From d0ce1b3e6c51c24a092ee2984eb831575966190f Mon Sep 17 00:00:00 2001
From: KP <109694228@qq.com>
Date: Thu, 7 Apr 2022 14:31:59 +0800
Subject: [PATCH] Delete mcd.py

---
 paddleaudio/paddleaudio/metric/mcd.py | 63 ---------------------------
 1 file changed, 63 deletions(-)
 delete mode 100644 paddleaudio/paddleaudio/metric/mcd.py

diff --git a/paddleaudio/paddleaudio/metric/mcd.py b/paddleaudio/paddleaudio/metric/mcd.py
deleted file mode 100644
index c973c852..00000000
--- a/paddleaudio/paddleaudio/metric/mcd.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) 2022 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.
-from typing import Callable
-
-import mcd.metrics as mt
-import numpy as np
-from mcd import dtw
-
-__all__ = [
-    'mcd_distance',
-]
-
-
-def mcd_distance(xs: np.ndarray,
-                 ys: np.ndarray,
-                 cost_fn: Callable=mt.logSpecDbDist) -> float:
-    """Mel cepstral distortion (MCD), dtw distance.
-
-    Dynamic Time Warping.
-    Uses dynamic programming to compute:
-
-    Examples:
-        .. code-block:: python
-
-            wps[i, j] = cost_fn(xs[i], ys[j]) + min(
-                            wps[i-1, j  ],  // vertical   / insertion / expansion
-                            wps[i  , j-1],  // horizontal / deletion  / compression
-                            wps[i-1, j-1])  // diagonal   / match
-
-            dtw = sqrt(wps[-1, -1])
-
-    Cost Function:
-    Examples:
-        .. code-block:: python
-
-            logSpecDbConst = 10.0 / math.log(10.0) * math.sqrt(2.0)
-
-            def logSpecDbDist(x, y):
-                diff = x - y
-                return logSpecDbConst * math.sqrt(np.inner(diff, diff))
-
-    Args:
-        xs (np.ndarray): ref sequence, [T,D]
-        ys (np.ndarray): hyp sequence, [T,D]
-        cost_fn (Callable, optional): Cost function. Defaults to mt.logSpecDbDist.
-
-    Returns:
-        float: dtw distance
-    """
-
-    min_cost, path = dtw.dtw(xs, ys, cost_fn)
-    return min_cost
-- 
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