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import os
import argparse
import numpy as np
import torch
from scipy.io.wavfile import read
from omegaconf import OmegaConf
MATPLOTLIB_FLAG = False
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
f0_bin = 256
f0_max = 1100.0
f0_min = 50.0
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
def f0_to_coarse(f0):
is_torch = isinstance(f0, torch.Tensor)
f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * \
np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * \
(f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
f0_coarse = (
f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
assert f0_coarse.max() <= 255 and f0_coarse.min(
) >= 1, (f0_coarse.max(), f0_coarse.min())
return f0_coarse
def get_hparams(init=True):
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, default="./configs/base.yaml",
help='YAML file for configuration')
args = parser.parse_args()
hparams = OmegaConf.load(args.config)
model_dir = os.path.join("./logs", hparams.train.model)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
config_save_path = os.path.join(model_dir, "config.json")
os.system(f"cp {args.config} {config_save_path}")
hparams.model_dir = model_dir
return hparams
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