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Update form of default value in docstring #802

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Jul 20, 2020
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Update form of default value in docstring #802

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44 changes: 22 additions & 22 deletions torchaudio/models/_wavernn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@ class _ResBlock(nn.Module):
The paper link is https://arxiv.org/pdf/1512.03385.pdf.

Args:
n_freq: the number of bins in a spectrogram (default=128)
n_freq: the number of bins in a spectrogram. (Default: ``128``)

Examples
>>> resblock = _ResBlock()
Expand Down Expand Up @@ -48,11 +48,11 @@ class _MelResNet(nn.Module):
r"""MelResNet layer uses a stack of ResBlocks on spectrogram.

Args:
n_res_block: the number of ResBlock in stack (default=10)
n_freq: the number of bins in a spectrogram (default=128)
n_hidden: the number of hidden dimensions of resblock (default=128)
n_output: the number of output dimensions of melresnet (default=128)
kernel_size: the number of kernel size in the first Conv1d layer (default=5)
n_res_block: the number of ResBlock in stack. (Default: ``10``)
n_freq: the number of bins in a spectrogram. (Default: ``128``)
n_hidden: the number of hidden dimensions of resblock. (Default: ``128``)
n_output: the number of output dimensions of melresnet. (Default: ``128``)
kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``)

Examples
>>> melresnet = _MelResNet()
Expand Down Expand Up @@ -129,12 +129,12 @@ class _UpsampleNetwork(nn.Module):
r"""Upscale the dimensions of a spectrogram.

Args:
upsample_scales: the list of upsample scales
n_res_block: the number of ResBlock in stack (default=10)
n_freq: the number of bins in a spectrogram (default=128)
n_hidden: the number of hidden dimensions of resblock (default=128)
n_output: the number of output dimensions of melresnet (default=128)
kernel_size: the number of kernel size in the first Conv1d layer (default=5)
upsample_scales: the list of upsample scales.
n_res_block: the number of ResBlock in stack. (Default: ``10``)
n_freq: the number of bins in a spectrogram. (Default: ``128``)
n_hidden: the number of hidden dimensions of resblock. (Default: ``128``)
n_output: the number of output dimensions of melresnet. (Default: ``128``)
kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``)

Examples
>>> upsamplenetwork = _UpsampleNetwork(upsample_scales=[4, 4, 16])
Expand Down Expand Up @@ -204,16 +204,16 @@ class _WaveRNN(nn.Module):
`upsample_scales` must equal `hop_length`.

Args:
upsample_scales: the list of upsample scales
n_classes: the number of output classes
hop_length: the number of samples between the starts of consecutive frames
n_res_block: the number of ResBlock in stack (default=10)
n_rnn: the dimension of RNN layer (default=512)
n_fc: the dimension of fully connected layer (default=512)
kernel_size: the number of kernel size in the first Conv1d layer (default=5)
n_freq: the number of bins in a spectrogram (default=128)
n_hidden: the number of hidden dimensions of resblock (default=128)
n_output: the number of output dimensions of melresnet (default=128)
upsample_scales: the list of upsample scales.
n_classes: the number of output classes.
hop_length: the number of samples between the starts of consecutive frames.
n_res_block: the number of ResBlock in stack. (Default: ``10``)
n_rnn: the dimension of RNN layer. (Default: ``512``)
n_fc: the dimension of fully connected layer. (Default: ``512``)
kernel_size: the number of kernel size in the first Conv1d layer. (Default: ``5``)
n_freq: the number of bins in a spectrogram. (Default: ``128``)
n_hidden: the number of hidden dimensions of resblock. (Default: ``128``)
n_output: the number of output dimensions of melresnet. (Default: ``128``)

Example
>>> wavernn = _waveRNN(upsample_scales=[5,5,8], n_classes=512, hop_length=200)
Expand Down