0.
        """,
    )

    parser.add_argument(
        "--keep-last-k",
        type=int,
        default=20,
        help="""Only keep this number of checkpoints on disk.
        For instance, if it is 3, there are only 3 checkpoints
        in the exp-dir with filenames `checkpoint-xxx.pt`.
        It does not affect checkpoints with name `epoch-xxx.pt`.
        """,
    )

    parser.add_argument(
        "--average-period",
        type=int,
        default=100,
        help="""Update the averaged model, namely `model_avg`, after processing
        this number of batches. `model_avg` is a separate version of model,
        in which each floating-point parameter is the average of all the
        parameters from the start of training. Each time we take the average,
        we do: `model_avg = model * (average_period / batch_idx_train) +
            model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
        """,
    )

    parser.add_argument(
        "--use-fp16",
        type=str2bool,
        default=False,
        help="Whether to use half precision training.",
    )

    add_model_arguments(parser)

    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.

        - attention_dim: Hidden dim for multi-head attention model.

        - num_decoder_layers: Number of decoder layer of transformer decoder.

        - 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 Emformer
            "feature_dim": 80,
            "subsampling_factor": 4,
            "vgg_frontend": False,
            # parameters for decoder
            "embedding_dim": 512,
            # parameters for Noam
            "warm_step": 80000,  # For the 100h subset, use 20000
            "env_info": get_env_info(),
        }
    )

    return params


def get_encoder_model(params: AttributeDict) -> nn.Module:
    encoder = Emformer(
        num_features=params.feature_dim,
        output_dim=params.vocab_size,
        subsampling_factor=params.subsampling_factor,
        d_model=params.attention_dim,
        nhead=params.nhead,
        dim_feedforward=params.dim_feedforward,
        num_encoder_layers=params.num_encoder_layers,
        vgg_frontend=params.vgg_frontend,
        left_context_length=params.left_context_length,
        segment_length=params.segment_length,
        right_context_length=params.right_context_length,
        max_memory_size=params.memory_size,
    )
    return encoder


def get_decoder_model(params: AttributeDict) -> nn.Module:
    decoder = Decoder(
        vocab_size=params.vocab_size,
        embedding_dim=params.embedding_dim,
        blank_id=params.blank_id,
        unk_id=params.unk_id,
        context_size=params.context_size,
    )
    return decoder


def get_joiner_model(params: AttributeDict) -> nn.Module:
    joiner = Joiner(
        input_dim=params.vocab_size,
        inner_dim=params.embedding_dim,
        output_dim=params.vocab_size,
    )
    return joiner


def get_transducer_model(params: AttributeDict) -> nn.Module:
    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,
    model_avg: Optional[nn.Module] = None,
    optimizer: Optional[torch.optim.Optimizer] = None,
) -> Optional[Dict[str, Any]]:
    """Load checkpoint from file.

    If params.start_batch is positive, it will load the checkpoint from
    `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if
    params.start_epoch is larger than 1, it will load the checkpoint from
    `params.start_epoch - 1`.

    Apart from loading state dict for `model` and `optimizer` 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.
      model_avg:
        The stored model averaged from the start of training.
      optimizer:
        The optimizer that we are using.
    Returns:
      Return a dict containing previously saved training info.
    """
    if params.start_batch > 0:
        filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
    elif params.start_epoch > 1:
        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
    else:
        return None

    assert filename.is_file(), f"{filename} does not exist!"

    saved_params = load_checkpoint(
        filename,
        model=model,
        model_avg=model_avg,
        optimizer=optimizer,
    )

    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]

    if params.start_batch > 0:
        if "cur_epoch" in saved_params:
            params["start_epoch"] = saved_params["cur_epoch"]

    return saved_params


def save_checkpoint(
    params: AttributeDict,
    model: Union[nn.Module, DDP],
    model_avg: Optional[nn.Module] = None,
    optimizer: Optional[torch.optim.Optimizer] = None,
    sampler: Optional[CutSampler] = None,
    scaler: Optional[GradScaler] = None,
    rank: int = 0,
) -> None:
    """Save model, optimizer, and training stats to file.

    Args:
      params:
        It is returned by :func:`get_params`.
      model:
        The training model.
      model_avg:
        The stored model averaged from the start of training.
      optimizer:
        The optimizer used in the training.
      sampler:
       The sampler for the training dataset.
      scaler:
        The scaler used for mix precision training.
    """
    if rank != 0:
        return
    filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
    save_checkpoint_impl(
        filename=filename,
        model=model,
        model_avg=model_avg,
        params=params,
        optimizer=optimizer,
        sampler=sampler,
        scaler=scaler,
        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: Union[nn.Module, DDP],
    sp: spm.SentencePieceProcessor,
    batch: dict,
    is_training: bool,
) -> Tuple[Tensor, MetricsTracker]:
    """
    Compute pruned RNN-T 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 Emformer 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 if isinstance(model, DDP) else next(model.parameters()).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):
        simple_loss, pruned_loss = model(
            x=feature,
            x_lens=feature_lens,
            y=y,
            prune_range=params.prune_range,
            am_scale=params.am_scale,
            lm_scale=params.lm_scale,
        )
        loss = params.simple_loss_scale * simple_loss + pruned_loss

    assert loss.requires_grad == is_training

    info = MetricsTracker()
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()

    # `utt_duration` and `utt_pad_proportion` would be normalized by `utterances`  # noqa
    info["utterances"] = feature.size(0)
    # averaged input duration in frames over utterances
    info["utt_duration"] = feature_lens.sum().item()
    # averaged padding proportion over utterances
    info["utt_pad_proportion"] = (
        ((feature.size(1) - feature_lens) / feature.size(1)).sum().item()
    )

    # Note: We use reduction=sum while computing the loss.
    info["loss"] = loss.detach().cpu().item()
    info["simple_loss"] = simple_loss.detach().cpu().item()
    info["pruned_loss"] = pruned_loss.detach().cpu().item()

    return loss, info


def compute_validation_loss(
    params: AttributeDict,
    model: Union[nn.Module, DDP],
    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: Union[nn.Module, DDP],
    optimizer: torch.optim.Optimizer,
    sp: spm.SentencePieceProcessor,
    train_dl: torch.utils.data.DataLoader,
    valid_dl: torch.utils.data.DataLoader,
    scaler: GradScaler,
    model_avg: Optional[nn.Module] = None,
    tb_writer: Optional[SummaryWriter] = None,
    world_size: int = 1,
    rank: int = 0,
) -> 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.
      scaler:
        The scaler used for mix precision training.
      model_avg:
        The stored model averaged from the start of training.
      tb_writer:
        Writer to write log messages to tensorboard.
      world_size:
        Number of nodes in DDP training. If it is 1, DDP is disabled.
      rank:
        The rank of the node in DDP training. If no DDP is used, it should
        be set to 0.
    """
    model.train()

    tot_loss = MetricsTracker()

    for batch_idx, batch in enumerate(train_dl):
        params.batch_idx_train += 1
        batch_size = len(batch["supervisions"]["text"])

        with torch.cuda.amp.autocast(enabled=params.use_fp16):
            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.
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
        optimizer.zero_grad()

        if params.print_diagnostics and batch_idx == 30:
            return

        if (
            rank == 0
            and params.batch_idx_train > 0
            and params.batch_idx_train % params.average_period == 0
        ):
            update_averaged_model(
                params=params,
                model_cur=model,
                model_avg=model_avg,
            )

        if (
            params.batch_idx_train > 0
            and params.batch_idx_train % params.save_every_n == 0
        ):
            save_checkpoint_with_global_batch_idx(
                out_dir=params.exp_dir,
                global_batch_idx=params.batch_idx_train,
                model=model,
                model_avg=model_avg,
                params=params,
                optimizer=optimizer,
                sampler=train_dl.sampler,
                scaler=scaler,
                rank=rank,
            )
            remove_checkpoints(
                out_dir=params.exp_dir,
                topk=params.keep_last_k,
                rank=rank,
            )

        if batch_idx % params.log_interval == 0:
            cur_lr = optimizer.rate()
            logging.info(
                f"Epoch {params.cur_epoch}, "
                f"batch {batch_idx}, loss[{loss_info}], "
                f"tot_loss[{tot_loss}], batch size: {batch_size}, "
                f"lr: {cur_lr:.2e}"
            )

            if tb_writer is not None:
                tb_writer.add_scalar(
                    "train/learning_rate", cur_lr, params.batch_idx_train
                )

                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 = 20000

    fix_random_seed(params.seed)
    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 <unk> are defined in local/train_bpe_model.py
    params.blank_id = sp.piece_to_id("<blk>")
    params.unk_id = sp.piece_to_id("<unk>")
    params.vocab_size = sp.get_piece_size()

    logging.info(params)

    logging.info("About to create model")
    model = get_transducer_model(params)

    num_param = sum([p.numel() for p in model.parameters()])
    logging.info(f"Number of model parameters: {num_param}")

    assert params.save_every_n >= params.average_period
    model_avg: Optional[nn.Module] = None
    if rank == 0:
        # model_avg is only used with rank 0
        model_avg = copy.deepcopy(model)

    assert params.start_epoch > 0, params.start_epoch
    checkpoints = load_checkpoint_if_available(
        params=params, model=model, model_avg=model_avg
    )

    model.to(device)
    if world_size > 1:
        logging.info("Using DDP")
        model = DDP(model, device_ids=[rank])

    optimizer = Noam(
        model.parameters(),
        model_size=params.attention_dim,
        factor=params.lr_factor,
        warm_step=params.warm_step,
    )

    if checkpoints and "optimizer" in checkpoints:
        logging.info("Loading optimizer state dict")
        optimizer.load_state_dict(checkpoints["optimizer"])

    if params.print_diagnostics:
        diagnostic = diagnostics.attach_diagnostics(model)

    librispeech = LibriSpeechAsrDataModule(args)

    if params.full_libri:
        train_cuts = librispeech.train_all_shuf_cuts()
    else:
        train_cuts = librispeech.train_clean_100_cuts()

    def remove_short_and_long_utt(c: Cut):
        # Keep only utterances with duration between 1 second and 20 seconds
        #
        # Caution: There is a reason to select 20.0 here. Please see
        # ../local/display_manifest_statistics.py
        #
        # You should use ../local/display_manifest_statistics.py to get
        # an utterance duration distribution for your dataset to select
        # the threshold
        if c.duration < 1.0 or c.duration > 20.0:
            logging.warning(
                f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
            )
            return False

        # In pruned RNN-T, we require that T >= S
        # where T is the number of feature frames after subsampling
        # and S is the number of tokens in the utterance

        # In ./emformer.py, the conv module uses the following expression
        # for subsampling
        T = ((c.num_frames - 1) // 2 - 1) // 2
        tokens = sp.encode(c.supervisions[0].text, out_type=str)

        if T < len(tokens):
            logging.warning(
                f"Exclude cut with ID {c.id} from training. "
                f"Number of frames (before subsampling): {c.num_frames}. "
                f"Number of frames (after subsampling): {T}. "
                f"Text: {c.supervisions[0].text}. "
                f"Tokens: {tokens}. "
                f"Number of tokens: {len(tokens)}"
            )
            return False

        return True

    train_cuts = train_cuts.filter(remove_short_and_long_utt)

    if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
        # We only load the sampler's state dict when it loads a checkpoint
        # saved in the middle of an epoch
        sampler_state_dict = checkpoints["sampler"]
    else:
        sampler_state_dict = None

    train_dl = librispeech.train_dataloaders(
        train_cuts, sampler_state_dict=sampler_state_dict
    )

    valid_cuts = librispeech.dev_clean_cuts()
    valid_cuts += librispeech.dev_other_cuts()
    valid_dl = librispeech.valid_dataloaders(valid_cuts)

    if not params.print_diagnostics:
        scan_pessimistic_batches_for_oom(
            model=model,
            train_dl=train_dl,
            optimizer=optimizer,
            sp=sp,
            params=params,
        )

    scaler = GradScaler(enabled=params.use_fp16)
    if checkpoints and "grad_scaler" in checkpoints:
        logging.info("Loading grad scaler state dict")
        scaler.load_state_dict(checkpoints["grad_scaler"])

    for epoch in range(params.start_epoch, params.num_epochs + 1):
        fix_random_seed(params.seed + epoch - 1)
        train_dl.sampler.set_epoch(epoch - 1)

        if tb_writer is not None:
            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)

        params.cur_epoch = epoch

        train_one_epoch(
            params=params,
            model=model,
            model_avg=model_avg,
            optimizer=optimizer,
            sp=sp,
            train_dl=train_dl,
            valid_dl=valid_dl,
            scaler=scaler,
            tb_writer=tb_writer,
            world_size=world_size,
            rank=rank,
        )

        if params.print_diagnostics:
            diagnostic.print_diagnostics()
            break

        save_checkpoint(
            params=params,
            model=model,
            model_avg=model_avg,
            optimizer=optimizer,
            sampler=train_dl.sampler,
            scaler=scaler,
            rank=rank,
        )

    logging.info("Done!")

    if world_size > 1:
        torch.distributed.barrier()
        cleanup_dist()


def scan_pessimistic_batches_for_oom(
    model: Union[nn.Module, DDP],
    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 1 would cause OOM."
    )
    batches, crit_values = find_pessimistic_batches(train_dl.sampler)
    for criterion, cuts in batches.items():
        batch = train_dl.dataset[cuts]
        try:
            with torch.cuda.amp.autocast(enabled=params.use_fp16):
                loss, _ = compute_loss(
                    params=params,
                    model=model,
                    sp=sp,
                    batch=batch,
                    is_training=True,
                )
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()
        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()