source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the K

labels = {idx: word for idx,word in enumerate(sentence.split(" ")) }

Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diff

Helper for setting incremental state for an nn.Module.

Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be

Initialize Conv1d module.

Initialize TFResidualStack module. Args: kernel_size (int): Kernel size. channles (int): Number of channels.

Initialize Parallel WaveGAN Generator module. Args: in_channels (int): Number of input channels. out_channels (in

Initialize MelGANGenerator module. Args: in_channels (int): Number of input channels. out_channels (int): Number of o

Construct an PositionalEncoding object.

Base class of train sampler. Args: indexes_hdf5_path: string batch_size: int black_list_csv: string

Assert the audio and feature lengths are correctly adjusted for upsamping.

f0_values: (batchsize, length, dim) where dim indicates fundamental tone and overtones

Initialize vocabulary with tokens from token_generator.

ph_encoding: [batch, t_p, hid] ph2word: tensor [batch, t_w] word_len: tensor [batch]

need to update like matlab

need to update like matlab

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Args: x: HxWxC image, [0, 1] sf: down-scale factor Return: bicubicly downsampled LR image

Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you

r"""Compute similarity between text and audio embeddings

connect_ Connects two adjacent clusters if their distance is <= n :param pairs: Clusters of iterateables e.g., [(1,5),(7,10)] :param n: d

connect_clusters_ Connects clustered predictions (0,1) in x with range n :param x: Input array. zero-one format :param n: Number of frame