Initialize tokens from a list of tokens. It is ok if reserved tokens appear in the vocab list. They will be removed. The set of token

x: [b, h, l, d] y: [h or 1, m, d] ret: [b, h, l, m]

x: [b, h, l, m] y: [h or 1, m, d] ret: [b, h, l, d]

clean_sentence: <str>, each word or punctuation should be separated by one blank.

words: <List of str>, each character in chinese is one item ph_words: <List of str>, each character in chinese is one item, represent

x: [b, h, l, 2*l-1] ret: [b, h, l, l]

:param sample: :param batch_idx: :return: total loss: torch.Tensor, loss_log: dict

:param outputs: :return: loss_output: dict

E.g., 1234567 -> 1,234,567

:param dur_pred: [B, T], float, log scale :param mel2ph: [B, T] :param txt_tokens: [B, T] :param losses: :ret

:param dur_pred: [B, T], float, log scale :param mel2ph: [B, T] :param txt_tokens: [B, T] :param losses: :re

:param front: front-head audio, like vocal [samples,channel], will be normlized so any scale will be fine :param noise: noise, [samples,chann

USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > thresho

generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1

generate an anisotropic Gaussian kernel Args: ksize : e.g., 15, kernel size theta : [0, pi], rotation angle range l1

Apply weight normalization module from all of the layers.

Apply weight normalization module from all of the layers.

Apply weight normalization module from all of the layers.

Apply weight normalization module from all of the layers.

Apply weight normalization module from all of the layers.

Inference audio tagging result of an audio clip.

Kai Zhang (github: https://github.com/cszn)

Create a 1D, 2D, or 3D average pooling module.

bgr version of rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]

Build vocabulary from csv file with a given threshold to drop all counts < threshold Args: input_json(string): Preprossessed json file. S

Build vocabulary from csv file with a given threshold to drop all counts < threshold Args: input_json(string): Preprossessed json file. S

Build vocabulary from csv file with a given threshold to drop all counts < threshold Args: input_json(string): Preprossessed json file. S