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| 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" | |
| ) | |
| ) | |