espnet2.legacy.nets.pytorch_backend.wavenet.WaveNet

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class espnet2.legacy.nets.pytorch_backend.wavenet.WaveNet(n_quantize=256, n_aux=28, n_resch=512, n_skipch=256, dilation_depth=10, dilation_repeat=3, kernel_size=2, upsampling_factor=0)

Bases: Module

Conditional wavenet.

• Parameters:
  • n_quantize (int) – Number of quantization.
  • n_aux (int) – Number of aux feature dimension.
  • n_resch (int) – Number of filter channels for residual block.
  • n_skipch (int) – Number of filter channels for skip connection.
  • dilation_depth (int) – Number of dilation depth (e.g. if set 10, max dilation = 2^(10-1)).
  • dilation_repeat (int) – Number of dilation repeat.
  • kernel_size (int) – Filter size of dilated causal convolution.
  • upsampling_factor (int) – Upsampling factor.

Initialize WaveNet class.

forward(x, h)

Calculate forward propagation.

• Parameters:
  • x (LongTensor) – Quantized input waveform tensor with the shape (B, T).
  • h (Tensor) – Auxiliary feature tensor with the shape (B, n_aux, T).
• Returns: Logits with the shape (B, T, n_quantize).
• Return type: Tensor

generate(x, h, n_samples, interval=None, mode='sampling')

Generate a waveform with fast genration algorithm.

This generation based on Fast WaveNet Generation Algorithm.

• Parameters:
  • x (LongTensor) – Initial waveform tensor with the shape (T,).
  • h (Tensor) – Auxiliary feature tensor with the shape (n_samples + T, n_aux).
  • n_samples (int) – Number of samples to be generated.
  • interval (int , optional) – Log interval.
  • mode (str , optional) – “sampling” or “argmax”.
• Returns: Generated quantized waveform (n_samples).
• Return type: ndarray