Source code for corpustools.corpus.io.text_transcription

import os
import re

from corpustools.corpus.classes import SpontaneousSpeechCorpus, Corpus, Word, Discourse, WordToken, Attribute

from corpustools.exceptions import DelimiterError, PCTOSError

from .helper import (compile_digraphs, parse_transcription, DiscourseData,
                    data_to_discourse, AnnotationType, text_to_lines,
                    Annotation, BaseAnnotation)

from .binary import load_binary

[docs]def inspect_discourse_transcription(path): """ Generate a list of AnnotationTypes for a specified text file for parsing it as a transcribed text Parameters ---------- path : str Full path to text file Returns ------- list of AnnotationTypes Autodetected AnnotationTypes for the text file """ trans_delimiters = ['.', ';', ','] att = Attribute('transcription','tier','Transcription') a = AnnotationType('transcription', None, None, attribute = att, base = True) if os.path.isdir(path): for root, subdirs, files in os.walk(path): for filename in files: if not filename.lower().endswith('.txt'): continue with open(os.path.join(root,filename), encoding='utf-8-sig', mode='r') as f: for line in f.readlines(): trial = line.strip().split() if a.trans_delimiter is None: for t in trial: for delim in trans_delimiters: if delim in t: a.trans_delimiter = delim break a.add(trial, save = False) else: with open(path, encoding='utf-8-sig', mode='r') as f: for line in f.readlines(): trial = line.strip().split() if a.trans_delimiter is None: for t in trial: for delim in trans_delimiters: if delim in t: a.trans_delimiter = delim break a.add(trial, save = False) annotation_types = [a] return annotation_types
def transcription_text_to_data(path, annotation_types = None, stop_check = None, call_back = None): name = os.path.splitext(os.path.split(path)[1])[0] if annotation_types is None: annotation_types = inspect_discourse_transcription(path) for a in annotation_types: a.reset() a = AnnotationType('spelling', None, None, attribute = Attribute('spelling','spelling','Spelling'), anchor = True) annotation_types.append(a) data = DiscourseData(name, annotation_types) lines = text_to_lines(path) if call_back is not None: call_back('Processing file...') call_back(0, len(lines)) cur = 0 trans_check = False n = 'transcription' for line in lines: if stop_check is not None and stop_check(): return if call_back is not None: cur += 1 if cur % 20 == 0: call_back(cur) if not line or line == '\n': continue for word in line: annotations = dict() trans = parse_transcription(word, data[n]) #if not trans_check and data[n].delimiter is not None and len(trans) > 1: # trans_check = True spell = ''.join(x.label for x in trans) if spell == '': continue word = Annotation(spell) tier_elements = trans level_count = data.level_length(n) word.references.append(n) word.begins.append(level_count) word.ends.append(level_count + len(tier_elements)) annotations[n] = tier_elements annotations['spelling'] = [word] data.add_annotations(**annotations) #if data[n].delimiter and not trans_check: # raise(DelimiterError('The transcription delimiter specified does not create multiple segments. Please specify another delimiter.')) return data
[docs]def load_directory_transcription(corpus_name, path, annotation_types = None, feature_system_path = None, stop_check = None, call_back = None): """ Loads a directory of transcribed texts. Parameters ---------- corpus_name : str Name of corpus path : str Path to directory of text files annotation_types : list of AnnotationType, optional List of AnnotationType specifying how to parse text files feature_system_path : str, optional File path of FeatureMatrix binary to specify segments stop_check : callable, optional Optional function to check whether to gracefully terminate early call_back : callable, optional Optional function to supply progress information during the loading Returns ------- SpontaneousSpeechCorpus Corpus containing Discourses corresponding to the text files """ if call_back is not None: call_back('Finding files...') call_back(0, 0) file_tuples = [] for root, subdirs, files in os.walk(path): for filename in files: if not filename.lower().endswith('.txt'): continue file_tuples.append((root, filename)) if call_back is not None: call_back('Parsing files...') call_back(0,len(file_tuples)) cur = 0 corpus = SpontaneousSpeechCorpus(corpus_name, path) for i, t in enumerate(file_tuples): if stop_check is not None and stop_check(): return if call_back is not None: call_back('Parsing file {} of {}...'.format(i+1,len(file_tuples))) call_back(i) root, filename = t name = os.path.splitext(filename)[0] d = load_discourse_transcription(name, os.path.join(root,filename), annotation_types, corpus.lexicon, None, stop_check, call_back) corpus.add_discourse(d) return corpus
[docs]def load_discourse_transcription(corpus_name, path, annotation_types = None, lexicon = None, feature_system_path = None, stop_check = None, call_back = None): """ Load a discourse from a text file containing running transcribed text Parameters ---------- corpus_name : str Informative identifier to refer to corpus path : str Full path to text file annotation_types : list of AnnotationType, optional List of AnnotationType specifying how to parse text files lexicon : Corpus, optional Corpus to store Discourse word information feature_system_path : str, optional Full path to pickled FeatureMatrix to use with the Corpus stop_check : callable, optional Optional function to check whether to gracefully terminate early call_back : callable, optional Optional function to supply progress information during the loading Returns ------- Discourse Discourse object generated from the text file """ if feature_system_path is not None: if not os.path.exists(feature_system_path): raise(PCTOSError("The feature path specified ({}) does not exist".format(feature_system_path))) data = transcription_text_to_data(path, annotation_types, stop_check, call_back) discourse = data_to_discourse(data, lexicon) discourse.name = corpus_name if feature_system_path is not None: feature_matrix = load_binary(feature_system_path) discourse.lexicon.set_feature_matrix(feature_matrix) return discourse
[docs]def export_discourse_transcription(discourse, path, trans_delim = '.', single_line = False): """ Export an transcribed discourse to a text file Parameters ---------- discourse : Discourse Discourse object to export path : str Path to export to trans_delim : str, optional Delimiter for segments, defaults to ``.`` single_line : bool, optional Flag to enforce all text to be on a single line, defaults to False. If False, lines are 10 words long. """ with open(path, encoding='utf-8', mode='w') as f: count = 0 for i, wt in enumerate(discourse): count += 1 f.write(trans_delim.join(wt.transcription)) if i != len(discourse) -1: if not single_line and count <= 10: f.write(' ') else: count = 0 f.write('\n')