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import argparse
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import glob
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import json
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import logging
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import os
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import subprocess
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import sys
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import traceback
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import librosa
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import numpy as np
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import torch
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logging.getLogger("numba").setLevel(logging.ERROR)
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logging.getLogger("matplotlib").setLevel(logging.ERROR)
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MATPLOTLIB_FLAG = False
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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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logger = logging
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def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
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iteration = checkpoint_dict["iteration"]
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learning_rate = checkpoint_dict["learning_rate"]
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if optimizer is not None and not skip_optimizer and checkpoint_dict["optimizer"] is not None:
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optimizer.load_state_dict(checkpoint_dict["optimizer"])
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saved_state_dict = checkpoint_dict["model"]
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if hasattr(model, "module"):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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new_state_dict = {}
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for k, v in state_dict.items():
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try:
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# assert "quantizer" not in k
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# print("load", k)
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new_state_dict[k] = saved_state_dict[k]
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assert saved_state_dict[k].shape == v.shape, (
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saved_state_dict[k].shape,
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v.shape,
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)
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except:
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traceback.print_exc()
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print("error, %s is not in the checkpoint" % k) # shape不对也会，比如text_embedding当cleaner修改时
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new_state_dict[k] = v
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if hasattr(model, "module"):
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model.module.load_state_dict(new_state_dict)
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else:
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model.load_state_dict(new_state_dict)
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print("load ")
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logger.info(
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"Loaded checkpoint '{}' (iteration {})".format(
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checkpoint_path,
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iteration,
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)
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)
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return model, optimizer, learning_rate, iteration
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import shutil
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from time import time as ttime
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def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path
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dir = os.path.dirname(path)
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name = os.path.basename(path)
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tmp_path = "%s.pth" % (ttime())
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torch.save(fea, tmp_path)
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shutil.move(tmp_path, "%s/%s" % (dir, name))
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def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
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logger.info("Saving model and optimizer state at iteration {} to {}".format(iteration, checkpoint_path))
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if hasattr(model, "module"):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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# torch.save(
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my_save(
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{
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"model": state_dict,
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"iteration": iteration,
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"optimizer": optimizer.state_dict(),
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"learning_rate": learning_rate,
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},
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checkpoint_path,
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)
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def summarize(
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writer,
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global_step,
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scalars={},
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histograms={},
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images={},
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audios={},
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audio_sampling_rate=22050,
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):
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for k, v in scalars.items():
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writer.add_scalar(k, v, global_step)
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for k, v in histograms.items():
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writer.add_histogram(k, v, global_step)
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for k, v in images.items():
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writer.add_image(k, v, global_step, dataformats="HWC")
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for k, v in audios.items():
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writer.add_audio(k, v, global_step, audio_sampling_rate)
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def latest_checkpoint_path(dir_path, regex="G_*.pth"):
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f_list = glob.glob(os.path.join(dir_path, regex))
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f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
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x = f_list[-1]
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print(x)
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return x
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def plot_spectrogram_to_numpy(spectrogram):
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger("matplotlib")
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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fig, ax = plt.subplots(figsize=(10, 2))
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im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
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plt.colorbar(im, ax=ax)
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plt.xlabel("Frames")
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plt.ylabel("Channels")
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plt.tight_layout()
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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def plot_alignment_to_numpy(alignment, info=None):
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global MATPLOTLIB_FLAG
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if not MATPLOTLIB_FLAG:
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import matplotlib
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matplotlib.use("Agg")
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MATPLOTLIB_FLAG = True
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mpl_logger = logging.getLogger("matplotlib")
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mpl_logger.setLevel(logging.WARNING)
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import matplotlib.pylab as plt
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fig, ax = plt.subplots(figsize=(6, 4))
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im = ax.imshow(
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alignment.transpose(),
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aspect="auto",
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origin="lower",
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interpolation="none",
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)
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fig.colorbar(im, ax=ax)
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xlabel = "Decoder timestep"
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if info is not None:
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xlabel += "\n\n" + info
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plt.xlabel(xlabel)
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plt.ylabel("Encoder timestep")
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plt.tight_layout()
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fig.canvas.draw()
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data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
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data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
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plt.close()
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return data
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def load_wav_to_torch(full_path):
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data, sampling_rate = librosa.load(full_path, sr=None)
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return torch.FloatTensor(data), sampling_rate
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def load_filepaths_and_text(filename, split="|"):
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with open(filename, encoding="utf-8") as f:
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filepaths_and_text = [line.strip().split(split) for line in f]
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return filepaths_and_text
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def get_hparams(init=True, stage=1):
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-c",
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"--config",
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type=str,
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default="./configs/s2.json",
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help="JSON file for configuration",
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)
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parser.add_argument("-p", "--pretrain", type=str, required=False, default=None, help="pretrain dir")
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parser.add_argument(
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"-rs",
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"--resume_step",
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type=int,
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required=False,
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default=None,
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help="resume step",
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)
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# parser.add_argument('-e', '--exp_dir', type=str, required=False,default=None,help='experiment directory')
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# parser.add_argument('-g', '--pretrained_s2G', type=str, required=False,default=None,help='pretrained sovits gererator weights')
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# parser.add_argument('-d', '--pretrained_s2D', type=str, required=False,default=None,help='pretrained sovits discriminator weights')
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args = parser.parse_args()
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config_path = args.config
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with open(config_path, "r") as f:
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data = f.read()
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config = json.loads(data)
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hparams = HParams(**config)
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hparams.pretrain = args.pretrain
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hparams.resume_step = args.resume_step
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# hparams.data.exp_dir = args.exp_dir
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if stage == 1:
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model_dir = hparams.s1_ckpt_dir
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else:
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model_dir = hparams.s2_ckpt_dir
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config_save_path = os.path.join(model_dir, "config.json")
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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with open(config_save_path, "w") as f:
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f.write(data)
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return hparams
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def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
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"""Freeing up space by deleting saved ckpts
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|
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|
Arguments:
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path_to_models -- Path to the model directory
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n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
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sort_by_time -- True -> chronologically delete ckpts
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False -> lexicographically delete ckpts
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"""
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import re
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ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
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name_key = lambda _f: int(re.compile("._(\d+)\.pth").match(_f).group(1))
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time_key = lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))
|
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|
sort_key = time_key if sort_by_time else name_key
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x_sorted = lambda _x: sorted(
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[f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
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key=sort_key,
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)
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to_del = [
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os.path.join(path_to_models, fn) for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
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]
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del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
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del_routine = lambda x: [os.remove(x), del_info(x)]
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rs = [del_routine(fn) for fn in to_del]
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|
|
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|
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def get_hparams_from_dir(model_dir):
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|
|
config_save_path = os.path.join(model_dir, "config.json")
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with open(config_save_path, "r") as f:
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data = f.read()
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config = json.loads(data)
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hparams = HParams(**config)
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hparams.model_dir = model_dir
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return hparams
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def get_hparams_from_file(config_path):
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with open(config_path, "r") as f:
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data = f.read()
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config = json.loads(data)
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hparams = HParams(**config)
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return hparams
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|
|
|
|
|
|
def check_git_hash(model_dir):
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|
source_dir = os.path.dirname(os.path.realpath(__file__))
|
|
|
if not os.path.exists(os.path.join(source_dir, ".git")):
|
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|
logger.warn(
|
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|
"{} is not a git repository, therefore hash value comparison will be ignored.".format(
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source_dir,
|
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|
)
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|
)
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return
|
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|
|
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cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
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|
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path = os.path.join(model_dir, "githash")
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if os.path.exists(path):
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saved_hash = open(path).read()
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if saved_hash != cur_hash:
|
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|
logger.warn(
|
|
|
"git hash values are different. {}(saved) != {}(current)".format(
|
|
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saved_hash[:8],
|
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|
cur_hash[:8],
|
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|
)
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|
|
)
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else:
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open(path, "w").write(cur_hash)
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|
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|
|
|
|
|
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def get_logger(model_dir, filename="train.log"):
|
|
|
global logger
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|
logger = logging.getLogger(os.path.basename(model_dir))
|
|
|
logger.setLevel(logging.INFO)
|
|
|
|
|
|
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
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|
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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|
h = logging.FileHandler(os.path.join(model_dir, filename))
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|
h.setLevel(logging.INFO)
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h.setFormatter(formatter)
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logger.addHandler(h)
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return logger
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|
|
|
|
|
|
|
|
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",
|
|
|
)
|
|
|
)
|