def predict_sequence(frame_probs, onset_probs, frame_predictions,
onset_predictions, offset_predictions, velocity_values,
hparams):
"""Predict a single sequence."""
if hparams.drums_only:
sequence_prediction = infer_util.predict_sequence(
frame_probs=frame_probs,
onset_probs=onset_probs,
frame_predictions=onset_predictions,
onset_predictions=onset_predictions,
offset_predictions=onset_predictions,
velocity_values=velocity_values,
min_pitch=constants.MIN_MIDI_PITCH,
hparams=hparams,
onsets_only=True)
for note in sequence_prediction.notes:
note.is_drum = True
else:
sequence_prediction = infer_util.predict_sequence(
frame_probs=frame_probs,
onset_probs=onset_probs,
frame_predictions=frame_predictions,
onset_predictions=onset_predictions,
offset_predictions=offset_predictions,
velocity_values=velocity_values,
min_pitch=constants.MIN_MIDI_PITCH,
hparams=hparams)
return sequence_prediction.SerializeToString()
def _predict(frame_predictions, onset_predictions, offset_predictions,
velocity_values):
sequence = infer_util.predict_sequence(
frame_predictions=frame_predictions,
onset_predictions=onset_predictions,
offset_predictions=offset_predictions,
velocity_values=velocity_values,
hparams=hparams, min_pitch=constants.MIN_MIDI_PITCH)
return sequence.SerializeToString()
def _predict(frame_probs, onset_probs, frame_predictions,
onset_predictions, offset_predictions, velocity_values):
"""Convert frame predictions into a sequence (Python)."""
sequence = infer_util.predict_sequence(
frame_probs=frame_probs,
onset_probs=onset_probs,
frame_predictions=frame_predictions,
onset_predictions=onset_predictions,
offset_predictions=offset_predictions,
velocity_values=velocity_values,
hparams=hparams,
min_pitch=constants.MIN_MIDI_PITCH)
return sequence.SerializeToString()
def magenta_decoding(onset_prob,
frame_prob,
offset_prob,
threshold=0.5,
viterbi=False):
config_map = configs.CONFIG_MAP
config = config_map['onsets_frames']
hparams = config.hparams
if viterbi:
hparams.viterbi_decoding = True
seq = predict_sequence(
frame_prob,
onset_prob,
frame_prob > threshold,
onset_prob > threshold,
offset_prob > 0.0,
# offset_prob > threshold,
velocity_values=None,
hparams=hparams,
min_pitch=21)
return seq
def _calculate_metrics_py(frame_probs,
onset_probs,
frame_predictions,
onset_predictions,
offset_predictions,
velocity_values,
sequence_label_str,
frame_labels,
sequence_id,
hparams,
min_pitch,
max_pitch,
onsets_only,
restrict_to_pitch=None):
"""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])
sequence_prediction = infer_util.predict_sequence(
frame_probs=frame_probs,
onset_probs=onset_probs,
frame_predictions=frame_predictions,
onset_predictions=onset_predictions,
offset_predictions=offset_predictions,
velocity_values=velocity_values,
min_pitch=min_pitch,
hparams=hparams,
onsets_only=onsets_only)
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 = (
sequence_to_valued_intervals(
sequence_prediction, restrict_to_pitch=restrict_to_pitch))
ref_intervals, ref_pitches, ref_velocities = (
sequence_to_valued_intervals(
sequence_label, restrict_to_pitch=restrict_to_pitch))
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], :])
if len(ref_pitches) == 0:
tf.logging.info(
'Reference pitches were length 0, returning empty metrics for %s:',
sequence_id)
return tuple([[]] * 12 + [processed_frame_predictions])
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_velocity_precision, note_with_velocity_recall,
note_with_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,
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))
tf.logging.info(
'Metrics for %s: Note F1 %f, Note w/ velocity F1 %f, Note w/ offsets F1 %f, '
'Note w/ offsets & velocity: %f', sequence_id, note_f1,
note_with_velocity_f1, note_with_offsets_f1,
note_with_offsets_velocity_f1)
# Return 1-d tensors for the metrics
return ([note_precision], [note_recall], [note_f1],
[note_with_velocity_precision], [note_with_velocity_recall],
[note_with_velocity_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])
