import os import glob import sys import argparse import logging import json import subprocess import traceback import librosa import numpy as np from scipy.io.wavfile import read import torch import logging logging.getLogger('numba').setLevel(logging.ERROR) logging.getLogger('matplotlib').setLevel(logging.ERROR) MATPLOTLIB_FLAG = False logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) logger = logging 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 "quantizer" not 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: traceback.print_exc() print("error, %s is not in the checkpoint" % k)#shape不对也会，比如text_embedding当cleaner修改时 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 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): data, sampling_rate = librosa.load(full_path, sr=None) return torch.FloatTensor(data), 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, stage=1): parser = argparse.ArgumentParser() parser.add_argument('-c', '--config', type=str, default="./configs/s2.json",help='JSON file for configuration') parser.add_argument('-p', '--pretrain', type=str, required=False,default=None,help='pretrain dir') parser.add_argument('-rs', '--resume_step', type=int, required=False,default=None,help='resume step') # parser.add_argument('-e', '--exp_dir', type=str, required=False,default=None,help='experiment directory') # parser.add_argument('-g', '--pretrained_s2G', type=str, required=False,default=None,help='pretrained sovits gererator weights') # parser.add_argument('-d', '--pretrained_s2D', type=str, required=False,default=None,help='pretrained sovits discriminator weights') args = parser.parse_args() config_path = args.config with open(config_path, "r") as f: data = f.read() config = json.loads(data) hparams = HParams(**config) hparams.pretrain = args.pretrain hparams.resume_step = args.resume_step # hparams.data.exp_dir = args.exp_dir if stage ==1: model_dir = hparams.s1_ckpt_dir else: model_dir = hparams.s2_ckpt_dir config_save_path = os.path.join(model_dir, "config.json") if not os.path.exists(model_dir): os.makedirs(model_dir) with open(config_save_path, "w") as f: f.write(data) return hparams 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 """ import re 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 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__() if __name__ == '__main__': print(load_wav_to_torch('/home/fish/wenetspeech/dataset_vq/Y0000022499_wHFSeHEx9CM/S00261.flac'))