fix mbmelgan static

parakeet/modules/kaiser.py

@@ -1,146 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import collections.abc as collections_abc
-
-import paddle
-
-_i0A = [
-    -4.41534164647933937950E-18, 3.33079451882223809783E-17,
-    -2.43127984654795469359E-16, 1.71539128555513303061E-15,
-    -1.16853328779934516808E-14, 7.67618549860493561688E-14,
-    -4.85644678311192946090E-13, 2.95505266312963983461E-12,
-    -1.72682629144155570723E-11, 9.67580903537323691224E-11,
-    -5.18979560163526290666E-10, 2.65982372468238665035E-9,
-    -1.30002500998624804212E-8, 6.04699502254191894932E-8,
-    -2.67079385394061173391E-7, 1.11738753912010371815E-6,
-    -4.41673835845875056359E-6, 1.64484480707288970893E-5,
-    -5.75419501008210370398E-5, 1.88502885095841655729E-4,
-    -5.76375574538582365885E-4, 1.63947561694133579842E-3,
-    -4.32430999505057594430E-3, 1.05464603945949983183E-2,
-    -2.37374148058994688156E-2, 4.93052842396707084878E-2,
-    -9.49010970480476444210E-2, 1.71620901522208775349E-1,
-    -3.04682672343198398683E-1, 6.76795274409476084995E-1
-]
-
-_i0B = [
-    -7.23318048787475395456E-18, -4.83050448594418207126E-18,
-    4.46562142029675999901E-17, 3.46122286769746109310E-17,
-    -2.82762398051658348494E-16, -3.42548561967721913462E-16,
-    1.77256013305652638360E-15, 3.81168066935262242075E-15,
-    -9.55484669882830764870E-15, -4.15056934728722208663E-14,
-    1.54008621752140982691E-14, 3.85277838274214270114E-13,
-    7.18012445138366623367E-13, -1.79417853150680611778E-12,
-    -1.32158118404477131188E-11, -3.14991652796324136454E-11,
-    1.18891471078464383424E-11, 4.94060238822496958910E-10,
-    3.39623202570838634515E-9, 2.26666899049817806459E-8,
-    2.04891858946906374183E-7, 2.89137052083475648297E-6,
-    6.88975834691682398426E-5, 3.36911647825569408990E-3,
-    8.04490411014108831608E-1
-]
-
-
-def piecewise(x, condlist, funclist, *args, **kw):
-    n2 = len(funclist)
-    # n = len(condlist)
-    n = 1
-    if n == n2 - 1:  # compute the "otherwise" condition.
-        condelse = ~paddle.any(condlist, axis=0, keepdim=True)
-        condlist = paddle.concat([condlist, condelse], axis=0)
-        n += 1
-    elif n != n2:
-        raise ValueError(
-            "with {} condition(s), either {} or {} functions are expected"
-            .format(n, n, n + 1))
-
-    y = paddle.zeros(paddle.shape(x), x.dtype)
-    for k in range(n):
-        item = funclist[k]
-        if not isinstance(item, collections_abc.Callable):
-            y[condlist[k]] = item
-        else:
-            temp = condlist[k]
-            if paddle.shape(x) == paddle.ones([1]):
-                vals = x
-                y = item(vals, *args, **kw)
-            else:
-                vals = x[temp]
-                y[temp] = item(vals, *args, **kw)
-    return y
-
-
-def _chbevl(x, vals):
-    b0 = vals[0]
-    b1 = 0.0
-    for i in range(1, len(vals)):
-        b2 = b1
-        b1 = b0
-        b0 = x * b1 - b2 + vals[i]
-
-    return 0.5 * (b0 - b2)
-
-
-def _i0_1(x):
-    out = paddle.exp(x) * _chbevl(x / 2.0 - 2, _i0A)
-    return paddle.cast(out, dtype="float32")
-
-
-def _i0_2(x):
-    out = paddle.exp(x) * _chbevl(32.0 / x - 2.0, _i0B) / paddle.sqrt(x)
-    return paddle.cast(out, dtype="float32")
-
-
-def _i0_dispatcher(x):
-    return (x, )
-
-
-def i0(x):
-    x = paddle.abs(x)
-    condlist = x <= paddle.full([1], 8.0)
-    condlist = condlist.unsqueeze(0)
-    return piecewise(x, condlist, [_i0_1, _i0_2])
-
-
-def _len_guards(M):
-    """Handle small or incorrect window lengths"""
-    if int(M) != M or M < 0:
-        raise ValueError('Window length M must be a non-negative integer')
-    return M <= 1
-
-
-def _extend(M, sym):
-    """Extend window by 1 sample if needed for DFT-even symmetry"""
-    if not sym:
-        return M + 1, True
-    else:
-        return M, False
-
-
-def _truncate(w, needed):
-    """Truncate window by 1 sample if needed for DFT-even symmetry"""
-    if needed:
-        return w[:-1]
-    else:
-        return w
-
-
-def kaiser(M, beta, sym=True):
-    if _len_guards(M):
-        return paddle.ones(M)
-    M, needs_trunc = _extend(M, sym)
-    n = paddle.arange(0, M)
-    alpha = (M - 1) / 2.0
-    a = i0(beta * paddle.sqrt(1 - ((n - alpha) / alpha)**2.0))
-    b = i0(paddle.full([1], beta))
-    w = a / b
-    return _truncate(w, needs_trunc)

parakeet/modules/pqmf.py

@@ -15,8 +15,7 @@
 import numpy as np
 import paddle
 import paddle.nn.functional as F
-
+from scipy.signal import kaiser
-from parakeet.modules.kaiser import kaiser
 
 
 def design_prototype_filter(taps=62, cutoff_ratio=0.142, beta=9.0):
@@ -41,16 +40,18 @@ def design_prototype_filter(taps=62, cutoff_ratio=0.142, beta=9.0):
     # check the arguments are valid
     assert taps % 2 == 0, "The number of taps mush be even number."
     assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
-
     # make initial filter
     omega_c = np.pi * cutoff_ratio
     with np.errstate(invalid="ignore"):
-        h_i = paddle.sin(omega_c * (paddle.arange(taps + 1) - 0.5 * taps)) / (
+        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) / (
-            np.pi * (paddle.arange(taps + 1) - 0.5 * taps))
+            np.pi * (np.arange(taps + 1) - 0.5 * taps))
-    h_i[taps // 2] = 1 * cutoff_ratio  # fix nan due to indeterminate form
+    h_i[taps //
+        2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form
+
     # apply kaiser window
     w = kaiser(taps + 1, beta)
     h = h_i * w
+
     return h
 
 
@@ -77,24 +78,24 @@ class PQMF(paddle.nn.Layer):
             Beta coefficient for kaiser window.
         """
         super().__init__()
+
         h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
-        h_proto_len = paddle.shape(h_proto)[0]
+        h_analysis = np.zeros((subbands, len(h_proto)))
-        h_analysis = paddle.zeros((subbands, h_proto_len))
+        h_synthesis = np.zeros((subbands, len(h_proto)))
-        h_synthesis = paddle.zeros((subbands, h_proto_len))
         for k in range(subbands):
             h_analysis[k] = (
-                2 * h_proto *
+                2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (
-                paddle.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (
+                    np.arange(taps + 1) - (taps / 2)) + (-1)**k * np.pi / 4))
-                    paddle.arange(taps + 1) - (taps / 2)) + (-1)**k * np.pi / 4)
-            )
             h_synthesis[k] = (
-                2 * h_proto *
+                2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (
-                paddle.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (
+                    np.arange(taps + 1) - (taps / 2)) - (-1)**k * np.pi / 4))
-                    paddle.arange(taps + 1) - (taps / 2)) - (-1)**k * np.pi / 4)
+
-            )
+        # convert to tensor
+        self.analysis_filter = paddle.to_tensor(
+            h_analysis, dtype="float32").unsqueeze(1)
+        self.synthesis_filter = paddle.to_tensor(
+            h_synthesis, dtype="float32").unsqueeze(0)
 
-        self.analysis_filter = h_analysis.unsqueeze(1)
-        self.synthesis_filter = h_synthesis.unsqueeze(0)
         # filter for downsampling & upsampling
         updown_filter = paddle.zeros(
             (subbands, subbands, subbands), dtype="float32")