Initial upload of medley-solos-db dataset script

README.md

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+# Medley-Solos-DB
+
+Medley-Solos-DB is a dataset of monophonic instrument solos across 8 musical classes:
+- clarinet
+- distorted electric guitar
+- female singer
+- flute
+- piano
+- tenor saxophone
+- trumpet
+- violin
+
+Each sample is labeled and paired with audio data at 44.1 kHz sampling rate.
+
+## Source
+Originally published on [Zenodo](https://zenodo.org/record/3464194).
+
+## License
+Creative Commons Attribution 4.0 International (CC BY 4.0)
+

medley-solos-db/medley-solos-db.py

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+# coding=utf-8
+
+"""Medley-Solos-DB dataset."""
+
+
+import os
+import gzip
+import shutil
+import pathlib
+import datasets
+import typing as tp
+import pandas as pd
+import urllib.request
+from pathlib import Path
+from rich import print
+from tqdm.auto import tqdm
+
+
+SAMPLE_RATE = 44_100
+
+# Cache location
+VERSION = "0.0.1"
+DEFAULT_XDG_CACHE_HOME = "~/.cache"
+XDG_CACHE_HOME = os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
+DEFAULT_HF_CACHE_HOME = os.path.join(XDG_CACHE_HOME, "huggingface")
+HF_CACHE_HOME = os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))
+DEFAULT_HF_DATASETS_CACHE = os.path.join(HF_CACHE_HOME, "datasets")
+HF_DATASETS_CACHE = Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))
+
+CLASSES = [
+    'clarinet', 'distorted electric guitar', 'female singer', 'flute', 'piano', 'tenor saxophone', 'trumpet', 'violin'
+]
+CLASS2INDEX = {cls:idx for idx, cls in enumerate(CLASSES)}
+INDEX2CLASS = {idx:cls for idx, cls in enumerate(CLASSES)}
+
+
+class MedleySolosDBConfig(datasets.BuilderConfig):
+    """BuilderConfig for Medley-Solos-DB."""
+    
+    def __init__(self, features, **kwargs):
+        super(MedleySolosDBConfig, self).__init__(version=datasets.Version(VERSION, ""), **kwargs)
+        self.features = features
+
+
+class MedleySolosDB(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        MedleySolosDBConfig(
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
+                    "instrument": datasets.Value("string"), 
+                    "label": datasets.features.ClassLabel(names=CLASSES), 
+                }
+            ),
+            name="v1.2", 
+            description="",
+        ), 
+    ]
+
+    DEFAULT_CONFIG_NAME = "v1.2"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="",
+            features=self.config.features,
+            supervised_keys=None,
+            homepage="",
+            citation="",
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        zip_file_url = "https://zenodo.org/records/3464194/files/Medley-solos-DB.tar.gz"
+        _filename = zip_file_url.split('/')[-1]
+        _save_path = os.path.join(
+            HF_DATASETS_CACHE, 'confit___medley-solos-db/v1.2', VERSION, _filename
+        )
+        download_file(zip_file_url, _save_path)
+        print(f"`{_filename}` is downloaded to {_save_path}")
+        archive_path = dl_manager.extract(_save_path)
+        print(f"`{_filename}` is now extracted to {archive_path}")
+
+        metadata_df = pd.read_csv("https://zenodo.org/records/3464194/files/Medley-solos-DB_metadata.csv")
+        train_df = metadata_df[metadata_df["subset"] == "training"].reset_index(drop=True)
+        validation_df = metadata_df[metadata_df["subset"] == "validation"].reset_index(drop=True)
+        test_df = metadata_df[metadata_df["subset"] == "test"].reset_index(drop=True)
+
+        extensions = ['.wav']
+        _, _walker = fast_scandir(archive_path, extensions, recursive=True)
+
+        def get_fileid2class(df, split=None):
+            fileid2class = {}
+            for idx, row in df.iterrows():
+                _fileid = row['uuid4']
+                class_ = row['instrument']
+                class_id = row['instrument_id']
+                fileid = f"Medley-solos-DB_{split}-{class_id}_{_fileid}"
+                fileid2class[fileid] = class_
+            return fileid2class
+
+        train_fileid2class = get_fileid2class(train_df, 'training')
+        validation_fileid2class = get_fileid2class(validation_df, 'validation')
+        test_fileid2class = get_fileid2class(test_df, 'test')
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepaths": _walker, "split": "train", "fileid2class": train_fileid2class}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": _walker, "split": "validation", "fileid2class": validation_fileid2class}
+            ), 
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepaths": _walker, "split": "test", "fileid2class": test_fileid2class}
+            ), 
+        ]
+
+    def _generate_examples(self, filepaths, split=None, fileid2class=None):
+        for guid, audio_path in enumerate(filepaths):
+            fileid = Path(audio_path).stem
+            if fileid not in fileid2class:
+                continue
+            instrument = fileid2class.get(fileid)
+            yield guid, {
+                "id": str(guid), 
+                "audio": audio_path, 
+                "instrument": instrument, 
+                "label": instrument, 
+            }
+
+
+
+def fast_scandir(path: str, exts: tp.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 exts:
+                        files.append(f.path)
+            except Exception:
+                pass
+    except Exception:
+        pass
+
+    if recursive:
+        for path in list(subfolders):
+            sf, f = fast_scandir(path, exts, 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 = pathlib.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)