import os import glob import datasets import pandas as pd from sklearn.model_selection import train_test_split _DESCRIPTION = """\ This new dataset is designed to solve this great NLP task and is crafted with a lot of care. """ _CITATION = """\ """ _CHANNEL_CONFIGS = sorted([ "CHANNEL0", "CHANNEL1", "CHANNEL2" ]) _GENDER_CONFIGS = sorted(["F", "M"]) _RACE_CONFIGS = sorted(["CHINESE", "MALAY", "INDIAN", "OTHERS"]) _HOMEPAGE = "https://huggingface.co/indonesian-nlp/librivox-indonesia" _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" _PATH_TO_DATA = './IMDA - National Speech Corpus/PART1' # _PATH_TO_DATA = './PART1/DATA' class Minds14Config(datasets.BuilderConfig): """BuilderConfig for xtreme-s""" def __init__( self, channel, gender, race, description, homepage, path_to_data ): super(Minds14Config, self).__init__( name=channel+gender+race, version=datasets.Version("1.0.0", ""), description=self.description, ) self.channel = channel self.gender = gender self.race = race self.description = description self.homepage = homepage self.path_to_data = path_to_data def _build_config(channel, gender, race): return Minds14Config( channel=channel, gender=gender, race=race, description=_DESCRIPTION, homepage=_HOMEPAGE, path_to_data=_PATH_TO_DATA, ) # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class NewDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.1.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [] for channel in _CHANNEL_CONFIGS + ["all"]: for gender in _GENDER_CONFIGS + ["all"]: for race in _RACE_CONFIGS + ["all"]: BUILDER_CONFIGS.append(_build_config(channel, gender, race)) # BUILDER_CONFIGS = [_build_config(name) for name in _CHANNEL_CONFIGS + ["all"]] DEFAULT_CONFIG_NAME = "allallall" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset task_templates = None # mics = _CHANNEL_CONFIGS features = datasets.Features( { "audio": datasets.features.Audio(sampling_rate=16000), "transcript": datasets.Value("string"), "mic": datasets.Value("string"), "audio_name": datasets.Value("string"), "gender": datasets.Value("string"), "race": datasets.Value("string"), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. supervised_keys=("audio", "transcript"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, task_templates=task_templates, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name mics = ( _CHANNEL_CONFIGS if self.config.channel == "all" else [self.config.channel] ) gender = ( _GENDER_CONFIGS if self.config.gender == "all" else [self.config.gender] ) race = ( _RACE_CONFIGS if self.config.race == "all" else [self.config.race] ) # augment speaker ids directly here # read the speaker information train_speaker_ids = [] test_speaker_ids = [] # path_to_speaker = os.path.join(self.config.path_to_data, "DOC", "Speaker Information (Part 1).XLSX") path_to_speaker = dl_manager.download(os.path.join(self.config.path_to_data, "DOC", "Speaker Information (Part 1).XLSX")) speaker_df = pd.read_excel(path_to_speaker, dtype={'SCD/PART1': object}) for g in gender: for r in race: X = speaker_df[(speaker_df["ACC"]==r) & (speaker_df["SEX"]==g)] X_train, X_test = train_test_split(X, test_size=0.3, random_state=42, shuffle=True) train_speaker_ids.extend(X_train["SCD/PART1"]) test_speaker_ids.extend(X_test["SCD/PART1"]) # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "path_to_data": self.config.path_to_data, "speaker_metadata":speaker_df, # "speaker_ids": train_speaker_ids, "speaker_ids":["0001"], "mics": mics, "dl_manager": dl_manager }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "path_to_data": self.config.path_to_data, "speaker_metadata":speaker_df, # "speaker_ids": test_speaker_ids, "speaker_ids": ["0003"], "mics": mics, "dl_manager": dl_manager }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples( self, path_to_data, speaker_metadata, speaker_ids, mics, dl_manager ): id_ = 0 for mic in mics: for speaker in speaker_ids: # TRANSCRIPT: in the case of error, if no file found then dictionary will b empty d = {} counter = 0 while counter < 10: data = dl_manager.download(os.path.join(path_to_data, "DATA", mic, "SCRIPT", mic[-1]+speaker+str(counter)+'.TXT')) try: line_num = 0 with open(data, encoding='utf-8-sig') as f: for line in f: if line_num == 0: key = line.split("\t")[0] line_num += 1 elif line_num == 1: d[key] = line.strip() line_num -= 1 except: print(f"{counter}") break counter+=1 # AUDIO: in the case of error it will skip the speaker # archive_path = os.path.join(path_to_data, "DATA", mic, "WAVE", "SPEAKER"+speaker+'.zip') archive_path = dl_manager.download(os.path.join(path_to_data, "DATA", mic, "WAVE", "SPEAKER"+speaker+'.zip')) # check that archive path exists, else will not open the archive if os.path.exists(archive_path): audio_files = dl_manager.iter_archive(archive_path) for path, f in audio_files: # bug catching if any error? result = {} full_path = os.path.join(archive_path, path) if archive_path else path # bug catching here result["audio"] = {"path": full_path, "bytes": f.read()} result["audio_name"] = path result["mic"] = mic metadata_row = speaker_metadata.loc[speaker_metadata["SCD/PART1"]==speaker].iloc[0] result["gender"]=metadata_row["SEX"] result["race"]=metadata_row["ACC"] try: result["transcript"] = d[f.name[-13:-4]] yield id_, result id_ += 1 except: print(f"unable to find transcript")