import os import glob import json import torch import argparse import numpy as np from scipy.io.wavfile import read def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1): assert os.path.isfile(checkpoint_path) checkpoint_dict = torch.load(checkpoint_path, map_location="cpu") 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] if saved_state_dict[k].shape != state_dict[k].shape: print( "shape-%s-mismatch|need-%s|get-%s", k, state_dict[k].shape, saved_state_dict[k].shape, ) raise KeyError except: print("%s is not in the checkpoint", k) new_state_dict[k] = v if hasattr(model, "module"): model.module.load_state_dict(new_state_dict, strict=False) else: model.load_state_dict(new_state_dict, strict=False) iteration = checkpoint_dict["iteration"] learning_rate = checkpoint_dict["learning_rate"] if optimizer is not None and load_opt == 1: optimizer.load_state_dict(checkpoint_dict["optimizer"]) print(f"Loaded checkpoint '{checkpoint_path}' (epoch {iteration})") return model, optimizer, learning_rate, iteration def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): print(f"Saved model '{checkpoint_path}' (epoch {iteration})") 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 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] return x def plot_spectrogram_to_numpy(spectrogram): 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 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(): parser = argparse.ArgumentParser() parser.add_argument( "-se", "--save_every_epoch", type=int, required=True, help="checkpoint save frequency (epoch)", ) parser.add_argument( "-te", "--total_epoch", type=int, required=True, help="total_epoch" ) parser.add_argument( "-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path" ) parser.add_argument( "-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path" ) parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -") parser.add_argument( "-bs", "--batch_size", type=int, required=True, help="batch size" ) parser.add_argument( "-e", "--experiment_dir", type=str, required=True, help="experiment dir" ) parser.add_argument( "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k" ) parser.add_argument( "-sw", "--save_every_weights", type=str, default="0", help="save the extracted model in weights directory when saving checkpoints", ) parser.add_argument( "-v", "--version", type=str, required=True, help="model version" ) parser.add_argument( "-f0", "--if_f0", type=int, required=True, help="use f0 as one of the inputs of the model, 1 or 0", ) parser.add_argument( "-l", "--if_latest", type=int, required=True, help="if only save the latest G/D pth file, 1 or 0", ) parser.add_argument( "-c", "--if_cache_data_in_gpu", type=int, required=True, help="if caching the dataset in GPU memory, 1 or 0", ) parser.add_argument( "-od", "--overtraining_detector", type=int, required=True, help="Detect overtraining or not, 1 or 0", ) parser.add_argument( "-ot", "--overtraining_threshold", type=int, default=50, help="overtraining_threshold", ) args = parser.parse_args() name = args.experiment_dir experiment_dir = os.path.join("./logs", args.experiment_dir) config_save_path = os.path.join(experiment_dir, "config.json") with open(config_save_path, "r") as f: config = json.load(f) hparams = HParams(**config) hparams.model_dir = hparams.experiment_dir = experiment_dir hparams.save_every_epoch = args.save_every_epoch hparams.name = name hparams.total_epoch = args.total_epoch hparams.pretrainG = args.pretrainG hparams.pretrainD = args.pretrainD hparams.version = args.version hparams.gpus = args.gpus hparams.train.batch_size = args.batch_size hparams.sample_rate = args.sample_rate hparams.if_f0 = args.if_f0 hparams.if_latest = args.if_latest hparams.save_every_weights = args.save_every_weights hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu hparams.data.training_files = f"{experiment_dir}/filelist.txt" hparams.overtraining_detector = args.overtraining_detector hparams.overtraining_threshold = args.overtraining_threshold return hparams 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__()