code: def inference(
      output_dir: str,
      batch_size: int,
      dtype: str,
      fs: int,
      ngpu: int,
      seed: int,
      num_workers: int,
      log_level: Union[int, str],
      data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
      key_file: Optional[str],
      train_config: Optional[str],
      model_file: Optional[str],
      model_tag: Optional[str],
      inference_config: Optional[str],
      allow_variable_data_keys: bool,
      ref_channel: Optional[int],
      enh_s2t_task: bool,
  ):
      assert check_argument_types()
      if batch_size > 1:
          raise NotImplementedError("batch decoding is not implemented")
      if ngpu > 1:
          raise NotImplementedError(
              "only single GPU decoding is supported"
          )
  
      logging.basicConfig(
          level=log_level,
          format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
      )
  
      if ngpu >= 1:
          device = "cuda"
      else:
          device = "cpu"
  
      # 1. Set random-seed
      set_all_random_seed(seed)
  
      # 2. Build separate_speech
      separate_speech_kwargs = dict(
          train_config=train_config,
          model_file=model_file,
          inference_config=inference_config,
          ref_channel=ref_channel,
          device=device,
          dtype=dtype,
          enh_s2t_task=enh_s2t_task,
      )
      separate_speech = SeparateSpeechStreaming.from_pretrained(
          model_tag=model_tag,
          **separate_speech_kwargs,
      )
  
      # 3. Build data-iterator
      loader = EnhancementTask.build_streaming_iterator(
          data_path_and_name_and_type,
          dtype=dtype,
          batch_size=batch_size,
          key_file=key_file,
          num_workers=num_workers,
          preprocess_fn=EnhancementTask.build_preprocess_fn(
              separate_speech.enh_train_args, False
          ),
          collate_fn=EnhancementTask.build_collate_fn(
              separate_speech.enh_train_args, False
          ),
          allow_variable_data_keys=allow_variable_data_keys,
          inference=True,
      )
  
      # 4. Start dataset for-loop
      output_dir = Path(output_dir).expanduser().resolve()
      writers = []
      for i in range(separate_speech.num_spk):
          writers.append(
              SoundScpWriter(
                  f"{output_dir}/wavs/{i + 1}",
                  f"{output_dir}/spk{i + 1}.scp",
              )
          )
  
      import tqdm
  
      for i, (keys, batch) in tqdm.tqdm(enumerate(loader)):
          logging.info(f"[{i}] Enhancing {keys}")
          assert isinstance(batch, dict), type(batch)
          assert all(isinstance(s, str) for s in keys), keys
          _bs = len(next(iter(batch.values())))
          assert len(keys) == _bs, f"{len(keys)} != {_bs}"
          batch = {
              k: v
              for k, v in batch.items()
              if not k.endswith("_lengths")
          }
  
          speech = batch["speech_mix"]
          lengths = speech.new_full(
              [batch_size], dtype=torch.long, fill_value=speech.size(1)
          )
  
          # split continuous speech into small chunks to simulate streaming
          speech_sim_chunks = separate_speech.frame(speech)
          output_chunks = [[] for ii in range(separate_speech.num_spk)]
  
          # the main loop for streaming processing
          for chunk in speech_sim_chunks:
              # process a single chunk
              output = separate_speech(chunk, fs=fs)
              for channel in range(separate_speech.num_spk):
                  # append processed chunks to ouput channels
                  output_chunks[channel].append(output[channel])
  
          # reset separator states after processing
          separate_speech.reset()
  
          # merge chunks
          waves = [
              separate_speech.merge(chunks, lengths)
              for chunks in output_chunks
          ]
  
          waves = [
              (w / abs(w).max(dim=1, keepdim=True)[0] * 0.9)
              .cpu()
              .numpy()
              for w in waves
          ]
  
          for spk, w in enumerate(waves):
              for b in range(batch_size):
                  writers[spk][keys[b]] = fs, w[b]
  
      for writer in writers:
          writer.close()
  
imports: import argparse
  import logging
  import math
  import sys
  from itertools import chain
  from pathlib import Path
  from typing import Any, List, Optional, Sequence, Tuple, Union
  import humanfriendly
  import numpy
  import torch
  import torch_complex
  import yaml
  from typeguard import check_argument_types
  from espnet2.bin.enh_inference import build_model_from_args_and_file, get_train_config, recursive_dict_update
  from espnet2.fileio.sound_scp import SoundScpWriter
  from espnet2.tasks.enh import EnhancementTask
  from espnet2.tasks.enh_s2t import EnhS2TTask
  from espnet2.torch_utils.device_funcs import to_device
  from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet2.utils import config_argparse
  from espnet2.utils.types import str2bool, str2triple_str, str_or_none
  from espnet.utils.cli_utils import get_commandline_args
label_desc: Use image node_id 2 for calling a global function with example usage: inference(output_dir, batch_size, dtype, fs, ngpu, seed, num_workers, log_level, data_path_and_name_and_type, key_file, train_config, model_file, model_tag, inference_config, allow_variable_data_keys, ref_channel, enh_s2t_task) without return types