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RVC-Boss
298
GPT_SoVITS/utils.py
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298
GPT_SoVITS/utils.py
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import os
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import glob
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import sys
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import argparse
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import logging
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import json
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import subprocess
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import traceback
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import librosa
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import numpy as np
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from scipy.io.wavfile import read
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import torch
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import logging
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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.DEBUG)
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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, (saved_state_dict[k].shape, v.shape)
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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("Loaded checkpoint '{}' (iteration {})".format(
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checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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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(
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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({'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}, checkpoint_path)
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def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
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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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import numpy as np
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fig, ax = plt.subplots(figsize=(10, 2))
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im = ax.imshow(spectrogram, aspect="auto", origin="lower",
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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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import numpy as np
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fig, ax = plt.subplots(figsize=(6, 4))
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im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
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interpolation='none')
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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('-c', '--config', type=str, default="./configs/s2.json",help='JSON file for configuration')
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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('-rs', '--resume_step', type=int, required=False,default=None,help='resume step')
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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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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([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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to_del = [os.path.join(path_to_models, fn) for fn in
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(x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
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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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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__))
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if not os.path.exists(os.path.join(source_dir, ".git")):
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logger.warn("{} 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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return
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cur_hash = subprocess.getoutput("git rev-parse HEAD")
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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], cur_hash[:8]))
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else:
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open(path, "w").write(cur_hash)
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def get_logger(model_dir, filename="train.log"):
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global logger
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logger = logging.getLogger(os.path.basename(model_dir))
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logger.setLevel(logging.DEBUG)
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formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
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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.DEBUG)
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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():
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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if type(v) == dict:
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v = HParams(**v)
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self[k] = v
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def keys(self):
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return self.__dict__.keys()
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def items(self):
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return self.__dict__.items()
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def values(self):
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return self.__dict__.values()
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def __len__(self):
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return len(self.__dict__)
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def __getitem__(self, key):
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return getattr(self, key)
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def __setitem__(self, key, value):
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return setattr(self, key, value)
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def __contains__(self, key):
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return key in self.__dict__
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def __repr__(self):
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return self.__dict__.__repr__()
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if __name__ == '__main__':
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print(load_wav_to_torch('/home/fish/wenetspeech/dataset_vq/Y0000022499_wHFSeHEx9CM/S00261.flac'))
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