utils.py

import os
import glob
import re
import sys
import argparse
import logging
import json
import subprocess

# import librosa
import numpy as np
import torchaudio
from scipy.io.wavfile import read
import torch
import torchvision
from torch.nn import functional as F
from commons import sequence_mask
from hubert import hubert_model
MATPLOTLIB_FLAG = False

logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging

f0_bin = 256
f0_max = 1100.0
f0_min = 50.0
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)

project_root_path = os.path.dirname(os.path.realpath(__file__))
hubert_path = os.path.join(project_root_path, "hubert/hubert-soft-0d54a1f4.pt")

def f0_to_coarse(f0):
  is_torch = isinstance(f0, torch.Tensor)
  f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
  f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1

  f0_mel[f0_mel <= 1] = 1
  f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
  f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
  assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
  return f0_coarse


def get_hubert_model(rank=None):
  hubert_soft = hubert_model.hubert_soft(hubert_path)
  if rank is not None:
    hubert_soft = hubert_soft.cuda(rank)
  return hubert_soft

def get_hubert_content(hmodel, y=None, path=None):
  if path is not None:
    source, sr = torchaudio.load(path)
    source = torchaudio.functional.resample(source, sr, 16000)
    if len(source.shape) == 2 and source.shape[1] >= 2:
      source = torch.mean(source, dim=0).unsqueeze(0)
  else:
    source = y
  source = source.unsqueeze(0)
  with torch.inference_mode():
    units = hmodel.units(source)
    return units.transpose(1,2)


def get_content(cmodel, y):
    with torch.no_grad():
        c = cmodel.extract_features(y.squeeze(1))[0]
    c = c.transpose(1, 2)
    return c



def transform(mel, height): # 68-92
    #r = np.random.random()
    #rate = r * 0.3 + 0.85 # 0.85-1.15
    #height = int(mel.size(-2) * rate)
    tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
    if height >= mel.size(-2):
        return tgt[:, :mel.size(-2), :]
    else:
        silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
        silence += torch.randn_like(silence) / 10
        return torch.cat((tgt, silence), 1)


def stretch(mel, width): # 0.5-2
    return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))


def keep_fixed_number_of_files(dir_path, num_files_to_keep):
  """

  Args:
    dir_path: The directory comprise target files
    num_files_to_keep: The number of file that was the latest generated files
  """
  def remove_old_checkpoints(paths):
      all_files = glob.glob(os.path.join(dir_path, paths))
      all_files.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
      # all_files.sort(key=lambda x: os.path.getmtime(x), reverse=True)
      files_to_delete = all_files[0:num_files_to_keep   ]
      for file_path in files_to_delete:
        os.remove(file_path)
        logger.warn("The file: {} has been deleted".format(file_path))
  remove_old_checkpoints('D_*.pth')
  remove_old_checkpoints('G_*.pth')


def load_checkpoint(checkpoint_path, model, optimizer=None):
  assert os.path.isfile(checkpoint_path)
  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
  iteration = checkpoint_dict['iteration']
  learning_rate = checkpoint_dict['learning_rate']
  if iteration is None:
    iteration = 1
  if learning_rate is None:
    learning_rate = 0.0002
  if optimizer is not None and checkpoint_dict['optimizer'] is not None:
    optimizer.load_state_dict(checkpoint_dict['optimizer'])
  saved_state_dict = checkpoint_dict['model']
  if hasattr(model, 'module'):
    state_dict = model.module.state_dict()
  else:
    state_dict = model.state_dict()
  new_state_dict= {}
  for k, v in state_dict.items():
    try:
      new_state_dict[k] = saved_state_dict[k]
    except:
      logger.info("%s is not in the checkpoint" % k)
      new_state_dict[k] = v
  if hasattr(model, 'module'):
    model.module.load_state_dict(new_state_dict)
  else:
    model.load_state_dict(new_state_dict)
  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
    checkpoint_path, iteration))
  return model, optimizer, learning_rate, iteration


def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
  logger.info("Saving model and optimizer state at iteration {} to {}".format(
    iteration, checkpoint_path))
  if hasattr(model, 'module'):
    state_dict = model.module.state_dict()
  else:
    state_dict = model.state_dict()
  torch.save({'model': state_dict,
              'iteration': iteration,
              'optimizer': optimizer.state_dict(),
              'learning_rate': learning_rate}, checkpoint_path)
  # clean_ckpt = False
  # if clean_ckpt:
  #   clean_checkpoints(path_to_models='logs/32k/', n_ckpts_to_keep=3, sort_by_time=True)

def clean_checkpoints(path_to_models='logs/48k/', n_ckpts_to_keep=2, sort_by_time=True):
  """Freeing up space by deleting saved ckpts

  Arguments:
  path_to_models    --  Path to the model directory
  n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
  sort_by_time      --  True -> chronologically delete ckpts
                        False -> lexicographically delete ckpts
  """
  ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
  name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
  time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
  sort_key = time_key if sort_by_time else name_key
  x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
  to_del = [os.path.join(path_to_models, fn) for fn in
            (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
  del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
  del_routine = lambda x: [os.remove(x), del_info(x)]
  rs = [del_routine(fn) for fn in to_del]

def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
  for k, v in scalars.items():
    writer.add_scalar(k, v, global_step)
  for k, v in histograms.items():
    writer.add_histogram(k, v, global_step)
  for k, v in images.items():
    writer.add_image(k, v, global_step, dataformats='HWC')
  for k, v in audios.items():
    writer.add_audio(k, v, global_step, audio_sampling_rate)


def latest_checkpoint_path(dir_path, regex="G_*.pth"):
  f_list = glob.glob(os.path.join(dir_path, regex))
  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
  x = f_list[-1]
  print(x)
  return x


def plot_spectrogram_to_numpy(spectrogram):
  global MATPLOTLIB_FLAG
  if not MATPLOTLIB_FLAG:
    import matplotlib
    matplotlib.use("Agg")
    MATPLOTLIB_FLAG = True
    mpl_logger = logging.getLogger('matplotlib')
    mpl_logger.setLevel(logging.WARNING)
  import matplotlib.pylab as plt
  import numpy as np

  fig, ax = plt.subplots(figsize=(10,2))
  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
                  interpolation='none')
  plt.colorbar(im, ax=ax)
  plt.xlabel("Frames")
  plt.ylabel("Channels")
  plt.tight_layout()

  fig.canvas.draw()
  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
  plt.close()
  return data


def plot_alignment_to_numpy(alignment, info=None):
  global MATPLOTLIB_FLAG
  if not MATPLOTLIB_FLAG:
    import matplotlib
    matplotlib.use("Agg")
    MATPLOTLIB_FLAG = True
    mpl_logger = logging.getLogger('matplotlib')
    mpl_logger.setLevel(logging.WARNING)
  import matplotlib.pylab as plt
  import numpy as np

  fig, ax = plt.subplots(figsize=(6, 4))
  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
                  interpolation='none')
  fig.colorbar(im, ax=ax)
  xlabel = 'Decoder timestep'
  if info is not None:
      xlabel += '\n\n' + info
  plt.xlabel(xlabel)
  plt.ylabel('Encoder timestep')
  plt.tight_layout()

  fig.canvas.draw()
  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
  plt.close()
  return data


def load_wav_to_torch(full_path):
  sampling_rate, data = read(full_path)
  return torch.FloatTensor(data.astype(np.float32)), sampling_rate


def load_filepaths_and_text(filename, split="|"):
  with open(filename, encoding='utf-8') as f:
    filepaths_and_text = [line.strip().split(split) for line in f]
  return filepaths_and_text


def get_hparams(init=True):
  parser = argparse.ArgumentParser()
  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
                      help='JSON file for configuration')
  parser.add_argument('-m', '--model', type=str, required=True,
                      help='Model name')

  args = parser.parse_args()
  model_dir = os.path.join("./logs", args.model)

  if not os.path.exists(model_dir):
    os.makedirs(model_dir)

  config_path = args.config
  config_save_path = os.path.join(model_dir, "config.json")
  if init:
    with open(config_path, "r") as f:
      data = f.read()
    with open(config_save_path, "w") as f:
      f.write(data)
  else:
    with open(config_save_path, "r") as f:
      data = f.read()
  config = json.loads(data)

  hparams = HParams(**config)
  hparams.model_dir = model_dir
  return hparams


def get_hparams_from_dir(model_dir):
  config_save_path = os.path.join(model_dir, "config.json")
  with open(config_save_path, "r") as f:
    data = f.read()
  config = json.loads(data)

  hparams =HParams(**config)
  hparams.model_dir = model_dir
  return hparams


def get_hparams_from_file(config_path):
  with open(config_path, "r") as f:
    data = f.read()
  config = json.loads(data)

  hparams =HParams(**config)
  return hparams


def check_git_hash(model_dir):
  source_dir = os.path.dirname(os.path.realpath(__file__))
  if not os.path.exists(os.path.join(source_dir, ".git")):
    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
      source_dir
    ))
    return

  cur_hash = subprocess.getoutput("git rev-parse HEAD")

  path = os.path.join(model_dir, "githash")
  if os.path.exists(path):
    saved_hash = open(path).read()
    if saved_hash != cur_hash:
      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
        saved_hash[:8], cur_hash[:8]))
  else:
    open(path, "w").write(cur_hash)


def get_logger(model_dir, filename="train.log"):
  global logger
  logger = logging.getLogger(os.path.basename(model_dir))
  logger.setLevel(logging.DEBUG)

  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
  if not os.path.exists(model_dir):
    os.makedirs(model_dir)
  h = logging.FileHandler(os.path.join(model_dir, filename))
  h.setLevel(logging.DEBUG)
  h.setFormatter(formatter)
  logger.addHandler(h)
  return logger


class HParams():
  def __init__(self, **kwargs):
    for k, v in kwargs.items():
      if type(v) == dict:
        v = HParams(**v)
      self[k] = v

  def keys(self):
    return self.__dict__.keys()

  def items(self):
    return self.__dict__.items()

  def values(self):
    return self.__dict__.values()

  def __len__(self):
    return len(self.__dict__)

  def __getitem__(self, key):
    return getattr(self, key)

  def __setitem__(self, key, value):
    return setattr(self, key, value)

  def __contains__(self, key):
    return key in self.__dict__

  def __repr__(self):
    return self.__dict__.__repr__()