def process(self, kv): # Seed random number generator based on key so that hop times are # deterministic. key, ns_str = kv m = hashlib.md5(key) random.seed(int(m.hexdigest(), 16)) # Deserialize NoteSequence proto. ns = music_pb2.NoteSequence.FromString(ns_str) # Apply sustain pedal. ns = sequences_lib.apply_sustain_control_changes(ns) # Remove control changes as there are potentially a lot of them and they are # no longer needed. del ns.control_changes[:] if (self._min_hop_size_seconds and ns.total_time < self._min_hop_size_seconds): Metrics.counter('extract_examples', 'sequence_too_short').inc() return sequences = [] for _ in range(self._num_replications): if self._max_hop_size_seconds: if self._max_hop_size_seconds == self._min_hop_size_seconds: # Split using fixed hop size. sequences += sequences_lib.split_note_sequence( ns, self._max_hop_size_seconds) else: # Sample random hop positions such that each segment size is within # the specified range. hop_times = [0.0] while hop_times[-1] <= ns.total_time - self._min_hop_size_seconds: if hop_times[-1] + self._max_hop_size_seconds < ns.total_time: # It's important that we get a valid hop size here, since the # remainder of the sequence is too long. max_offset = min( self._max_hop_size_seconds, ns.total_time - self._min_hop_size_seconds - hop_times[-1]) else: # It's okay if the next hop time is invalid (in which case we'll # just stop). max_offset = self._max_hop_size_seconds offset = random.uniform(self._min_hop_size_seconds, max_offset) hop_times.append(hop_times[-1] + offset) # Split at the chosen hop times (ignoring zero and the final invalid # time). sequences += sequences_lib.split_note_sequence(ns, hop_times[1:-1]) else: sequences += [ns] for performance_sequence in sequences: if self._encode_score_fns: # We need to extract a score. if not self._absolute_timing: # Beats are required to extract a score with metric timing. beats = [ ta for ta in performance_sequence.text_annotations if (ta.annotation_type == music_pb2.NoteSequence.TextAnnotation.BEAT) and ta.time <= performance_sequence.total_time ] if len(beats) < 2: Metrics.counter('extract_examples', 'not_enough_beats').inc() continue # Ensure the sequence starts and ends on a beat. performance_sequence = sequences_lib.extract_subsequence( performance_sequence, start_time=min(beat.time for beat in beats), end_time=max(beat.time for beat in beats) ) # Infer beat-aligned chords (only for relative timing). try: chord_inference.infer_chords_for_sequence( performance_sequence, chord_change_prob=0.25, chord_note_concentration=50.0, add_key_signatures=True) except chord_inference.ChordInferenceError: Metrics.counter('extract_examples', 'chord_inference_failed').inc() continue # Infer melody regardless of relative/absolute timing. try: melody_instrument = melody_inference.infer_melody_for_sequence( performance_sequence, melody_interval_scale=2.0, rest_prob=0.1, instantaneous_non_max_pitch_prob=1e-15, instantaneous_non_empty_rest_prob=0.0, instantaneous_missing_pitch_prob=1e-15) except melody_inference.MelodyInferenceError: Metrics.counter('extract_examples', 'melody_inference_failed').inc() continue if not self._absolute_timing: # Now rectify detected beats to occur at fixed tempo. # TODO(iansimon): also include the alignment score_sequence, unused_alignment = sequences_lib.rectify_beats( performance_sequence, beats_per_minute=SCORE_BPM) else: # Score uses same timing as performance. score_sequence = copy.deepcopy(performance_sequence) # Remove melody notes from performance. performance_notes = [] for note in performance_sequence.notes: if note.instrument != melody_instrument: performance_notes.append(note) del performance_sequence.notes[:] performance_sequence.notes.extend(performance_notes) # Remove non-melody notes from score. score_notes = [] for note in score_sequence.notes: if note.instrument == melody_instrument: score_notes.append(note) del score_sequence.notes[:] score_sequence.notes.extend(score_notes) # Remove key signatures and beat/chord annotations from performance. del performance_sequence.key_signatures[:] del performance_sequence.text_annotations[:] Metrics.counter('extract_examples', 'extracted_score').inc() for augment_fn in self._augment_fns: # Augment and encode the performance. try: augmented_performance_sequence = augment_fn(performance_sequence) except DataAugmentationError: Metrics.counter( 'extract_examples', 'augment_performance_failed').inc() continue example_dict = { 'targets': self._encode_performance_fn( augmented_performance_sequence) } if not example_dict['targets']: Metrics.counter('extract_examples', 'skipped_empty_targets').inc() continue if self._encode_score_fns: # Augment the extracted score. try: augmented_score_sequence = augment_fn(score_sequence) except DataAugmentationError: Metrics.counter('extract_examples', 'augment_score_failed').inc() continue # Apply all score encoding functions. skip = False for name, encode_score_fn in self._encode_score_fns.items(): example_dict[name] = encode_score_fn(augmented_score_sequence) if not example_dict[name]: Metrics.counter('extract_examples', 'skipped_empty_%s' % name).inc() skip = True break if skip: continue Metrics.counter('extract_examples', 'encoded_example').inc() Metrics.distribution( 'extract_examples', 'performance_length_in_seconds').update( int(augmented_performance_sequence.total_time)) yield generator_utils.to_example(example_dict)
def process_midi(self, f): def augment_note_sequence(ns, stretch_factor, transpose_amount): """Augment a NoteSequence by time stretch and pitch transposition.""" augmented_ns = sequences_lib.stretch_note_sequence(ns, stretch_factor, in_place=False) try: _, num_deleted_notes = sequences_lib.transpose_note_sequence( augmented_ns, transpose_amount, min_allowed_pitch=MIN_PITCH, max_allowed_pitch=MAX_PITCH, in_place=True) except chord_symbols_lib.ChordSymbolError: raise datagen_beam.DataAugmentationError( 'Transposition of chord symbol(s) failed.') if num_deleted_notes: raise datagen_beam.DataAugmentationError( 'Transposition caused out-of-range pitch(es).') return augmented_ns self._min_hop_size_seconds = 0.0 self._max_hop_size_seconds = 0.0 self._num_replications = 1 self._encode_performance_fn = self.performance_encoder( ).encode_note_sequence self._encode_score_fns = dict( (name, encoder.encode_note_sequence) for name, encoder in self.score_encoders()) augment_params = itertools.product(self.stretch_factors, self.transpose_amounts) augment_fns = [ functools.partial(augment_note_sequence, stretch_factor=s, transpose_amount=t) for s, t in augment_params ] self._augment_fns = augment_fns self._absolute_timing = self.absolute_timing self._random_crop_length = self.random_crop_length_in_datagen if self._random_crop_length is not None: self._augment_fns = self._augment_fns rets = [] ns = magenta.music.midi_file_to_sequence_proto(f) # Apply sustain pedal. ns = sequences_lib.apply_sustain_control_changes(ns) # Remove control changes as there are potentially a lot of them and they are # no longer needed. del ns.control_changes[:] if (self._min_hop_size_seconds and ns.total_time < self._min_hop_size_seconds): print("sequence_too_short") return [] sequences = [] for _ in range(self._num_replications): if self._max_hop_size_seconds: if self._max_hop_size_seconds == self._min_hop_size_seconds: # Split using fixed hop size. sequences += sequences_lib.split_note_sequence( ns, self._max_hop_size_seconds) else: # Sample random hop positions such that each segment size is within # the specified range. hop_times = [0.0] while hop_times[ -1] <= ns.total_time - self._min_hop_size_seconds: if hop_times[ -1] + self._max_hop_size_seconds < ns.total_time: # It's important that we get a valid hop size here, since the # remainder of the sequence is too long. max_offset = min( self._max_hop_size_seconds, ns.total_time - self._min_hop_size_seconds - hop_times[-1]) else: # It's okay if the next hop time is invalid (in which case we'll # just stop). max_offset = self._max_hop_size_seconds offset = random.uniform(self._min_hop_size_seconds, max_offset) hop_times.append(hop_times[-1] + offset) # Split at the chosen hop times (ignoring zero and the final invalid # time). sequences += sequences_lib.split_note_sequence( ns, hop_times[1:-1]) else: sequences += [ns] for performance_sequence in sequences: if self._encode_score_fns: # We need to extract a score. if not self._absolute_timing: # Beats are required to extract a score with metric timing. beats = [ ta for ta in performance_sequence.text_annotations if (ta.annotation_type == music_pb2.NoteSequence.TextAnnotation.BEAT) and ta.time <= performance_sequence.total_time ] if len(beats) < 2: print('not_enough_beats') continue # Ensure the sequence starts and ends on a beat. performance_sequence = sequences_lib.extract_subsequence( performance_sequence, start_time=min(beat.time for beat in beats), end_time=max(beat.time for beat in beats)) # Infer beat-aligned chords (only for relative timing). try: chord_inference.infer_chords_for_sequence( performance_sequence, chord_change_prob=0.25, chord_note_concentration=50.0, add_key_signatures=True) except chord_inference.ChordInferenceError: print("chord_inference_failed") continue # Infer melody regardless of relative/absolute timing. try: melody_instrument = melody_inference.infer_melody_for_sequence( performance_sequence, melody_interval_scale=2.0, rest_prob=0.1, instantaneous_non_max_pitch_prob=1e-15, instantaneous_non_empty_rest_prob=0.0, instantaneous_missing_pitch_prob=1e-15) except melody_inference.MelodyInferenceError: print('melody_inference_failed') continue if not self._absolute_timing: # Now rectify detected beats to occur at fixed tempo. # TODO(iansimon): also include the alignment score_sequence, unused_alignment = sequences_lib.rectify_beats( performance_sequence, beats_per_minute=SCORE_BPM) else: # Score uses same timing as performance. score_sequence = copy.deepcopy(performance_sequence) # Remove melody notes from performance. performance_notes = [] for note in performance_sequence.notes: if note.instrument != melody_instrument: performance_notes.append(note) del performance_sequence.notes[:] performance_sequence.notes.extend(performance_notes) # Remove non-melody notes from score. score_notes = [] for note in score_sequence.notes: if note.instrument == melody_instrument: score_notes.append(note) del score_sequence.notes[:] score_sequence.notes.extend(score_notes) # Remove key signatures and beat/chord annotations from performance. del performance_sequence.key_signatures[:] del performance_sequence.text_annotations[:] for augment_fn in self._augment_fns: # Augment and encode the performance. try: augmented_performance_sequence = augment_fn( performance_sequence) except DataAugmentationError as e: print("augment_performance_failed", e) continue example_dict = { 'targets': self._encode_performance_fn(augmented_performance_sequence) } if not example_dict['targets']: print('skipped_empty_targets') continue if (self._random_crop_length and len(example_dict['targets']) > self._random_crop_length): # Take a random crop of the encoded performance. max_offset = len( example_dict['targets']) - self._random_crop_length offset = random.randrange(max_offset + 1) example_dict['targets'] = example_dict['targets'][ offset:offset + self._random_crop_length] if self._encode_score_fns: # Augment the extracted score. try: augmented_score_sequence = augment_fn(score_sequence) except DataAugmentationError: print('augment_score_failed') continue # Apply all score encoding functions. skip = False for name, encode_score_fn in self._encode_score_fns.items( ): example_dict[name] = encode_score_fn( augmented_score_sequence) if not example_dict[name]: print('skipped_empty_%s' % name) skip = True break if skip: continue rets.append(example_dict) return rets