mirror of
https://github.com/svc-develop-team/so-vits-svc.git
synced 2025-01-09 04:27:31 +08:00
356 lines
11 KiB
Python
356 lines
11 KiB
Python
import os
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import glob
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import re
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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 librosa
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import numpy as np
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import torchaudio
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from scipy.io.wavfile import read
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import torch
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import torchvision
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from torch.nn import functional as F
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from commons import sequence_mask
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from hubert import hubert_model
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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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f0_bin = 256
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f0_max = 1100.0
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f0_min = 50.0
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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def f0_to_coarse(f0):
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is_torch = isinstance(f0, torch.Tensor)
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f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
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f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
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assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
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return f0_coarse
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def get_hubert_model(rank=None):
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hubert_soft = hubert_model.hubert_soft("hubert/hubert-soft-0d54a1f4.pt")
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if rank is not None:
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hubert_soft = hubert_soft.cuda(rank)
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return hubert_soft
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def get_hubert_content(hmodel, y=None, path=None):
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if path is not None:
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source, sr = torchaudio.load(path)
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source = torchaudio.functional.resample(source, sr, 16000)
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if len(source.shape) == 2 and source.shape[1] >= 2:
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source = torch.mean(source, dim=0).unsqueeze(0)
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else:
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source = y
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source = source.unsqueeze(0)
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with torch.inference_mode():
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units = hmodel.units(source)
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return units.transpose(1,2)
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def get_content(cmodel, y):
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with torch.no_grad():
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c = cmodel.extract_features(y.squeeze(1))[0]
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c = c.transpose(1, 2)
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return c
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def transform(mel, height): # 68-92
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#r = np.random.random()
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#rate = r * 0.3 + 0.85 # 0.85-1.15
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#height = int(mel.size(-2) * rate)
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tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
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if height >= mel.size(-2):
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return tgt[:, :mel.size(-2), :]
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else:
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silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
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silence += torch.randn_like(silence) / 10
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return torch.cat((tgt, silence), 1)
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def stretch(mel, width): # 0.5-2
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return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
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def load_checkpoint(checkpoint_path, model, optimizer=None):
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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 iteration is None:
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iteration = 1
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if learning_rate is None:
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learning_rate = 0.0002
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if optimizer is not None 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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new_state_dict[k] = saved_state_dict[k]
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except:
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logger.info("%s is not in the checkpoint" % k)
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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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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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# clean_ckpt = False
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# if clean_ckpt:
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# clean_checkpoints(path_to_models='logs/32k/', n_ckpts_to_keep=3, sort_by_time=True)
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def clean_checkpoints(path_to_models='logs/48k/', 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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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')], 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 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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sampling_rate, data = read(full_path)
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return torch.FloatTensor(data.astype(np.float32)), 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):
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parser = argparse.ArgumentParser()
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parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
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help='JSON file for configuration')
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parser.add_argument('-m', '--model', type=str, required=True,
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help='Model name')
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args = parser.parse_args()
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model_dir = os.path.join("./logs", args.model)
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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config_path = args.config
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config_save_path = os.path.join(model_dir, "config.json")
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if init:
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with open(config_path, "r") as f:
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data = f.read()
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with open(config_save_path, "w") as f:
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f.write(data)
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else:
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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_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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