text: from datetime import datetime
  from functools import partial
  from multiprocessing import Pool
  from pathlib import Path
  
  import numpy as np
  from tqdm import tqdm
  
  from encoder import audio
  from encoder.config import librispeech_datasets, anglophone_nationalites
  from encoder.params_data import *
  
  
  _AUDIO_EXTENSIONS = ("wav", "flac", "m4a", "mp3")
  
  class DatasetLog:
      """
      Registers metadata about the dataset in a text file.
      """
      def __init__(self, root, name):
          self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
          self.sample_data = dict()
  
          start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
          self.write_line("Creating dataset %s on %s" % (name, start_time))
          self.write_line("-----")
          self._log_params()
  
      def _log_params(self):
          from encoder import params_data
          self.write_line("Parameter values:")
          for param_name in (p for p in dir(params_data) if not p.startswith("__")):
              value = getattr(params_data, param_name)
              self.write_line("\t%s: %s" % (param_name, value))
          self.write_line("-----")
  
      def write_line(self, line):
          self.text_file.write("%s\n" % line)
  
      def add_sample(self, **kwargs):
          for param_name, value in kwargs.items():
              if not param_name in self.sample_data:
                  self.sample_data[param_name] = []
              self.sample_data[param_name].append(value)
  
      def finalize(self):
          self.write_line("Statistics:")
          for param_name, values in self.sample_data.items():
              self.write_line("\t%s:" % param_name)
              self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
              self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
          self.write_line("-----")
          end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
          self.write_line("Finished on %s" % end_time)
          self.text_file.close()
  
  
  def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
      dataset_root = datasets_root.joinpath(dataset_name)
      if not dataset_root.exists():
          print("Couldn\'t find %s, skipping this dataset." % dataset_root)
          return None, None
      return dataset_root, DatasetLog(out_dir, dataset_name)
  
  
  def _preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, skip_existing: bool):
      # Give a name to the speaker that includes its dataset
      speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
  
      # Create an output directory with that name, as well as a txt file containing a
      # reference to each source file.
      speaker_out_dir = out_dir.joinpath(speaker_name)
      speaker_out_dir.mkdir(exist_ok=True)
      sources_fpath = speaker_out_dir.joinpath("_sources.txt")
  
      # There's a possibility that the preprocessing was interrupted earlier, check if
      # there already is a sources file.
      if sources_fpath.exists():
          try:
              with sources_fpath.open("r") as sources_file:
                  existing_fnames = {line.split(",")[0] for line in sources_file}
          except:
              existing_fnames = {}
      else:
          existing_fnames = {}
  
      # Gather all audio files for that speaker recursively
      sources_file = sources_fpath.open("a" if skip_existing else "w")
      audio_durs = []
      for extension in _AUDIO_EXTENSIONS:
          for in_fpath in speaker_dir.glob("**/*.%s" % extension):
              # Check if the target output file already exists
              out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
              out_fname = out_fname.replace(".%s" % extension, ".npy")
              if skip_existing and out_fname in existing_fnames:
                  continue
  
              # Load and preprocess the waveform
              wav = audio.preprocess_wav(in_fpath)
              if len(wav) == 0:
                  continue
  
              # Create the mel spectrogram, discard those that are too short
              frames = audio.wav_to_mel_spectrogram(wav)
              if len(frames) < partials_n_frames:
                  continue
  
              out_fpath = speaker_out_dir.joinpath(out_fname)
              np.save(out_fpath, frames)
              sources_file.write("%s,%s\n" % (out_fname, in_fpath))
              audio_durs.append(len(wav) / sampling_rate)
  
      sources_file.close()
  
      return audio_durs
  
  
  def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger):
      print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
  
      # Process the utterances for each speaker
      work_fn = partial(_preprocess_speaker, datasets_root=datasets_root, out_dir=out_dir, skip_existing=skip_existing)
      with Pool(4) as pool:
          tasks = pool.imap(work_fn, speaker_dirs)
          for sample_durs in tqdm(tasks, dataset_name, len(speaker_dirs), unit="speakers"):
              for sample_dur in sample_durs:
                  logger.add_sample(duration=sample_dur)
  
      logger.finalize()
      print("Done preprocessing %s.\n" % dataset_name)
  
  
  def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
      for dataset_name in librispeech_datasets["train"]["other"]:
          # Initialize the preprocessing
          dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
          if not dataset_root:
              return
  
          # Preprocess all speakers
          speaker_dirs = list(dataset_root.glob("*"))
          _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger)
  
  
  def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
      # Initialize the preprocessing
      dataset_name = "VoxCeleb1"
      dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
      if not dataset_root:
          return
  
      # Get the contents of the meta file
      with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
          metadata = [line.split("\t") for line in metafile][1:]
  
      # Select the ID and the nationality, filter out non-anglophone speakers
      nationalities = {line[0]: line[3] for line in metadata}
      keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if
                          nationality.lower() in anglophone_nationalites]
      print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." %
            (len(keep_speaker_ids), len(nationalities)))
  
      # Get the speaker directories for anglophone speakers only
      speaker_dirs = dataset_root.joinpath("wav").glob("*")
      speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
                      speaker_dir.name in keep_speaker_ids]
      print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." %
            (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))
  
      # Preprocess all speakers
      _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger)
  
  
  def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
      # Initialize the preprocessing
      dataset_name = "VoxCeleb2"
      dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
      if not dataset_root:
          return
  
      # Get the speaker directories
      # Preprocess all speakers
      speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
      _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger)
  