107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
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# Copyright 2016 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Create a dataset of SequenceExamples from NoteSequence protos.
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This script will extract melodies from NoteSequence protos and save them to
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TensorFlow's SequenceExample protos for input to the melody RNN models.
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"""
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import os
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# internal imports
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import tensorflow as tf
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from magenta.music import melodies_lib
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from magenta.pipelines import dag_pipeline
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from magenta.pipelines import pipeline
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from magenta.pipelines import pipelines_common
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from magenta.protobuf import music_pb2
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FLAGS = tf.app.flags.FLAGS
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tf.app.flags.DEFINE_string('input', None,
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'TFRecord to read NoteSequence protos from.')
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tf.app.flags.DEFINE_string('output_dir', None,
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'Directory to write training and eval TFRecord '
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'files. The TFRecord files are populated with '
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'SequenceExample protos.')
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tf.app.flags.DEFINE_float('eval_ratio', 0.0,
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'Fraction of input to set aside for eval set. '
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'Partition is randomly selected.')
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tf.app.flags.DEFINE_string('log', 'INFO',
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'The threshold for what messages will be logged '
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'DEBUG, INFO, WARN, ERROR, or FATAL.')
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class EncoderPipeline(pipeline.Pipeline):
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"""A Module that converts monophonic melodies to a model specific encoding."""
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def __init__(self, melody_encoder_decoder):
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"""Constructs a EncoderPipeline.
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A melodies_lib.MelodyEncoderDecoder is needed to provide the
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`encode` function.
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Args:
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melody_encoder_decoder: A melodies_lib.MelodyEncoderDecoder object.
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"""
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super(EncoderPipeline, self).__init__(
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input_type=melodies_lib.MonophonicMelody,
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output_type=tf.train.SequenceExample)
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self.melody_encoder_decoder = melody_encoder_decoder
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def transform(self, melody):
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encoded = self.melody_encoder_decoder.squash_and_encode(melody)
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return [encoded]
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def get_stats(self):
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return {}
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def get_pipeline(melody_encoder_decoder):
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"""Returns the Pipeline instance which creates the RNN dataset.
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Args:
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melody_encoder_decoder: A melodies_lib.MelodyEncoderDecoder object.
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Returns:
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A pipeline.Pipeline instance.
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"""
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quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
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melody_extractor = pipelines_common.MonophonicMelodyExtractor(
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min_bars=7, min_unique_pitches=5,
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gap_bars=1.0, ignore_polyphonic_notes=False)
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encoder_pipeline = EncoderPipeline(melody_encoder_decoder)
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partitioner = pipelines_common.RandomPartition(
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tf.train.SequenceExample,
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['eval_melodies', 'training_melodies'],
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[FLAGS.eval_ratio])
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dag = {quantizer: dag_pipeline.Input(music_pb2.NoteSequence),
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melody_extractor: quantizer,
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encoder_pipeline: melody_extractor,
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partitioner: encoder_pipeline,
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dag_pipeline.Output(): partitioner}
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return dag_pipeline.DAGPipeline(dag)
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def run_from_flags(pipeline_instance):
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tf.logging.set_verbosity(FLAGS.log)
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FLAGS.input = os.path.expanduser(FLAGS.input)
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FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir)
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pipeline.run_pipeline_serial(
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pipeline_instance,
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pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type),
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FLAGS.output_dir)
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