utils.py

import os
import glob
import re
import sys
import argparse
import logging
import json
import subprocess
import warnings
import random
import functools

import librosa
import numpy as np
from scipy.io.wavfile import read
import torch
from torch.nn import functional as F
from modules.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)


# def normalize_f0(f0, random_scale=True):
#     f0_norm = f0.clone()  # create a copy of the input Tensor
#     batch_size, _, frame_length = f0_norm.shape
#     for i in range(batch_size):
#         means = torch.mean(f0_norm[i, 0, :])
#         if random_scale:
#             factor = random.uniform(0.8, 1.2)
#         else:
#             factor = 1
#         f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor
#     return f0_norm
# def normalize_f0(f0, random_scale=True):
#     means = torch.mean(f0[:, 0, :], dim=1, keepdim=True)
#     if random_scale:
#         factor = torch.Tensor(f0.shape[0],1).uniform_(0.8, 1.2).to(f0.device)
#     else:
#         factor = torch.ones(f0.shape[0], 1, 1).to(f0.device)
#     f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
#     return f0_norm

def deprecated(func):
    """This is a decorator which can be used to mark functions
    as deprecated. It will result in a warning being emitted
    when the function is used."""
    @functools.wraps(func)
    def new_func(*args, **kwargs):
        warnings.simplefilter('always', DeprecationWarning)  # turn off filter
        warnings.warn("Call to deprecated function {}.".format(func.__name__),
                      category=DeprecationWarning,
                      stacklevel=2)
        warnings.simplefilter('default', DeprecationWarning)  # reset filter
        return func(*args, **kwargs)
    return new_func

def normalize_f0(f0, x_mask, uv, random_scale=True):
    # calculate means based on x_mask
    uv_sum = torch.sum(uv, dim=1, keepdim=True)
    uv_sum[uv_sum == 0] = 9999
    means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum

    if random_scale:
        factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
    else:
        factor = torch.ones(f0.shape[0], 1).to(f0.device)
    # normalize f0 based on means and factor
    f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
    if torch.isnan(f0_norm).any():
        exit(0)
    return f0_norm * x_mask

def compute_f0_uv_torchcrepe(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512,device=None,cr_threshold=0.05):
    from modules.crepe import CrepePitchExtractor
    x = wav_numpy
    if p_len is None:
        p_len = x.shape[0]//hop_length
    else:
        assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
    
    f0_min = 50
    f0_max = 1100
    F0Creper = CrepePitchExtractor(hop_length=hop_length,f0_min=f0_min,f0_max=f0_max,device=device,threshold=cr_threshold)
    f0,uv = F0Creper(x[None,:].float(),sampling_rate,pad_to=p_len)
    return f0,uv

def plot_data_to_numpy(x, y):
    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))
    plt.plot(x)
    plt.plot(y)
    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 interpolate_f0(f0):

    data = np.reshape(f0, (f0.size, 1))

    vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
    vuv_vector[data > 0.0] = 1.0
    vuv_vector[data <= 0.0] = 0.0

    ip_data = data

    frame_number = data.size
    last_value = 0.0
    for i in range(frame_number):
        if data[i] <= 0.0:
            j = i + 1
            for j in range(i + 1, frame_number):
                if data[j] > 0.0:
                    break
            if j < frame_number - 1:
                if last_value > 0.0:
                    step = (data[j] - data[i - 1]) / float(j - i)
                    for k in range(i, j):
                        ip_data[k] = data[i - 1] + step * (k - i + 1)
                else:
                    for k in range(i, j):
                        ip_data[k] = data[j]
            else:
                for k in range(i, frame_number):
                    ip_data[k] = last_value
        else:
            ip_data[i] = data[i] # this may not be necessary
            last_value = data[i]

    return ip_data[:,0], vuv_vector[:,0]


def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
    import parselmouth
    x = wav_numpy
    if p_len is None:
        p_len = x.shape[0]//hop_length
    else:
        assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
    time_step = hop_length / sampling_rate * 1000
    f0_min = 50
    f0_max = 1100
    f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac(
        time_step=time_step / 1000, voicing_threshold=0.6,
        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']

    pad_size=(p_len - len(f0) + 1) // 2
    if(pad_size>0 or p_len - len(f0) - pad_size>0):
        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
    return f0

def resize_f0(x, target_len):
    source = np.array(x)
    source[source<0.001] = np.nan
    target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
    res = np.nan_to_num(target)
    return res

def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
    import pyworld
    if p_len is None:
        p_len = wav_numpy.shape[0]//hop_length
    f0, t = pyworld.dio(
        wav_numpy.astype(np.double),
        fs=sampling_rate,
        f0_ceil=800,
        frame_period=1000 * hop_length / sampling_rate,
    )
    f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
    for index, pitch in enumerate(f0):
        f0[index] = round(pitch, 1)
    return resize_f0(f0, p_len)

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).int() 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():
  vec_path = "hubert/checkpoint_best_legacy_500.pt"
  print("load model(s) from {}".format(vec_path))
  from fairseq import checkpoint_utils
  models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
    [vec_path],
    suffix="",
  )
  model = models[0]
  model.eval()
  return model

def get_hubert_content(hmodel, wav_16k_tensor):
  feats = wav_16k_tensor
  if feats.dim() == 2:  # double channels
    feats = feats.mean(-1)
  assert feats.dim() == 1, feats.dim()
  feats = feats.view(1, -1)
  padding_mask = torch.BoolTensor(feats.shape).fill_(False)
  inputs = {
    "source": feats.to(wav_16k_tensor.device),
    "padding_mask": padding_mask.to(wav_16k_tensor.device),
    "output_layer": 9,  # layer 9
  }
  with torch.no_grad():
    logits = hmodel.extract_features(**inputs)
    feats = hmodel.final_proj(logits[0])
  return feats.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 load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
    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 optimizer is not None and not skip_optimizer 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:
            # assert "dec" in k or "disc" in k
            # print("load", k)
            new_state_dict[k] = saved_state_dict[k]
            assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
        except:
            print("error, %s is not in the checkpoint" % k)
            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)
    print("load ")
    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)

def clean_checkpoints(path_to_models='logs/44k/', 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


def repeat_expand_2d(content, target_len):
    # content : [h, t]

    src_len = content.shape[-1]
    target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
    temp = torch.arange(src_len+1) * target_len / src_len
    current_pos = 0
    for i in range(target_len):
        if i < temp[current_pos+1]:
            target[:, i] = content[:, current_pos]
        else:
            current_pos += 1
            target[:, i] = content[:, current_pos]

    return target


def mix_model(model_paths,mix_rate,mode):
  mix_rate = torch.FloatTensor(mix_rate)/100
  model_tem = torch.load(model_paths[0])
  models = [torch.load(path)["model"] for path in model_paths]
  if mode == 0:
     mix_rate = F.softmax(mix_rate,dim=0)
  for k in model_tem["model"].keys():
     model_tem["model"][k] = torch.zeros_like(model_tem["model"][k])
     for i,model in enumerate(models):
        model_tem["model"][k] += model[k]*mix_rate[i]
  torch.save(model_tem,os.path.join(os.path.curdir,"output.pth"))
  return os.path.join(os.path.curdir,"output.pth")
  
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__()