add loading script

musdb18.py

@@ -0,0 +1,94 @@
+import datasets
+from pathlib import Path
+import stempeg
+import numpy as np
+
+
+_DESCRIPTION = """\
+    MUSDB18 music source separation dataset
+
+    to open original stem file (mp4), which is done internally you need stempeg library.
+    Outcome of mp4 file is a 3 dimensional np_array [n_stems, n_samples, sample_rate].
+    
+    firt dimension meanings: {
+        0: mixture.
+        1: drugs,
+        2: bass,
+        3: others,
+        4:vocals,
+    }
+
+    Original dataset is not cutted in any parts, but here I cut each song in 10 seconds chunks with 1 sec overlap.
+    """
+
+_DESCRIPTION = "musdb dataset"
+
+
+class Musdb18Dataset(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 300
+    SAMPLING_RATE = 44100
+    WINDOW_SIZE = SAMPLING_RATE * 10  # 10s windows
+    INSTRUMENT_NAMES = ["mixture", "drums", "bass", "other", "vocals"]
+
+    #! To configure different configurations (length of window is the only thing)
+    # use datasets.BuilderConfig
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "name": datasets.Value("string"),
+                "n_window": datasets.Value("int16"),
+                **{name: datasets.Audio(sampling_rate=self.SAMPLING_RATE, mono=False)
+                    for name in self.INSTRUMENT_NAMES}
+            })
+        )
+
+    def _split_generators(self, dl_manager):
+        #! you must have your folder locally!
+        archive_path = dl_manager.download_and_extract(
+            "https://zenodo.org/record/1117372/files/musdb18.zip?download=1")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "audio_path": f"{archive_path}/train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "audio_path": f"{archive_path}/test"
+                }
+            )
+        ]
+
+    def _generate_stem_dict(self, S, song_name, start):
+        return {name: {"path": f"{song_name}/{name}",
+                       "array": S[i, start:start+self.WINDOW_SIZE, :], "sampling_rate": self.SAMPLING_RATE}
+                for i, name in enumerate(self.INSTRUMENT_NAMES)}
+
+    def _generate_examples(self, audio_path):
+        id_ = 0
+        for stems_path in Path(audio_path).iterdir():
+            song_name = stems_path.stem
+            S, sr = stempeg.read_stems(
+                str(stems_path), dtype=np.float32, multiprocess=False)
+
+            for idx, start in enumerate(range(0, S.shape[1], self.WINDOW_SIZE)):
+                yield id_, {
+                    "name": song_name,
+                    "n_window": idx,
+                    **self._generate_stem_dict(S, song_name, start)
+                }
+
+                id_ += 1
+
+            # It's very rare for song to have exactly 3 minutes
+            yield id_, {
+                "name": song_name,
+                "n_window": idx+1,
+                **self._generate_stem_dict(S, song_name, start=S.shape[1] - self.WINDOW_SIZE)
+            }
+
+            id_ += 1