# coding=utf-8 # Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. # # 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. # Lint as: python3 """AMI Corpus""" import os import warnings import xml.etree.ElementTree as ET import numpy as np import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @inproceedings{10.1007/11677482_3, author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain and Post, Wilfried and Reidsma, Dennis and Wellner, Pierre}, title = {The AMI Meeting Corpus: A Pre-Announcement}, year = {2005}, isbn = {3540325492}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, url = {https://doi.org/10.1007/11677482_3}, doi = {10.1007/11677482_3}, abstract = {The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. It is being created in the context of a project that is developing meeting browsing technology and will eventually be released publicly. Some of the meetings it contains are naturally occurring, and some are elicited, particularly using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The corpus is being recorded using a wide range of devices including close-talking and far-field microphones, individual and room-view video cameras, projection, a whiteboard, and individual pens, all of which produce output signals that are synchronized with each other. It is also being hand-annotated for many different phenomena, including orthographic transcription, discourse properties such as named entities and dialogue acts, summaries, emotions, and some head and hand gestures. We describe the data set, including the rationale behind using elicited material, and explain how the material is being recorded, transcribed and annotated.}, booktitle = {Proceedings of the Second International Conference on Machine Learning for Multimodal Interaction}, pages = {28–39}, numpages = {12}, location = {Edinburgh, UK}, series = {MLMI'05} } """ _URL = "https://groups.inf.ed.ac.uk/ami/corpus/" _DL_URL_ANNOTATIONS = "http://groups.inf.ed.ac.uk/ami/AMICorpusAnnotations/ami_public_manual_1.6.2.zip" _DL_SAMPLE_FORMAT = "https://groups.inf.ed.ac.uk/ami/AMICorpusMirror//amicorpus/{}/audio/{}" _SPEAKERS = ["A", "B", "C", "D", "E"] # Commented out samples don't seem to exist _TRAIN_SAMPLE_IDS = [ "ES2002a", "ES2002b", "ES2002c", "ES2002d", "ES2003a", "ES2003b", "ES2003c", "ES2003d", "ES2005a", "ES2005b", "ES2005c", "ES2005d", "ES2006a", "ES2006b", "ES2006c", "ES2006d", "ES2007a", "ES2007b", "ES2007c", "ES2007d", "ES2008a", "ES2008b", "ES2008c", "ES2008d", "ES2009a", "ES2009b", "ES2009c", "ES2009d", "ES2010a", "ES2010b", "ES2010c", "ES2010d", "ES2012a", "ES2012b", "ES2012c", "ES2012d", "ES2013a", "ES2013b", "ES2013c", "ES2013d", "ES2014a", "ES2014b", "ES2014c", "ES2014d", "ES2015a", "ES2015b", "ES2015c", "ES2015d", "ES2016a", "ES2016b", "ES2016c", "ES2016d", "IS1000a", "IS1000b", "IS1000c", "IS1000d", "IS1001a", "IS1001b", "IS1001c", "IS1001d", "IS1002b", "IS1002c", "IS1002d", "IS1003a", "IS1003b", "IS1003c", "IS1003d", "IS1004a", "IS1004b", "IS1004c", "IS1004d", "IS1005a", "IS1005b", "IS1005c", "IS1006a", "IS1006b", "IS1006c", "IS1006d", "IS1007a", "IS1007b", "IS1007c", "IS1007d", "TS3005a", "TS3005b", "TS3005c", "TS3005d", "TS3006a", "TS3006b", "TS3006c", "TS3006d", "TS3007a", "TS3007b", "TS3007c", "TS3007d", "TS3008a", "TS3008b", "TS3008c", "TS3008d", "TS3009a", "TS3009b", "TS3009c", "TS3009d", "TS3010a", "TS3010b", "TS3010c", "TS3010d", "TS3011a", "TS3011b", "TS3011c", "TS3011d", "TS3012a", "TS3012b", "TS3012c", "TS3012d", "EN2001a", "EN2001b", "EN2001d", "EN2001e", "EN2003a", "EN2004a", "EN2005a", "EN2006a", "EN2006b", "EN2009b", "EN2009c", "EN2009d", "IN1001", "IN1002", "IN1005", "IN1007", "IN1008", "IN1009", "IN1012", "IN1013", "IN1014", "IN1016", ] _VALIDATION_SAMPLE_IDS = [ "ES2011a", "ES2011b", "ES2011c", "ES2011d", "IS1008a", "IS1008b", "IS1008c", "IS1008d", "TS3004a", "TS3004b", "TS3004c", "TS3004d", "IB4001", "IB4002", "IB4003", "IB4004", "IB4010", "IB4011", ] _EVAL_SAMPLE_IDS = [ "ES2004a", "ES2004b", "ES2004c", "ES2004d", "IS1009a", "IS1009b", "IS1009c", "IS1009d", "TS3003a", "TS3003b", "TS3003c", "TS3003d", "EN2002a", "EN2002b", "EN2002c", "EN2002d", ] _DESCRIPTION = """\ The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals synchronized to a common timeline. These include close-talking and far-field microphones, individual and room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings, the participants also have unsynchronized pens available to them that record what is written. The meetings were recorded in English using three different rooms with different acoustic properties, and include mostly non-native speakers. \n """ class AMIConfig(datasets.BuilderConfig): """BuilderConfig for LibriSpeechASR.""" def __init__(self, formats, missing_files=None, **kwargs): """ Args: formats: `List[string]`, a list of audio file formats missing_files: `List[string]`, a list of missing audio file ids **kwargs: keyword arguments forwarded to super. """ self.dl_path_formats = [_DL_SAMPLE_FORMAT + "." + f + ".wav" for f in formats] # for microphone configs some audio files are missing self.missing_files = missing_files if missing_files is not None else [] super(AMIConfig, self).__init__(version=datasets.Version("1.6.2", ""), **kwargs) class AMI(datasets.GeneratorBasedBuilder): """AMI dataset.""" BUILDER_CONFIGS = [ AMIConfig(name="headset-single", formats=["Mix-Headset"], description=""), AMIConfig(name="headset-multi", formats=["Headset-0", "Headset-1", "Headset-2", "Headset-3"], description=""), AMIConfig( name="microphone-single", formats=["Array1-01"], missing_files=["IS1003b", "IS1007d"], ), AMIConfig( name="microphone-multi", formats=[ "Array1-01", "Array1-02", "Array1-03", "Array1-04", "Array1-05", "Array1-06", "Array1-07", "Array1-08", ], missing_files=["IS1003b", "IS1007d"], ), ] def _info(self): warnings.warn( """ This version of the AMI dataset is deprecated. You can download the latest one (based on the official Kaldi recipes) with >>> load_dataset(\"edinburghcstr/ami\", \"ihm\") # for the "independent headset microphone" part or >>> load_dataset(\"edinburghcstr/ami\", \"sdm\") # for the "single distant microphone" part """ ) features_dict = { "word_ids": datasets.Sequence(datasets.Value("string")), "word_start_times": datasets.Sequence(datasets.Value("float")), "word_end_times": datasets.Sequence(datasets.Value("float")), "word_speakers": datasets.Sequence(datasets.Value("string")), "segment_ids": datasets.Sequence(datasets.Value("string")), "segment_start_times": datasets.Sequence(datasets.Value("float")), "segment_end_times": datasets.Sequence(datasets.Value("float")), "segment_speakers": datasets.Sequence(datasets.Value("string")), "words": datasets.Sequence(datasets.Value("string")), "channels": datasets.Sequence(datasets.Value("string")), } if self.config.name == "headset-single": features_dict.update({"file": datasets.Value("string")}) features_dict.update({"audio": datasets.features.Audio(sampling_rate=16_000)}) config_description = ( "Close talking audio of single headset. " "This configuration only includes audio belonging to the " "headset of the person currently speaking." ) elif self.config.name == "microphone-single": features_dict.update({"file": datasets.Value("string")}) features_dict.update({"audio": datasets.features.Audio(sampling_rate=16_000)}) config_description = ( "Far field audio of single microphone. " "This configuration only includes audio belonging the first microphone, " "*i.e.* 1-1, of the microphone array." ) elif self.config.name == "headset-multi": features_dict.update({f"file-{i}": datasets.Value("string") for i in range(4)}) features_dict.update({f"file-{i}": datasets.features.Audio(sampling_rate=16_000) for i in range(4)}) config_description = ( "Close talking audio of four individual headset. " "This configuration includes audio belonging to four individual headsets." " For each annotation there are 4 audio files 0, 1, 2, 3." ) elif self.config.name == "microphone-multi": features_dict.update({f"file-1-{i}": datasets.Value("string") for i in range(1, 8)}) features_dict.update({f"file-1-{i}": datasets.features.Audio(sampling_rate=16_000) for i in range(1, 8)}) config_description = ( "Far field audio of microphone array. " "This configuration includes audio of " "the first microphone array 1-1, 1-2, ..., 1-8." ) else: raise ValueError(f"Configuration {self.config.name} does not exist.") return datasets.DatasetInfo( description=_DESCRIPTION + config_description, features=datasets.Features(features_dict), homepage=_URL, citation=_CITATION, ) def _split_generators(self, dl_manager): # multi-processing doesn't work for AMI if hasattr(dl_manager, "download_config") and dl_manager.download_config.num_proc != 1: logger.warning( "AMI corpus cannot be downloaded using multi-processing. " "Setting number of downloaded processes `num_proc` to 1. " ) dl_manager.download_config.num_proc = 1 annotation_path = dl_manager.download_and_extract(_DL_URL_ANNOTATIONS) # train train_files = [path.format(_id, _id) for _id in _TRAIN_SAMPLE_IDS for path in self.config.dl_path_formats] train_files = list( filter(lambda f: f.split("/")[-1].split(".")[0] not in self.config.missing_files, train_files) ) train_ids = [f.split("/")[-1].split(".")[0] for f in train_files] train_path = dl_manager.download_and_extract(train_files) # validation validation_files = [ path.format(_id, _id) for _id in _VALIDATION_SAMPLE_IDS for path in self.config.dl_path_formats ] validation_ids = [f.split("/")[-1].split(".")[0] for f in validation_files] validation_path = dl_manager.download_and_extract(validation_files) # test eval_files = [path.format(_id, _id) for _id in _EVAL_SAMPLE_IDS for path in self.config.dl_path_formats] eval_ids = [f.split("/")[-1].split(".")[0] for f in eval_files] eval_path = dl_manager.download_and_extract(eval_files) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "annotation_path": annotation_path, "samples_paths": train_path, "ids": tuple(_TRAIN_SAMPLE_IDS), "verification_ids": train_ids, }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "annotation_path": annotation_path, "samples_paths": validation_path, "ids": tuple(_VALIDATION_SAMPLE_IDS), "verification_ids": validation_ids, }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "annotation_path": annotation_path, "samples_paths": eval_path, "ids": tuple(_EVAL_SAMPLE_IDS), "verification_ids": eval_ids, }, ), ] @staticmethod def _sort(key, lists): indices = np.argsort(key) sorted_lists = [np.array(array)[indices].tolist() for array in lists] return sorted_lists @staticmethod def _extract_words_annotations(paths): word_ids = [] word_start_times = [] word_end_times = [] words = [] word_speakers = [] for path in paths: # retrive speaker speaker = path.split(".")[-3] with open(path, "r", encoding="utf-8") as words_file: root = ET.parse(words_file).getroot() for type_tag in root.findall("w"): word_id = type_tag.get("{http://nite.sourceforge.net/}id") word_start_time = type_tag.get("starttime") word_end_time = type_tag.get("endtime") text = type_tag.text if word_start_time is not None and word_end_time is not None: word_ids.append(word_id) word_start_times.append(float(word_start_time)) word_end_times.append(float(word_end_time)) words.append(text) word_speakers.append(speaker) else: logger.warning( f"Annotation {word_id} of file {path} is missing information about" "either word_start_time or word_end_time. Skipping sample..." ) return AMI._sort(word_start_times, [word_ids, word_start_times, word_end_times, words, word_speakers]) @staticmethod def _extract_segments_annotations(paths): segment_ids = [] channels = [] segment_start_times = [] segment_end_times = [] segment_speakers = [] for path in paths: speaker = path.split(".")[-3] with open(path, "r", encoding="utf-8") as segments_file: root = ET.parse(segments_file).getroot() for type_tag in root.findall("segment"): segment_ids.append(type_tag.get("{http://nite.sourceforge.net/}id")) segment_start_times.append(float(type_tag.get("transcriber_start"))) segment_end_times.append(float(type_tag.get("transcriber_end"))) channels.append(type_tag.get("channel")) segment_speakers.append(speaker) return AMI._sort( segment_start_times, [segment_ids, segment_start_times, segment_end_times, channels, segment_speakers] ) def _generate_examples(self, annotation_path, samples_paths, ids, verification_ids): logger.info(f"⏳ Generating {', '.join(ids)}") # number of audio files of config num_audios = len(self.config.dl_path_formats) # filter missing ids ids = list(filter(lambda n: n not in self.config.missing_files, ids)) # audio samples_paths_dict = {} for i, _id in enumerate(ids): sample_paths = samples_paths[num_audios * i : num_audios * (i + 1)] sample_verification_id = set(verification_ids[num_audios * i : num_audios * (i + 1)]) # make sure that multi microphone samples are correctly attributed to labels if len(sample_verification_id) > 1 or next(iter(sample_verification_id)) != _id: raise ValueError( f"Incorrect dataset generation. The files {sample_paths} of id {_id} have incorrect verification_ids {sample_verification_id}." ) # set correct files correctly samples_paths_dict[_id] = sample_paths # words words_paths = { _id: [os.path.join(annotation_path, f"words/{_id}.{speaker}.words.xml") for speaker in _SPEAKERS] for _id in ids } words_paths = {_id: list(filter(lambda path: os.path.isfile(path), words_paths[_id])) for _id in ids} words_paths = {key: words_paths[key] for key in words_paths if len(words_paths[key]) > 0} # segments segments_paths = { _id: [os.path.join(annotation_path, f"segments/{_id}.{speaker}.segments.xml") for speaker in _SPEAKERS] for _id in ids } segments_paths = {_id: list(filter(lambda path: os.path.isfile(path), segments_paths[_id])) for _id in ids} segments_paths = {key: segments_paths[key] for key in segments_paths if len(segments_paths[key]) > 0} for _id in words_paths.keys(): word_ids, word_start_times, word_end_times, words, word_speakers = self._extract_words_annotations( words_paths[_id] ) ( segment_ids, segment_start_times, segment_end_times, channels, segment_speakers, ) = self._extract_segments_annotations(segments_paths[_id]) result = { "word_ids": word_ids, "word_start_times": word_start_times, "word_end_times": word_end_times, "word_speakers": word_speakers, "segment_ids": segment_ids, "segment_start_times": segment_start_times, "segment_end_times": segment_end_times, "segment_speakers": segment_speakers, "channels": channels, "words": words, } if self.config.name in ["headset-single", "microphone-single"]: result.update({"file": samples_paths_dict[_id][0], "audio": samples_paths_dict[_id][0]}) elif self.config.name in ["headset-multi"]: result.update({f"file-{i}": samples_paths_dict[_id][i] for i in range(num_audios)}) result.update({f"audio-{i}": samples_paths_dict[_id][i] for i in range(num_audios)}) elif self.config.name in ["microphone-multi"]: result.update({f"file-1-{i+1}": samples_paths_dict[_id][i] for i in range(num_audios)}) result.update({f"audio-1-{i+1}": samples_paths_dict[_id][i] for i in range(num_audios)}) else: raise ValueError(f"Configuration {self.config.name} does not exist.") yield _id, result