sep28k.py

# coding=utf-8

"""SEP-28K dataset."""

import os
import gzip
import shutil
import urllib.request
from typing import List
from pathlib import Path

import librosa
import datasets
import pandas as pd
from rich import print
from tqdm.auto import tqdm

SAMPLING_RATE = 16_000

CLASSES = ['block', 'prolongation', 'sound_rep', 'word_rep', 'interjection', 'no_dysfluencies']


class SEP28KConfig(datasets.BuilderConfig):
    """BuilderConfig for SEP-28K."""
    
    def __init__(self, features, **kwargs):
        super(SEP28KConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
        self.features = features


class SEP28K(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        SEP28KConfig(
            features=datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=SAMPLING_RATE),
                    # "speaker": datasets.Value("string"), 
                    # "duration": datasets.Value("int32"), 
                    "start": datasets.Value("int32"), 
                    "end": datasets.Value("int32"), 
                    "stutter": datasets.Sequence(datasets.Value("string")), 
                    "label": datasets.Sequence(datasets.features.ClassLabel(names=CLASSES)), 
                }
            ),
            name="sep28k", 
            description="",
        ), 
        SEP28KConfig(
            features=datasets.Features(
                {
                    "audio": datasets.Audio(sampling_rate=SAMPLING_RATE),
                    # "speaker": datasets.Value("string"), 
                    # "duration": datasets.Value("int32"), 
                    "start": datasets.Value("int32"), 
                    "end": datasets.Value("int32"), 
                    "stutter": datasets.Sequence(datasets.Value("string")), 
                    "label": datasets.Sequence(datasets.features.ClassLabel(names=CLASSES)), 
                }
            ),
            name="fluencybank", 
            description="",
        ), 
    ]

    DEFAULT_CONFIG_NAME = "sep28k"

    def __init__(
        self, 
        cache_dir = None, 
        dataset_name = None, 
        config_name = None, 
        hash = None, 
        base_path = None, 
        info = None, 
        features = None, 
        token = None, 
        repo_id = None, 
        data_files = None, 
        data_dir = None, 
        storage_options = None, 
        writer_batch_size = None, 
        **config_kwargs
    ):
        super().__init__(
            cache_dir, 
            dataset_name, 
            config_name, 
            hash, 
            base_path, 
            info, 
            features, 
            token, 
            repo_id, 
            data_files, 
            data_dir, 
            storage_options, 
            writer_batch_size, 
            **config_kwargs
        )

    def _info(self):
        return datasets.DatasetInfo(
            description="SEP-28K dataset",
            features=self.config.features,
        )

    def _split_generators(self, dl_manager):
        if dl_manager.manual_dir is None:
            from datasets.config import HF_DATASETS_CACHE

            data_dir = os.path.join(HF_DATASETS_CACHE, "downloads")
            print(f'`data_dir` is None, set the path to {data_dir}')
        else:
            data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))

        if not os.path.exists(os.path.join(data_dir, 'clips')):
            download_file(
                'https://huggingface.co/datasets/confit/sep-28k/resolve/main/archive.zip', 
                dest=os.path.join(data_dir, 'archive.zip'), 
                unpack=True, 
                write_permissions=True
            )

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir}),
        ]

    def _generate_examples(self, data_dir):
        """Generate examples from SEP-28K"""
        if self.config.name == 'sep28k':
            metadata_df = pd.read_csv(os.path.join(data_dir, 'SEP-28k_labels.csv'))
        elif self.config.name == 'fluencybank':
            metadata_df = pd.read_csv(os.path.join(data_dir, 'fluencybank_labels.csv'))
        metadata_df = metadata_df[metadata_df['Unsure'] == 0].reset_index(drop=True)
        print(metadata_df)
        
        threshold = 2 # https://arxiv.org/pdf/2102.12394
        _mapping = {}
        for idx, row in metadata_df.iterrows():
            filename = f"{row['Show']}_{row['EpId']}_{row['ClipId']}"
            start = row['Start']
            end = row['Stop']
            block = 1 if row['Block'] >= threshold else 0
            prolongation = 1 if row['Prolongation'] >= threshold else 0
            sound_rep = 1 if row['SoundRep'] >= threshold else 0
            word_rep = 1 if row['WordRep'] >= threshold else 0
            interjection = 1 if row['Interjection'] >= threshold else 0
            # no_stuttered_words = 1 if row['NoStutteredWords'] >= 1 else 0
            dysfluencies = sum([prolongation, block, sound_rep, word_rep, interjection])
            no_dysfluencies = 1 if dysfluencies == 0 else 0

            stutter = []
            if block == 1:
                stutter.append('block')
            if prolongation == 1:
                stutter.append('prolongation')
            if sound_rep == 1:
                stutter.append('sound_rep')
            if word_rep == 1:
                stutter.append('word_rep')
            if interjection == 1:
                stutter.append('interjection')
            if no_dysfluencies == 1:
                stutter.append('no_dysfluencies')

            _mapping[filename] = {
                'filename': filename,
                'start': start,
                'end': end,
                'block': block,
                'prolongation': prolongation,
                'sound_rep': sound_rep,
                'word_rep': word_rep,
                'interjection': interjection,
                'no_dysfluencies': no_dysfluencies,
                'stutter': stutter, 
            }

        # Iterating the contents of the data to extract the relevant information
        extensions = ['.wav']

        _, wav_paths = fast_scandir(data_dir, extensions, recursive=True)

        for guid, wav_path in enumerate(wav_paths):
            # duration = librosa.get_duration(path=wav_path)
            # if duration <= 0:
            #     continue
            try:
                fileid = Path(wav_path).stem
                info = _mapping[fileid]
                yield guid, {
                    "id": str(guid),
                    "audio": wav_path, 
                    "stutter": info['stutter'], 
                    "label": info['stutter'],
                    "start": start, 
                    "end": end, 
                }
            except:
                continue


def fast_scandir(path: str, extensions: List[str], recursive: bool = False):
    # Scan files recursively faster than glob
    # From github.com/drscotthawley/aeiou/blob/main/aeiou/core.py
    subfolders, files = [], []

    try: # hope to avoid 'permission denied' by this try
        for f in os.scandir(path):
            try:  # 'hope to avoid too many levels of symbolic links' error
                if f.is_dir():
                    subfolders.append(f.path)
                elif f.is_file():
                    if os.path.splitext(f.name)[1].lower() in extensions:
                        files.append(f.path)
            except Exception:
                pass
    except Exception:
        pass

    if recursive:
        for path in list(subfolders):
            sf, f = fast_scandir(path, extensions, recursive=recursive)
            subfolders.extend(sf)
            files.extend(f)  # type: ignore

    return subfolders, files


def download_file(
    source,
    dest,
    unpack=False,
    dest_unpack=None,
    replace_existing=False,
    write_permissions=False,
):
    """Downloads the file from the given source and saves it in the given
    destination path.
     Arguments
    ---------
    source : path or url
        Path of the source file. If the source is an URL, it downloads it from
        the web.
    dest : path
        Destination path.
    unpack : bool
        If True, it unpacks the data in the dest folder.
    dest_unpack: path
        Path where to store the unpacked dataset
    replace_existing : bool
        If True, replaces the existing files.
    write_permissions: bool
        When set to True, all the files in the dest_unpack directory will be granted write permissions.
        This option is active only when unpack=True.
    """
    class DownloadProgressBar(tqdm):
        """DownloadProgressBar class."""

        def update_to(self, b=1, bsize=1, tsize=None):
            """Needed to support multigpu training."""
            if tsize is not None:
                self.total = tsize
            self.update(b * bsize - self.n)

    # Create the destination directory if it doesn't exist
    dest_dir = Path(dest).resolve().parent
    dest_dir.mkdir(parents=True, exist_ok=True)
    if "http" not in source:
        shutil.copyfile(source, dest)

    elif not os.path.isfile(dest) or (
        os.path.isfile(dest) and replace_existing
    ):
        print(f"Downloading {source} to {dest}")
        with DownloadProgressBar(
            unit="B",
            unit_scale=True,
            miniters=1,
            desc=source.split("/")[-1],
        ) as t:
            urllib.request.urlretrieve(
                source, filename=dest, reporthook=t.update_to
            )
    else:
        print(f"{dest} exists. Skipping download")

    # Unpack if necessary
    if unpack:
        if dest_unpack is None:
            dest_unpack = os.path.dirname(dest)
        print(f"Extracting {dest} to {dest_unpack}")
        # shutil unpack_archive does not work with tar.gz files
        if (
            source.endswith(".tar.gz")
            or source.endswith(".tgz")
            or source.endswith(".gz")
        ):
            out = dest.replace(".gz", "")
            with gzip.open(dest, "rb") as f_in:
                with open(out, "wb") as f_out:
                    shutil.copyfileobj(f_in, f_out)
        else:
            shutil.unpack_archive(dest, dest_unpack)
        if write_permissions:
            set_writing_permissions(dest_unpack)


def set_writing_permissions(folder_path):
    """
    This function sets user writing permissions to all the files in the given folder.
    Arguments
    ---------
    folder_path : folder
        Folder whose files will be granted write permissions.
    """
    for root, dirs, files in os.walk(folder_path):
        for file_name in files:
            file_path = os.path.join(root, file_name)
            # Set writing permissions (mode 0o666) to the file
            os.chmod(file_path, 0o666)