text: #!/usr/bin/env python3
  # encoding: utf-8
  
  # Copyright 2017 Johns Hopkins University (Shinji Watanabe)
  #  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
  
  """Training/decoding definition for the speech recognition task."""
  
  import copy
  import json
  import logging
  import math
  import os
  import sys
  
  from chainer import reporter as reporter_module
  from chainer import training
  from chainer.training import extensions
  from chainer.training.updater import StandardUpdater
  import numpy as np
  from tensorboardX import SummaryWriter
  import torch
  from torch.nn.parallel import data_parallel
  
  from espnet.asr.asr_utils import adadelta_eps_decay
  from espnet.asr.asr_utils import add_results_to_json
  from espnet.asr.asr_utils import CompareValueTrigger
  from espnet.asr.asr_utils import format_mulenc_args
  from espnet.asr.asr_utils import get_model_conf
  from espnet.asr.asr_utils import plot_spectrogram
  from espnet.asr.asr_utils import restore_snapshot
  from espnet.asr.asr_utils import snapshot_object
  from espnet.asr.asr_utils import torch_load
  from espnet.asr.asr_utils import torch_resume
  from espnet.asr.asr_utils import torch_snapshot
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.asr.pytorch_backend.asr_init import load_trained_modules
  import espnet.lm.pytorch_backend.extlm as extlm_pytorch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.pytorch_backend.e2e_asr import pad_list
  import espnet.nets.pytorch_backend.lm.default as lm_pytorch
  from espnet.nets.pytorch_backend.streaming.segment import SegmentStreamingE2E
  from espnet.nets.pytorch_backend.streaming.window import WindowStreamingE2E
  from espnet.transform.spectrogram import IStft
  from espnet.transform.transformation import Transformation
  from espnet.utils.cli_writers import file_writer_helper
  from espnet.utils.dataset import ChainerDataLoader
  from espnet.utils.dataset import TransformDataset
  from espnet.utils.deterministic_utils import set_deterministic_pytorch
  from espnet.utils.dynamic_import import dynamic_import
  from espnet.utils.io_utils import LoadInputsAndTargets
  from espnet.utils.training.batchfy import make_batchset
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop
  from espnet.utils.training.train_utils import set_early_stop
  
  import matplotlib
  matplotlib.use('Agg')
  
  if sys.version_info[0] == 2:
      from itertools import izip_longest as zip_longest
  else:
      from itertools import zip_longest as zip_longest
  
  
  class CustomEvaluator(BaseEvaluator):
      """Custom Evaluator for Pytorch.
  
      Args:
          model (torch.nn.Module): The model to evaluate.
          iterator (chainer.dataset.Iterator) : The train iterator.
  
          target (link | dict[str, link]) :Link object or a dictionary of
              links to evaluate. If this is just a link object, the link is
              registered by the name ``'main'``.
  
          device (torch.device): The device used.
          ngpu (int): The number of GPUs.
  
      """
  
      def __init__(self, model, iterator, target, device, ngpu=None):
          super(CustomEvaluator, self).__init__(iterator, target)
          self.model = model
          self.device = device
          if ngpu is not None:
              self.ngpu = ngpu
          elif device.type == "cpu":
              self.ngpu = 0
          else:
              self.ngpu = 1
  
      # The core part of the update routine can be customized by overriding
      def evaluate(self):
          """Main evaluate routine for CustomEvaluator."""
          iterator = self._iterators['main']
  
          if self.eval_hook:
              self.eval_hook(self)
  
          if hasattr(iterator, 'reset'):
              iterator.reset()
              it = iterator
          else:
              it = copy.copy(iterator)
  
          summary = reporter_module.DictSummary()
  
          self.model.eval()
          with torch.no_grad():
              for batch in it:
                  x = tuple(arr.to(self.device) if arr is not None else None
                            for arr in batch)
                  observation = {}
                  with reporter_module.report_scope(observation):
                      # read scp files
                      # x: original json with loaded features
                      #    will be converted to chainer variable later
                      if self.ngpu == 0:
                          self.model(*x)
                      else:
                          # apex does not support torch.nn.DataParallel
                          data_parallel(self.model, x, range(self.ngpu))
  
                  summary.add(observation)
          self.model.train()
  
          return summary.compute_mean()
  
  
  class CustomUpdater(StandardUpdater):
      """Custom Updater for Pytorch.
  
      Args:
          model (torch.nn.Module): The model to update.
          grad_clip_threshold (float): The gradient clipping value to use.
          train_iter (chainer.dataset.Iterator): The training iterator.
          optimizer (torch.optim.optimizer): The training optimizer.
  
          device (torch.device): The device to use.
          ngpu (int): The number of gpus to use.
          use_apex (bool): The flag to use Apex in backprop.
  
      """
  
      def __init__(self, model, grad_clip_threshold, train_iter,
                   optimizer, device, ngpu, grad_noise=False, accum_grad=1, use_apex=False):
          super(CustomUpdater, self).__init__(train_iter, optimizer)
          self.model = model
          self.grad_clip_threshold = grad_clip_threshold
          self.device = device
          self.ngpu = ngpu
          self.accum_grad = accum_grad
          self.forward_count = 0
          self.grad_noise = grad_noise
          self.iteration = 0
          self.use_apex = use_apex
  
      # The core part of the update routine can be customized by overriding.
      def update_core(self):
          """Main update routine of the CustomUpdater."""
          # When we pass one iterator and optimizer to StandardUpdater.__init__,
          # they are automatically named 'main'.
          train_iter = self.get_iterator('main')
          optimizer = self.get_optimizer('main')
          epoch = train_iter.epoch
  
          # Get the next batch (a list of json files)
          batch = train_iter.next()
          # self.iteration += 1 # Increase may result in early report, which is done in other place automatically.
          x = tuple(arr.to(self.device) if arr is not None else None
                    for arr in batch)
          is_new_epoch = train_iter.epoch != epoch
          # When the last minibatch in the current epoch is given,
          # gradient accumulation is turned off in order to evaluate the model
          # on the validation set in every epoch.
          # see details in https://github.com/espnet/espnet/pull/1388
  
          # Compute the loss at this time step and accumulate it
          if self.ngpu == 0:
              loss = self.model(*x).mean() / self.accum_grad
          else:
              # apex does not support torch.nn.DataParallel
              loss = data_parallel(self.model, x, range(self.ngpu)).mean() / self.accum_grad
          if self.use_apex:
              from apex import amp
              # NOTE: for a compatibility with noam optimizer
              opt = optimizer.optimizer if hasattr(optimizer, "optimizer") else optimizer
              with amp.scale_loss(loss, opt) as scaled_loss:
                  scaled_loss.backward()
          else:
              loss.backward()
          # gradient noise injection
          if self.grad_noise:
              from espnet.asr.asr_utils import add_gradient_noise
              add_gradient_noise(self.model, self.iteration, duration=100, eta=1.0, scale_factor=0.55)
          loss.detach()  # Truncate the graph
  
          # update parameters
          self.forward_count += 1
          if not is_new_epoch and self.forward_count != self.accum_grad:
              return
          self.forward_count = 0
          # compute the gradient norm to check if it is normal or not
          grad_norm = torch.nn.utils.clip_grad_norm_(
              self.model.parameters(), self.grad_clip_threshold)
          logging.info('grad norm={}'.format(grad_norm))
          if math.isnan(grad_norm):
              logging.warning('grad norm is nan. Do not update model.')
          else:
              optimizer.step()
          optimizer.zero_grad()
  
      def update(self):
          self.update_core()
          # #iterations with accum_grad > 1
          # Ref.: https://github.com/espnet/espnet/issues/777
          if self.forward_count == 0:
              self.iteration += 1
  
  
  class CustomConverter(object):
      """Custom batch converter for Pytorch.
  
      Args:
          subsampling_factor (int): The subsampling factor.
          dtype (torch.dtype): Data type to convert.
  
      """
  
      def __init__(self, subsampling_factor=1, dtype=torch.float32):
          """Construct a CustomConverter object."""
          self.subsampling_factor = subsampling_factor
          self.ignore_id = -1
          self.dtype = dtype
  
      def __call__(self, batch, device=torch.device('cpu')):
          """Transform a batch and send it to a device.
  
          Args:
              batch (list): The batch to transform.
              device (torch.device): The device to send to.
  
          Returns:
              tuple(torch.Tensor, torch.Tensor, torch.Tensor)
  
          """
          # batch should be located in list
          assert len(batch) == 1
          xs, ys = batch[0]
  
          # perform subsampling
          if self.subsampling_factor > 1:
              xs = [x[::self.subsampling_factor, :] for x in xs]
  
          # get batch of lengths of input sequences
          ilens = np.array([x.shape[0] for x in xs])
  
          # perform padding and convert to tensor
          # currently only support real number
          if xs[0].dtype.kind == 'c':
              xs_pad_real = pad_list(
                  [torch.from_numpy(x.real).float() for x in xs], 0).to(device, dtype=self.dtype)
              xs_pad_imag = pad_list(
                  [torch.from_numpy(x.imag).float() for x in xs], 0).to(device, dtype=self.dtype)
              # Note(kamo):
              # {'real': ..., 'imag': ...} will be changed to ComplexTensor in E2E.
              # Don't create ComplexTensor and give it E2E here
              # because torch.nn.DataParellel can't handle it.
              xs_pad = {'real': xs_pad_real, 'imag': xs_pad_imag}
          else:
              xs_pad = pad_list([torch.from_numpy(x).float() for x in xs], 0).to(device, dtype=self.dtype)
  
          ilens = torch.from_numpy(ilens).to(device)
          # NOTE: this is for multi-output (e.g., speech translation)
          ys_pad = pad_list([torch.from_numpy(np.array(y[0][:]) if isinstance(y, tuple) else y).long()
                             for y in ys], self.ignore_id).to(device)
  
          return xs_pad, ilens, ys_pad
  
  
  class CustomConverterMulEnc(object):
      """Custom batch converter for Pytorch in multi-encoder case.
  
      Args:
          subsampling_factors (list): List of subsampling factors for each encoder.
          dtype (torch.dtype): Data type to convert.
  
      """
  
      def __init__(self, subsamping_factors=[1, 1], dtype=torch.float32):
          """Initialize the converter."""
          self.subsamping_factors = subsamping_factors
          self.ignore_id = -1
          self.dtype = dtype
          self.num_encs = len(subsamping_factors)
  
      def __call__(self, batch, device):
          """Transform a batch and send it to a device.
  
          Args:
              batch (list): The batch to transform.
              device (torch.device): The device to send to.
  
          Returns:
              tuple( list(torch.Tensor), list(torch.Tensor), torch.Tensor)
  
          """
          # batch should be located in list
          assert len(batch) == 1
          xs_list = batch[0][:self.num_encs]
          ys = batch[0][-1]
  
          # perform subsampling
          if np.sum(self.subsamping_factors) > self.num_encs:
              xs_list = [[x[::self.subsampling_factors[i], :] for x in xs_list[i]] for i in range(self.num_encs)]
  
          # get batch of lengths of input sequences
          ilens_list = [np.array([x.shape[0] for x in xs_list[i]]) for i in range(self.num_encs)]
  
          # perform padding and convert to tensor
          # currently only support real number
          xs_list_pad = [pad_list([torch.from_numpy(x).float() for x in xs_list[i]], 0).to(device, dtype=self.dtype) for i
                         in range(self.num_encs)]
  
          ilens_list = [torch.from_numpy(ilens_list[i]).to(device) for i in range(self.num_encs)]
          # NOTE: this is for multi-task learning (e.g., speech translation)
          ys_pad = pad_list([torch.from_numpy(np.array(y[0]) if isinstance(y, tuple) else y).long()
                             for y in ys], self.ignore_id).to(device)
  
          return xs_list_pad, ilens_list, ys_pad
  
  
  def train(args):
      """Train with the given args.
  
      Args:
          args (namespace): The program arguments.
  
      """
      set_deterministic_pytorch(args)
      if args.num_encs > 1:
          args = format_mulenc_args(args)
  
      # check cuda availability
      if not torch.cuda.is_available():
          logging.warning('cuda is not available')
  
      # get input and output dimension info
      with open(args.valid_json, 'rb') as f:
          valid_json = json.load(f)['utts']
      utts = list(valid_json.keys())
      idim_list = [int(valid_json[utts[0]]['input'][i]['shape'][-1]) for i in range(args.num_encs)]
      odim = int(valid_json[utts[0]]['output'][0]['shape'][-1])
      for i in range(args.num_encs):
          logging.info('stream{}: input dims : {}'.format(i + 1, idim_list[i]))
      logging.info('#output dims: ' + str(odim))
  
      # specify attention, CTC, hybrid mode
      if args.mtlalpha == 1.0:
          mtl_mode = 'ctc'
          logging.info('Pure CTC mode')
      elif args.mtlalpha == 0.0:
          mtl_mode = 'att'
          logging.info('Pure attention mode')
      else:
          mtl_mode = 'mtl'
          logging.info('Multitask learning mode')
  
      if (args.enc_init is not None or args.dec_init is not None) and args.num_encs == 1:
          model = load_trained_modules(idim_list[0], odim, args)
      else:
          model_class = dynamic_import(args.model_module)
          model = model_class(idim_list[0] if args.num_encs == 1 else idim_list, odim, args)
      assert isinstance(model, ASRInterface)
  
      if args.rnnlm is not None:
          rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
          rnnlm = lm_pytorch.ClassifierWithState(
              lm_pytorch.RNNLM(
                  len(args.char_list), rnnlm_args.layer, rnnlm_args.unit))
          torch_load(args.rnnlm, rnnlm)
          model.rnnlm = rnnlm
  
      # write model config
      if not os.path.exists(args.outdir):
          os.makedirs(args.outdir)
      model_conf = args.outdir + '/model.json'
      with open(model_conf, 'wb') as f:
          logging.info('writing a model config file to ' + model_conf)
          f.write(json.dumps((idim_list[0] if args.num_encs == 1 else idim_list, odim, vars(args)),
                             indent=4, ensure_ascii=False, sort_keys=True).encode('utf_8'))
      for key in sorted(vars(args).keys()):
          logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
  
      reporter = model.reporter
  
      # check the use of multi-gpu
      if args.ngpu > 1:
          if args.batch_size != 0:
              logging.warning('batch size is automatically increased (%d -> %d)' % (
                  args.batch_size, args.batch_size * args.ngpu))
              args.batch_size *= args.ngpu
          if args.num_encs > 1:
              # TODO(ruizhili): implement data parallel for multi-encoder setup.
              raise NotImplementedError("Data parallel is not supported for multi-encoder setup.")
  
      # set torch device
      device = torch.device("cuda" if args.ngpu > 0 else "cpu")
      if args.train_dtype in ("float16", "float32", "float64"):
          dtype = getattr(torch, args.train_dtype)
      else:
          dtype = torch.float32
      model = model.to(device=device, dtype=dtype)
  
      # Setup an optimizer
      if args.opt == 'adadelta':
          optimizer = torch.optim.Adadelta(
              model.parameters(), rho=0.95, eps=args.eps,
              weight_decay=args.weight_decay)
      elif args.opt == 'adam':
          optimizer = torch.optim.Adam(model.parameters(),
                                       weight_decay=args.weight_decay)
      elif args.opt == 'noam':
          from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt
          optimizer = get_std_opt(model, args.adim, args.transformer_warmup_steps, args.transformer_lr)
      else:
          raise NotImplementedError("unknown optimizer: " + args.opt)
  
      # setup apex.amp
      if args.train_dtype in ("O0", "O1", "O2", "O3"):
          try:
              from apex import amp
          except ImportError as e:
              logging.error(f"You need to install apex for --train-dtype {args.train_dtype}. "
                            "See https://github.com/NVIDIA/apex#linux")
              raise e
          if args.opt == 'noam':
              model, optimizer.optimizer = amp.initialize(model, optimizer.optimizer, opt_level=args.train_dtype)
          else:
              model, optimizer = amp.initialize(model, optimizer, opt_level=args.train_dtype)
          use_apex = True
      else:
          use_apex = False
  
      # FIXME: TOO DIRTY HACK
      setattr(optimizer, "target", reporter)
      setattr(optimizer, "serialize", lambda s: reporter.serialize(s))
  
      # Setup a converter
      if args.num_encs == 1:
          converter = CustomConverter(subsampling_factor=model.subsample[0], dtype=dtype)
      else:
          converter = CustomConverterMulEnc([i[0] for i in model.subsample_list], dtype=dtype)
  
      # read json data
      with open(args.train_json, 'rb') as f:
          train_json = json.load(f)['utts']
      with open(args.valid_json, 'rb') as f:
          valid_json = json.load(f)['utts']
  
      use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
      # make minibatch list (variable length)
      train = make_batchset(train_json, args.batch_size,
                            args.maxlen_in, args.maxlen_out, args.minibatches,
                            min_batch_size=args.ngpu if args.ngpu > 1 else 1,
                            shortest_first=use_sortagrad,
                            count=args.batch_count,
                            batch_bins=args.batch_bins,
                            batch_frames_in=args.batch_frames_in,
                            batch_frames_out=args.batch_frames_out,
                            batch_frames_inout=args.batch_frames_inout,
                            iaxis=0, oaxis=0)
      valid = make_batchset(valid_json, args.batch_size,
                            args.maxlen_in, args.maxlen_out, args.minibatches,
                            min_batch_size=args.ngpu if args.ngpu > 1 else 1,
                            count=args.batch_count,
                            batch_bins=args.batch_bins,
                            batch_frames_in=args.batch_frames_in,
                            batch_frames_out=args.batch_frames_out,
                            batch_frames_inout=args.batch_frames_inout,
                            iaxis=0, oaxis=0)
  
      load_tr = LoadInputsAndTargets(
          mode='asr', load_output=True, preprocess_conf=args.preprocess_conf,
          preprocess_args={'train': True}  # Switch the mode of preprocessing
      )
      load_cv = LoadInputsAndTargets(
          mode='asr', load_output=True, preprocess_conf=args.preprocess_conf,
          preprocess_args={'train': False}  # Switch the mode of preprocessing
      )
      # hack to make batchsize argument as 1
      # actual bathsize is included in a list
      # default collate function converts numpy array to pytorch tensor
      # we used an empty collate function instead which returns list
      train_iter = {'main': ChainerDataLoader(
          dataset=TransformDataset(train, lambda data: converter([load_tr(data)])),
          batch_size=1, num_workers=args.n_iter_processes,
          shuffle=not use_sortagrad, collate_fn=lambda x: x[0])}
      valid_iter = {'main': ChainerDataLoader(
          dataset=TransformDataset(valid, lambda data: converter([load_cv(data)])),
          batch_size=1, shuffle=False, collate_fn=lambda x: x[0],
          num_workers=args.n_iter_processes)}
  
      # Set up a trainer
      updater = CustomUpdater(
          model, args.grad_clip, train_iter, optimizer,
          device, args.ngpu, args.grad_noise, args.accum_grad, use_apex=use_apex)
      trainer = training.Trainer(
          updater, (args.epochs, 'epoch'), out=args.outdir)
  
      if use_sortagrad:
          trainer.extend(ShufflingEnabler([train_iter]),
                         trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs, 'epoch'))
  
      # Resume from a snapshot
      if args.resume:
          logging.info('resumed from %s' % args.resume)
          torch_resume(args.resume, trainer)
  
      # Evaluate the model with the test dataset for each epoch
      if args.save_interval_iters > 0:
          trainer.extend(CustomEvaluator(model, valid_iter, reporter, device, args.ngpu),
                         trigger=(args.save_interval_iters, 'iteration'))
      else:
          trainer.extend(CustomEvaluator(model, valid_iter, reporter, device, args.ngpu))
  
      # Save attention weight each epoch
      if args.num_save_attention > 0 and args.mtlalpha != 1.0:
          data = sorted(list(valid_json.items())[:args.num_save_attention],
                        key=lambda x: int(x[1]['input'][0]['shape'][1]), reverse=True)
          if hasattr(model, "module"):
              att_vis_fn = model.module.calculate_all_attentions
              plot_class = model.module.attention_plot_class
          else:
              att_vis_fn = model.calculate_all_attentions
              plot_class = model.attention_plot_class
          att_reporter = plot_class(
              att_vis_fn, data, args.outdir + "/att_ws",
              converter=converter, transform=load_cv, device=device)
          trainer.extend(att_reporter, trigger=(1, 'epoch'))
      else:
          att_reporter = None
  
      # Make a plot for training and validation values
      if args.num_encs > 1:
          report_keys_loss_ctc = ['main/loss_ctc{}'.format(i + 1) for i in range(model.num_encs)] + [
              'validation/main/loss_ctc{}'.format(i + 1) for i in range(model.num_encs)]
          report_keys_cer_ctc = ['main/cer_ctc{}'.format(i + 1) for i in range(model.num_encs)] + [
              'validation/main/cer_ctc{}'.format(i + 1) for i in range(model.num_encs)]
      trainer.extend(extensions.PlotReport(['main/loss', 'validation/main/loss',
                                            'main/loss_ctc', 'validation/main/loss_ctc',
                                            'main/loss_att',
                                            'validation/main/loss_att'] +
                                           ([] if args.num_encs == 1 else report_keys_loss_ctc),
                                           'epoch', file_name='loss.png'))
      trainer.extend(extensions.PlotReport(['main/acc', 'validation/main/acc'],
                                           'epoch', file_name='acc.png'))
      trainer.extend(extensions.PlotReport(
          ['main/cer_ctc', 'validation/main/cer_ctc'] + ([] if args.num_encs == 1 else report_keys_loss_ctc),
          'epoch', file_name='cer.png'))
  
      # Save best models
      trainer.extend(snapshot_object(model, 'model.loss.best'),
                     trigger=training.triggers.MinValueTrigger('validation/main/loss'))
      if mtl_mode != 'ctc':
          trainer.extend(snapshot_object(model, 'model.acc.best'),
                         trigger=training.triggers.MaxValueTrigger('validation/main/acc'))
  
      # save snapshot which contains model and optimizer states
      if args.save_interval_iters > 0:
          trainer.extend(torch_snapshot(filename='snapshot.iter.{.updater.iteration}'),
                         trigger=(args.save_interval_iters, 'iteration'))
      else:
          trainer.extend(torch_snapshot(), trigger=(1, 'epoch'))
  
      # epsilon decay in the optimizer
      if args.opt == 'adadelta':
          if args.criterion == 'acc' and mtl_mode != 'ctc':
              trainer.extend(restore_snapshot(model, args.outdir + '/model.acc.best', load_fn=torch_load),
                             trigger=CompareValueTrigger(
                                 'validation/main/acc',
                                 lambda best_value, current_value: best_value > current_value))
              trainer.extend(adadelta_eps_decay(args.eps_decay),
                             trigger=CompareValueTrigger(
                                 'validation/main/acc',
                                 lambda best_value, current_value: best_value > current_value))
          elif args.criterion == 'loss':
              trainer.extend(restore_snapshot(model, args.outdir + '/model.loss.best', load_fn=torch_load),
                             trigger=CompareValueTrigger(
                                 'validation/main/loss',
                                 lambda best_value, current_value: best_value < current_value))
              trainer.extend(adadelta_eps_decay(args.eps_decay),
                             trigger=CompareValueTrigger(
                                 'validation/main/loss',
                                 lambda best_value, current_value: best_value < current_value))
  
      # Write a log of evaluation statistics for each epoch
      trainer.extend(extensions.LogReport(trigger=(args.report_interval_iters, 'iteration')))
      report_keys = ['epoch', 'iteration', 'main/loss', 'main/loss_ctc', 'main/loss_att',
                     'validation/main/loss', 'validation/main/loss_ctc', 'validation/main/loss_att',
                     'main/acc', 'validation/main/acc', 'main/cer_ctc', 'validation/main/cer_ctc',
                     'elapsed_time'] + ([] if args.num_encs == 1 else report_keys_cer_ctc + report_keys_loss_ctc)
      if args.opt == 'adadelta':
          trainer.extend(extensions.observe_value(
              'eps', lambda trainer: trainer.updater.get_optimizer('main').param_groups[0]["eps"]),
              trigger=(args.report_interval_iters, 'iteration'))
          report_keys.append('eps')
      if args.report_cer:
          report_keys.append('validation/main/cer')
      if args.report_wer:
          report_keys.append('validation/main/wer')
      trainer.extend(extensions.PrintReport(
          report_keys), trigger=(args.report_interval_iters, 'iteration'))
  
      trainer.extend(extensions.ProgressBar(update_interval=args.report_interval_iters))
      set_early_stop(trainer, args)
  
      if args.tensorboard_dir is not None and args.tensorboard_dir != "":
          trainer.extend(TensorboardLogger(SummaryWriter(args.tensorboard_dir), att_reporter),
                         trigger=(args.report_interval_iters, "iteration"))
      # Run the training
      trainer.run()
      check_early_stop(trainer, args.epochs)
  
  
  def recog(args):
      """Decode with the given args.
  
      Args:
          args (namespace): The program arguments.
      """
      set_deterministic_pytorch(args)
      model, train_args = load_trained_model(args.model)
      assert isinstance(model, ASRInterface)
      model.recog_args = args
  
      # read rnnlm
      if args.rnnlm:
          rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
          if getattr(rnnlm_args, "model_module", "default") != "default":
              raise ValueError("use '--api v2' option to decode with non-default language model")
          rnnlm = lm_pytorch.ClassifierWithState(
              lm_pytorch.RNNLM(
                  len(train_args.char_list), rnnlm_args.layer, rnnlm_args.unit))
          torch_load(args.rnnlm, rnnlm)
          rnnlm.eval()
      else:
          rnnlm = None
  
      if args.word_rnnlm:
          rnnlm_args = get_model_conf(args.word_rnnlm, args.word_rnnlm_conf)
          word_dict = rnnlm_args.char_list_dict
          char_dict = {x: i for i, x in enumerate(train_args.char_list)}
          word_rnnlm = lm_pytorch.ClassifierWithState(lm_pytorch.RNNLM(
              len(word_dict), rnnlm_args.layer, rnnlm_args.unit))
          torch_load(args.word_rnnlm, word_rnnlm)
          word_rnnlm.eval()
  
          if rnnlm is not None:
              rnnlm = lm_pytorch.ClassifierWithState(
                  extlm_pytorch.MultiLevelLM(word_rnnlm.predictor,
                                             rnnlm.predictor, word_dict, char_dict))
          else:
              rnnlm = lm_pytorch.ClassifierWithState(
                  extlm_pytorch.LookAheadWordLM(word_rnnlm.predictor,
                                                word_dict, char_dict))
  
      # gpu
      if args.ngpu == 1:
          gpu_id = list(range(args.ngpu))
          logging.info('gpu id: ' + str(gpu_id))
          model.cuda()
          if rnnlm:
              rnnlm.cuda()
  
      # read json data
      with open(args.recog_json, 'rb') as f:
          js = json.load(f)['utts']
      new_js = {}
  
      load_inputs_and_targets = LoadInputsAndTargets(
          mode='asr', load_output=False, sort_in_input_length=False,
          preprocess_conf=train_args.preprocess_conf
          if args.preprocess_conf is None else args.preprocess_conf,
          preprocess_args={'train': False})
  
      if args.batchsize == 0:
          with torch.no_grad():
              for idx, name in enumerate(js.keys(), 1):
                  logging.info('(%d/%d) decoding ' + name, idx, len(js.keys()))
                  batch = [(name, js[name])]
                  feat = load_inputs_and_targets(batch)
                  feat = feat[0][0] if args.num_encs == 1 else [feat[idx][0] for idx in range(model.num_encs)]
                  if args.streaming_mode == 'window' and args.num_encs == 1:
                      logging.info('Using streaming recognizer with window size %d frames', args.streaming_window)
                      se2e = WindowStreamingE2E(e2e=model, recog_args=args, rnnlm=rnnlm)
                      for i in range(0, feat.shape[0], args.streaming_window):
                          logging.info('Feeding frames %d - %d', i, i + args.streaming_window)
                          se2e.accept_input(feat[i:i + args.streaming_window])
                      logging.info('Running offline attention decoder')
                      se2e.decode_with_attention_offline()
                      logging.info('Offline attention decoder finished')
                      nbest_hyps = se2e.retrieve_recognition()
                  elif args.streaming_mode == 'segment' and args.num_encs == 1:
                      logging.info('Using streaming recognizer with threshold value %d', args.streaming_min_blank_dur)
                      nbest_hyps = []
                      for n in range(args.nbest):
                          nbest_hyps.append({'yseq': [], 'score': 0.0})
                      se2e = SegmentStreamingE2E(e2e=model, recog_args=args, rnnlm=rnnlm)
                      r = np.prod(model.subsample)
                      for i in range(0, feat.shape[0], r):
                          hyps = se2e.accept_input(feat[i:i + r])
                          if hyps is not None:
                              text = ''.join([train_args.char_list[int(x)]
                                              for x in hyps[0]['yseq'][1:-1] if int(x) != -1])
                              text = text.replace('\u2581', ' ').strip()  # for SentencePiece
                              text = text.replace(model.space, ' ')
                              text = text.replace(model.blank, '')
                              logging.info(text)
                              for n in range(args.nbest):
                                  nbest_hyps[n]['yseq'].extend(hyps[n]['yseq'])
                                  nbest_hyps[n]['score'] += hyps[n]['score']
                  else:
                      nbest_hyps = model.recognize(feat, args, train_args.char_list, rnnlm)
                  new_js[name] = add_results_to_json(js[name], nbest_hyps, train_args.char_list)
  
      else:
          def grouper(n, iterable, fillvalue=None):
              kargs = [iter(iterable)] * n
              return zip_longest(*kargs, fillvalue=fillvalue)
  
          # sort data if batchsize > 1
          keys = list(js.keys())
          if args.batchsize > 1:
              feat_lens = [js[key]['input'][0]['shape'][0] for key in keys]
              sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i])
              keys = [keys[i] for i in sorted_index]
  
          with torch.no_grad():
              for names in grouper(args.batchsize, keys, None):
                  names = [name for name in names if name]
                  batch = [(name, js[name]) for name in names]
                  feats = load_inputs_and_targets(batch)[0] if args.num_encs == 1 else load_inputs_and_targets(batch)
                  if args.streaming_mode == 'window' and args.num_encs == 1:
                      raise NotImplementedError
                  elif args.streaming_mode == 'segment' and args.num_encs == 1:
                      if args.batchsize > 1:
                          raise NotImplementedError
                      feat = feats[0]
                      nbest_hyps = []
                      for n in range(args.nbest):
                          nbest_hyps.append({'yseq': [], 'score': 0.0})
                      se2e = SegmentStreamingE2E(e2e=model, recog_args=args, rnnlm=rnnlm)
                      r = np.prod(model.subsample)
                      for i in range(0, feat.shape[0], r):
                          hyps = se2e.accept_input(feat[i:i + r])
                          if hyps is not None:
                              text = ''.join([train_args.char_list[int(x)]
                                              for x in hyps[0]['yseq'][1:-1] if int(x) != -1])
                              text = text.replace('\u2581', ' ').strip()  # for SentencePiece
                              text = text.replace(model.space, ' ')
                              text = text.replace(model.blank, '')
                              logging.info(text)
                              for n in range(args.nbest):
                                  nbest_hyps[n]['yseq'].extend(hyps[n]['yseq'])
                                  nbest_hyps[n]['score'] += hyps[n]['score']
                      nbest_hyps = [nbest_hyps]
                  else:
                      nbest_hyps = model.recognize_batch(feats, args, train_args.char_list, rnnlm=rnnlm)
  
                  for i, nbest_hyp in enumerate(nbest_hyps):
                      name = names[i]
                      new_js[name] = add_results_to_json(js[name], nbest_hyp, train_args.char_list)
  
      with open(args.result_label, 'wb') as f:
          f.write(json.dumps({'utts': new_js}, indent=4, ensure_ascii=False, sort_keys=True).encode('utf_8'))
  
  
  def enhance(args):
      """Dumping enhanced speech and mask.
  
      Args:
          args (namespace): The program arguments.
      """
      set_deterministic_pytorch(args)
      # read training config
      idim, odim, train_args = get_model_conf(args.model, args.model_conf)
  
      # TODO(ruizhili): implement enhance for multi-encoder model
      assert args.num_encs == 1, "number of encoder should be 1 ({} is given)".format(args.num_encs)
  
      # load trained model parameters
      logging.info('reading model parameters from ' + args.model)
      model_class = dynamic_import(train_args.model_module)
      model = model_class(idim, odim, train_args)
      assert isinstance(model, ASRInterface)
      torch_load(args.model, model)
      model.recog_args = args
  
      # gpu
      if args.ngpu == 1:
          gpu_id = list(range(args.ngpu))
          logging.info('gpu id: ' + str(gpu_id))
          model.cuda()
  
      # read json data
      with open(args.recog_json, 'rb') as f:
          js = json.load(f)['utts']
  
      load_inputs_and_targets = LoadInputsAndTargets(
          mode='asr', load_output=False, sort_in_input_length=False,
          preprocess_conf=None  # Apply pre_process in outer func
      )
      if args.batchsize == 0:
          args.batchsize = 1
  
      # Creates writers for outputs from the network
      if args.enh_wspecifier is not None:
          enh_writer = file_writer_helper(args.enh_wspecifier,
                                          filetype=args.enh_filetype)
      else:
          enh_writer = None
  
      # Creates a Transformation instance
      preprocess_conf = (
          train_args.preprocess_conf if args.preprocess_conf is None
          else args.preprocess_conf)
      if preprocess_conf is not None:
          logging.info('Use preprocessing'.format(preprocess_conf))
          transform = Transformation(preprocess_conf)
      else:
          transform = None
  
      # Creates a IStft instance
      istft = None
      frame_shift = args.istft_n_shift  # Used for plot the spectrogram
      if args.apply_istft:
          if preprocess_conf is not None:
              # Read the conffile and find stft setting
              with open(preprocess_conf) as f:
                  # Json format: e.g.
                  #    {"process": [{"type": "stft",
                  #                  "win_length": 400,
                  #                  "n_fft": 512, "n_shift": 160,
                  #                  "window": "han"},
                  #                 {"type": "foo", ...}, ...]}
                  conf = json.load(f)
                  assert 'process' in conf, conf
                  # Find stft setting
                  for p in conf['process']:
                      if p['type'] == 'stft':
                          istft = IStft(win_length=p['win_length'],
                                        n_shift=p['n_shift'],
                                        window=p.get('window', 'hann'))
                          logging.info('stft is found in {}. '
                                       'Setting istft config from it\n{}'
                                       .format(preprocess_conf, istft))
                          frame_shift = p['n_shift']
                          break
          if istft is None:
              # Set from command line arguments
              istft = IStft(win_length=args.istft_win_length,
                            n_shift=args.istft_n_shift,
                            window=args.istft_window)
              logging.info('Setting istft config from the command line args\n{}'
                           .format(istft))
  
      # sort data
      keys = list(js.keys())
      feat_lens = [js[key]['input'][0]['shape'][0] for key in keys]
      sorted_index = sorted(range(len(feat_lens)), key=lambda i: -feat_lens[i])
      keys = [keys[i] for i in sorted_index]
  
      def grouper(n, iterable, fillvalue=None):
          kargs = [iter(iterable)] * n
          return zip_longest(*kargs, fillvalue=fillvalue)
  
      num_images = 0
      if not os.path.exists(args.image_dir):
          os.makedirs(args.image_dir)
  
      for names in grouper(args.batchsize, keys, None):
          batch = [(name, js[name]) for name in names]
  
          # May be in time region: (Batch, [Time, Channel])
          org_feats = load_inputs_and_targets(batch)[0]
          if transform is not None:
              # May be in time-freq region: : (Batch, [Time, Channel, Freq])
              feats = transform(org_feats, train=False)
          else:
              feats = org_feats
  
          with torch.no_grad():
              enhanced, mask, ilens = model.enhance(feats)
  
          for idx, name in enumerate(names):
              # Assuming mask, feats : [Batch, Time, Channel. Freq]
              #          enhanced    : [Batch, Time, Freq]
              enh = enhanced[idx][:ilens[idx]]
              mas = mask[idx][:ilens[idx]]
              feat = feats[idx]
  
              # Plot spectrogram
              if args.image_dir is not None and num_images < args.num_images:
                  import matplotlib.pyplot as plt
                  num_images += 1
                  ref_ch = 0
  
                  plt.figure(figsize=(20, 10))
                  plt.subplot(4, 1, 1)
                  plt.title('Mask [ref={}ch]'.format(ref_ch))
                  plot_spectrogram(plt, mas[:, ref_ch].T, fs=args.fs,
                                   mode='linear', frame_shift=frame_shift,
                                   bottom=False, labelbottom=False)
  
                  plt.subplot(4, 1, 2)
                  plt.title('Noisy speech [ref={}ch]'.format(ref_ch))
                  plot_spectrogram(plt, feat[:, ref_ch].T, fs=args.fs,
                                   mode='db', frame_shift=frame_shift,
                                   bottom=False, labelbottom=False)
  
                  plt.subplot(4, 1, 3)
                  plt.title('Masked speech [ref={}ch]'.format(ref_ch))
                  plot_spectrogram(
                      plt, (feat[:, ref_ch] * mas[:, ref_ch]).T,
                      frame_shift=frame_shift,
                      fs=args.fs, mode='db', bottom=False, labelbottom=False)
  
                  plt.subplot(4, 1, 4)
                  plt.title('Enhanced speech')
                  plot_spectrogram(plt, enh.T, fs=args.fs,
                                   mode='db', frame_shift=frame_shift)
  
                  plt.savefig(os.path.join(args.image_dir, name + '.png'))
                  plt.clf()
  
              # Write enhanced wave files
              if enh_writer is not None:
                  if istft is not None:
                      enh = istft(enh)
                  else:
                      enh = enh
  
                  if args.keep_length:
                      if len(org_feats[idx]) < len(enh):
                          # Truncate the frames added by stft padding
                          enh = enh[:len(org_feats[idx])]
                      elif len(org_feats) > len(enh):
                          padwidth = [(0, (len(org_feats[idx]) - len(enh)))] \
                              + [(0, 0)] * (enh.ndim - 1)
                          enh = np.pad(enh, padwidth, mode='constant')
  
                  if args.enh_filetype in ('sound', 'sound.hdf5'):
                      enh_writer[name] = (args.fs, enh)
                  else:
                      # Hint: To dump stft_signal, mask or etc,
                      # enh_filetype='hdf5' might be convenient.
                      enh_writer[name] = enh
  
              if num_images >= args.num_images and enh_writer is None:
                  logging.info('Breaking the process.')
                  break
  