Revert "the script 'forced_alignment_novo.py' which is to run novo_api on Python 3.6 environment is added."

This reverts commit b87a81eb9d.

novoapi_for_python3x/asr/segments/segments.py

@@ -1,99 +0,0 @@
-#!/usr/bin/env python
-# (c) 2015--2018 NovoLanguage, author: David A. van Leeuwen
-
-## These classes can be initialized with dictionaries, as they are returned by the python spraaklab recognition system.
-
-class Segment(object):
-    def __init__(self, segment):
-        self.begin = segment["begin"]
-        self.end = segment["end"]
-        self.begintime = segment.get("beginTime", self.begin / 100.0)
-        self.endtime = segment.get("endTime", self.end / 100.0)
-        self.label = segment["label"]
-        self.score = segment["score"]
-        if "llh" in segment:
-            self.llh = segment["llh"]
-        if "phones" in segment:
-            self.type = "word"
-            self.phones = Segmentation(segment["phones"], ["sil"])
-            if hasattr(self.phones[0], "llh"):
-                self.minllh = min([s.llh for s in self.phones]) ## the current word llh for error detection
-        else:
-            self.type = "phone"
-
-    def __repr__(self):
-        res = "%8.3f -- %8.3f score %8.3f " % (self.begintime, self.endtime, self.score)
-        if hasattr(self, "llh"):
-            res += "llh %8.3f " % self.llh
-        res += self.label.encode("utf8")
-        return res
-
-    def export(self):
-        r = {"begin": self.begin, "end": self.end, "label": self.label, "score": self.score, "type": self.type}
-        if hasattr(self, "llh"):
-            r["llh"] = self.llh
-        if hasattr(self, "phones"):
-            r["phones"] = self.phones.export()
-        return r
-
-class Segmentation(object):
-    def __init__(self, segments, sils=["<s>", "</s>", "!sil"]):
-        """Create a segmentation from a spraaklab recognition structure.
-        segments: an array of words (or phones), represented by a dict with
-        "begin", "end", "label", "score", and "llh" keys.  Words can also have
-        "phones" which is another array of segments."""
-        self.segments = [Segment(s) for s in segments]
-        if self.segments:
-            self.type = self.segments[0].type
-        else:
-            self.type = None
-        self.sils = sils
-        self.orig = segments ## in case we want to have access to the original recognition structure
-
-    def __getitem__(self, item):
-        return self.segments[item]
-
-    def __repr__(self):
-        ns = len(self.segments)
-        res = "Segmentation with %d %s%s" % (ns, self.type, "" if ns==1 else "s")
-        for seg in self.segments:
-            res += "\n " + repr(seg)
-        return res
-
-    def __len__(self):
-        return len(self.segments)
-
-    def score(self, skip=None):
-        if not skip:
-            skip = self.sils
-        s = 0.0
-        for seg in self.segments:
-            if seg.label not in skip:
-                s += seg.score
-        return s
-
-    def llhs(self, skip=None):
-        if not skip:
-            skip = self.sils
-        return [seg.llh for seg in self.segments if hasattr(seg, "llh") and seg.label not in skip]
-
-    def llh(self, skip=None):
-        return sum(self.llhs(skip))
-
-    def minllh(self, skip=None):
-        llhs = self.llhs(skip)
-        if llhs:
-            return min(llhs)
-        else:
-            return None
-
-    def labels(self, skip=None):
-        if not skip:
-            skip = self.sils
-        return [seg.label for seg in self.segments if seg.label not in skip]
-
-    def sentence(self, skip=None):
-        return " ".join(self.labels(skip))
-
-    def export(self):
-        return [seg.export() for seg in self.segments]