Source code for torchaudio.datasets.musdb_hq

import os
from pathlib import Path
from typing import List, Optional, Tuple, Union

import torch
import torchaudio
from torch.hub import download_url_to_file
from import Dataset
from torchaudio.datasets.utils import _extract_zip

_URL = ""
_CHECKSUM = "baac80d0483c61d74b2e5f3be75fa557eec52898339e6aa45c1fa48833c5d21d"
_EXT = ".wav"
_SAMPLE_RATE = 44100
    "Actions - One Minute Smile",
    "Clara Berry And Wooldog - Waltz For My Victims",
    "Johnny Lokke - Promises & Lies",
    "Patrick Talbot - A Reason To Leave",
    "Triviul - Angelsaint",
    "Alexander Ross - Goodbye Bolero",
    "Fergessen - Nos Palpitants",
    "Leaf - Summerghost",
    "Skelpolu - Human Mistakes",
    "Young Griffo - Pennies",
    "ANiMAL - Rockshow",
    "James May - On The Line",
    "Meaxic - Take A Step",
    "Traffic Experiment - Sirens",

[docs]class MUSDB_HQ(Dataset): """*MUSDB_HQ* :cite:`MUSDB18HQ` dataset. Args: root (str or Path): Root directory where the dataset's top level directory is found subset (str): Subset of the dataset to use. Options: [``"train"``, ``"test"``]. sources (List[str] or None, optional): Sources extract data from. List can contain the following options: [``"bass"``, ``"drums"``, ``"other"``, ``"mixture"``, ``"vocals"``]. If ``None``, dataset consists of tracks except mixture. (default: ``None``) split (str or None, optional): Whether to split training set into train and validation set. If ``None``, no splitting occurs. If ``train`` or ``validation``, returns respective set. (default: ``None``) download (bool, optional): Whether to download the dataset if it is not found at root path. (default: ``False``) """ def __init__( self, root: Union[str, Path], subset: str, sources: Optional[List[str]] = None, split: Optional[str] = None, download: bool = False, ) -> None: self.sources = ["bass", "drums", "other", "vocals"] if not sources else sources self.split = split basename = os.path.basename(_URL) archive = os.path.join(root, basename) basename = basename.rsplit(".", 2)[0] if subset not in ["test", "train"]: raise ValueError("`subset` must be one of ['test', 'train']") if self.split is not None and self.split not in ["train", "validation"]: raise ValueError("`split` must be one of ['train', 'validation']") base_path = os.path.join(root, basename) self._path = os.path.join(base_path, subset) if not os.path.isdir(self._path): if not os.path.isfile(archive): if not download: raise RuntimeError("Dataset not found. Please use `download=True` to download") download_url_to_file(_URL, archive, hash_prefix=_CHECKSUM) os.makedirs(base_path, exist_ok=True) _extract_zip(archive, base_path) self.names = self._collect_songs() def _get_track(self, name, source): return Path(self._path) / name / f"{source}{_EXT}" def _load_sample(self, n: int) -> Tuple[torch.Tensor, int, int, str]: name = self.names[n] wavs = [] num_frames = None for source in self.sources: track = self._get_track(name, source) wav, sr = torchaudio.load(str(track)) if sr != _SAMPLE_RATE: raise ValueError(f"expected sample rate {_SAMPLE_RATE}, but got {sr}") if num_frames is None: num_frames = wav.shape[-1] else: if wav.shape[-1] != num_frames: raise ValueError("num_frames do not match across sources") wavs.append(wav) stacked = torch.stack(wavs) return stacked, _SAMPLE_RATE, num_frames, name def _collect_songs(self): if self.split == "validation": return _VALIDATION_SET path = Path(self._path) names = [] for root, folders, _ in os.walk(path, followlinks=True): root = Path(root) if".") or folders or root == path: continue name = str(root.relative_to(path)) if self.split and name in _VALIDATION_SET: continue names.append(name) return sorted(names)
[docs] def __getitem__(self, n: int) -> Tuple[torch.Tensor, int, int, str]: """Load the n-th sample from the dataset. Args: n (int): The index of the sample to be loaded Returns: Tuple of the following items; Tensor: Waveform int: Sample rate int: Num frames str: Track name """ return self._load_sample(n)
def __len__(self) -> int: return len(self.names)


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources