added slakh processing, tests, and test data, tox passing.

basic_pitch/data/datasets/slakh.py

@@ -0,0 +1,226 @@
+#!/usr/bin/env python
+# encoding: utf-8
+#
+# Copyright 2024 Spotify AB
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import logging
+import os
+import time
+
+from typing import List, Tuple
+
+import apache_beam as beam
+import mirdata
+
+from basic_pitch.data import commandline, pipeline
+
+
+class SlakhFilterInvalidTracks(beam.DoFn):
+    DOWNLOAD_ATTRIBUTES = ["audio_path", "metadata_path", "midi_path"]
+
+    def __init__(self, source: str):
+        self.source = source
+
+    def setup(self):
+        import mirdata
+
+        self.slakh_remote = mirdata.initialize("slakh", data_home=self.source)
+        self.filesystem = beam.io.filesystems.FileSystems()
+
+    def process(self, element: Tuple[str, str]):
+        import tempfile
+
+        import apache_beam as beam
+        import sox
+
+        from basic_pitch.constants import (
+            AUDIO_N_CHANNELS,
+            AUDIO_SAMPLE_RATE,
+        )
+
+        track_id, split = element
+        if split == "omitted":
+            return None
+
+        logging.info(f"Processing (track_id, split): ({track_id}, {split})")
+
+        track_remote = self.slakh_remote.track(track_id)
+
+        with tempfile.TemporaryDirectory() as local_tmp_dir:
+            slakh_local = mirdata.initialize("slakh", local_tmp_dir)
+            track_local = slakh_local.track(track_id)
+
+            for attr in self.DOWNLOAD_ATTRIBUTES:
+                source = getattr(track_remote, attr)
+                dest = getattr(track_local, attr)
+                if not dest:
+                    return None
+                logging.info(f"Downloading {attr} from {source} to {dest}")
+                os.makedirs(os.path.dirname(dest), exist_ok=True)
+                with self.filesystem.open(source) as s, open(dest, "wb") as d:
+                    d.write(s.read())
+
+            if track_local.is_drum:
+                return None
+
+            local_wav_path = "{}_tmp.wav".format(track_local.audio_path)
+            tfm = sox.Transformer()
+            tfm.rate(AUDIO_SAMPLE_RATE)
+            tfm.channels(AUDIO_N_CHANNELS)
+            try:
+                tfm.build(track_local.audio_path, local_wav_path)
+            except Exception as e:
+                logging.info(f"Could not process {local_wav_path}. Exception: {e}")
+                return None
+
+            # if there are no notes, skip this track
+            if track_local.notes is None or len(track_local.notes.intervals) == 0:
+                return None
+
+            yield beam.pvalue.TaggedOutput(split, track_id)
+
+
+class SlakhToTfExample(beam.DoFn):
+    DOWNLOAD_ATTRIBUTES = ["audio_path", "metadata_path", "midi_path"]
+
+    def __init__(self, source: str, download: bool) -> None:
+        self.source = source
+        self.download = download
+
+    def setup(self):
+        import apache_beam as beam
+        import os
+        import mirdata
+
+        self.slakh_remote = mirdata.initialize("slakh", data_home=self.source)
+        self.filesystem = beam.io.filesystems.FileSystems()  # TODO: replace with fsspec
+        if self.download:
+            self.slakh_remote.download()
+
+    def process(self, element: List[str]):
+        import tempfile
+
+        import numpy as np
+        import sox
+
+        from basic_pitch.constants import (
+            AUDIO_N_CHANNELS,
+            AUDIO_SAMPLE_RATE,
+            FREQ_BINS_CONTOURS,
+            FREQ_BINS_NOTES,
+            ANNOTATION_HOP,
+            N_FREQ_BINS_NOTES,
+            N_FREQ_BINS_CONTOURS,
+        )
+        from basic_pitch.data import tf_example_serialization
+
+        logging.info(f"Processing {element}")
+        batch = []
+
+        for track_id in element:
+            track_remote = self.slakh_remote.track(track_id)
+
+            with tempfile.TemporaryDirectory() as local_tmp_dir:
+                slakh_local = mirdata.initialize("slakh", local_tmp_dir)
+                track_local = slakh_local.track(track_id)
+
+                for attr in self.DOWNLOAD_ATTRIBUTES:
+                    source = getattr(track_remote, attr)
+                    dest = getattr(track_local, attr)
+                    logging.info(f"Downloading {attr} from {source} to {dest}")
+                    os.makedirs(os.path.dirname(dest), exist_ok=True)
+                    with self.filesystem.open(source) as s, open(dest, "wb") as d:
+                        d.write(s.read())
+
+                local_wav_path = "{}_tmp.wav".format(track_local.audio_path)
+                tfm = sox.Transformer()
+                tfm.rate(AUDIO_SAMPLE_RATE)
+                tfm.channels(AUDIO_N_CHANNELS)
+                tfm.build(track_local.audio_path, local_wav_path)
+
+                duration = sox.file_info.duration(local_wav_path)
+                time_scale = np.arange(0, duration + ANNOTATION_HOP, ANNOTATION_HOP)
+                n_time_frames = len(time_scale)
+
+                note_indices, note_values = track_local.notes.to_sparse_index(time_scale, "s", FREQ_BINS_NOTES, "hz")
+                onset_indices, onset_values = track_local.notes.to_sparse_index(
+                    time_scale, "s", FREQ_BINS_NOTES, "hz", onsets_only=True
+                )
+                contour_indices, contour_values = track_local.multif0.to_sparse_index(
+                    time_scale, "s", FREQ_BINS_CONTOURS, "hz"
+                )
+
+                batch.append(
+                    tf_example_serialization.to_transcription_tfexample(
+                        track_id,
+                        "slakh",
+                        local_wav_path,
+                        note_indices,
+                        note_values,
+                        onset_indices,
+                        onset_values,
+                        contour_indices,
+                        contour_values,
+                        (n_time_frames, N_FREQ_BINS_NOTES),
+                        (n_time_frames, N_FREQ_BINS_CONTOURS),
+                    )
+                )
+
+        logging.info(f"Finished processing batch of length {len(batch)}")
+        return [batch]
+
+
+def create_input_data() -> List[Tuple[str, str]]:
+    slakh = mirdata.initialize("slakh")
+    return [(track_id, track.data_split) for track_id, track in slakh.load_tracks().items()]
+
+
+def main(known_args, pipeline_args):
+    time_created = int(time.time())
+    destination = commandline.resolve_destination(known_args, time_created)
+    input_data = create_input_data()
+
+    pipeline_options = {
+        "runner": known_args.runner,
+        "job_name": f"slakh-tfrecords-{time_created}",
+        "machine_type": "e2-standard-4",
+        "num_workers": 25,
+        "disk_size_gb": 128,
+        "experiments": ["use_runner_v2"],
+        "save_main_session": True,
+        "sdk_container_image": known_args.sdk_container_image,
+        "job_endpoint": known_args.job_endpoint,
+        "environment_type": "DOCKER",
+        "environment_config": known_args.sdk_container_image,
+    }
+    pipeline.run(
+        pipeline_options,
+        pipeline_args,
+        input_data,
+        SlakhToTfExample(known_args.source, download=True),
+        SlakhFilterInvalidTracks(known_args.source),
+        destination,
+        known_args.batch_size,
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    commandline.add_default(parser, os.path.basename(os.path.splitext(__file__)[0]))
+    commandline.add_split(parser)
+    known_args, pipeline_args = parser.parse_known_args()  # sys.argv)
+
+    main(known_args, pipeline_args)

tests/data/test_slakh.py

@@ -0,0 +1,144 @@
+#!/usr/bin/env python
+# encoding: utf-8
+#
+# Copyright 2024 Spotify AB
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import apache_beam as beam
+import itertools
+import os
+import pathlib
+
+from typing import List
+
+from apache_beam.testing.test_pipeline import TestPipeline
+
+from basic_pitch.data.datasets.slakh import (
+    SlakhFilterInvalidTracks,
+    SlakhToTfExample,
+    create_input_data,
+)
+from basic_pitch.data.pipeline import WriteBatchToTfRecord
+
+RESOURCES_PATH = pathlib.Path(__file__).parent.parent / "resources"
+TRAIN_PIANO_TRACK_ID = "Track00001-S02"
+TRAIN_DRUMS_TRACK_ID = "Track00001-S01"
+VALID_PIANO_TRACK_ID = "Track01501-S06"
+VALID_DRUMS_TRACK_ID = "Track01501-S03"
+TEST_PIANO_TRACK_ID = "Track01876-S01"
+TEST_DRUMS_TRACK_ID = "Track01876-S08"
+OMITTED_PIANO_TRACK_ID = "Track00049-S05"
+OMITTED_DRUMS_TRACK_ID = "Track00049-S06"
+
+
+def test_slakh_to_tf_example(tmpdir: str) -> None:
+    input_data: List[str] = [TRAIN_PIANO_TRACK_ID]
+    with TestPipeline() as p:
+        (
+            p
+            | "Create PCollection of track IDs" >> beam.Create([input_data])
+            | "Create tf.Example"
+            >> beam.ParDo(SlakhToTfExample(str(RESOURCES_PATH / "data" / "slakh"), download=False))
+            | "Write to tfrecord" >> beam.ParDo(WriteBatchToTfRecord(tmpdir))
+        )
+
+    assert len(os.listdir(tmpdir)) == 1
+    assert os.path.splitext(os.listdir(tmpdir)[0])[-1] == ".tfrecord"
+    with open(os.path.join(tmpdir, os.listdir(tmpdir)[0]), "rb") as fp:
+        data = fp.read()
+        assert len(data) != 0
+
+
+def test_slakh_invalid_tracks(tmpdir: str) -> None:
+    split_labels = ["train", "validation", "test"]
+    input_data = [(TRAIN_PIANO_TRACK_ID, "train"),
+                  (VALID_PIANO_TRACK_ID, "validation"),
+                  (TEST_PIANO_TRACK_ID, "test")]
+
+    with TestPipeline() as p:
+        splits = (
+            p
+            | "Create PCollection" >> beam.Create(input_data)
+            | "Tag it" >> beam.ParDo(
+                SlakhFilterInvalidTracks(str(RESOURCES_PATH / "data" / "slakh"))).with_outputs(*split_labels)
+        )
+
+        for split in split_labels:
+            (
+                getattr(splits, split)
+                | f"Write {split} to text"
+                >> beam.io.WriteToText(os.path.join(tmpdir, f"output_{split}.txt"), shard_name_template="")
+            )
+
+    for track_id, split in input_data:
+        with open(os.path.join(tmpdir, f"output_{split}.txt"), "r") as fp:
+            assert fp.read().strip() == track_id
+
+
+def test_slakh_invalid_tracks_omitted(tmpdir: str) -> None:
+    split_labels = ["train", "omitted"]
+    input_data = [(TRAIN_PIANO_TRACK_ID, "train"),
+                  (OMITTED_PIANO_TRACK_ID, "omitted")]
+
+    with TestPipeline() as p:
+        splits = (
+            p
+            | "Create PCollection" >> beam.Create(input_data)
+            | "Tag it" >> beam.ParDo(
+                SlakhFilterInvalidTracks(str(RESOURCES_PATH / "data" / "slakh"))).with_outputs(*split_labels)
+        )
+
+        for split in split_labels:
+            (
+                getattr(splits, split)
+                | f"Write {split} to text"
+                >> beam.io.WriteToText(os.path.join(tmpdir, f"output_{split}.txt"), shard_name_template="")
+            )
+
+    with open(os.path.join(tmpdir, "output_train.txt"), "r") as fp:
+        assert fp.read().strip() == TRAIN_PIANO_TRACK_ID
+
+    with open(os.path.join(tmpdir, "output_omitted.txt"), "r") as fp:
+        assert fp.read().strip() == ""
+
+
+def test_slakh_invalid_tracks_drums(tmpdir: str) -> None:
+    split_labels = ["train", "validation", "test"]
+    input_data = [(TRAIN_DRUMS_TRACK_ID, "train"),
+                  (VALID_DRUMS_TRACK_ID, "validation"),
+                  (TEST_DRUMS_TRACK_ID, "test")]
+
+    with TestPipeline() as p:
+        splits = (
+            p
+            | "Create PCollection" >> beam.Create(input_data)
+            | "Tag it" >> beam.ParDo(
+                SlakhFilterInvalidTracks(str(RESOURCES_PATH / "data" / "slakh"))).with_outputs(*split_labels)
+        )
+
+        for split in split_labels:
+            (
+                getattr(splits, split)
+                | f"Write {split} to text"
+                >> beam.io.WriteToText(os.path.join(tmpdir, f"output_{split}.txt"), shard_name_template="")
+            )
+
+    for track_id, split in input_data:
+        with open(os.path.join(tmpdir, f"output_{split}.txt"), "r") as fp:
+            assert fp.read().strip() == ""
+
+
+def test_create_input_data() -> None:
+    data = create_input_data()
+    for key, group in itertools.groupby(data, lambda el: el[1]):
+        assert len(list(group))