text: #!/usr/bin/env python3
  
  """
  This script is used for multi-speaker speech recognition.
  
  Copyright 2017 Johns Hopkins University (Shinji Watanabe)
   Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
  """
  import json
  import logging
  import os
  
  # chainer related
  from chainer import training
  from chainer.training import extensions
  from itertools import zip_longest as zip_longest
  import numpy as np
  from tensorboardX import SummaryWriter
  import torch
  
  from espnet.asr.asr_mix_utils import add_results_to_json
  from espnet.asr.asr_utils import adadelta_eps_decay
  
  from espnet.asr.asr_utils import CompareValueTrigger
  from espnet.asr.asr_utils import get_model_conf
  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 import CustomEvaluator
  from espnet.asr.pytorch_backend.asr import CustomUpdater
  from espnet.asr.pytorch_backend.asr import load_trained_model
  import espnet.lm.pytorch_backend.extlm as extlm_pytorch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.pytorch_backend.e2e_asr_mix import E2E
  from espnet.nets.pytorch_backend.e2e_asr_mix import pad_list
  import espnet.nets.pytorch_backend.lm.default as lm_pytorch
  from espnet.utils.dataset import ChainerDataLoader
  from espnet.utils.dataset import TransformDataset
  from espnet.utils.deterministic_utils import set_deterministic_pytorch
  from espnet.utils.io_utils import LoadInputsAndTargets
  from espnet.utils.training.batchfy import make_batchset
  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')
  
  
  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):
          """Initialize the converter."""
          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(tuple(str, dict[str, dict[str, Any]]))): The batch to transform.
              device (torch.device): The device to send to.
  
          Returns:
              tuple(torch.Tensor, torch.Tensor, torch.Tensor): Transformed batch.
  
          """
          # batch should be located in list
          assert len(batch) == 1
          xs, ys = batch[0]
          # Convert zip object to list in python 3.x
          ys = list(ys)
  
          # 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 to E2E here
              # because torch.nn.DataParallel 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)
          # TODO(Xuankai): try to make this neat
          if not isinstance(ys[0], np.ndarray):
              ys_pad = [torch.from_numpy(y[0]).long() for y in ys] + [torch.from_numpy(y[1]).long() for y in ys]
              ys_pad = pad_list(ys_pad, self.ignore_id)
              ys_pad = ys_pad.view(2, -1, ys_pad.size(1)).transpose(0, 1).to(device)  # (num_spkrs, B, Tmax)
          else:
              ys_pad = pad_list([torch.from_numpy(y).long() for y in ys], self.ignore_id).to(device)
  
          return xs_pad, ilens, ys_pad
  
  
  def train(args):
      """Train with the given args.
  
      Args:
          args (namespace): The program arguments.
  
      """
      set_deterministic_pytorch(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 = int(valid_json[utts[0]]['input'][0]['shape'][-1])
      odim = int(valid_json[utts[0]]['output'][0]['shape'][-1])
      logging.info('#input dims : ' + str(idim))
      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')
  
      # specify model architecture
      model = E2E(idim, odim, args)
      subsampling_factor = model.subsample[0]
  
      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,
                  getattr(rnnlm_args, "embed_unit", None),  # for backward compatibility
              )
          )
          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, 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
  
      # 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':  # 自定义更新lr
          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
      converter = CustomConverter(subsampling_factor=subsampling_factor, 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=-1)
      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=-1)
  
      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=True, 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
      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
      trainer.extend(extensions.PlotReport(['main/loss', 'validation/main/loss',
                                            'main/loss_ctc', 'validation/main/loss_ctc',
                                            'main/loss_att', 'validation/main/loss_att'],
                                           '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'],
                                           '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
      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.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,
                  getattr(rnnlm_args, "embed_unit", None),  # for backward compatibility
              )
          )
          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)[0][0]
                  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]
                  nbest_hyps = model.recognize_batch(feats, args, train_args.char_list, rnnlm=rnnlm)
  
                  for i, name in enumerate(names):
                      nbest_hyp = [hyp[i] for hyp in nbest_hyps]
                      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'))
  