rename
This commit is contained in:
qinzy
420
melo/utils.py
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420
melo/utils.py
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import os
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import glob
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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 numpy as np
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from scipy.io.wavfile import read
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import torch
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import torchaudio
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import librosa
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from .text import cleaned_text_to_sequence, get_bert
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from .text.cleaner import clean_text
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from . import commons
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MATPLOTLIB_FLAG = False
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logger = logging.getLogger(__name__)
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def get_text_for_tts_infer(text, language_str, hps, device, symbol_to_id=None):
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norm_text, phone, tone, word2ph = clean_text(text, language_str)
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str, symbol_to_id)
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if hps.data.add_blank:
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phone = commons.intersperse(phone, 0)
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tone = commons.intersperse(tone, 0)
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language = commons.intersperse(language, 0)
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for i in range(len(word2ph)):
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word2ph[i] = word2ph[i] * 2
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word2ph[0] += 1
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if getattr(hps.data, "disable_bert", False):
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bert = torch.zeros(1024, len(phone))
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ja_bert = torch.zeros(768, len(phone))
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else:
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bert = get_bert(norm_text, word2ph, language_str, device)
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del word2ph
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assert bert.shape[-1] == len(phone), phone
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if language_str == "ZH":
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bert = bert
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ja_bert = torch.zeros(768, len(phone))
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elif language_str in ["JP", "EN", "ZH_MIX_EN", 'KR', 'SP', 'ES', 'FR', 'DE', 'RU']:
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ja_bert = bert
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bert = torch.zeros(1024, len(phone))
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else:
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raise NotImplementedError()
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assert bert.shape[-1] == len(
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phone
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), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
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phone = torch.LongTensor(phone)
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tone = torch.LongTensor(tone)
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language = torch.LongTensor(language)
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return bert, ja_bert, phone, tone, language
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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 (
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optimizer is not None
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and not skip_optimizer
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and checkpoint_dict["optimizer"] is not None
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):
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optimizer.load_state_dict(checkpoint_dict["optimizer"])
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elif optimizer is None and not skip_optimizer:
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# else: Disable this line if Infer and resume checkpoint,then enable the line upper
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new_opt_dict = optimizer.state_dict()
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new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
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new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
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new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
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optimizer.load_state_dict(new_opt_dict)
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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 "emb_g" not in 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 Exception as e:
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# For upgrading from the old version
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if "ja_bert_proj" in k:
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v = torch.zeros_like(v)
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logger.warn(
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f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
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)
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else:
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logger.error(f"{k} is not in the checkpoint")
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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, strict=False)
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else:
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model.load_state_dict(new_state_dict, strict=False)
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logger.info(
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"Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
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)
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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(
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"Saving model and optimizer state at iteration {} to {}".format(
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iteration, checkpoint_path
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)
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)
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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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{
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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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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", 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(
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alignment.transpose(), aspect="auto", origin="lower", 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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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_wav_to_torch_new(full_path):
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audio_norm, sampling_rate = torchaudio.load(full_path, frame_offset=0, num_frames=-1, normalize=True, channels_first=True)
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audio_norm = audio_norm.mean(dim=0)
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return audio_norm, sampling_rate
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def load_wav_to_torch_librosa(full_path, sr):
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audio_norm, sampling_rate = librosa.load(full_path, sr=sr, mono=True)
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return torch.FloatTensor(audio_norm.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(
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"-c",
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"--config",
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type=str,
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default="./configs/base.json",
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help="JSON file for configuration",
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)
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parser.add_argument('--local-rank', type=int, default=0)
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parser.add_argument("-m", "--model", type=str, required=True, help="Model name")
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parser.add_argument('--pretrain_G', type=str, default=None,
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help='pretrain model')
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parser.add_argument('--pretrain_D', type=str, default=None,
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help='pretrain model D')
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parser.add_argument('--pretrain_dur', type=str, default=None,
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help='pretrain model duration')
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args = parser.parse_args()
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model_dir = os.path.join("./logs", args.model)
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os.makedirs(model_dir, exist_ok=True)
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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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hparams.pretrain_G = args.pretrain_G
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hparams.pretrain_D = args.pretrain_D
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hparams.pretrain_dur = args.pretrain_dur
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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 = [
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f
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for f in os.listdir(path_to_models)
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if os.path.isfile(os.path.join(path_to_models, f))
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]
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def name_key(_f):
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return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
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def time_key(_f):
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return 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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def x_sorted(_x):
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return 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)
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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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def del_info(fn):
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return logger.info(f".. Free up space by deleting ckpt {fn}")
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def del_routine(x):
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return [os.remove(x), del_info(x)]
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[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", encoding="utf-8") 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", encoding="utf-8") 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(
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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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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(
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"git hash values are different. {}(saved) != {}(current)".format(
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saved_hash[:8], 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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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, exist_ok=True)
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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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Block a user