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

MATPLOTLIB_FLAG = False

logging.basicConfig(stream=sys.stdout, level=logging.WARN)
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, 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 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 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_content(cmodel, y):
    with torch.no_grad():
        c = cmodel.extract_features(y.squeeze(1))[0]
    c = c.transpose(1, 2)
    return c

def get_f0_predictor(f0_predictor,hop_length,sampling_rate,**kargs):
    if f0_predictor == "pm":
        from modules.F0Predictor.PMF0Predictor import PMF0Predictor
        f0_predictor_object = PMF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
    elif f0_predictor == "crepe":
        from modules.F0Predictor.CrepeF0Predictor import CrepeF0Predictor
        f0_predictor_object = CrepeF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate,device=kargs["device"],threshold=kargs["threshold"])
    elif f0_predictor == "harvest":
        from modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
        f0_predictor_object = HarvestF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
    elif f0_predictor == "dio":
        from modules.F0Predictor.DioF0Predictor import DioF0Predictor
        f0_predictor_object = DioF0Predictor(hop_length=hop_length,sampling_rate=sampling_rate)
    else:
        raise Exception("Unknown f0 predictor")
    return f0_predictor_object

def get_speech_encoder(speech_encoder,device=None,**kargs):
    if speech_encoder == "vec768l12":
        from vencoder.ContentVec768L12 import ContentVec768L12
        speech_encoder_object = ContentVec768L12(device = device)
    elif speech_encoder == "vec256l9":
        from vencoder.ContentVec256L9 import ContentVec256L9
        speech_encoder_object = ContentVec256L9(device = device)
    elif speech_encoder == "vec256l9-onnx":
        from vencoder.ContentVec256L9_Onnx import ContentVec256L9_Onnx
        speech_encoder_object = ContentVec256L9(device = device)
    elif speech_encoder == "vec256l12-onnx":
        from vencoder.ContentVec256L12_Onnx import ContentVec256L12_Onnx
        speech_encoder_object = ContentVec256L9(device = device)
    elif speech_encoder == "vec768l9-onnx":
        from vencoder.ContentVec768L9_Onnx import ContentVec768L9_Onnx
        speech_encoder_object = ContentVec256L9(device = device)
    elif speech_encoder == "vec768l12-onnx":
        from vencoder.ContentVec768L12_Onnx import ContentVec768L12_Onnx
        speech_encoder_object = ContentVec256L9(device = device)
    elif speech_encoder == "hubertsoft-onnx":
        from vencoder.HubertSoft_Onnx import HubertSoft_Onnx
        speech_encoder_object = HubertSoft(device = device)
    elif speech_encoder == "hubertsoft":
        from vencoder.HubertSoft import HubertSoft
        speech_encoder_object = HubertSoft(device = device)
    elif speech_encoder == "whisper-ppg":
        from vencoder.WhisperPPG import WhisperPPG
        speech_encoder_object = WhisperPPG(device = device)
    else:
        raise Exception("Unknown speech encoder")
    return speech_encoder_object 

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/config.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__()

  def get(self,index):
    return self.__dict__.get(index)

class Volume_Extractor:
    def __init__(self, hop_size = 512):
        self.hop_size = hop_size
        
    def extract(self, audio): # audio: 2d tensor array
        if not isinstance(audio,torch.Tensor):
           audio = torch.Tensor(audio)
        n_frames = int(audio.size(-1) // self.hop_size)
        audio2 = audio ** 2
        audio2 = torch.nn.functional.pad(audio2, (int(self.hop_size // 2), int((self.hop_size + 1) // 2)), mode = 'reflect')
        volume = torch.FloatTensor([torch.mean(audio2[:,int(n * self.hop_size) : int((n + 1) * self.hop_size)]) for n in range(n_frames)])
        volume = torch.sqrt(volume)
        return volume