r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads

Encoder layer module. Args: size (int): Input dimension. self_attn (torch.nn.Module): Self-attention module instance.

Relative positional encoding module (new implementation). Details can be found in https://github.com/espnet/espnet/pull/2816. See : Appendix

Linear transform the input without subsampling Args: idim (int): Input dimension. odim (int): Output dimension. pos_enc_c

Positionwise feed forward layer. FeedForward are appied on each position of the sequence. The output dim is same with the input dim. Arg

Multi-Head Attention layer with relative position encoding. Paper: https://arxiv.org/abs/1901.02860 Args: n_head (int): The number of

Residual block module in HiFiGAN/BigVGAN.

Implementation of a sine-based periodic activation function Shape: - Input: (B, C, T) - Output: (B, C, T), same shape as the

A 1D upsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and output

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This decoder requires an input with the same shape of the target. So, if your text content is shorter or longer than the outputs, please re-s

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r""" A feed-forward layer. Parameters: dim (`int`): The number of channels in the input. dim_out (`int`, *optional*): The num

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HiFTNet Generator: Neural Source Filter + ISTFTNet https://arxiv.org/abs/2309.09493

Definition of sine generator SineGen(samp_rate, harmonic_num = 0, sine_amp = 0.1, noise_std = 0.003, voiced_threshold = 0

Definition of sine generator SineGen(samp_rate, harmonic_num = 0, sine_amp = 0.1, noise_std = 0.003, voiced_threshold = 0

A modified Snake function which uses separate parameters for the magnitude of the periodic components Shape: - Input: (B, C, T)

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A 1D upsampling layer with an optional convolution. Parameters: channels (`int`): number of channels in the inputs and output

Args: input_size (int): input dim output_size (int): dimension of attention attention_heads (int): the number of heads of

This decoder requires an input with the same shape of the target. So, if your text content is shorter or longer than the outputs, please re-s

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Multi-Head Attention layer. Args: n_head (int): The number of heads. n_feat (int): The number of features. dropout_rate (

SourceModule for hn-nsf SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, add_noise_std=0.003, voiced_threshod=0) sa

SourceModule for hn-nsf SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, add_noise_std=0.003, voiced_threshod=0) sa

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