We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
scipy ImportError on multiple platforms
### Describe the bug

When attempting to Infer, the process is cancelled with the following error message:

[10:53:57] ERROR    [10:53:57] cannot import name 'kaiser' from 'scipy.signal'                                gui.py:764
                    (C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\scipy\signal\_
                    _init__.py)
                    Traceback (most recent call last):
                      File
                    "C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\so_vits_svc_fo
                    rk\gui.py", line 723, in main
                        from so_vits_svc_fork.inference.main import infer
                      File
                    "C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\so_vits_svc_fo
                    rk\inference\main.py", line 14, in <module>
                        from so_vits_svc_fork.inference.core import RealtimeVC, RealtimeVC2, Svc
                      File
                    "C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\so_vits_svc_fo
                    rk\inference\core.py", line 18, in <module>
                        from ..modules.synthesizers import SynthesizerTrn
                      File
                    "C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\so_vits_svc_fo
                    rk\modules\synthesizers.py", line 13, in <module>
                        from so_vits_svc_fork.modules.decoders.mb_istft import (
                      File
                    "C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\so_vits_svc_fo
                    rk\modules\decoders\mb_istft\__init__.py", line 1, in <module>
                        from ._generators import (
                      File
                    "C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\so_vits_svc_fo
                    rk\modules\decoders\mb_istft\_generators.py", line 11, in <module>
                        from ._pqmf import PQMF
                      File
                    "C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\so_vits_svc_fo
                    rk\modules\decoders\mb_istft\_pqmf.py", line 9, in <module>
                        from scipy.signal import kaiser
                    ImportError: cannot import name 'kaiser' from 'scipy.signal'
                    (C:\Users\Marcello\AppData\Roaming\so-vits-svc-fork\venv\lib\site-packages\scipy\signal\_
                    _init__.py)


### To Reproduce

1. Install so-vits-svc-fork using option 1, 2 or 3 natively or within Anaconda
2. Select model & config
3. Select input file, click on Infer 

### Additional context

The same error message appears on every operating system I've tried.

### Version

4.1.58

### Platform

Windows 11 / MacOS Sonoma 14.1.1 / Anaconda3

### Code of Conduct

- [X] I agree to follow this project's Code of Conduct.

### No Duplicate

- [X] I have checked existing issues to avoid duplicates.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py`
Content:
```
1 # Copyright 2020 Tomoki Hayashi
2 #  MIT License (https://opensource.org/licenses/MIT)
3 
4 """Pseudo QMF modules."""
5 
6 import numpy as np
7 import torch
8 import torch.nn.functional as F
9 from scipy.signal import kaiser
10 
11 
12 def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
13     """Design prototype filter for PQMF.
14     This method is based on `A Kaiser window approach for the design of prototype
15     filters of cosine modulated filterbanks`_.
16     Args:
17         taps (int): The number of filter taps.
18         cutoff_ratio (float): Cut-off frequency ratio.
19         beta (float): Beta coefficient for kaiser window.
20     Returns:
21         ndarray: Impluse response of prototype filter (taps + 1,).
22     .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
23         https://ieeexplore.ieee.org/abstract/document/681427
24     """
25     # check the arguments are valid
26     assert taps % 2 == 0, "The number of taps mush be even number."
27     assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
28 
29     # make initial filter
30     omega_c = np.pi * cutoff_ratio
31     with np.errstate(invalid="ignore"):
32         h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) / (
33             np.pi * (np.arange(taps + 1) - 0.5 * taps)
34         )
35     h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form
36 
37     # apply kaiser window
38     w = kaiser(taps + 1, beta)
39     h = h_i * w
40 
41     return h
42 
43 
44 class PQMF(torch.nn.Module):
45     """PQMF module.
46     This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
47     .. _`Near-perfect-reconstruction pseudo-QMF banks`:
48         https://ieeexplore.ieee.org/document/258122
49     """
50 
51     def __init__(self, device, subbands=8, taps=62, cutoff_ratio=0.15, beta=9.0):
52         """Initialize PQMF module.
53         Args:
54             subbands (int): The number of subbands.
55             taps (int): The number of filter taps.
56             cutoff_ratio (float): Cut-off frequency ratio.
57             beta (float): Beta coefficient for kaiser window.
58         """
59         super().__init__()
60 
61         # define filter coefficient
62         h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
63         h_analysis = np.zeros((subbands, len(h_proto)))
64         h_synthesis = np.zeros((subbands, len(h_proto)))
65         for k in range(subbands):
66             h_analysis[k] = (
67                 2
68                 * h_proto
69                 * np.cos(
70                     (2 * k + 1)
71                     * (np.pi / (2 * subbands))
72                     * (np.arange(taps + 1) - ((taps - 1) / 2))
73                     + (-1) ** k * np.pi / 4
74                 )
75             )
76             h_synthesis[k] = (
77                 2
78                 * h_proto
79                 * np.cos(
80                     (2 * k + 1)
81                     * (np.pi / (2 * subbands))
82                     * (np.arange(taps + 1) - ((taps - 1) / 2))
83                     - (-1) ** k * np.pi / 4
84                 )
85             )
86 
87         # convert to tensor
88         analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1).to(device)
89         synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0).to(device)
90 
91         # register coefficients as buffer
92         self.register_buffer("analysis_filter", analysis_filter)
93         self.register_buffer("synthesis_filter", synthesis_filter)
94 
95         # filter for downsampling & upsampling
96         updown_filter = torch.zeros((subbands, subbands, subbands)).float().to(device)
97         for k in range(subbands):
98             updown_filter[k, k, 0] = 1.0
99         self.register_buffer("updown_filter", updown_filter)
100         self.subbands = subbands
101 
102         # keep padding info
103         self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
104 
105     def analysis(self, x):
106         """Analysis with PQMF.
107         Args:
108             x (Tensor): Input tensor (B, 1, T).
109         Returns:
110             Tensor: Output tensor (B, subbands, T // subbands).
111         """
112         x = F.conv1d(self.pad_fn(x), self.analysis_filter)
113         return F.conv1d(x, self.updown_filter, stride=self.subbands)
114 
115     def synthesis(self, x):
116         """Synthesis with PQMF.
117         Args:
118             x (Tensor): Input tensor (B, subbands, T // subbands).
119         Returns:
120             Tensor: Output tensor (B, 1, T).
121         """
122         # NOTE(kan-bayashi): Power will be dreased so here multiply by # subbands.
123         #   Not sure this is the correct way, it is better to check again.
124         # TODO(kan-bayashi): Understand the reconstruction procedure
125         x = F.conv_transpose1d(
126             x, self.updown_filter * self.subbands, stride=self.subbands
127         )
128         return F.conv1d(self.pad_fn(x), self.synthesis_filter)
129
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py b/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py
--- a/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py
+++ b/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py
@@ -6,7 +6,7 @@
 import numpy as np
 import torch
 import torch.nn.functional as F
-from scipy.signal import kaiser
+from scipy.signal.windows import kaiser
 
 
 def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):

{"golden_diff": "diff --git a/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py b/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py\n--- a/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py\n+++ b/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py\n@@ -6,7 +6,7 @@\n import numpy as np\n import torch\n import torch.nn.functional as F\n-from scipy.signal import kaiser\n+from scipy.signal.windows import kaiser\n \n \n def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):\n", "issue": "scipy ImportError on multiple platforms\n### Describe the bug\n\nWhen attempting to Infer, the process is cancelled with the following error message:\r\n\r\n[10:53:57] ERROR    [10:53:57] cannot import name 'kaiser' from 'scipy.signal'                                gui.py:764\r\n                    (C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\scipy\\signal\\_\r\n                    _init__.py)\r\n                    Traceback (most recent call last):\r\n                      File\r\n                    \"C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\so_vits_svc_fo\r\n                    rk\\gui.py\", line 723, in main\r\n                        from so_vits_svc_fork.inference.main import infer\r\n                      File\r\n                    \"C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\so_vits_svc_fo\r\n                    rk\\inference\\main.py\", line 14, in <module>\r\n                        from so_vits_svc_fork.inference.core import RealtimeVC, RealtimeVC2, Svc\r\n                      File\r\n                    \"C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\so_vits_svc_fo\r\n                    rk\\inference\\core.py\", line 18, in <module>\r\n                        from ..modules.synthesizers import SynthesizerTrn\r\n                      File\r\n                    \"C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\so_vits_svc_fo\r\n                    rk\\modules\\synthesizers.py\", line 13, in <module>\r\n                        from so_vits_svc_fork.modules.decoders.mb_istft import (\r\n                      File\r\n                    \"C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\so_vits_svc_fo\r\n                    rk\\modules\\decoders\\mb_istft\\__init__.py\", line 1, in <module>\r\n                        from ._generators import (\r\n                      File\r\n                    \"C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\so_vits_svc_fo\r\n                    rk\\modules\\decoders\\mb_istft\\_generators.py\", line 11, in <module>\r\n                        from ._pqmf import PQMF\r\n                      File\r\n                    \"C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\so_vits_svc_fo\r\n                    rk\\modules\\decoders\\mb_istft\\_pqmf.py\", line 9, in <module>\r\n                        from scipy.signal import kaiser\r\n                    ImportError: cannot import name 'kaiser' from 'scipy.signal'\r\n                    (C:\\Users\\Marcello\\AppData\\Roaming\\so-vits-svc-fork\\venv\\lib\\site-packages\\scipy\\signal\\_\r\n                    _init__.py)\r\n\n\n### To Reproduce\n\n1. Install so-vits-svc-fork using option 1, 2 or 3 natively or within Anaconda\r\n2. Select model & config\r\n3. Select input file, click on Infer \n\n### Additional context\n\nThe same error message appears on every operating system I've tried.\n\n### Version\n\n4.1.58\n\n### Platform\n\nWindows 11 / MacOS Sonoma 14.1.1 / Anaconda3\n\n### Code of Conduct\n\n- [X] I agree to follow this project's Code of Conduct.\n\n### No Duplicate\n\n- [X] I have checked existing issues to avoid duplicates.\n", "before_files": [{"content": "# Copyright 2020 Tomoki Hayashi\n#  MIT License (https://opensource.org/licenses/MIT)\n\n\"\"\"Pseudo QMF modules.\"\"\"\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom scipy.signal import kaiser\n\n\ndef design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):\n    \"\"\"Design prototype filter for PQMF.\n    This method is based on `A Kaiser window approach for the design of prototype\n    filters of cosine modulated filterbanks`_.\n    Args:\n        taps (int): The number of filter taps.\n        cutoff_ratio (float): Cut-off frequency ratio.\n        beta (float): Beta coefficient for kaiser window.\n    Returns:\n        ndarray: Impluse response of prototype filter (taps + 1,).\n    .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:\n        https://ieeexplore.ieee.org/abstract/document/681427\n    \"\"\"\n    # check the arguments are valid\n    assert taps % 2 == 0, \"The number of taps mush be even number.\"\n    assert 0.0 < cutoff_ratio < 1.0, \"Cutoff ratio must be > 0.0 and < 1.0.\"\n\n    # make initial filter\n    omega_c = np.pi * cutoff_ratio\n    with np.errstate(invalid=\"ignore\"):\n        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) / (\n            np.pi * (np.arange(taps + 1) - 0.5 * taps)\n        )\n    h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form\n\n    # apply kaiser window\n    w = kaiser(taps + 1, beta)\n    h = h_i * w\n\n    return h\n\n\nclass PQMF(torch.nn.Module):\n    \"\"\"PQMF module.\n    This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.\n    .. _`Near-perfect-reconstruction pseudo-QMF banks`:\n        https://ieeexplore.ieee.org/document/258122\n    \"\"\"\n\n    def __init__(self, device, subbands=8, taps=62, cutoff_ratio=0.15, beta=9.0):\n        \"\"\"Initialize PQMF module.\n        Args:\n            subbands (int): The number of subbands.\n            taps (int): The number of filter taps.\n            cutoff_ratio (float): Cut-off frequency ratio.\n            beta (float): Beta coefficient for kaiser window.\n        \"\"\"\n        super().__init__()\n\n        # define filter coefficient\n        h_proto = design_prototype_filter(taps, cutoff_ratio, beta)\n        h_analysis = np.zeros((subbands, len(h_proto)))\n        h_synthesis = np.zeros((subbands, len(h_proto)))\n        for k in range(subbands):\n            h_analysis[k] = (\n                2\n                * h_proto\n                * np.cos(\n                    (2 * k + 1)\n                    * (np.pi / (2 * subbands))\n                    * (np.arange(taps + 1) - ((taps - 1) / 2))\n                    + (-1) ** k * np.pi / 4\n                )\n            )\n            h_synthesis[k] = (\n                2\n                * h_proto\n                * np.cos(\n                    (2 * k + 1)\n                    * (np.pi / (2 * subbands))\n                    * (np.arange(taps + 1) - ((taps - 1) / 2))\n                    - (-1) ** k * np.pi / 4\n                )\n            )\n\n        # convert to tensor\n        analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1).to(device)\n        synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0).to(device)\n\n        # register coefficients as buffer\n        self.register_buffer(\"analysis_filter\", analysis_filter)\n        self.register_buffer(\"synthesis_filter\", synthesis_filter)\n\n        # filter for downsampling & upsampling\n        updown_filter = torch.zeros((subbands, subbands, subbands)).float().to(device)\n        for k in range(subbands):\n            updown_filter[k, k, 0] = 1.0\n        self.register_buffer(\"updown_filter\", updown_filter)\n        self.subbands = subbands\n\n        # keep padding info\n        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)\n\n    def analysis(self, x):\n        \"\"\"Analysis with PQMF.\n        Args:\n            x (Tensor): Input tensor (B, 1, T).\n        Returns:\n            Tensor: Output tensor (B, subbands, T // subbands).\n        \"\"\"\n        x = F.conv1d(self.pad_fn(x), self.analysis_filter)\n        return F.conv1d(x, self.updown_filter, stride=self.subbands)\n\n    def synthesis(self, x):\n        \"\"\"Synthesis with PQMF.\n        Args:\n            x (Tensor): Input tensor (B, subbands, T // subbands).\n        Returns:\n            Tensor: Output tensor (B, 1, T).\n        \"\"\"\n        # NOTE(kan-bayashi): Power will be dreased so here multiply by # subbands.\n        #   Not sure this is the correct way, it is better to check again.\n        # TODO(kan-bayashi): Understand the reconstruction procedure\n        x = F.conv_transpose1d(\n            x, self.updown_filter * self.subbands, stride=self.subbands\n        )\n        return F.conv1d(self.pad_fn(x), self.synthesis_filter)\n", "path": "src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py"}], "after_files": [{"content": "# Copyright 2020 Tomoki Hayashi\n#  MIT License (https://opensource.org/licenses/MIT)\n\n\"\"\"Pseudo QMF modules.\"\"\"\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom scipy.signal.windows import kaiser\n\n\ndef design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):\n    \"\"\"Design prototype filter for PQMF.\n    This method is based on `A Kaiser window approach for the design of prototype\n    filters of cosine modulated filterbanks`_.\n    Args:\n        taps (int): The number of filter taps.\n        cutoff_ratio (float): Cut-off frequency ratio.\n        beta (float): Beta coefficient for kaiser window.\n    Returns:\n        ndarray: Impluse response of prototype filter (taps + 1,).\n    .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:\n        https://ieeexplore.ieee.org/abstract/document/681427\n    \"\"\"\n    # check the arguments are valid\n    assert taps % 2 == 0, \"The number of taps mush be even number.\"\n    assert 0.0 < cutoff_ratio < 1.0, \"Cutoff ratio must be > 0.0 and < 1.0.\"\n\n    # make initial filter\n    omega_c = np.pi * cutoff_ratio\n    with np.errstate(invalid=\"ignore\"):\n        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) / (\n            np.pi * (np.arange(taps + 1) - 0.5 * taps)\n        )\n    h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form\n\n    # apply kaiser window\n    w = kaiser(taps + 1, beta)\n    h = h_i * w\n\n    return h\n\n\nclass PQMF(torch.nn.Module):\n    \"\"\"PQMF module.\n    This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.\n    .. _`Near-perfect-reconstruction pseudo-QMF banks`:\n        https://ieeexplore.ieee.org/document/258122\n    \"\"\"\n\n    def __init__(self, device, subbands=8, taps=62, cutoff_ratio=0.15, beta=9.0):\n        \"\"\"Initialize PQMF module.\n        Args:\n            subbands (int): The number of subbands.\n            taps (int): The number of filter taps.\n            cutoff_ratio (float): Cut-off frequency ratio.\n            beta (float): Beta coefficient for kaiser window.\n        \"\"\"\n        super().__init__()\n\n        # define filter coefficient\n        h_proto = design_prototype_filter(taps, cutoff_ratio, beta)\n        h_analysis = np.zeros((subbands, len(h_proto)))\n        h_synthesis = np.zeros((subbands, len(h_proto)))\n        for k in range(subbands):\n            h_analysis[k] = (\n                2\n                * h_proto\n                * np.cos(\n                    (2 * k + 1)\n                    * (np.pi / (2 * subbands))\n                    * (np.arange(taps + 1) - ((taps - 1) / 2))\n                    + (-1) ** k * np.pi / 4\n                )\n            )\n            h_synthesis[k] = (\n                2\n                * h_proto\n                * np.cos(\n                    (2 * k + 1)\n                    * (np.pi / (2 * subbands))\n                    * (np.arange(taps + 1) - ((taps - 1) / 2))\n                    - (-1) ** k * np.pi / 4\n                )\n            )\n\n        # convert to tensor\n        analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1).to(device)\n        synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0).to(device)\n\n        # register coefficients as buffer\n        self.register_buffer(\"analysis_filter\", analysis_filter)\n        self.register_buffer(\"synthesis_filter\", synthesis_filter)\n\n        # filter for downsampling & upsampling\n        updown_filter = torch.zeros((subbands, subbands, subbands)).float().to(device)\n        for k in range(subbands):\n            updown_filter[k, k, 0] = 1.0\n        self.register_buffer(\"updown_filter\", updown_filter)\n        self.subbands = subbands\n\n        # keep padding info\n        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)\n\n    def analysis(self, x):\n        \"\"\"Analysis with PQMF.\n        Args:\n            x (Tensor): Input tensor (B, 1, T).\n        Returns:\n            Tensor: Output tensor (B, subbands, T // subbands).\n        \"\"\"\n        x = F.conv1d(self.pad_fn(x), self.analysis_filter)\n        return F.conv1d(x, self.updown_filter, stride=self.subbands)\n\n    def synthesis(self, x):\n        \"\"\"Synthesis with PQMF.\n        Args:\n            x (Tensor): Input tensor (B, subbands, T // subbands).\n        Returns:\n            Tensor: Output tensor (B, 1, T).\n        \"\"\"\n        # NOTE(kan-bayashi): Power will be dreased so here multiply by # subbands.\n        #   Not sure this is the correct way, it is better to check again.\n        # TODO(kan-bayashi): Understand the reconstruction procedure\n        x = F.conv_transpose1d(\n            x, self.updown_filter * self.subbands, stride=self.subbands\n        )\n        return F.conv1d(self.pad_fn(x), self.synthesis_filter)\n", "path": "src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py"}]}