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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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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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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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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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