Merge branch 'master' into indian_raga

docs/source/mirdata.rst

@@ -108,6 +108,14 @@ filosax
    :inherited-members:
 
 
+four_way_tabla
+^^^^^^^^^^^^^^
+
+.. automodule:: mirdata.datasets.four_way_tabla
+   :members:
+   :inherited-members:
+
+
 freesound_one_shot_percussive_sounds
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

docs/source/table.rst

@@ -135,6 +135,14 @@
      - 48
      - ❌
 
+   * - Four-Way Tabla Stroke
+     - - audio: ✅
+       - annotations: ✅
+     - - :ref:`tags`
+     - 236
+     - .. image:: https://licensebuttons.net/l/by-sa/4.0/80x15.png
+          :target: https://creativecommons.org/licenses/by-sa/4.0
+
    * - Freesound One-Shot Percussive Sounds
      - - audio: ✅
        - annotations: ✅

mirdata/datasets/four_way_tabla.py

@@ -0,0 +1,237 @@
+"""Four-Way Tabla Stroke Transcription and Classification Loader
+
+.. admonition:: Dataset Info
+    :class: dropdown
+
+    The Four-Way Tabla Dataset includes audio recordings of tabla solo with onset annotations for particular
+    strokes types. This dataset was published in 2021 in the context of ISMIR2021 (Online), and may be used for
+    tasks related to tabla analysis, including problems such as onset detection and stroke classification.
+
+    Total audio samples: We do have a total of 226 samples for training and 10 for testing. Each audio has
+    an approximate duration of 1 minute.
+
+    Audio specifications:
+
+    * Sampling frequency: 44.1 kHz
+    * Bit-depth: 16 bit
+    * Audio format: .wav
+
+    Dataset usage: This dataset may be used for the data-driven research of tabla stroke transcription and 
+    identification. In this dataset, four important tabla characteristic strokes are considered.
+
+    Dataset structure: The dataset is split in two subsets, containing training and testing samples. Within each
+    subset, there is a folder containing the audios, and another folder containing the onset annotations. The onset
+    annotations are organized in a folder per each stroke type: b, d, rb, rt. Therefore, the paths to onsets would
+    look like:
+
+    .. code-block:: bash
+
+        train/onsets/<StrokeType>/<ID>.onsets
+
+    The dataset is made available by CompMusic under a Creative Commons
+    Attribution 3.0 Unported (CC BY 3.0) License.
+
+"""
+
+import csv
+
+import librosa
+import numpy as np
+from typing import BinaryIO, Optional, Tuple
+
+from deprecated.sphinx import deprecated
+
+from mirdata import annotations, core, download_utils, io, jams_utils
+
+BIBTEX = """@article{RohitMA2021,
+    author = {M.A, Rohit and Bhattacharjee, Amitrajit and Rao, Preeti},
+    journal = {Proc. of the 22nd Int. Society for Music Information Retrieval Conf., Online, 2021},
+    title = {{Four-way Classification of Tabla Strokes with Models Adapted from Automatic Drum Transcription}},
+    year = {2021}
+}"""
+
+INDEXES = {
+    "default": "1.0",
+    "test": "1.0",
+    "1.0": core.Index(filename="four_way_tabla_index.json"),
+}
+
+REMOTES = {
+    "remote_data": download_utils.RemoteFileMetadata(
+        filename="4way-tabla-ismir21-dataset.zip",
+        url="https://zenodo.org/record/7110248/files/4way-tabla-ismir21-dataset.zip?download=1",
+        checksum="fcddb565f260d4170877f70a7c33d69d",  # the md5 checksum
+    ),
+}
+
+LICENSE_INFO = "Creative Commons Attribution 4.0 International License."
+
+
+class Track(core.Track):
+    """Four-Way Tabla track class
+
+    Args:
+        track_id (str): track id of the track
+        data_home (str): Local path where the dataset is stored.
+
+    Attributes:
+        track_id (str): track id
+        audio_path (str): audio path
+        onsets_b_path (str): path to B onsets
+        onsets_d_path (str): path to D onsets
+        onsets_rb_path (str): path to RB onsets
+        onsets_rt_path (str): path to RT onsets
+
+    """
+
+    def __init__(
+        self,
+        track_id,
+        data_home,
+        dataset_name,
+        index,
+        metadata,
+    ):
+        super().__init__(
+            track_id,
+            data_home,
+            dataset_name,
+            index,
+            metadata,
+        )
+
+        self.audio_path = self.get_path("audio")
+        self.onsets_b_path = self.get_path("onsets_b")
+        self.onsets_d_path = self.get_path("onsets_d")
+        self.onsets_rb_path = self.get_path("onsets_rb")
+        self.onsets_rt_path = self.get_path("onsets_rt")
+
+        self.train = True if "train" in self.audio_path else False
+
+    @property
+    def audio(self) -> Optional[Tuple[np.ndarray, float]]:
+        """The track's audio
+
+        Returns:
+            * np.ndarray - audio signal
+            * float - sample rate
+
+        """
+        return load_audio(self.audio_path)
+
+    @property
+    def onsets_b(self) -> Optional[annotations.BeatData]:
+        """Onsets for stroke B
+
+        Returns:
+            * annotations.BeatData - onsets annotation
+
+        """
+        return load_onsets(self.onsets_b_path)
+
+    @property
+    def onsets_d(self) -> Optional[annotations.BeatData]:
+        """Onsets for stroke D
+
+        Returns:
+            * annotations.BeatData - onsets annotation
+
+        """
+        return load_onsets(self.onsets_d_path)
+
+    @property
+    def onsets_rb(self) -> Optional[annotations.BeatData]:
+        """Onsets for stroke RB
+
+        Returns:
+            * annotations.BeatData - onsets annotation
+
+        """
+        return load_onsets(self.onsets_rb_path)
+
+    @property
+    def onsets_rt(self) -> Optional[annotations.BeatData]:
+        """Onsets for stroke RT
+
+        Returns:
+            * annotations.BeatData - onsets annotation
+
+        """
+        return load_onsets(self.onsets_rt_path)
+
+    def to_jams(self):
+        """Get the track's data in jams format
+
+        Returns:
+            jams.JAMS: the track's data in jams format
+
+        """
+        return jams_utils.jams_converter(
+            audio_path=self.audio_path,
+            beat_data=[
+                (self.onsets_b, "onsets_b"),
+                (self.onsets_d, "onsets_d"),
+                (self.onsets_rb, "onsets_rb"),
+                (self.onsets_rt, "onsets_rt"),
+            ],
+            metadata={"train": self.train},
+        )
+
+
+@io.coerce_to_bytes_io
+def load_audio(fhandle: BinaryIO) -> Tuple[np.ndarray, float]:
+    """Load a Mridangam Stroke Dataset audio file.
+
+    Args:
+        fhandle (str or file-like): File-like object or path to audio file
+
+    Returns:
+        * np.ndarray - the mono audio signal
+        * float - The sample rate of the audio file
+    """
+    return librosa.load(fhandle, sr=44100, mono=True)
+
+
+@io.coerce_to_string_io
+def load_onsets(fhandle):
+    """Load stroke onsets
+
+    Args:
+        fhandle (str or file-like): Local path where the pitch annotation is stored.
+
+    Returns:
+        EventData: onset annotations
+
+    """
+    onsets = []
+    reader = csv.reader(fhandle, delimiter="\n")
+    for line in reader:
+        onsets.append(float(line[0]))
+
+    if not onsets:
+        return None
+    onsets = np.array(onsets)
+
+    # All strokes are out of an annotated metric
+    beat_position = np.zeros(onsets.shape)
+
+    return annotations.BeatData(onsets, "s", beat_position, "global_index")
+
+
+@core.docstring_inherit(core.Dataset)
+class Dataset(core.Dataset):
+    """
+    The Four-Way Tabla dataset
+    """
+
+    def __init__(self, data_home=None, version="default"):
+        super().__init__(
+            data_home,
+            version,
+            name="four_way_tabla",
+            track_class=Track,
+            bibtex=BIBTEX,
+            indexes=INDEXES,
+            remotes=REMOTES,
+            license_info=LICENSE_INFO,
+        )