Initial commit

.gitignore

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+
+# Created by https://www.gitignore.io/api/python,pycharm
+# Edit at https://www.gitignore.io/?templates=python,pycharm
+
+### PyCharm ###
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+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
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+# auto-import.
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+### PyCharm Patch ###
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+!train/TRAINED_MODEL_HERE.md
+
+# End of https://www.gitignore.io/api/python,pycharm

W2M.py

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+import tensorflow as tf
+import os
+
+from magenta.protobuf import music_pb2
+from magenta.music import sequences_lib, midi_io
+from magenta.models.onsets_frames_transcription import configs, data, train_util, audio_label_data_utils, constants
+
+# Trained Model Directory
+MODEL_DIR = './train'
+
+# Hyperparamter
+config = configs.CONFIG_MAP['onsets_frames']
+hparams = config.hparams
+hparams.batch_size = 1
+hparams.use_cudnn = False
+hparams.audio_transform = False
+
+example = tf.placeholder(tf.string, [None])
+
+# Logging
+tf.logging.info('model_dir=%s', MODEL_DIR)
+tf.logging.info('checkpoint_path=%s', 'checkpoint')
+
+# 배치 생성
+dataset = data.provide_batch(examples=example, preprocess_examples=True,
+    params=hparams, is_training=False, shuffle_examples=False,
+    skip_n_initial_records=0)
+
+# Estimator
+estimator = train_util.create_estimator(config.model_fn, MODEL_DIR, hparams)
+
+# 배치를 순환하는 이터레이터
+iterator =dataset.make_initializable_iterator()
+next_record = iterator.get_next()
+
+sess = tf.Session()
+
+#전역변수 및 로컬변수 초기화
+sess.run([tf.initializers.global_variables(), tf.initializers.local_variables()])
+
+#tensor로부터 사용하려는 데이터를 불러와 Dataset 인스턴스 생성
+def input_fn(params):
+    del params
+    return tf.data.Dataset.from_tensors(sess.run(next_record))
+
+def infer(filename):
+    # WAV 파일 Binary로 읽기
+    wav = open(filename,'rb')
+    wav_data = wav.read()
+    wav.close()
+
+    tf.logging.info('User .WAV FIle %s length %s bytes', filename, len(wav_data))
+
+    ## 전처리
+    # 청크로 분할 후, Protocol Buffers 로 변환
+    to_process = []
+    examples = list(audio_label_data_utils.process_record(wav_data=wav_data, ns=music_pb2.NoteSequence(),
+                                                          example_id=filename, min_length=0, max_length=-1, allow_empty_notesequence=True))
+
+    # 분할된 버퍼를 시리얼라이즈
+    to_process.append(examples[0].SerializeToString())
+
+    #############################################################
+
+    #시리얼라이즈한 버퍼를 iterator에 주입
+    sess.run(iterator.initializer, {example:to_process})
+
+    # Inference
+    predictions = list(estimator.predict(input_fn, yield_single_examples=False))
+    #가정 설정문으로 prediction size를 1로 보장
+    assert len(predictions) == 1
+
+    #예측 결과 불러오기
+    frame_predictions = predictions[0]['frame_predictions'][0]
+    onset_predictions = predictions[0]['onset_predictions'][0]  # 치는 순간
+    velocity_values = predictions[0]['velocity_values'][0]      #강약
+
+    #MIDI로 인코딩
+    sequence_prediction = sequences_lib.pianoroll_to_note_sequence(
+        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,
+        velocity_values=velocity_values)
+
+    basename = os.path.split(os.path.splitext(filename)[0])[1] + '.mid'
+    output_filename = os.path.join('', basename)
+
+    midi_filename = (output_filename)
+    midi_io.sequence_proto_to_midi_file(sequence_prediction, midi_filename)
+
+    print('Program Ended, Your MIDI File is in', output_filename)
+
+    sess.close()
+
+if __name__ == '__main__':
+    file = input('Input Your .WAV Filename:')
+    infer(file)

train/TRAINED_MODEL_HERE.md

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+# Download or Create Trained Model AND Place It Here
+
+You can download Model from [Here](https://github.com/tensorflow/magenta/tree/master/magenta/models/onsets_frames_transcription).