Merge pull request #92 from sp-nitech/minor_fix

Prepare release

README.md

@@ -3,7 +3,7 @@
 *diffsptk* is a differentiable version of [SPTK](https://github.com/sp-nitech/SPTK) based on the PyTorch framework.
 
 [![Latest Manual](https://img.shields.io/badge/docs-latest-blue.svg)](https://sp-nitech.github.io/diffsptk/latest/)
-[![Stable Manual](https://img.shields.io/badge/docs-stable-blue.svg)](https://sp-nitech.github.io/diffsptk/2.1.1/)
+[![Stable Manual](https://img.shields.io/badge/docs-stable-blue.svg)](https://sp-nitech.github.io/diffsptk/2.2.0/)
 [![Downloads](https://static.pepy.tech/badge/diffsptk)](https://pepy.tech/project/diffsptk)
 [![Python Version](https://img.shields.io/pypi/pyversions/diffsptk.svg)](https://pypi.python.org/pypi/diffsptk)
 [![PyTorch Version](https://img.shields.io/badge/pytorch-2.0.0%20%7C%202.4.0-orange.svg)](https://pypi.python.org/pypi/diffsptk)

diffsptk/modules/ap.py

@@ -123,11 +123,11 @@ def forward(self, x, f0):
         d = x.dim()
         if d == 1:
             x = x.unsqueeze(0)
-        assert x.dim() == 2, "Input must be 2D tensor"
+        assert x.dim() == 2, "Input must be 2D tensor."
 
         if f0.dim() == 1:
             f0 = f0.unsqueeze(0)
-        assert f0.dim() == 2, "Input must be 2D tensor"
+        assert f0.dim() == 2, "Input must be 2D tensor."
 
         ap = self.convert(self.extractor(x, f0))
 

diffsptk/modules/linear_intpl.py

@@ -76,7 +76,7 @@ def _forward(x, upsampling_factor):
             x = x.view(1, -1, 1)
         elif d == 2:
             x = x.unsqueeze(0)
-        assert x.dim() == 3, "Input must be 3D tensor"
+        assert x.dim() == 3, "Input must be 3D tensor."
         B, T, D = x.shape
 
         x = x.transpose(-2, -1).contiguous()  # (B, D, T)

diffsptk/modules/magic_intpl.py

@@ -94,7 +94,7 @@ def forward(ctx, x, magic_number):
             x = x.view(1, -1, 1)
         elif d == 2:
             x = x.unsqueeze(0)
-        assert x.dim() == 3, "Input must be 3D tensor"
+        assert x.dim() == 3, "Input must be 3D tensor."
         B, T, D = x.shape
 
         def compute_lerp_inputs(x, magic_number):