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import os |
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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 numpy as np |
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from scipy.io.wavfile import read |
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import torch |
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from torch.nn import functional as F |
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from commons import sequence_mask |
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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 get_cmodel(rank): |
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checkpoint = torch.load('wavlm/WavLM-Large.pt') |
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cfg = WavLMConfig(checkpoint['cfg']) |
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cmodel = WavLM(cfg).cuda(rank) |
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cmodel.load_state_dict(checkpoint['model']) |
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cmodel.eval() |
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return cmodel |
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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 get_vocoder(rank): |
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with open("hifigan/config.json", "r") as f: |
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config = json.load(f) |
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config = hifigan.AttrDict(config) |
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vocoder = hifigan.Generator(config) |
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ckpt = torch.load("hifigan/generator_v1") |
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vocoder.load_state_dict(ckpt["generator"]) |
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vocoder.eval() |
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vocoder.remove_weight_norm() |
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vocoder.cuda(rank) |
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return vocoder |
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def transform(mel, height): |
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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): |
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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 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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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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