diff --git a/basic_pitch/commandline_printing.py b/basic_pitch/commandline_printing.py
index 6cf12488..d8e08718 100644
--- a/basic_pitch/commandline_printing.py
+++ b/basic_pitch/commandline_printing.py
@@ -42,7 +42,9 @@ def generating_file_message(output_type: str) -> None:
     print(f"\n\n  Creating {output_type.replace('_', ' ').lower()}...")
 
 
-def file_saved_confirmation(output_type: str, save_path: Union[pathlib.Path, str]) -> None:
+def file_saved_confirmation(
+    output_type: str, save_path: Union[pathlib.Path, str]
+) -> None:
     """Print a confirmation that the file was saved succesfully
 
     Args:
@@ -61,7 +63,9 @@ def failed_to_save(output_type: str, save_path: Union[pathlib.Path, str]) -> Non
         save_path: The path to output file.
 
     """
-    print(f"\n🚨 Failed to save {output_type.replace('_', ' ').lower()} to {save_path} \n")
+    print(
+        f"\n🚨 Failed to save {output_type.replace('_', ' ').lower()} to {save_path} \n"
+    )
 
 
 @contextmanager
diff --git a/basic_pitch/constants.py b/basic_pitch/constants.py
index a78a487b..809119b2 100644
--- a/basic_pitch/constants.py
+++ b/basic_pitch/constants.py
@@ -51,11 +51,17 @@
 }
 
 
-def _freq_bins(bins_per_semitone: int, base_frequency: float, n_semitones: int) -> np.array:
+def _freq_bins(
+    bins_per_semitone: int, base_frequency: float, n_semitones: int
+) -> np.array:
     d = 2.0 ** (1.0 / (12 * bins_per_semitone))
     bin_freqs = base_frequency * d ** np.arange(bins_per_semitone * n_semitones)
     return bin_freqs
 
 
-FREQ_BINS_NOTES = _freq_bins(NOTES_BINS_PER_SEMITONE, ANNOTATIONS_BASE_FREQUENCY, ANNOTATIONS_N_SEMITONES)
-FREQ_BINS_CONTOURS = _freq_bins(CONTOURS_BINS_PER_SEMITONE, ANNOTATIONS_BASE_FREQUENCY, ANNOTATIONS_N_SEMITONES)
+FREQ_BINS_NOTES = _freq_bins(
+    NOTES_BINS_PER_SEMITONE, ANNOTATIONS_BASE_FREQUENCY, ANNOTATIONS_N_SEMITONES
+)
+FREQ_BINS_CONTOURS = _freq_bins(
+    CONTOURS_BINS_PER_SEMITONE, ANNOTATIONS_BASE_FREQUENCY, ANNOTATIONS_N_SEMITONES
+)
diff --git a/basic_pitch/data/commandline.py b/basic_pitch/data/commandline.py
index ab178f8d..61bcde45 100644
--- a/basic_pitch/data/commandline.py
+++ b/basic_pitch/data/commandline.py
@@ -1,11 +1,11 @@
 #!/usr/bin/env python
 # encoding: utf-8
 #
-# Copyright 2022 Spotify AB
+# Cos.pathyright 2022 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
+# You may obtain a cos.pathy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
@@ -17,33 +17,57 @@
 
 import argparse
 import os
-import os.path as op
 
+from typing import Optional
 
-def add_default(parser: argparse.ArgumentParser, dataset_name: str):
-    parser.add_argument("source", nargs='?', default=op.join(op.expanduser('~'), 'mir_datasets'),
-                        help="Source directory for mir data. Defaults to local mir_datasets folder.")
-    parser.add_argument("destination", nargs='?',
-                        default=op.join(op.expanduser('~'), 'data', 'basic_pitch', dataset_name),
-                        help="Output directory to write results to. Defaults to local ~/data/basic_pitch/{dataset}/")
-    parser.add_argument("--runner", choices=["DataflowRunner", "DirectRunner"], default="DirectRunner")
-    parser.add_argument("--timestamped", default=False, action="store_true",
-                        help="If passed, the dataset will be put into a timestamp directory instead of 'splits'")
-    parser.add_argument("--batch-size", default=5, type=int, help="Number of examples per tfrecord")
-    parser.add_argument("--worker-harness-container-image", default="",
-                        help="Container image to run dataset generation job with. \
-                        Required due to non-python dependencies.")
 
+def add_default(parser: argparse.ArgumentParser, dataset_name: str = "") -> None:
+    parser.add_argument(
+        "--source",
+        default=os.path.join(os.path.expanduser("~"), "mir_datasets", dataset_name),
+        help="Source directory for mir data. Defaults to local mir_datasets folder.",
+    )
+    parser.add_argument(
+        "--destination",
+        default=os.path.join(
+            os.path.expanduser("~"), "data", "basic_pitch", dataset_name
+        ),
+        help="Output directory to write results to. Defaults to local ~/data/basic_pitch/{dataset}/",
+    )
+    parser.add_argument(
+        "--runner",
+        choices=["DataflowRunner", "DirectRunner"],
+        default="DirectRunner",
+        help="Whether to run the download and process locally or on GCP Dataflow",
+    )
+    parser.add_argument(
+        "--timestamped",
+        default=False,
+        action="store_true",
+        help="If passed, the dataset will be put into a timestamp directory instead of 'splits'",
+    )
+    parser.add_argument(
+        "--batch-size", default=5, type=int, help="Number of examples per tfrecord"
+    )
+    parser.add_argument(
+        "--worker-harness-container-image",
+        default="",
+        help="Container image to run dataset generation job with. \
+                        Required due to non-python dependencies.",
+    )
 
-def resolve_destination(namespace: argparse.Namespace, dataset: str, time_created: int) -> str:
-    return os.path.join(namespace.destination, str(time_created) if namespace.timestamped else "splits")
+
+def resolve_destination(namespace: argparse.Namespace, time_created: int) -> str:
+    return os.path.join(
+        namespace.destination, str(time_created) if namespace.timestamped else "splits"
+    )
 
 
 def add_split(
     parser: argparse.ArgumentParser,
     train_percent: float = 0.8,
     validation_percent: float = 0.1,
-    split_seed: int = None,
+    split_seed: Optional[int] = None,
 ):
     parser.add_argument(
         "--train-percent",
@@ -51,7 +75,8 @@ def add_split(
         default=train_percent,
         help="Percentage of tracks to mark as train",
     )
-    parser.add_argument( "--validation-percent",
+    parser.add_argument(
+        "--validation-percent",
         type=float,
         default=validation_percent,
         help="Percentage of tracks to mark as validation",
diff --git a/basic_pitch/data/datasets/guitarset.py b/basic_pitch/data/datasets/guitarset.py
index c4b08036..70cf6a50 100644
--- a/basic_pitch/data/datasets/guitarset.py
+++ b/basic_pitch/data/datasets/guitarset.py
@@ -18,7 +18,6 @@
 import argparse
 import logging
 import os
-import os.path as op
 import random
 import time
 from typing import List, Tuple, Optional
@@ -28,11 +27,9 @@
 
 from basic_pitch.data import commandline, pipeline
 
-DIRNAME = "guitarset"
-
 
 class GuitarSetInvalidTracks(beam.DoFn):
-    def process(self, element: Tuple[str, str]):
+    def process(self, element: Tuple[str, str], *args, **kwargs):
         track_id, split = element
         yield beam.pvalue.TaggedOutput(split, track_id)
 
@@ -41,17 +38,21 @@ class GuitarSetToTfExample(beam.DoFn):
     DOWNLOAD_ATTRIBUTES = ["audio_mic_path", "jams_path"]
 
     def __init__(self, source: str):
-        print(f"source_dir: {source}")
         self.source = source
 
     def setup(self):
         import apache_beam as beam
         import mirdata
 
-        self.guitarset_remote = mirdata.initialize("guitarset", data_home=os.path.join(self.source, DIRNAME))
+        self.guitarset_remote = mirdata.initialize("guitarset", data_home=self.source)
         self.filesystem = beam.io.filesystems.FileSystems()
+        if (
+            type(self.filesystem.get_filesystem(self.source))
+            == beam.io.localfilesystem.LocalFileSystem
+        ):
+            self.guitarset_remote.download()
 
-    def process(self, element: List[str]):
+    def process(self, element: List[str], *args, **kwargs):
         import tempfile
 
         import mirdata
@@ -80,10 +81,11 @@ def process(self, element: List[str]):
 
                 for attribute in self.DOWNLOAD_ATTRIBUTES:
                     source = getattr(track_remote, attribute)
-                    print(f"source: {source}")
                     destination = getattr(track_local, attribute)
                     os.makedirs(os.path.dirname(destination), exist_ok=True)
-                    with self.filesystem.open(source) as s, open(destination, "wb") as d:
+                    with self.filesystem.open(source) as s, open(
+                        destination, "wb"
+                    ) as d:
                         d.write(s.read())
 
                 local_wav_path = f"{track_local.audio_mic_path}_tmp.wav"
@@ -146,15 +148,17 @@ def determine_split() -> str:
         return "test"
 
     guitarset = mirdata.initialize("guitarset")
-    guitarset.download()
+    # guitarset.download()
 
     return [(track_id, determine_split()) for track_id in guitarset.track_ids]
 
 
 def main(known_args, pipeline_args):
     time_created = int(time.time())
-    destination = commandline.resolve_destination(known_args, DIRNAME, time_created)
-    input_data = create_input_data(known_args.train_percent, known_args.validation_percent, known_args.split_seed)
+    destination = commandline.resolve_destination(known_args, time_created)
+    input_data = create_input_data(
+        known_args.train_percent, known_args.validation_percent, known_args.split_seed
+    )
 
     pipeline_options = {
         "runner": known_args.runner,
@@ -178,7 +182,7 @@ def main(known_args, pipeline_args):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    commandline.add_default(parser, op.basename(op.splittext(__file__)[0]))
+    commandline.add_default(parser, os.path.basename(os.path.splitext(__file__)[0]))
     commandline.add_split(parser)
     known_args, pipeline_args = parser.parse_known_args()
 
diff --git a/basic_pitch/data/download.py b/basic_pitch/data/download.py
index ffd76657..8ed74b35 100644
--- a/basic_pitch/data/download.py
+++ b/basic_pitch/data/download.py
@@ -1,25 +1,41 @@
 import argparse
+import logging
+import sys
 
 from basic_pitch.data import commandline
 from basic_pitch.data.datasets.guitarset import main as guitarset_main
 
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+handler = logging.StreamHandler(sys.stdout)
+formatter = logging.Formatter("%(levelname)s:: %(message)s")
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
 DATASET_DICT = {
-    'guitarset': guitarset_main,
+    "guitarset": guitarset_main,
 }
 
+
 def main():
     dataset_parser = argparse.ArgumentParser()
-    dataset_parser.add_argument("dataset", choices=list(DATASET_DICT.keys()), help="The dataset to download / process.")
+    dataset_parser.add_argument(
+        "dataset",
+        choices=list(DATASET_DICT.keys()),
+        help="The dataset to download / process.",
+    )
     args, remaining_args = dataset_parser.parse_known_args()
     dataset = args.dataset
+    logger.info(f"Downloading and processing {dataset}")
 
-    print(f'got the arg: {dataset}')
     cl_parser = argparse.ArgumentParser()
     commandline.add_default(cl_parser, dataset)
     commandline.add_split(cl_parser)
     known_args, pipeline_args = cl_parser.parse_known_args(remaining_args)
+    for arg in vars(known_args):
+        logger.info(f"{arg} = {getattr(known_args, arg)}")
     DATASET_DICT[dataset](known_args, pipeline_args)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/basic_pitch/data/pipeline.py b/basic_pitch/data/pipeline.py
index 35227854..23c0b374 100644
--- a/basic_pitch/data/pipeline.py
+++ b/basic_pitch/data/pipeline.py
@@ -18,7 +18,7 @@
 import logging
 import os
 import uuid
-from typing import Dict, List, Tuple
+from typing import Dict, List, Tuple, Callable, Union
 
 import apache_beam as beam
 import tensorflow as tf
@@ -40,23 +40,28 @@ def __init__(self, destination):
         self.destination = destination
 
     def process(self, element):
+        if not isinstance(element, list):
+            element = [element]
+
         logging.info(f"Writing to file batch of length {len(element)}")
         # hopefully uuids are unique enough
-        with tf.io.TFRecordWriter(os.path.join(self.destination, f"{uuid.uuid4()}.tfrecord")) as writer:
+        with tf.io.TFRecordWriter(
+            os.path.join(self.destination, f"{uuid.uuid4()}.tfrecord")
+        ) as writer:
             for example in element:
                 writer.write(example.SerializeToString())
 
 
 def transcription_dataset_writer(
-    pcoll,
+    p: beam.Pipeline,
     input_data: List[Tuple[str, str]],
-    to_tf_example: beam.DoFn,
-    filter_invalid_tracks: beam.DoFn,
+    to_tf_example: Union[beam.DoFn, Callable],
+    filter_invalid_tracks: beam.PTransform,
     destination: str,
     batch_size: int,
 ):
     valid_track_ids = (
-        pcoll
+        p
         | "Create PCollection of track IDS" >> beam.Create(input_data)
         | "Remove invalid track IDs"
         >> beam.ParDo(filter_invalid_tracks).with_outputs(
@@ -73,9 +78,12 @@ def transcription_dataset_writer(
             | f"Batch {split}" >> beam.ParDo(Batch(batch_size))
             | f"Reshuffle {split}" >> beam.Reshuffle()  # To prevent fuses
             | f"Create tf.Example {split} batch" >> beam.ParDo(to_tf_example)
-            | f"Write {split} batch to tfrecord" >> beam.ParDo(WriteBatchToTfRecord(os.path.join(destination, split)))
+            | f"Write {split} batch to tfrecord"
+            >> beam.ParDo(WriteBatchToTfRecord(os.path.join(destination, split)))
         )
-        getattr(valid_track_ids, split) | f"Write {split} index file" >> beam.io.textio.WriteToText(
+        getattr(
+            valid_track_ids, split
+        ) | f"Write {split} index file" >> beam.io.textio.WriteToText(
             os.path.join(destination, split, "index.csv"),
             num_shards=1,
             header="track_id",
@@ -92,4 +100,6 @@ def run(
     batch_size: int,
 ):
     with beam.Pipeline(options=PipelineOptions(**pipeline_options)) as p:
-        transcription_dataset_writer(p, input_data, to_tf_example, filter_invalid_tracks, destination, batch_size)
+        transcription_dataset_writer(
+            p, input_data, to_tf_example, filter_invalid_tracks, destination, batch_size
+        )
diff --git a/basic_pitch/data/tf_example_serialization.py b/basic_pitch/data/tf_example_serialization.py
index c4985c71..a7e0432e 100644
--- a/basic_pitch/data/tf_example_serialization.py
+++ b/basic_pitch/data/tf_example_serialization.py
@@ -62,14 +62,30 @@ def _to_transcription_tfex(
                 "file_id": bytes_feature(bytes(file_id, "utf-8")),
                 "source": bytes_feature(bytes(source, "utf-8")),
                 "audio_wav": bytes_feature(encoded_wav),
-                "notes_indices": bytes_feature(tf.io.serialize_tensor(np.array(notes_indices, np.int64))),
-                "notes_values": bytes_feature(tf.io.serialize_tensor(np.array(notes_values, np.float32))),
-                "onsets_indices": bytes_feature(tf.io.serialize_tensor(np.array(onsets_indices, np.int64))),
-                "onsets_values": bytes_feature(tf.io.serialize_tensor(np.array(onsets_values, np.float32))),
-                "contours_indices": bytes_feature(tf.io.serialize_tensor(np.array(contours_indices, np.int64))),
-                "contours_values": bytes_feature(tf.io.serialize_tensor(np.array(contours_values, np.float32))),
-                "notes_onsets_shape": bytes_feature(tf.io.serialize_tensor(np.array(notes_onsets_shape, np.int64))),
-                "contours_shape": bytes_feature(tf.io.serialize_tensor(np.array(contours_shape, np.int64))),
+                "notes_indices": bytes_feature(
+                    tf.io.serialize_tensor(np.array(notes_indices, np.int64))
+                ),
+                "notes_values": bytes_feature(
+                    tf.io.serialize_tensor(np.array(notes_values, np.float32))
+                ),
+                "onsets_indices": bytes_feature(
+                    tf.io.serialize_tensor(np.array(onsets_indices, np.int64))
+                ),
+                "onsets_values": bytes_feature(
+                    tf.io.serialize_tensor(np.array(onsets_values, np.float32))
+                ),
+                "contours_indices": bytes_feature(
+                    tf.io.serialize_tensor(np.array(contours_indices, np.int64))
+                ),
+                "contours_values": bytes_feature(
+                    tf.io.serialize_tensor(np.array(contours_values, np.float32))
+                ),
+                "notes_onsets_shape": bytes_feature(
+                    tf.io.serialize_tensor(np.array(notes_onsets_shape, np.int64))
+                ),
+                "contours_shape": bytes_feature(
+                    tf.io.serialize_tensor(np.array(contours_shape, np.int64))
+                ),
             }
         )
     )
diff --git a/basic_pitch/inference.py b/basic_pitch/inference.py
index be521a15..d4716d98 100644
--- a/basic_pitch/inference.py
+++ b/basic_pitch/inference.py
@@ -42,7 +42,9 @@
 )
 
 
-def window_audio_file(audio_original: Tensor, hop_size: int) -> Tuple[Tensor, List[Dict[str, int]]]:
+def window_audio_file(
+    audio_original: Tensor, hop_size: int
+) -> Tuple[Tensor, List[Dict[str, int]]]:
     """
     Pad appropriately an audio file, and return as
     windowed signal, with window length = AUDIO_N_SAMPLES
@@ -56,7 +58,9 @@ def window_audio_file(audio_original: Tensor, hop_size: int) -> Tuple[Tensor, Li
     from tensorflow import expand_dims  # imporing this here so the module loads faster
 
     audio_windowed = expand_dims(
-        signal.frame(audio_original, AUDIO_N_SAMPLES, hop_size, pad_end=True, pad_value=0),
+        signal.frame(
+            audio_original, AUDIO_N_SAMPLES, hop_size, pad_end=True, pad_value=0
+        ),
         axis=-1,
     )
     window_times = [
@@ -84,17 +88,23 @@ def get_audio_input(
             length of original audio file, in frames, BEFORE padding.
 
     """
-    assert overlap_len % 2 == 0, "overlap_length must be even, got {}".format(overlap_len)
+    assert overlap_len % 2 == 0, "overlap_length must be even, got {}".format(
+        overlap_len
+    )
 
     audio_original, _ = librosa.load(str(audio_path), sr=AUDIO_SAMPLE_RATE, mono=True)
 
     original_length = audio_original.shape[0]
-    audio_original = np.concatenate([np.zeros((int(overlap_len / 2),), dtype=np.float32), audio_original])
+    audio_original = np.concatenate(
+        [np.zeros((int(overlap_len / 2),), dtype=np.float32), audio_original]
+    )
     audio_windowed, window_times = window_audio_file(audio_original, hop_size)
     return audio_windowed, window_times, original_length
 
 
-def unwrap_output(output: Tensor, audio_original_length: int, n_overlapping_frames: int) -> np.array:
+def unwrap_output(
+    output: Tensor, audio_original_length: int, n_overlapping_frames: int
+) -> np.array:
     """Unwrap batched model predictions to a single matrix.
 
     Args:
@@ -115,9 +125,15 @@ def unwrap_output(output: Tensor, audio_original_length: int, n_overlapping_fram
         raw_output = raw_output[:, n_olap:-n_olap, :]
 
     output_shape = raw_output.shape
-    n_output_frames_original = int(np.floor(audio_original_length * (ANNOTATIONS_FPS / AUDIO_SAMPLE_RATE)))
-    unwrapped_output = raw_output.reshape(output_shape[0] * output_shape[1], output_shape[2])
-    return unwrapped_output[:n_output_frames_original, :]  # trim to original audio length
+    n_output_frames_original = int(
+        np.floor(audio_original_length * (ANNOTATIONS_FPS / AUDIO_SAMPLE_RATE))
+    )
+    unwrapped_output = raw_output.reshape(
+        output_shape[0] * output_shape[1], output_shape[2]
+    )
+    return unwrapped_output[
+        :n_output_frames_original, :
+    ]  # trim to original audio length
 
 
 def run_inference(
@@ -140,10 +156,15 @@ def run_inference(
     overlap_len = n_overlapping_frames * FFT_HOP
     hop_size = AUDIO_N_SAMPLES - overlap_len
 
-    audio_windowed, _, audio_original_length = get_audio_input(audio_path, overlap_len, hop_size)
+    audio_windowed, _, audio_original_length = get_audio_input(
+        audio_path, overlap_len, hop_size
+    )
 
     output = model(audio_windowed)
-    unwrapped_output = {k: unwrap_output(output[k], audio_original_length, n_overlapping_frames) for k in output}
+    unwrapped_output = {
+        k: unwrap_output(output[k], audio_original_length, n_overlapping_frames)
+        for k in output
+    }
 
     if debug_file:
         with open(debug_file, "w") as f:
@@ -153,7 +174,9 @@ def run_inference(
                     "audio_original_length": audio_original_length,
                     "hop_size_samples": hop_size,
                     "overlap_length_samples": overlap_len,
-                    "unwrapped_output": {k: v.tolist() for k, v in unwrapped_output.items()},
+                    "unwrapped_output": {
+                        k: v.tolist() for k, v in unwrapped_output.items()
+                    },
                 },
                 f,
             )
@@ -251,7 +274,9 @@ def save_note_events(
 
     with open(save_path, "w") as fhandle:
         writer = csv.writer(fhandle, delimiter=",")
-        writer.writerow(["start_time_s", "end_time_s", "pitch_midi", "velocity", "pitch_bend"])
+        writer.writerow(
+            ["start_time_s", "end_time_s", "pitch_midi", "velocity", "pitch_bend"]
+        )
         for start_time, end_time, note_number, amplitude, pitch_bend in note_events:
             row = [start_time, end_time, note_number, int(np.round(127 * amplitude))]
             if pitch_bend:
@@ -271,7 +296,11 @@ def predict(
     melodia_trick: bool = True,
     debug_file: Optional[pathlib.Path] = None,
     midi_tempo: float = 120,
-) -> Tuple[Dict[str, np.array], pretty_midi.PrettyMIDI, List[Tuple[float, float, int, float, Optional[List[int]]]],]:
+) -> Tuple[
+    Dict[str, np.array],
+    pretty_midi.PrettyMIDI,
+    List[Tuple[float, float, int, float, Optional[List[int]]]],
+]:
     """Run a single prediction.
 
     Args:
@@ -301,7 +330,9 @@ def predict(
         print(f"Predicting MIDI for {audio_path}...")
 
         model_output = run_inference(audio_path, model, debug_file)
-        min_note_len = int(np.round(minimum_note_length / 1000 * (AUDIO_SAMPLE_RATE / FFT_HOP)))
+        min_note_len = int(
+            np.round(minimum_note_length / 1000 * (AUDIO_SAMPLE_RATE / FFT_HOP))
+        )
         midi_data, note_events = infer.model_output_to_notes(
             model_output,
             onset_thresh=onset_threshold,
@@ -400,17 +431,25 @@ def predict_and_save(
             )
 
             if save_model_outputs:
-                model_output_path = build_output_path(audio_path, output_directory, OutputExtensions.MODEL_OUTPUT_NPZ)
+                model_output_path = build_output_path(
+                    audio_path, output_directory, OutputExtensions.MODEL_OUTPUT_NPZ
+                )
                 try:
                     np.savez(model_output_path, basic_pitch_model_output=model_output)
-                    file_saved_confirmation(OutputExtensions.MODEL_OUTPUT_NPZ.name, model_output_path)
+                    file_saved_confirmation(
+                        OutputExtensions.MODEL_OUTPUT_NPZ.name, model_output_path
+                    )
                 except Exception as e:
-                    failed_to_save(OutputExtensions.MODEL_OUTPUT_NPZ.name, model_output_path)
+                    failed_to_save(
+                        OutputExtensions.MODEL_OUTPUT_NPZ.name, model_output_path
+                    )
                     raise e
 
             if save_midi:
                 try:
-                    midi_path = build_output_path(audio_path, output_directory, OutputExtensions.MIDI)
+                    midi_path = build_output_path(
+                        audio_path, output_directory, OutputExtensions.MIDI
+                    )
                 except IOError as e:
                     raise e
                 try:
@@ -421,19 +460,31 @@ def predict_and_save(
                     raise e
 
             if sonify_midi:
-                midi_sonify_path = build_output_path(audio_path, output_directory, OutputExtensions.MIDI_SONIFICATION)
+                midi_sonify_path = build_output_path(
+                    audio_path, output_directory, OutputExtensions.MIDI_SONIFICATION
+                )
                 try:
-                    infer.sonify_midi(midi_data, midi_sonify_path, sr=sonification_samplerate)
-                    file_saved_confirmation(OutputExtensions.MIDI_SONIFICATION.name, midi_sonify_path)
+                    infer.sonify_midi(
+                        midi_data, midi_sonify_path, sr=sonification_samplerate
+                    )
+                    file_saved_confirmation(
+                        OutputExtensions.MIDI_SONIFICATION.name, midi_sonify_path
+                    )
                 except Exception as e:
-                    failed_to_save(OutputExtensions.MIDI_SONIFICATION.name, midi_sonify_path)
+                    failed_to_save(
+                        OutputExtensions.MIDI_SONIFICATION.name, midi_sonify_path
+                    )
                     raise e
 
             if save_notes:
-                note_events_path = build_output_path(audio_path, output_directory, OutputExtensions.NOTE_EVENTS)
+                note_events_path = build_output_path(
+                    audio_path, output_directory, OutputExtensions.NOTE_EVENTS
+                )
                 try:
                     save_note_events(note_events, note_events_path)
-                    file_saved_confirmation(OutputExtensions.NOTE_EVENTS.name, note_events_path)
+                    file_saved_confirmation(
+                        OutputExtensions.NOTE_EVENTS.name, note_events_path
+                    )
                 except Exception as e:
                     failed_to_save(OutputExtensions.NOTE_EVENTS.name, note_events_path)
                     raise e
diff --git a/basic_pitch/layers/nnaudio.py b/basic_pitch/layers/nnaudio.py
index 883f1c54..078b060b 100644
--- a/basic_pitch/layers/nnaudio.py
+++ b/basic_pitch/layers/nnaudio.py
@@ -56,7 +56,9 @@ def create_lowpass_filter(
     gain_at_key_frequencies = [1.0, 1.0, 0.0, 0.0]
 
     # This command produces the filter kernel coefficients
-    filter_kernel = scipy.signal.firwin2(kernel_length, key_frequencies, gain_at_key_frequencies)
+    filter_kernel = scipy.signal.firwin2(
+        kernel_length, key_frequencies, gain_at_key_frequencies
+    )
 
     return tf.constant(filter_kernel, dtype=dtype)
 
@@ -74,7 +76,9 @@ def early_downsample(
     filter_cutoff_hz: float,
 ) -> Tuple[Union[float, int], int, int]:
     """Return new sampling rate and hop length after early downsampling"""
-    downsample_count = early_downsample_count(nyquist_hz, filter_cutoff_hz, hop_length, n_octaves)
+    downsample_count = early_downsample_count(
+        nyquist_hz, filter_cutoff_hz, hop_length, n_octaves
+    )
     downsample_factor = 2 ** (downsample_count)
 
     hop_length //= downsample_factor  # Getting new hop_length
@@ -86,10 +90,14 @@ def early_downsample(
 # The following two downsampling count functions are obtained from librosa CQT
 # They are used to determine the number of pre resamplings if the starting and ending frequency
 # are both in low frequency regions.
-def early_downsample_count(nyquist_hz: float, filter_cutoff_hz: float, hop_length: int, n_octaves: int) -> int:
+def early_downsample_count(
+    nyquist_hz: float, filter_cutoff_hz: float, hop_length: int, n_octaves: int
+) -> int:
     """Compute the number of early downsampling operations"""
 
-    downsample_count1 = max(0, int(np.ceil(np.log2(0.85 * nyquist_hz / filter_cutoff_hz)) - 1) - 1)
+    downsample_count1 = max(
+        0, int(np.ceil(np.log2(0.85 * nyquist_hz / filter_cutoff_hz)) - 1) - 1
+    )
     num_twos = next_power_of_2(hop_length)
     downsample_count2 = max(0, num_twos - n_octaves + 1)
 
@@ -97,13 +105,20 @@ def early_downsample_count(nyquist_hz: float, filter_cutoff_hz: float, hop_lengt
 
 
 def get_early_downsample_params(
-    sr: Union[float, int], hop_length: int, fmax_t: float, Q: float, n_octaves: int, dtype: tf.dtypes.DType
+    sr: Union[float, int],
+    hop_length: int,
+    fmax_t: float,
+    Q: float,
+    n_octaves: int,
+    dtype: tf.dtypes.DType,
 ) -> Tuple[Union[float, int], int, float, np.array, bool]:
     """Compute downsampling parameters used for early downsampling"""
 
     window_bandwidth = 1.5  # for hann window
     filter_cutoff = fmax_t * (1 + 0.5 * window_bandwidth / Q)
-    sr, hop_length, downsample_factor = early_downsample(sr, hop_length, n_octaves, sr // 2, filter_cutoff)
+    sr, hop_length, downsample_factor = early_downsample(
+        sr, hop_length, n_octaves, sr // 2, filter_cutoff
+    )
     if downsample_factor != 1:
         earlydownsample = True
         early_downsample_filter = create_lowpass_filter(
@@ -119,7 +134,9 @@ def get_early_downsample_params(
     return sr, hop_length, downsample_factor, early_downsample_filter, earlydownsample
 
 
-def get_window_dispatch(window: Union[str, Tuple[str, float]], N: int, fftbins: bool = True) -> np.array:
+def get_window_dispatch(
+    window: Union[str, Tuple[str, float]], N: int, fftbins: bool = True
+) -> np.array:
     if isinstance(window, str):
         return scipy.signal.get_window(window, N, fftbins=fftbins)
     elif isinstance(window, tuple):
@@ -130,9 +147,15 @@ def get_window_dispatch(window: Union[str, Tuple[str, float]], N: int, fftbins:
         else:
             Warning("Tuple windows may have undesired behaviour regarding Q factor")
     elif isinstance(window, float):
-        Warning("You are using Kaiser window with beta factor " + str(window) + ". Correct behaviour not checked.")
+        Warning(
+            "You are using Kaiser window with beta factor "
+            + str(window)
+            + ". Correct behaviour not checked."
+        )
     else:
-        raise Exception("The function get_window from scipy only supports strings, tuples and floats.")
+        raise Exception(
+            "The function get_window from scipy only supports strings, tuples and floats."
+        )
 
 
 def create_cqt_kernels(
@@ -153,7 +176,9 @@ def create_cqt_kernels(
     fftLen = 2 ** next_power_of_2(np.ceil(Q * fs / fmin))
 
     if (fmax is not None) and (n_bins is None):
-        n_bins = np.ceil(bins_per_octave * np.log2(fmax / fmin))  # Calculate the number of bins
+        n_bins = np.ceil(
+            bins_per_octave * np.log2(fmax / fmin)
+        )  # Calculate the number of bins
         freqs = fmin * 2.0 ** (np.r_[0:n_bins] / float(bins_per_octave))
 
     elif (fmax is None) and (n_bins is not None):
@@ -161,12 +186,16 @@ def create_cqt_kernels(
 
     else:
         warnings.warn("If fmax is given, n_bins will be ignored", SyntaxWarning)
-        n_bins = np.ceil(bins_per_octave * np.log2(fmax / fmin))  # Calculate the number of bins
+        n_bins = np.ceil(
+            bins_per_octave * np.log2(fmax / fmin)
+        )  # Calculate the number of bins
         freqs = fmin * 2.0 ** (np.r_[0:n_bins] / float(bins_per_octave))
 
     if np.max(freqs) > fs / 2 and topbin_check is True:
         raise ValueError(
-            "The top bin {}Hz has exceeded the Nyquist frequency, please reduce the n_bins".format(np.max(freqs))
+            "The top bin {}Hz has exceeded the Nyquist frequency, please reduce the n_bins".format(
+                np.max(freqs)
+            )
         )
 
     tempKernel = np.zeros((int(n_bins), int(fftLen)), dtype=np.complex64)
@@ -206,14 +235,18 @@ def get_cqt_complex(
     a constant Q transform.” (1992)."""
 
     try:
-        x = padding(x)  # When center is True, we need padding at the beginning and ending
+        x = padding(
+            x
+        )  # When center is True, we need padding at the beginning and ending
     except Exception:
         warnings.warn(
             f"\ninput size = {x.shape}\tkernel size = {cqt_kernels_real.shape[-1]}\n"
             "padding with reflection mode might not be the best choice, try using constant padding",
             UserWarning,
         )
-        x = tf.pad(x, (cqt_kernels_real.shape[-1] // 2, cqt_kernels_real.shape[-1] // 2))
+        x = tf.pad(
+            x, (cqt_kernels_real.shape[-1] // 2, cqt_kernels_real.shape[-1] // 2)
+        )
     CQT_real = tf.transpose(
         tf.nn.conv1d(
             tf.transpose(x, [0, 2, 1]),
@@ -236,7 +269,9 @@ def get_cqt_complex(
     return tf.stack((CQT_real, CQT_imag), axis=-1)
 
 
-def downsampling_by_n(x: tf.Tensor, filter_kernel: tf.Tensor, n: float, match_torch_exactly: bool = True) -> tf.Tensor:
+def downsampling_by_n(
+    x: tf.Tensor, filter_kernel: tf.Tensor, n: float, match_torch_exactly: bool = True
+) -> tf.Tensor:
     """
     Downsample the given tensor using the given filter kernel.
     The input tensor is expected to have shape `(n_batches, channels, width)`,
@@ -256,10 +291,14 @@ def downsampling_by_n(x: tf.Tensor, filter_kernel: tf.Tensor, n: float, match_to
 
         # Store this tensor in the shape `(n_batches, width, channels)`
         padded_nwc = tf.transpose(padded, [0, 2, 1])
-        result_nwc = tf.nn.conv1d(padded_nwc, filter_kernel[:, None, None], padding="VALID", stride=n)
+        result_nwc = tf.nn.conv1d(
+            padded_nwc, filter_kernel[:, None, None], padding="VALID", stride=n
+        )
     else:
         x_nwc = tf.transpose(x, [0, 2, 1])
-        result_nwc = tf.nn.conv1d(x_nwc, filter_kernel[:, None, None], padding="SAME", stride=n)
+        result_nwc = tf.nn.conv1d(
+            x_nwc, filter_kernel[:, None, None], padding="SAME", stride=n
+        )
     result_ncw = tf.transpose(result_nwc, [0, 2, 1])
     return result_ncw
 
@@ -275,7 +314,15 @@ def __init__(self, padding: Union[int, Tuple[int]] = 1, **kwargs: Any):
         super(ReflectionPad1D, self).__init__(**kwargs)
 
     def compute_output_shape(self, s: List[int]) -> Tuple[int, int, int]:
-        return (s[0], s[1], s[2] + 2 * self.padding if isinstance(self.padding, int) else self.padding[0])
+        return (
+            s[0],
+            s[1],
+            (
+                s[2] + 2 * self.padding
+                if isinstance(self.padding, int)
+                else self.padding[0]
+            ),
+        )
 
     def call(self, x: tf.Tensor) -> tf.Tensor:
         return tf.pad(x, [[0, 0], [0, 0], [self.padding, self.padding]], "REFLECT")
@@ -286,20 +333,34 @@ class ConstantPad1D(tf.keras.layers.Layer):
     Replica of Torch's nn.ConstantPad1D in TF.
     """
 
-    def __init__(self, padding: Union[int, Tuple[int]] = 1, value: int = 0, **kwargs: Any):
+    def __init__(
+        self, padding: Union[int, Tuple[int]] = 1, value: int = 0, **kwargs: Any
+    ):
         self.padding = padding
         self.value = value
         self.input_spec = [tf.keras.layers.InputSpec(ndim=3)]
         super(ConstantPad1D, self).__init__(**kwargs)
 
     def compute_output_shape(self, s: List[int]) -> Tuple[int, int, int]:
-        return (s[0], s[1], s[2] + 2 * self.padding if isinstance(self.padding, int) else self.padding[0])
+        return (
+            s[0],
+            s[1],
+            (
+                s[2] + 2 * self.padding
+                if isinstance(self.padding, int)
+                else self.padding[0]
+            ),
+        )
 
     def call(self, x: tf.Tensor) -> tf.Tensor:
-        return tf.pad(x, [[0, 0], [0, 0], [self.padding, self.padding]], "CONSTANT", self.value)
+        return tf.pad(
+            x, [[0, 0], [0, 0], [self.padding, self.padding]], "CONSTANT", self.value
+        )
 
 
-def pad_center(data: np.ndarray, size: int, axis: int = -1, **kwargs: Any) -> np.ndarray:
+def pad_center(
+    data: np.ndarray, size: int, axis: int = -1, **kwargs: Any
+) -> np.ndarray:
     """Wrapper for np.pad to automatically center an array prior to padding.
     This is analogous to `str.center()`
 
@@ -362,7 +423,9 @@ def pad_center(data: np.ndarray, size: int, axis: int = -1, **kwargs: Any) -> np
     lengths[axis] = (lpad, int(size - n - lpad))
 
     if lpad < 0:
-        raise ValueError(("Target size ({:d}) must be at least input size ({:d})").format(size, n))
+        raise ValueError(
+            ("Target size ({:d}) must be at least input size ({:d})").format(size, n)
+        )
 
     return np.pad(data, lengths, **kwargs)
 
@@ -511,7 +574,9 @@ def build(self, input_shape: tf.TensorShape) -> None:
         # This will be used to calculate filter_cutoff and creating CQT kernels
         Q = float(self.filter_scale) / (2 ** (1 / self.bins_per_octave) - 1)
 
-        self.lowpass_filter = create_lowpass_filter(band_center=0.5, kernel_length=256, transition_bandwidth=0.001)
+        self.lowpass_filter = create_lowpass_filter(
+            band_center=0.5, kernel_length=256, transition_bandwidth=0.001
+        )
 
         # Calculate num of filter requires for the kernel
         # n_octaves determines how many resampling requires for the CQT
@@ -524,25 +589,35 @@ def build(self, input_shape: tf.TensorShape) -> None:
 
         if remainder == 0:
             # Calculate the top bin frequency
-            fmax_t = self.fmin_t * 2 ** ((self.bins_per_octave - 1) / self.bins_per_octave)
+            fmax_t = self.fmin_t * 2 ** (
+                (self.bins_per_octave - 1) / self.bins_per_octave
+            )
         else:
             # Calculate the top bin frequency
             fmax_t = self.fmin_t * 2 ** ((remainder - 1) / self.bins_per_octave)
 
-        self.fmin_t = fmax_t / 2 ** (1 - 1 / self.bins_per_octave)  # Adjusting the top minium bins
+        self.fmin_t = fmax_t / 2 ** (
+            1 - 1 / self.bins_per_octave
+        )  # Adjusting the top minium bins
         if fmax_t > self.sample_rate / 2:
             raise ValueError(
-                "The top bin {}Hz has exceeded the Nyquist frequency, please reduce the n_bins".format(fmax_t)
+                "The top bin {}Hz has exceeded the Nyquist frequency, please reduce the n_bins".format(
+                    fmax_t
+                )
             )
 
-        if self.earlydownsample is True:  # Do early downsampling if this argument is True
+        if (
+            self.earlydownsample is True
+        ):  # Do early downsampling if this argument is True
             (
                 self.sample_rate,
                 self.hop_length,
                 self.downsample_factor,
                 early_downsample_filter,
                 self.earlydownsample,
-            ) = get_early_downsample_params(self.sample_rate, self.hop_length, fmax_t, Q, self.n_octaves, self.dtype)
+            ) = get_early_downsample_params(
+                self.sample_rate, self.hop_length, fmax_t, Q, self.n_octaves, self.dtype
+            )
 
             self.early_downsample_filter = early_downsample_filter
         else:
@@ -563,7 +638,9 @@ def build(self, input_shape: tf.TensorShape) -> None:
         # The freqs returned by create_cqt_kernels cannot be used
         # Since that returns only the top octave bins
         # We need the information for all freq bin
-        freqs = self.fmin * 2.0 ** (np.r_[0 : self.n_bins] / float(self.bins_per_octave))
+        freqs = self.fmin * 2.0 ** (
+            np.r_[0 : self.n_bins] / float(self.bins_per_octave)
+        )
         self.frequencies = freqs
 
         self.lengths = np.ceil(Q * self.sample_rate / freqs)
@@ -576,8 +653,12 @@ def build(self, input_shape: tf.TensorShape) -> None:
         self.cqt_kernels_imag = tf.expand_dims(basis.imag.astype(self.dtype), 1)
 
         if self.trainable:
-            self.cqt_kernels_real = tf.Variable(initial_value=self.cqt_kernels_real, trainable=True)
-            self.cqt_kernels_imag = tf.Variable(initial_value=self.cqt_kernels_imag, trainable=True)
+            self.cqt_kernels_real = tf.Variable(
+                initial_value=self.cqt_kernels_real, trainable=True
+            )
+            self.cqt_kernels_imag = tf.Variable(
+                initial_value=self.cqt_kernels_imag, trainable=True
+            )
 
         # If center==True, the STFT window will be put in the middle, and paddings at the beginning
         # and ending are required.
@@ -594,25 +675,38 @@ def build(self, input_shape: tf.TensorShape) -> None:
         elif rank == 3:
             self.reshape_input = lambda x: x
         else:
-            raise ValueError(f"Input shape must be rank <= 3, found shape {input_shape}")
+            raise ValueError(
+                f"Input shape must be rank <= 3, found shape {input_shape}"
+            )
 
     def call(self, x: tf.Tensor) -> tf.Tensor:
         x = self.reshape_input(x)  # type: ignore
 
         if self.earlydownsample is True:
-            x = downsampling_by_n(x, self.early_downsample_filter, self.downsample_factor, self.match_torch_exactly)
+            x = downsampling_by_n(
+                x,
+                self.early_downsample_filter,
+                self.downsample_factor,
+                self.match_torch_exactly,
+            )
 
         hop = self.hop_length
 
         # Getting the top octave CQT
-        CQT = get_cqt_complex(x, self.cqt_kernels_real, self.cqt_kernels_imag, hop, self.padding)
+        CQT = get_cqt_complex(
+            x, self.cqt_kernels_real, self.cqt_kernels_imag, hop, self.padding
+        )
 
         x_down = x  # Preparing a new variable for downsampling
 
         for _ in range(self.n_octaves - 1):
             hop = hop // 2
-            x_down = downsampling_by_n(x_down, self.lowpass_filter, 2, self.match_torch_exactly)
-            CQT1 = get_cqt_complex(x_down, self.cqt_kernels_real, self.cqt_kernels_imag, hop, self.padding)
+            x_down = downsampling_by_n(
+                x_down, self.lowpass_filter, 2, self.match_torch_exactly
+            )
+            CQT1 = get_cqt_complex(
+                x_down, self.cqt_kernels_real, self.cqt_kernels_imag, hop, self.padding
+            )
             CQT = tf.concat((CQT1, CQT), axis=1)
 
         CQT = CQT[:, -self.n_bins :, :]  # Removing unwanted bottom bins
@@ -629,12 +723,18 @@ def call(self, x: tf.Tensor) -> tf.Tensor:
         elif self.normalization_type == "wrap":
             CQT *= 2
         else:
-            raise ValueError("The normalization_type %r is not part of our current options." % self.normalization_type)
+            raise ValueError(
+                "The normalization_type %r is not part of our current options."
+                % self.normalization_type
+            )
 
         # Transpose the output to match the output of the other spectrogram layers.
         if self.output_format.lower() == "magnitude":
             # Getting CQT Amplitude
-            return tf.transpose(tf.math.sqrt(tf.math.reduce_sum(tf.math.pow(CQT, 2), axis=-1)), [0, 2, 1])
+            return tf.transpose(
+                tf.math.sqrt(tf.math.reduce_sum(tf.math.pow(CQT, 2), axis=-1)),
+                [0, 2, 1],
+            )
 
         elif self.output_format.lower() == "complex":
             return CQT
diff --git a/basic_pitch/layers/signal.py b/basic_pitch/layers/signal.py
index f5f629f0..dc69fae9 100644
--- a/basic_pitch/layers/signal.py
+++ b/basic_pitch/layers/signal.py
@@ -67,7 +67,9 @@ def build(self, input_shape: tf.TensorShape) -> None:
             lpad = (self.fft_length - self.window_length) // 2
             rpad = self.fft_length - self.window_length - lpad
 
-            def padded_window(window_length: int, dtype: tf.dtypes.DType = tf.float32) -> tf.Tensor:
+            def padded_window(
+                window_length: int, dtype: tf.dtypes.DType = tf.float32
+            ) -> tf.Tensor:
                 # This is a trick to match librosa's way of handling window lengths < their fft_lengths
                 # In that case the window is 0 padded such that the window is centered around 0s
                 # In the Tensorflow case, the window is computed, multiplied against the frame and then
@@ -80,7 +82,8 @@ def padded_window(window_length: int, dtype: tf.dtypes.DType = tf.float32) -> tf
             self.spec = tf.keras.layers.Lambda(
                 lambda x: tf.pad(
                     x,
-                    [[0, 0] for _ in range(input_shape.rank - 1)] + [[self.fft_length // 2, self.fft_length // 2]],
+                    [[0, 0] for _ in range(input_shape.rank - 1)]
+                    + [[self.fft_length // 2, self.fft_length // 2]],
                     mode=self.pad_mode,
                 )
             )
@@ -163,10 +166,14 @@ def build(self, input_shape: tf.Tensor) -> None:
         self.squeeze_batch = lambda batch: batch
         rank = input_shape.rank
         if rank == 4:
-            assert input_shape[1] == 1, "If the rank is 4, the second dimension must be length 1"
+            assert (
+                input_shape[1] == 1
+            ), "If the rank is 4, the second dimension must be length 1"
             self.squeeze_batch = lambda batch: tf.squeeze(batch, axis=1)
         else:
-            assert rank == 3, f"Only ranks 3 and 4 are supported!. Received rank {rank} for {input_shape}."
+            assert (
+                rank == 3
+            ), f"Only ranks 3 and 4 are supported!. Received rank {rank} for {input_shape}."
 
     def call(self, inputs: tf.Tensor) -> tf.Tensor:
         inputs = self.squeeze_batch(inputs)  # type: ignore
@@ -174,12 +181,16 @@ def call(self, inputs: tf.Tensor) -> tf.Tensor:
         power = tf.math.square(inputs)
         log_power = 10 * log_base_b(power + 1e-10, 10)
 
-        log_power_min = tf.reshape(tf.math.reduce_min(log_power, axis=[1, 2]), [tf.shape(inputs)[0], 1, 1])
+        log_power_min = tf.reshape(
+            tf.math.reduce_min(log_power, axis=[1, 2]), [tf.shape(inputs)[0], 1, 1]
+        )
         log_power_offset = log_power - log_power_min
         log_power_offset_max = tf.reshape(
             tf.math.reduce_max(log_power_offset, axis=[1, 2]),
             [tf.shape(inputs)[0], 1, 1],
         )
-        log_power_normalized = tf.math.divide_no_nan(log_power_offset, log_power_offset_max)
+        log_power_normalized = tf.math.divide_no_nan(
+            log_power_offset, log_power_offset_max
+        )
 
         return tf.reshape(log_power_normalized, tf.shape(inputs))
diff --git a/basic_pitch/models.py b/basic_pitch/models.py
index 0e726510..b5d8e9a6 100644
--- a/basic_pitch/models.py
+++ b/basic_pitch/models.py
@@ -33,10 +33,14 @@
 
 tfkl = tf.keras.layers
 
-MAX_N_SEMITONES = int(np.floor(12.0 * np.log2(0.5 * AUDIO_SAMPLE_RATE / ANNOTATIONS_BASE_FREQUENCY)))
+MAX_N_SEMITONES = int(
+    np.floor(12.0 * np.log2(0.5 * AUDIO_SAMPLE_RATE / ANNOTATIONS_BASE_FREQUENCY))
+)
 
 
-def transcription_loss(y_true: tf.Tensor, y_pred: tf.Tensor, label_smoothing: float) -> tf.Tensor:
+def transcription_loss(
+    y_true: tf.Tensor, y_pred: tf.Tensor, label_smoothing: float
+) -> tf.Tensor:
     """Really a binary cross entropy loss. Used to calculate the loss between the predicted
     posteriorgrams and the ground truth matrices.
 
@@ -48,12 +52,17 @@ def transcription_loss(y_true: tf.Tensor, y_pred: tf.Tensor, label_smoothing: fl
     Returns:
         The transcription loss.
     """
-    bce = tf.keras.losses.binary_crossentropy(y_true, y_pred, label_smoothing=label_smoothing)
+    bce = tf.keras.losses.binary_crossentropy(
+        y_true, y_pred, label_smoothing=label_smoothing
+    )
     return bce
 
 
 def weighted_transcription_loss(
-    y_true: tf.Tensor, y_pred: tf.Tensor, label_smoothing: float, positive_weight: float = 0.5
+    y_true: tf.Tensor,
+    y_pred: tf.Tensor,
+    label_smoothing: float,
+    positive_weight: float = 0.5,
 ) -> tf.Tensor:
     """The transcription loss where the positive and negative true labels are balanced by a weighting factor.
 
@@ -103,7 +112,9 @@ def onset_loss(
     return lambda x, y: transcription_loss(x, y, label_smoothing=label_smoothing)
 
 
-def loss(label_smoothing: float = 0.2, weighted: bool = False, positive_weight: float = 0.5) -> Dict[str, Any]:
+def loss(
+    label_smoothing: float = 0.2, weighted: bool = False, positive_weight: float = 0.5
+) -> Dict[str, Any]:
     """Creates a keras-compatible dictionary of loss functions to calculate
     the loss for the contour, note and onset posteriorgrams.
 
@@ -127,7 +138,9 @@ def loss(label_smoothing: float = 0.2, weighted: bool = False, positive_weight:
 
 
 def _initializer() -> tf.keras.initializers.VarianceScaling:
-    return tf.keras.initializers.VarianceScaling(scale=2.0, mode="fan_avg", distribution="uniform", seed=None)
+    return tf.keras.initializers.VarianceScaling(
+        scale=2.0, mode="fan_avg", distribution="uniform", seed=None
+    )
 
 
 def _kernel_constraint() -> tf.keras.constraints.UnitNorm:
diff --git a/basic_pitch/nn.py b/basic_pitch/nn.py
index 2a856488..4633f859 100644
--- a/basic_pitch/nn.py
+++ b/basic_pitch/nn.py
@@ -40,14 +40,19 @@ class HarmonicStacking(tf.keras.layers.Layer):
     """
 
     def __init__(
-        self, bins_per_semitone: int, harmonics: List[float], n_output_freqs: int, name: str = "harmonic_stacking"
+        self,
+        bins_per_semitone: int,
+        harmonics: List[float],
+        n_output_freqs: int,
+        name: str = "harmonic_stacking",
     ):
         """Downsample frequency by stride, upsample channels by 4."""
         super().__init__(trainable=False, name=name)
         self.bins_per_semitone = bins_per_semitone
         self.harmonics = harmonics
         self.shifts = [
-            int(tf.math.round(12.0 * self.bins_per_semitone * log_base_b(float(h), 2))) for h in self.harmonics
+            int(tf.math.round(12.0 * self.bins_per_semitone * log_base_b(float(h), 2)))
+            for h in self.harmonics
         ]
         self.n_output_freqs = n_output_freqs
 
@@ -81,7 +86,9 @@ def call(self, x: tf.Tensor) -> tf.Tensor:
 
             channels.append(padded)
         x = tf.concat(channels, axis=-1)
-        x = x[:, :, : self.n_output_freqs, :]  # return only the first n_output_freqs frequency channels
+        x = x[
+            :, :, : self.n_output_freqs, :
+        ]  # return only the first n_output_freqs frequency channels
         return x
 
 
@@ -109,4 +116,6 @@ class FlattenFreqCh(tf.keras.layers.Layer):
 
     def call(self, x: tf.Tensor) -> tf.Tensor:
         shapes = K.int_shape(x)
-        return tf.keras.layers.Reshape([shapes[1], shapes[2] * shapes[3]])(x)  # ignore batch size
+        return tf.keras.layers.Reshape([shapes[1], shapes[2] * shapes[3]])(
+            x
+        )  # ignore batch size
diff --git a/basic_pitch/note_creation.py b/basic_pitch/note_creation.py
index 5a8072ca..296b21f0 100644
--- a/basic_pitch/note_creation.py
+++ b/basic_pitch/note_creation.py
@@ -55,7 +55,9 @@ def model_output_to_notes(
     multiple_pitch_bends: bool = False,
     melodia_trick: bool = True,
     midi_tempo: float = 120,
-) -> Tuple[pretty_midi.PrettyMIDI, List[Tuple[float, float, int, float, Optional[List[int]]]]]:
+) -> Tuple[
+    pretty_midi.PrettyMIDI, List[Tuple[float, float, int, float, Optional[List[int]]]]
+]:
     """Convert model output to MIDI
 
     Args:
@@ -97,20 +99,29 @@ def model_output_to_notes(
     if include_pitch_bends:
         estimated_notes_with_pitch_bend = get_pitch_bends(contours, estimated_notes)
     else:
-        estimated_notes_with_pitch_bend = [(note[0], note[1], note[2], note[3], None) for note in estimated_notes]
+        estimated_notes_with_pitch_bend = [
+            (note[0], note[1], note[2], note[3], None) for note in estimated_notes
+        ]
 
     times_s = model_frames_to_time(contours.shape[0])
     estimated_notes_time_seconds = [
-        (times_s[note[0]], times_s[note[1]], note[2], note[3], note[4]) for note in estimated_notes_with_pitch_bend
+        (times_s[note[0]], times_s[note[1]], note[2], note[3], note[4])
+        for note in estimated_notes_with_pitch_bend
     ]
 
     return (
-        note_events_to_midi(estimated_notes_time_seconds, multiple_pitch_bends, midi_tempo),
+        note_events_to_midi(
+            estimated_notes_time_seconds, multiple_pitch_bends, midi_tempo
+        ),
         estimated_notes_time_seconds,
     )
 
 
-def sonify_midi(midi: pretty_midi.PrettyMIDI, save_path: Union[pathlib.Path, str], sr: Optional[int] = 44100) -> None:
+def sonify_midi(
+    midi: pretty_midi.PrettyMIDI,
+    save_path: Union[pathlib.Path, str],
+    sr: Optional[int] = 44100,
+) -> None:
     """Sonify a pretty_midi midi object and save to a file.
 
     Args:
@@ -123,7 +134,10 @@ def sonify_midi(midi: pretty_midi.PrettyMIDI, save_path: Union[pathlib.Path, str
 
 
 def sonify_salience(
-    gram: np.array, semitone_resolution: float, save_path: Optional[str] = None, thresh: float = 0.2
+    gram: np.array,
+    semitone_resolution: float,
+    save_path: Optional[str] = None,
+    thresh: float = 0.2,
 ) -> Tuple[np.array, int]:
     """Sonify a salience matrix.
 
@@ -151,7 +165,9 @@ def sonify_salience(
         hop_length=AUDIO_N_SAMPLES / ANNOT_N_FRAMES,  # THIS IS THE CORRECT HOP!!
     )
     gram[gram < thresh] = 0
-    y = mir_eval.sonify.time_frequency(gram[:max_freq_idx, :], freqs[:max_freq_idx], times, fs=SONIFY_FS)
+    y = mir_eval.sonify.time_frequency(
+        gram[:max_freq_idx, :], freqs[:max_freq_idx], times, fs=SONIFY_FS
+    )
     if save_path:
         y_resamp = resampy.resample(y, SONIFY_FS, 44100)
         wavfile.write(save_path, 44100, y_resamp)
@@ -170,11 +186,17 @@ def midi_pitch_to_contour_bin(pitch_midi: int) -> np.array:
 
     """
     pitch_hz = librosa.midi_to_hz(pitch_midi)
-    return 12.0 * CONTOURS_BINS_PER_SEMITONE * np.log2(pitch_hz / ANNOTATIONS_BASE_FREQUENCY)
+    return (
+        12.0
+        * CONTOURS_BINS_PER_SEMITONE
+        * np.log2(pitch_hz / ANNOTATIONS_BASE_FREQUENCY)
+    )
 
 
 def get_pitch_bends(
-    contours: np.ndarray, note_events: List[Tuple[int, int, int, float]], n_bins_tolerance: int = 25
+    contours: np.ndarray,
+    note_events: List[Tuple[int, int, int, float]],
+    n_bins_tolerance: int = 25,
 ) -> List[Tuple[int, int, int, float, Optional[List[int]]]]:
     """Given note events and contours, estimate pitch bends per note.
     Pitch bends are represented as a sequence of evenly spaced midi pitch bend control units.
@@ -209,12 +231,16 @@ def get_pitch_bends(
         bends: Optional[List[int]] = list(
             np.argmax(pitch_bend_submatrix, axis=1) - pb_shift
         )  # this is in units of 1/3 semitones
-        note_events_with_pitch_bends.append((start_idx, end_idx, pitch_midi, amplitude, bends))
+        note_events_with_pitch_bends.append(
+            (start_idx, end_idx, pitch_midi, amplitude, bends)
+        )
     return note_events_with_pitch_bends
 
 
 def note_events_to_midi(
-    note_events_with_pitch_bends: List[Tuple[float, float, int, float, Optional[List[int]]]],
+    note_events_with_pitch_bends: List[
+        Tuple[float, float, int, float, Optional[List[int]]]
+    ],
     multiple_pitch_bends: bool = False,
     midi_tempo: float = 120,
 ) -> pretty_midi.PrettyMIDI:
@@ -233,14 +259,24 @@ def note_events_to_midi(
     """
     mid = pretty_midi.PrettyMIDI(initial_tempo=midi_tempo)
     if not multiple_pitch_bends:
-        note_events_with_pitch_bends = drop_overlapping_pitch_bends(note_events_with_pitch_bends)
+        note_events_with_pitch_bends = drop_overlapping_pitch_bends(
+            note_events_with_pitch_bends
+        )
 
     piano_program = pretty_midi.instrument_name_to_program("Electric Piano 1")
     instruments: DefaultDict[int, pretty_midi.Instrument] = defaultdict(
         lambda: pretty_midi.Instrument(program=piano_program)
     )
-    for start_time, end_time, note_number, amplitude, pitch_bend in note_events_with_pitch_bends:
-        instrument = instruments[note_number] if multiple_pitch_bends else instruments[0]
+    for (
+        start_time,
+        end_time,
+        note_number,
+        amplitude,
+        pitch_bend,
+    ) in note_events_with_pitch_bends:
+        instrument = (
+            instruments[note_number] if multiple_pitch_bends else instruments[0]
+        )
         note = pretty_midi.Note(
             velocity=int(np.round(127 * amplitude)),
             pitch=note_number,
@@ -251,11 +287,17 @@ def note_events_to_midi(
         if not pitch_bend:
             continue
         pitch_bend_times = np.linspace(start_time, end_time, len(pitch_bend))
-        pitch_bend_midi_ticks = np.round(np.array(pitch_bend) * 4096 / CONTOURS_BINS_PER_SEMITONE).astype(int)
+        pitch_bend_midi_ticks = np.round(
+            np.array(pitch_bend) * 4096 / CONTOURS_BINS_PER_SEMITONE
+        ).astype(int)
         # This supports pitch bends up to 2 semitones
         # If we estimate pitch bends above/below 2 semitones, crop them here when adding them to the midi file
-        pitch_bend_midi_ticks[pitch_bend_midi_ticks > N_PITCH_BEND_TICKS - 1] = N_PITCH_BEND_TICKS - 1
-        pitch_bend_midi_ticks[pitch_bend_midi_ticks < -N_PITCH_BEND_TICKS] = -N_PITCH_BEND_TICKS
+        pitch_bend_midi_ticks[pitch_bend_midi_ticks > N_PITCH_BEND_TICKS - 1] = (
+            N_PITCH_BEND_TICKS - 1
+        )
+        pitch_bend_midi_ticks[pitch_bend_midi_ticks < -N_PITCH_BEND_TICKS] = (
+            -N_PITCH_BEND_TICKS
+        )
         for pb_time, pb_midi in zip(pitch_bend_times, pitch_bend_midi_ticks):
             instrument.pitch_bends.append(pretty_midi.PitchBend(pb_midi, pb_time))
     mid.instruments.extend(instruments.values())
@@ -264,7 +306,9 @@ def note_events_to_midi(
 
 
 def drop_overlapping_pitch_bends(
-    note_events_with_pitch_bends: List[Tuple[float, float, int, float, Optional[List[int]]]]
+    note_events_with_pitch_bends: List[
+        Tuple[float, float, int, float, Optional[List[int]]]
+    ]
 ) -> List[Tuple[float, float, int, float, Optional[List[int]]]]:
     """Drop pitch bends from any notes that overlap in time with another note"""
     note_events = sorted(note_events_with_pitch_bends)
@@ -296,15 +340,22 @@ def get_infered_onsets(onsets: np.array, frames: np.array, n_diff: int = 2) -> n
     frame_diff = np.min(diffs, axis=0)
     frame_diff[frame_diff < 0] = 0
     frame_diff[:n_diff, :] = 0
-    frame_diff = np.max(onsets) * frame_diff / np.max(frame_diff)  # rescale to have the same max as onsets
+    frame_diff = (
+        np.max(onsets) * frame_diff / np.max(frame_diff)
+    )  # rescale to have the same max as onsets
 
-    max_onsets_diff = np.max([onsets, frame_diff], axis=0)  # use the max of the predicted onsets and the differences
+    max_onsets_diff = np.max(
+        [onsets, frame_diff], axis=0
+    )  # use the max of the predicted onsets and the differences
 
     return max_onsets_diff
 
 
 def constrain_frequency(
-    onsets: np.array, frames: np.array, max_freq: Optional[float], min_freq: Optional[float]
+    onsets: np.array,
+    frames: np.array,
+    max_freq: Optional[float],
+    min_freq: Optional[float],
 ) -> Tuple[np.array, np.array]:
     """Zero out activations above or below the max/min frequencies
 
@@ -437,7 +488,9 @@ def output_to_notes_polyphonic(
         energy_shape = remaining_energy.shape
 
         while np.max(remaining_energy) > frame_thresh:
-            i_mid, freq_idx = np.unravel_index(np.argmax(remaining_energy), energy_shape)
+            i_mid, freq_idx = np.unravel_index(
+                np.argmax(remaining_energy), energy_shape
+            )
             remaining_energy[i_mid, freq_idx] = 0
 
             # forward pass
diff --git a/basic_pitch/predict.py b/basic_pitch/predict.py
index 76bb8862..b9eefa81 100644
--- a/basic_pitch/predict.py
+++ b/basic_pitch/predict.py
@@ -30,7 +30,12 @@ def main() -> None:
     """Handle command line arguments. Entrypoint for this script."""
     parser = argparse.ArgumentParser(description="Predict midi from audio.")
     parser.add_argument("output_dir", type=str, help="directory to save outputs")
-    parser.add_argument("audio_paths", type=str, nargs="+", help="Space separated paths to the input audio files.")
+    parser.add_argument(
+        "audio_paths",
+        type=str,
+        nargs="+",
+        help="Space separated paths to the input audio files.",
+    )
     parser.add_argument(
         "--model_path",
         type=str,
@@ -106,8 +111,17 @@ def main() -> None:
         default=120,
         help="The tempo for the midi file.",
     )
-    parser.add_argument("--debug-file", default=None, help="Optional file for debug output for inference.")
-    parser.add_argument("--no-melodia", default=False, action="store_true", help="Skip the melodia trick.")
+    parser.add_argument(
+        "--debug-file",
+        default=None,
+        help="Optional file for debug output for inference.",
+    )
+    parser.add_argument(
+        "--no-melodia",
+        default=False,
+        action="store_true",
+        help="Skip the melodia trick.",
+    )
     args = parser.parse_args()
 
     print("")
@@ -119,7 +133,11 @@ def main() -> None:
     # tensorflow is very slow to import
     # this import is here so that the help messages print faster
     print("Importing Tensorflow (this may take a few seconds)...")
-    from basic_pitch.inference import predict_and_save, verify_output_dir, verify_input_path
+    from basic_pitch.inference import (
+        predict_and_save,
+        verify_output_dir,
+        verify_input_path,
+    )
 
     output_dir = pathlib.Path(args.output_dir)
     verify_output_dir(output_dir)
diff --git a/tests/resources/data/guitarset/annotation/00_BN1-129-Eb_comp.jams b/tests/resources/data/guitarset/annotation/00_BN1-129-Eb_comp.jams
new file mode 100644
index 00000000..28657b71
--- /dev/null
+++ b/tests/resources/data/guitarset/annotation/00_BN1-129-Eb_comp.jams
@@ -0,0 +1,79476 @@
+{
+  "annotations": [
+    {
+      "annotation_metadata": {
+        "curator": {
+          "name": "",
+          "email": ""
+        },
+        "annotator": {},
+        "version": "",
+        "corpus": "",
+        "annotation_tools": "",
+        "annotation_rules": "",
+        "validation": "",
+        "data_source": "0"
+      },
+      "namespace": "pitch_contour",
+      "data": {
+        "time": [
+          7.463079364999999,
+          7.468884353000021,
+          7.474689342000033,
+          7.480494331000045,
+          7.486299319000068,
+          7.49210430800008,
+          7.497909297000092,
+          7.503714285000001,
+          7.509519274000013,
+          7.515324263000025,
+          7.521129251000048,
+          7.52693424000006,
+          7.532739229000072,
+          7.538544217000094,
+          7.544349205999993,
+          7.550154195000005,
+          7.555959183000027,
+          7.561764172000039,
+          7.567569160000062,
+          7.573374149000074,
+          7.579179138000086,
+          7.584984125999995,
+          7.590789115000007,
+          7.596594104000019,
+          7.6023990920000415,
+          7.6082040810000535,
+          7.6140090700000655,
+          7.619814058000088,
+          7.6256190469999865,
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+          "time": 20.46511627906977,
+          "duration": 0.0,
+          "value": {
+            "position": 1,
+            "beat_units": 4,
+            "measure": 12,
+            "num_beats": 4
+          },
+          "confidence": null
+        },
+        {
+          "time": 20.930232558139533,
+          "duration": 0.0,
+          "value": {
+            "position": 2,
+            "beat_units": 4,
+            "measure": 12,
+            "num_beats": 4
+          },
+          "confidence": null
+        },
+        {
+          "time": 21.3953488372093,
+          "duration": 0.0,
+          "value": {
+            "position": 3,
+            "beat_units": 4,
+            "measure": 12,
+            "num_beats": 4
+          },
+          "confidence": null
+        },
+        {
+          "time": 21.86046511627907,
+          "duration": 0.0,
+          "value": {
+            "position": 4,
+            "beat_units": 4,
+            "measure": 12,
+            "num_beats": 4
+          },
+          "confidence": null
+        }
+      ],
+      "sandbox": {},
+      "time": 0,
+      "duration": 22.32439999999997
+    },
+    {
+      "annotation_metadata": {
+        "curator": {
+          "name": "",
+          "email": ""
+        },
+        "annotator": {},
+        "version": "",
+        "corpus": "",
+        "annotation_tools": "",
+        "annotation_rules": "",
+        "validation": "",
+        "data_source": ""
+      },
+      "namespace": "tempo",
+      "data": [
+        {
+          "time": 0.0,
+          "duration": 22.32439999999997,
+          "value": 129.0,
+          "confidence": 1.0
+        }
+      ],
+      "sandbox": {},
+      "time": 0,
+      "duration": 22.32439999999997
+    },
+    {
+      "annotation_metadata": {
+        "curator": {
+          "name": "",
+          "email": ""
+        },
+        "annotator": {},
+        "version": "",
+        "corpus": "",
+        "annotation_tools": "",
+        "annotation_rules": "",
+        "validation": "",
+        "data_source": ""
+      },
+      "namespace": "chord",
+      "data": [
+        {
+          "time": 0.0,
+          "duration": 7.441860465116279,
+          "value": "D#:maj",
+          "confidence": null
+        },
+        {
+          "time": 7.441860465116279,
+          "duration": 3.72093023255814,
+          "value": "G#:maj",
+          "confidence": null
+        },
+        {
+          "time": 11.162790697674419,
+          "duration": 3.720930232558139,
+          "value": "D#:maj",
+          "confidence": null
+        },
+        {
+          "time": 14.883720930232558,
+          "duration": 1.8604651162790713,
+          "value": "A#:maj",
+          "confidence": null
+        },
+        {
+          "time": 16.74418604651163,
+          "duration": 1.8604651162790695,
+          "value": "G#:maj",
+          "confidence": null
+        },
+        {
+          "time": 18.6046511627907,
+          "duration": 3.71974883720927,
+          "value": "D#:maj",
+          "confidence": null
+        }
+      ],
+      "sandbox": {},
+      "time": 0,
+      "duration": 22.32439999999997
+    },
+    {
+      "annotation_metadata": {
+        "curator": {
+          "name": "",
+          "email": ""
+        },
+        "annotator": {},
+        "version": 0.9,
+        "corpus": "",
+        "annotation_tools": "",
+        "annotation_rules": "Chord sheet-informed symbolic chord transcription based on the included separate string note transcriptions with the chord segmentation and root derived from sheet music.",
+        "validation": "",
+        "data_source": "Semi-automatic chord transcription with manual verification"
+      },
+      "namespace": "chord",
+      "data": [
+        {
+          "time": 0.0,
+          "duration": 7.441860465116279,
+          "value": "D#:sus2(7)/1",
+          "confidence": null
+        },
+        {
+          "time": 7.441860465116279,
+          "duration": 3.72093023255814,
+          "value": "G#:maj6(*5)/1",
+          "confidence": null
+        },
+        {
+          "time": 11.162790697674419,
+          "duration": 3.720930232558139,
+          "value": "D#:maj7/1",
+          "confidence": null
+        },
+        {
+          "time": 14.883720930232558,
+          "duration": 1.8604651162790713,
+          "value": "A#:maj/1",
+          "confidence": null
+        },
+        {
+          "time": 16.74418604651163,
+          "duration": 1.8604651162790695,
+          "value": "G#:maj6(2,b5,*5)/1",
+          "confidence": null
+        },
+        {
+          "time": 18.6046511627907,
+          "duration": 3.71974883720927,
+          "value": "D#:maj7/1",
+          "confidence": null
+        }
+      ],
+      "sandbox": {},
+      "time": 0,
+      "duration": null
+    },
+    {
+      "annotation_metadata": {
+        "curator": {
+          "name": "",
+          "email": ""
+        },
+        "annotator": {},
+        "version": "",
+        "corpus": "",
+        "annotation_tools": "",
+        "annotation_rules": "",
+        "validation": "",
+        "data_source": ""
+      },
+      "namespace": "key_mode",
+      "data": [
+        {
+          "time": 0.0,
+          "duration": 22.32439999999997,
+          "value": "Eb:major",
+          "confidence": 1
+        }
+      ],
+      "sandbox": {},
+      "time": 0,
+      "duration": 22.32439999999997
+    }
+  ],
+  "file_metadata": {
+    "title": "00_BN1-129-Eb_comp",
+    "artist": "",
+    "release": "",
+    "duration": 22.32439999999997,
+    "identifiers": {},
+    "jams_version": "0.3.1"
+  },
+  "sandbox": {}
+}
\ No newline at end of file
diff --git a/tests/resources/data/guitarset/audio_mono-mic/00_BN1-129-Eb_comp_mic.wav b/tests/resources/data/guitarset/audio_mono-mic/00_BN1-129-Eb_comp_mic.wav
new file mode 100644
index 00000000..b38acec4
Binary files /dev/null and b/tests/resources/data/guitarset/audio_mono-mic/00_BN1-129-Eb_comp_mic.wav differ
diff --git a/tests/test_guitarset.py b/tests/test_guitarset.py
new file mode 100644
index 00000000..e38c1c65
--- /dev/null
+++ b/tests/test_guitarset.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+# encoding: utf-8
+#
+# Copyright 2022 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 apache_beam.testing.test_pipeline import TestPipeline
+from typing import List
+
+from basic_pitch.data.datasets.guitarset import (
+    GuitarSetToTfExample,
+    GuitarSetInvalidTracks,
+    create_input_data,
+)
+from basic_pitch.data.pipeline import WriteBatchToTfRecord
+
+RESOURCES_PATH = pathlib.Path(__file__).parent / "resources"
+TRACK_ID = "00_BN1-129-Eb_comp"
+
+
+def test_guitar_set_to_tf_example(tmpdir: str):
+    input_data: List[str] = [TRACK_ID]
+    with TestPipeline() as p:
+        (
+            p
+            | "Create PCollection of track IDs" >> beam.Create([input_data])
+            | "Create tf.Example"
+            >> beam.ParDo(
+                GuitarSetToTfExample(str(RESOURCES_PATH / "data" / "guitarset"))
+            )
+            | "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_guitar_set_invalid_tracks(tmpdir):
+    split_labels = ["train", "test", "validation"]
+    input_data = [(str(i), split) for i, split in enumerate(split_labels)]
+    with TestPipeline() as p:
+        splits = (
+            p
+            | "Create PCollection" >> beam.Create(input_data)
+            | "Tag it"
+            >> beam.ParDo(GuitarSetInvalidTracks()).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 i, split in enumerate(split_labels):
+        with open(os.path.join(tmpdir, f"output_{split}.txt"), "r") as fp:
+            assert fp.read().strip() == str(i)
+
+
+def test_create_input_data():
+    data = create_input_data(train_percent=0.33, validation_percent=0.33)
+    data.sort(key=lambda el: el[1])  # sort by split
+    tolerance = 0.05
+    for key, group in itertools.groupby(data, lambda el: el[1]):
+        assert (
+            (0.33 - tolerance) * len(data)
+            <= len(list(group))
+            <= (0.33 + tolerance) * len(data)
+        )
+        # print(f"total length: {len(data)}, label: {key}, len: {len(list(group))}")
+
+
+def test_create_input_data_overallocate():
+    try:
+        create_input_data(train_percent=0.6, validation_percent=0.6)
+    except AssertionError:
+        return True
diff --git a/tests/test_inference.py b/tests/test_inference.py
index 23a477e5..d3617311 100644
--- a/tests/test_inference.py
+++ b/tests/test_inference.py
@@ -42,7 +42,9 @@ def test_predict(self) -> None:
         assert isinstance(midi_data, pretty_midi.PrettyMIDI)
         lowest_supported_midi = 21
         note_pitch_min = [n[2] >= lowest_supported_midi for n in note_events]
-        note_pitch_max = [n[2] <= lowest_supported_midi + ANNOTATIONS_N_SEMITONES for n in note_events]
+        note_pitch_max = [
+            n[2] <= lowest_supported_midi + ANNOTATIONS_N_SEMITONES for n in note_events
+        ]
         assert all(note_pitch_min)
         assert all(note_pitch_max)
         assert isinstance(note_events, list)
@@ -63,7 +65,12 @@ def test_predict_with_saves(self) -> None:
             expected_npz_path = tmpdir / pathlib.Path("vocadito_10_basic_pitch.npz")
             expected_sonif_path = tmpdir / pathlib.Path("vocadito_10_basic_pitch.wav")
 
-            for output_path in [expected_midi_path, expected_csv_path, expected_npz_path, expected_sonif_path]:
+            for output_path in [
+                expected_midi_path,
+                expected_csv_path,
+                expected_npz_path,
+                expected_sonif_path,
+            ]:
                 assert os.path.exists(output_path)
 
     def test_predict_onset_threshold(self) -> None:
diff --git a/tests/test_nn.py b/tests/test_nn.py
index 7e2b1c05..f9e0e187 100644
--- a/tests/test_nn.py
+++ b/tests/test_nn.py
@@ -37,10 +37,12 @@
 class TestHarmonicStacking(unittest.TestCase):
     def _audio_data_gen(self) -> Iterator[Tuple[np.array, np.array]]:
         while True:
-            audio = np.random.uniform(size=(BATCH_SIZE, AUDIO_N_SAMPLES, 1)).astype(np.float32)
-            output = np.random.uniform(size=(BATCH_SIZE, ANNOT_N_FRAMES, ANNOTATIONS_N_SEMITONES * 3, 1)).astype(
+            audio = np.random.uniform(size=(BATCH_SIZE, AUDIO_N_SAMPLES, 1)).astype(
                 np.float32
             )
+            output = np.random.uniform(
+                size=(BATCH_SIZE, ANNOT_N_FRAMES, ANNOTATIONS_N_SEMITONES * 3, 1)
+            ).astype(np.float32)
             yield (audio, output)
 
     def _dummy_dataset(self) -> tf.data.Dataset:
@@ -49,7 +51,9 @@ def _dummy_dataset(self) -> tf.data.Dataset:
             (tf.float32, tf.float32),
             (
                 tf.TensorShape([BATCH_SIZE, AUDIO_N_SAMPLES, 1]),
-                tf.TensorShape([BATCH_SIZE, ANNOT_N_FRAMES, ANNOTATIONS_N_SEMITONES * 3, 1]),
+                tf.TensorShape(
+                    [BATCH_SIZE, ANNOT_N_FRAMES, ANNOTATIONS_N_SEMITONES * 3, 1]
+                ),
             ),
         )
         return ds
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
new file mode 100644
index 00000000..7a967509
--- /dev/null
+++ b/tests/test_pipeline.py
@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+# encoding: utf-8
+#
+# Copyright 2022 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 csv
+import os
+import unittest
+
+from itertools import islice
+
+import apache_beam as beam
+import numpy as np
+import tensorflow as tf
+
+from apache_beam import Create
+from apache_beam.testing.test_pipeline import TestPipeline
+
+from basic_pitch.data.tf_example_serialization import bytes_feature, int64_feature
+from basic_pitch.data.pipeline import (
+    Batch,
+    WriteBatchToTfRecord,
+    transcription_dataset_writer,
+)
+
+
+def test_batch() -> None:
+    element_size = 10
+    batch_size = 3
+
+    element = np.random.random([element_size])
+    batcher = Batch(batch_size)
+    batches = list(batcher.process(element))
+    for batch in batches[:-1]:
+        assert len(batch) == element_size // batch_size
+
+    assert len(batches[-1]) == element_size % batch_size
+
+
+def test_write_batch_to_tf_record(tmpdir: str) -> None:
+    data = np.random.random([10, 10])
+    features = {
+        f"feat{i}": bytes_feature(tf.io.serialize_tensor(vec))
+        for i, vec in enumerate(list(data))
+    }
+    example = tf.train.Example(features=tf.train.Features(feature=features))
+
+    writer = WriteBatchToTfRecord(tmpdir)
+    writer.process([example])
+    assert len(os.listdir(tmpdir)) == 1
+    dataset = tf.data.TFRecordDataset(
+        [os.path.join(tmpdir, f) for f in os.listdir(tmpdir)]
+    )
+
+    # assert dataset.cardinality() == 1
+    for d in dataset:
+        ex = tf.train.Example()
+        ex.ParseFromString(d.numpy())
+
+    assert ex == example
+
+
+def test_transcription_dataset_writer(tmpdir: str) -> None:
+    input_data = [
+        (f"track{i}", ("train", "test", "validation")[i % 3]) for i in range(9)
+    ] + [("foo", "bar")]
+    batch_size = 2
+
+    with TestPipeline() as p:
+        transcription_dataset_writer(
+            p=p,
+            input_data=input_data,
+            to_tf_example=lambda el: [
+                tf.train.Example(
+                    features=tf.train.Features(feature={"foo": int64_feature(1)})
+                )
+            ],
+            filter_invalid_tracks=lambda el: [beam.pvalue.TaggedOutput(el[1], el[0])],
+            destination=tmpdir,
+            batch_size=batch_size,
+        )
+
+    for i, split in enumerate(["train", "test", "validation"]):
+        assert os.path.exists(os.path.join(tmpdir, split, "index.csv"))
+        with open(os.path.join(tmpdir, split, "index.csv")) as fp:
+            reader = csv.reader(fp, delimiter=",")
+            for row in islice(reader, 1, None):
+                # print(row)
+                assert int(row[0][-1]) % 3 == i
diff --git a/tests/test_tf_example_serialization.py b/tests/test_tf_example_serialization.py
new file mode 100644
index 00000000..61ccb3f8
--- /dev/null
+++ b/tests/test_tf_example_serialization.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+# encoding: utf-8
+#
+# Copyright 2022 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.
+
+from basic_pitch.data.tf_example_serialization import to_transcription_tfexample
+
+
+def test_to_transcription_tfexample(tmp_path: str) -> None:
+    pass