adding local loading script

loadingScript_imda.py

@@ -0,0 +1,220 @@
+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 = r'C:\Users\calic\Downloads\huggingface-dataset\imda-dataset\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")
+        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
+                metadata_path = os.path.join(path_to_data, "DATA", mic, "SCRIPT", mic[-1]+speaker+'*.TXT')
+                script_list = glob.glob(metadata_path)
+                d = {}
+                for script in script_list:
+                    line_num = 0
+                    with open(script, 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
+                # 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')
+                # 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["transcript"] = d[f.name[-13:-4]]
+                        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"]
+                        yield id_, result
+                        id_ += 1