Source code for anndata.aio.xtrans

# -*- coding: UTF-8 -*-
:author:   Brigitte Bigi
:summary:  Input/Output of XTrans.

.. _This file is part of SPPAS:

     ___   __    __    __    ___
    /     |  \  |  \  |  \  /              the automatic
    \__   |__/  |__/  |___| \__             annotation and
       \  |     |     |   |    \             analysis
    ___/  |     |     |   | ___/              of speech

    Copyright (C) 2011-2021  Brigitte Bigi
    Laboratoire Parole et Langage, Aix-en-Provence, France

    Use of this software is governed by the GNU Public License, version 3.

    SPPAS is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    SPPAS is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with SPPAS. If not, see <>.

    This banner notice must not be removed.


XTrans is a multi-platform, multilingual, multi-channel transcription tool
that supports manual transcription and annotation of audio recordings.
Last version of Xtrans was released in 2009.


import codecs

from sppas.src.config import sg

from ..anndataexc import AioLineFormatError
from ..anndataexc import AnnDataTypeError
from ..ann.annlocation import sppasLocation
from ..ann.annlocation import sppasPoint
from ..ann.annlocation import sppasInterval

from .text import sppasBaseText
from .aioutils import load
from .aioutils import format_labels

# ----------------------------------------------------------------------------

[docs]class sppasTDF(sppasBaseText): """SPPAS TDF reader. This class implements a TDF reader, but not a writer. TDF is a Tab-Delimited Format. It contains 13 columns but SPPAS only extracts 8 of them. TDF does not support alternatives labels nor locations. Only the ones with the best score are saved. TDF can save several tiers. TDF does not support controlled vocabularies. TDF does not support hierarchy. TDF does not support metadata. TDF supports media assignment. TDF supports intervals only. TDF does not support alternative tags. TDF does not support radius. """
[docs] @staticmethod def detect(filename): """Check whether a file is of TDF format or not. :param filename: (str) Name of the file to check. :returns: (bool) """ # Open and load the content. try: with, 'r', sg.__encoding__) as fp: lines = fp.readlines() fp.close() except IOError: # can't open the file return False except UnicodeDecodeError: # can't open with SPPAS default encoding return False # Check each line for line in lines: if sppasTDF.is_comment(line): continue tab = line.split('\t') if len(tab) < 10: # expected is 13 return False return True
# -----------------------------------------------------------------------
[docs] @staticmethod def make_point(midpoint): """The localization is a time value, so always a float.""" try: midpoint = float(midpoint) except ValueError: raise AnnDataTypeError(midpoint, "float") return sppasPoint(midpoint, radius=0.005)
# -----------------------------------------------------------------------
[docs] def __init__(self, name=None): """Initialize a new sppasTDF instance. :param name: (str) This transcription name. """ if name is None: name = self.__class__.__name__ super(sppasTDF, self).__init__(name) self.default_extension = "tdf" = "Xtrans" # override all self._accept_multi_tiers = True self._accept_no_tiers = True self._accept_metadata = False self._accept_ctrl_vocab = False self._accept_media = False self._accept_hierarchy = False self._accept_point = True self._accept_interval = True self._accept_disjoint = False self._accept_alt_localization = False self._accept_alt_tag = False self._accept_radius = False self._accept_gaps = True self._accept_overlaps = True
# -----------------------------------------------------------------------
[docs] def read(self, filename): """Read a raw file and fill the sppasTranscription. It creates a tier for each speaker-channel observed in the file. :param filename: (str) """ lines = load(filename) if len(lines) < 2: return # The first line is the name of the columns first_line = lines[0] lines.pop(0) self._extract_lines(first_line, lines)
# ----------------------------------------------------------------------- def _extract_lines(self, first_line, lines): """Extract the content of the TDF file. :param first_line: The first line of the TDF file (column' names) :param lines: the content of the file """ # The 1st line indicates the names of the columns. column_names = first_line.split('\t') # Find indexes of the relevant information try: # index function raises a ValueError if the string is missing channel = column_names.index('channel;int') speaker = column_names.index('speaker;unicode') speaker_type = column_names.index('speakerType;unicode') speaker_dialect = column_names.index('speakerDialect;unicode') tag = column_names.index('transcript;unicode') begin = column_names.index('start;float') end = column_names.index('end;float') media_url = column_names.index('file;unicode') except ValueError: raise AioLineFormatError(1, first_line) # Extract rows, create tiers and metadata. for i, line in enumerate(lines): line = line.strip() # ignore blank lines if len(line) == 0: continue # a comment can contain metadata if sppasBaseText.is_comment(line): sppasBaseText._parse_comment(line, self) continue # a tab-delimited line line = line.split('\t') if len(line) < 10: raise AioLineFormatError(i + 1, line) # check for the tier (find it or create it) tier_name = line[speaker] + '-' + line[channel] tier = self.find(tier_name) if tier is None: # Create the media linked to the tier media = sppasBaseText.create_media(line[media_url].strip(), self) # Create the tier and set metadata tier = self.create_tier(tier_name, media=media) tier.set_meta("media_channel", line[channel]) tier.set_meta("speaker_name", line[speaker]) tier.set_meta("speaker_type", line[speaker_type]) tier.set_meta("speaker_dialect", line[speaker_dialect]) # Add the new annotation location = sppasLocation(sppasInterval( sppasTDF.make_point(line[begin]), sppasTDF.make_point(line[end]))) labels = format_labels(line[tag], separator="\n", empty="") tier.create_annotation(location, labels)