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audio/c2H2c.wav

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audio/gzH2c.wav

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syncpoints/CJH2c-syncpoints.json

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syncpoints/GTH2c-syncpoints.json

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syncpoints/JTH2c-syncpoints.json

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syncpoints/K732c-syncpoints.json

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syncpoints/WTH2c-syncpoints.json

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syncpoints/cHH2c-syncpoints.json

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syncpoints/fW32c-syncpoints.json

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syncpoints/gqH2c-syncpoints.json

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syncpoints/rGX2c-syncpoints.json

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syncpoints/xFC2c-syncpoints.json

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syncpoints/xHH2c-syncpoints.json

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transcription-evaluation-midi/dataset-eval.py

@@ -0,0 +1,167 @@
+# /// script
+# requires-python = "<3.10"
+# dependencies = [
+#     "mir-eval",
+#     "numpy",
+#     "pandas",
+#     "pretty-midi",
+#     "setuptools",
+# ]
+# ///
+import mir_eval
+from pathlib import Path
+import pretty_midi as pm
+import numpy as np
+import pandas as pd
+import json
+
+pm.pretty_midi.MAX_TICK = 1e10
+
+def evaluate(midi_path='*.crepe_notes.mid',
+             midi_replace_str='.crepe_notes.mid',
+             midi_replace_with='.mid',
+             output_label='crepe_notes',
+             base_dir='./data/audio'):
+    results = []
+
+    merge_tracks = True
+
+    # gastro_test_scorehashes = ['Zpswc', 'rc1wc', 'bV1wc', 'XD1wc', '1M1wc', 'pM1wc', '5N1wc', 'bc1wc', 'Vpswc', 'hf1wc', 'vpswc', 'mf1wc', 'BSswc', 'y41wc', 'VD1wc', 'Jw1wc', 'Lf1wc', 'GD1wc', 'c41wc', 'gk1wc', 'Bc1wc', '-k1wc', 'sy1wc', '441wc', 'W41wc', 'xf1wc', 'Ny1wc', '3Sswc', '4w1wc', '1w1wc', 'cw1wc']
+    hashes_to_skip = ['3HH2c', 'xFC2c'] # these were present in the FiloBass training data
+
+    paths = sorted(Path(base_dir).rglob(midi_path))
+    for path in paths:
+        scorehash = path.stem.replace('-fine-aligned', '')
+        if scorehash in hashes_to_skip:
+            continue
+
+        print(f"{output_label}: {path}")
+        est_path = str(path)
+        ref_path = str(path).replace(midi_replace_str, midi_replace_with)
+
+        try:
+            ref = pm.PrettyMIDI(ref_path)
+        except:
+            print(f"Couldn't load {ref_path}")
+            continue
+
+        try:
+            est = pm.PrettyMIDI(est_path)
+        except:
+            print(f"Couldn't load {ref_path}")
+            continue
+
+        ref_times = np.array([[n.start, n.end]
+                              for n in ref.instruments[0].notes])
+        ref_pitches = np.array(
+            [pm.note_number_to_hz(n.pitch) for n in ref.instruments[0].notes])
+
+        if merge_tracks:
+            est_times = np.array(
+                [[n.start, n.end]
+                 for inst_notes in map(lambda i: i.notes, est.instruments)
+                 for n in inst_notes])
+            est_pitches = np.array([
+                pm.note_number_to_hz(n.pitch)
+                for inst_notes in map(lambda i: i.notes, est.instruments)
+                for n in inst_notes
+            ])
+        else:
+            est_times = np.array([[n.start, n.end]
+                                  for n in est.instruments[0].notes])
+            est_pitches = np.array([
+                pm.note_number_to_hz(n.pitch) for n in est.instruments[0].notes
+            ])
+
+        # downbeats_json = json.load(open(Path("../syncpoints") / Path(ref_path).name.replace('-transcribed.mid', '-syncpoints.json')))
+        # downbeats = [x[1] for x in downbeats_json if len(x) == 2 or x[2] == 0]
+        # first_ref_note = downbeats[2]
+        # last_ref_note = downbeats[-2]
+
+        first_ref_note = ref_times[0]
+        last_ref_note = ref_times[-1]
+
+        ref_times_valid_idxs = np.unique(np.where((ref_times > first_ref_note) * (ref_times < last_ref_note))[0])
+        ref_times = ref_times[ref_times_valid_idxs]
+        ref_pitches = ref_pitches[ref_times_valid_idxs]
+
+        est_times_valid_idxs = np.unique(
+            np.where((est_times > first_ref_note)
+                     & (est_times < last_ref_note))[0])
+
+        est_times = est_times[est_times_valid_idxs]
+        est_pitches = est_pitches[est_times_valid_idxs]
+
+        eval_result = mir_eval.transcription.evaluate(ref_times, ref_pitches,
+                                                      est_times, est_pitches,
+                                                      onset_tolerance=0.05)
+        eval_result['file'] = str(path)
+        results.append(eval_result)
+    df = pd.DataFrame(results)
+    df.to_pickle(f'{output_label}_eval.pkl')
+    pd.set_option('display.max_colwidth', None)
+    print(df.describe()[['Precision_no_offset', 'Recall_no_offset', 'F-measure_no_offset', 'Average_Overlap_Ratio_no_offset']])
+    print(df[['F-measure_no_offset', 'file']])
+
+
+# evaluate('Sax.mt3.mid', '.mt3.mid', 'mt3')
+# evaluate('Sax.crepe_notes_amp_trimming.mid', '.crepe_notes_amp_trimming.mid', 'crepe_notes_amp_trimming')
+# evaluate('Sax_vamp_pyin_pyin_notes.mid', '_vamp_pyin_pyin_notes.mid', 'pyin_notes')
+# evaluate('Sax.crepe_notes_with_onsets.mid', '.crepe_notes_with_onsets.mid', 'crepe_notes_with_onsets')
+
+# evaluate('*_basic_pitch.mid', '_basic_pitch.mid', 'basic_pitch')
+# evaluate('*.mt3.mid', '.mt3.mid', 'mt3')
+# evaluate('*.cn_25ms_min.mid', '.cn_25ms_min.mid', 'crepe_notes-min-dur-25ms')
+# evaluate('*.crepe_notes-min-dur-11ms.mid', '.crepe_notes-min-dur-11ms.mid', 'crepe_notes-min-dur-11ms')
+# evaluate('*.crepe_notes-min-dur-11ms-no-tuning.mid',
+# evaluate('*.crepe_notes-min-dur-25ms-no-tuning.mid',
+#          '.crepe_notes-min-dur-25ms-no-tuning.mid',
+#          'crepe_notes-min-dur-25ms-no-tuning')
+# evaluate('*_vamp_pyin_pyin_notes.mid', '_vamp_pyin_pyin_notes.mid', 'pyin_notes')
+# evaluate('*.cn_transition_a.mid', '.cn_transition_a.mid', 'crepe_notes-transitions-a')
+
+# evaluate('bass.transcription.mid',  '.transcription.mid', '-fine-aligned.mid', 'filobass_cn_sens_2_min_50ms')
+# evaluate('md-bass.mid',  'md-bass.mid', 'bass-fine-aligned.mid', 'melodyne_melodic')
+# evaluate('bass_basic_pitch.mid',  '_basic_pitch.mid', '-fine-aligned.mid', 'filobass_bp')
+# evaluate('bass_basic_pitch.mid',  '_basic_pitch.mid', 'filobass_cn')
+# evaluate('*-fine-aligned.mid',  '-fine-aligned.mid', '-transcribed.mid', 'gastro')
+# evaluate('*-fine-aligned.mid',  '-fine-aligned.mid', '-transcribed.mid', 'BassBooks')
+
+import tempfile
+import shutil
+
+with tempfile.TemporaryDirectory() as tmpdirname:
+    for f in Path("./GroundTruthAligned").rglob("*.mid"):
+        shutil.copy(f, tmpdirname)
+    for f in Path("./KongFiloBassResults").rglob("*.mid"):
+        shutil.copy(f, tmpdirname)
+
+    evaluate('*-fine-aligned.mid',
+        '-fine-aligned.mid',
+        '-transcribed.mid',
+        'BassBooksKongFiloBass',
+        base_dir=tmpdirname)
+
+with tempfile.TemporaryDirectory() as tmpdirname:
+    for f in Path("./GroundTruthAligned").rglob("*.mid"):
+        shutil.copy(f, tmpdirname)
+    for f in Path("./KongHRGResults").rglob("*.mid"):
+        shutil.copy(f, tmpdirname)
+
+    evaluate('*-fine-aligned.mid',
+        '-fine-aligned.mid',
+        '-transcribed-guitar.mid',
+        'BassBooksKongHRG',
+        base_dir=tmpdirname)
+
+with tempfile.TemporaryDirectory() as tmpdirname:
+    for f in Path("./GroundTruthAligned").rglob("*.mid"):
+        shutil.copy(f, tmpdirname)
+    for f in Path("./MelodyneResults").rglob("*.mid"):
+        shutil.copy(f, tmpdirname)
+
+    evaluate('*-fine-aligned.mid',
+        '-fine-aligned.mid',
+        '.mid',
+        'BassBooksMelodyne',
+        base_dir=tmpdirname)