def _note_metrics(labels, predictions):
"""A pyfunc that wraps a call to precision_recall_f1_overlap."""
est_sequence = pianoroll_to_note_sequence(
predictions,
frames_per_second=data.hparams_frames_per_second(hparams),
min_duration_ms=hparams.min_duration_ms)
ref_sequence = pianoroll_to_note_sequence(
labels,
frames_per_second=data.hparams_frames_per_second(hparams),
min_duration_ms=hparams.min_duration_ms)
est_intervals, est_pitches = sequence_to_valued_intervals(
est_sequence, hparams.min_duration_ms)
ref_intervals, ref_pitches = sequence_to_valued_intervals(
ref_sequence, hparams.min_duration_ms)
if est_intervals.size == 0 or ref_intervals.size == 0:
return 0., 0., 0.
note_precision, note_recall, note_f1, _ = precision_recall_f1_overlap(
ref_intervals,
pretty_midi.note_number_to_hz(ref_pitches),
est_intervals,
pretty_midi.note_number_to_hz(est_pitches),
offset_ratio=offset_ratio)
return note_precision, note_recall, note_f1
def _note_metrics(labels, predictions):
"""A pyfunc that wraps a call to precision_recall_f1_overlap."""
est_sequence = sequences_lib.pianoroll_to_note_sequence(
predictions,
frames_per_second=data.hparams_frames_per_second(hparams),
min_duration_ms=hparams.min_duration_ms)
ref_sequence = sequences_lib.pianoroll_to_note_sequence(
labels,
frames_per_second=data.hparams_frames_per_second(hparams),
min_duration_ms=hparams.min_duration_ms)
est_intervals, est_pitches, _ = infer_util.sequence_to_valued_intervals(
est_sequence, hparams.min_duration_ms)
ref_intervals, ref_pitches, _ = infer_util.sequence_to_valued_intervals(
ref_sequence, hparams.min_duration_ms)
if est_intervals.size == 0 or ref_intervals.size == 0:
return 0., 0., 0.
note_precision, note_recall, note_f1, _ = precision_recall_f1_overlap(
ref_intervals,
pretty_midi.note_number_to_hz(ref_pitches),
est_intervals,
pretty_midi.note_number_to_hz(est_pitches),
offset_ratio=offset_ratio)
return note_precision, note_recall, note_f1
def testSequenceToValuedIntervals(self):
sequence = music_pb2.NoteSequence()
sequence.notes.add(pitch=60, start_time=1.0, end_time=2.0)
# Should be dropped because it is 0 duration.
sequence.notes.add(pitch=60, start_time=3.0, end_time=3.0)
intervals, pitches = infer_util.sequence_to_valued_intervals(
sequence, min_duration_ms=0)
np.testing.assert_array_equal([[1., 2.]], intervals)
np.testing.assert_array_equal([60], pitches)
def testSequenceToValuedIntervals(self):
sequence = music_pb2.NoteSequence()
sequence.notes.add(pitch=60, start_time=1.0, end_time=2.0)
# Should be dropped because it is 0 duration.
sequence.notes.add(pitch=60, start_time=3.0, end_time=3.0)
intervals, pitches = infer_util.sequence_to_valued_intervals(
sequence, min_duration_ms=0)
np.testing.assert_array_equal([[1., 2.]], intervals)
np.testing.assert_array_equal([60], pitches)
def _calculate_metrics_py(
frame_predictions, onset_predictions, offset_predictions, velocity_values,
sequence_label_str, frame_labels, sequence_id, hparams):
"""Python logic for calculating metrics on a single example."""
tf.logging.info('Calculating metrics for %s with length %d', sequence_id,
frame_labels.shape[0])
if not hparams.predict_onset_threshold:
onset_predictions = None
if not hparams.predict_offset_threshold:
offset_predictions = None
sequence_prediction = sequences_lib.pianoroll_to_note_sequence(
frames=frame_predictions,
frames_per_second=data.hparams_frames_per_second(hparams),
min_duration_ms=0,
min_midi_pitch=constants.MIN_MIDI_PITCH,
onset_predictions=onset_predictions,
offset_predictions=offset_predictions,
velocity_values=velocity_values)
sequence_label = music_pb2.NoteSequence.FromString(sequence_label_str)
if hparams.backward_shift_amount_ms:
def shift_notesequence(ns_time):
return ns_time + hparams.backward_shift_amount_ms / 1000.
shifted_sequence_label, skipped_notes = (
sequences_lib.adjust_notesequence_times(sequence_label,
shift_notesequence))
assert skipped_notes == 0
sequence_label = shifted_sequence_label
est_intervals, est_pitches, est_velocities = (
infer_util.sequence_to_valued_intervals(sequence_prediction))
ref_intervals, ref_pitches, ref_velocities = (
infer_util.sequence_to_valued_intervals(sequence_label))
note_precision, note_recall, note_f1, _ = (
mir_eval.transcription.precision_recall_f1_overlap(
ref_intervals,
pretty_midi.note_number_to_hz(ref_pitches),
est_intervals,
pretty_midi.note_number_to_hz(est_pitches),
offset_ratio=None))
(note_with_offsets_precision, note_with_offsets_recall, note_with_offsets_f1,
_) = (
mir_eval.transcription.precision_recall_f1_overlap(
ref_intervals, pretty_midi.note_number_to_hz(ref_pitches),
est_intervals, pretty_midi.note_number_to_hz(est_pitches)))
(note_with_offsets_velocity_precision, note_with_offsets_velocity_recall,
note_with_offsets_velocity_f1, _) = (
mir_eval.transcription_velocity.precision_recall_f1_overlap(
ref_intervals=ref_intervals,
ref_pitches=pretty_midi.note_number_to_hz(ref_pitches),
ref_velocities=ref_velocities,
est_intervals=est_intervals,
est_pitches=pretty_midi.note_number_to_hz(est_pitches),
est_velocities=est_velocities))
processed_frame_predictions = sequences_lib.sequence_to_pianoroll(
sequence_prediction,
frames_per_second=data.hparams_frames_per_second(hparams),
min_pitch=constants.MIN_MIDI_PITCH,
max_pitch=constants.MAX_MIDI_PITCH).active
if processed_frame_predictions.shape[0] < frame_labels.shape[0]:
# Pad transcribed frames with silence.
pad_length = frame_labels.shape[0] - processed_frame_predictions.shape[0]
processed_frame_predictions = np.pad(processed_frame_predictions,
[(0, pad_length), (0, 0)], 'constant')
elif processed_frame_predictions.shape[0] > frame_labels.shape[0]:
# Truncate transcribed frames.
processed_frame_predictions = (
processed_frame_predictions[:frame_labels.shape[0], :])
tf.logging.info(
'Metrics for %s: Note F1 %f, Note w/ offsets F1 %f, '
'Note w/ offsets & velocity: %f', sequence_id, note_f1,
note_with_offsets_f1, note_with_offsets_velocity_f1)
return (note_precision, note_recall, note_f1, note_with_offsets_precision,
note_with_offsets_recall, note_with_offsets_f1,
note_with_offsets_velocity_precision,
note_with_offsets_velocity_recall, note_with_offsets_velocity_f1,
processed_frame_predictions)
def _calculate_metrics_py(
frame_predictions, onset_predictions, offset_predictions, velocity_values,
sequence_label_str, frame_labels, sequence_id, hparams, min_pitch,
max_pitch):
"""Python logic for calculating metrics on a single example."""
tf.logging.info('Calculating metrics for %s with length %d', sequence_id,
frame_labels.shape[0])
if not hparams.predict_onset_threshold:
onset_predictions = None
if not hparams.predict_offset_threshold:
offset_predictions = None
sequence_prediction = sequences_lib.pianoroll_to_note_sequence(
frames=frame_predictions,
frames_per_second=data.hparams_frames_per_second(hparams),
min_duration_ms=0,
min_midi_pitch=min_pitch,
onset_predictions=onset_predictions,
offset_predictions=offset_predictions,
velocity_values=velocity_values)
sequence_label = music_pb2.NoteSequence.FromString(sequence_label_str)
if hparams.backward_shift_amount_ms:
def shift_notesequence(ns_time):
return ns_time + hparams.backward_shift_amount_ms / 1000.
shifted_sequence_label, skipped_notes = (
sequences_lib.adjust_notesequence_times(sequence_label,
shift_notesequence))
assert skipped_notes == 0
sequence_label = shifted_sequence_label
est_intervals, est_pitches, est_velocities = (
infer_util.sequence_to_valued_intervals(sequence_prediction))
ref_intervals, ref_pitches, ref_velocities = (
infer_util.sequence_to_valued_intervals(sequence_label))
note_precision, note_recall, note_f1, _ = (
mir_eval.transcription.precision_recall_f1_overlap(
ref_intervals,
pretty_midi.note_number_to_hz(ref_pitches),
est_intervals,
pretty_midi.note_number_to_hz(est_pitches),
offset_ratio=None))
(note_with_offsets_precision, note_with_offsets_recall, note_with_offsets_f1,
_) = (
mir_eval.transcription.precision_recall_f1_overlap(
ref_intervals, pretty_midi.note_number_to_hz(ref_pitches),
est_intervals, pretty_midi.note_number_to_hz(est_pitches)))
(note_with_offsets_velocity_precision, note_with_offsets_velocity_recall,
note_with_offsets_velocity_f1, _) = (
mir_eval.transcription_velocity.precision_recall_f1_overlap(
ref_intervals=ref_intervals,
ref_pitches=pretty_midi.note_number_to_hz(ref_pitches),
ref_velocities=ref_velocities,
est_intervals=est_intervals,
est_pitches=pretty_midi.note_number_to_hz(est_pitches),
est_velocities=est_velocities))
processed_frame_predictions = sequences_lib.sequence_to_pianoroll(
sequence_prediction,
frames_per_second=data.hparams_frames_per_second(hparams),
min_pitch=min_pitch, max_pitch=max_pitch).active
if processed_frame_predictions.shape[0] < frame_labels.shape[0]:
# Pad transcribed frames with silence.
pad_length = frame_labels.shape[0] - processed_frame_predictions.shape[0]
processed_frame_predictions = np.pad(processed_frame_predictions,
[(0, pad_length), (0, 0)], 'constant')
elif processed_frame_predictions.shape[0] > frame_labels.shape[0]:
# Truncate transcribed frames.
processed_frame_predictions = (
processed_frame_predictions[:frame_labels.shape[0], :])
tf.logging.info(
'Metrics for %s: Note F1 %f, Note w/ offsets F1 %f, '
'Note w/ offsets & velocity: %f', sequence_id, note_f1,
note_with_offsets_f1, note_with_offsets_velocity_f1)
return (note_precision, note_recall, note_f1, note_with_offsets_precision,
note_with_offsets_recall, note_with_offsets_f1,
note_with_offsets_velocity_precision,
note_with_offsets_velocity_recall, note_with_offsets_velocity_f1,
processed_frame_predictions)
