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| import numpy as np | |
| import os | |
| REPO_PATH = '/'.join(os.path.abspath(__file__).split('/')[:-3]) + '/' | |
| AUDIO_PATH = REPO_PATH + 'data/music/audio/' | |
| MIDI_PATH = REPO_PATH + 'data/music/midi/' | |
| MUSIC_PATH = REPO_PATH + 'data/music/' | |
| PROCESSED_PATH = REPO_PATH + 'data/music/processed/' | |
| ENCODED_PATH = REPO_PATH + 'data/music/encoded/' | |
| HANDCODED_REP_PATH = MUSIC_PATH + 'handcoded_reps/' | |
| DATASET_PATH = REPO_PATH + 'data/music/encoded_new_structured/diverse_piano/' | |
| SYNTH_RECORDED_AUDIO_PATH = AUDIO_PATH + 'synth_audio_recorded/' | |
| SYNTH_RECORDED_MIDI_PATH = MIDI_PATH + 'synth_midi_recorded/' | |
| CHECKPOINTS_PATH = REPO_PATH + 'checkpoints/' | |
| EXPERIMENT_PATH = REPO_PATH + 'experiments/' | |
| SEED = 0 | |
| # params for data download | |
| ALL_URL_PATH = REPO_PATH + 'data/music/audio/all_urls.pickle' | |
| ALL_FAILED_URL_PATH = REPO_PATH + 'data/music/audio/all_failed_urls.pickle' | |
| RATE_AUDIO_SAVE = 16000 | |
| FROM_URL_PATH = AUDIO_PATH + 'from_url/' | |
| # params transcription | |
| CHKPT_PATH_TRANSCRIPTION = REPO_PATH + 'checkpoints/piano_transcription/note_F1=0.9677_pedal_F1=0.9186.pth' # transcriptor chkpt path | |
| FPS = 16000 | |
| RANDOM_CROP = True # whether to use random crops in case of cropped audio | |
| CROP_LEN = 26 * 60 | |
| # params midi scrubbing and processing | |
| MAX_DEPTH = 5 # max depth when searching in folders for audio files | |
| MAX_GAP_IN_SONG = 10 # in secs | |
| MIN_LEN = 20 # actual min len could go down to MIN_LEN - 2 * (REMOVE_FIRST_AND_LAST / 5) | |
| MAX_LEN = 25 * 60 # maximum audio len for playlist downloads, and maximum audio length for transcription (in sec) | |
| MIN_NB_NOTES = 80 # min nb of notes per minute of recording | |
| REMOVE_FIRST_AND_LAST = 10 # will be divided by 5 if cutting this makes the song fall below min len | |
| # parameters encoding | |
| NOISE_INJECTED = True | |
| AUGMENTATION = True | |
| NB_AUG = 4 if AUGMENTATION else 0 | |
| RANGE_NOTE_ON = 128 | |
| RANGE_NOTE_OFF = 128 | |
| RANGE_VEL = 32 | |
| RANGE_TIME_SHIFT = 100 | |
| MAX_EMBEDDING = RANGE_VEL + RANGE_NOTE_OFF + RANGE_TIME_SHIFT + RANGE_NOTE_ON | |
| MAX_TEST_SIZE = 1000 | |
| CHECKSUM_PATH = REPO_PATH + 'data/music/midi/checksum.pickle' | |
| CHUNK_SIZE = 512 | |
| ALL_AUGMENTATIONS = [] | |
| for p in [-3, -2, -1, 1, 2, 3]: | |
| ALL_AUGMENTATIONS.append((p)) | |
| ALL_AUGMENTATIONS = np.array(ALL_AUGMENTATIONS) | |
| ALL_NOISE = [] | |
| for s in [-5, -2.5, 0, 2.5, 5]: | |
| for p in np.arange(-6, 7): | |
| if not ((s == 0) and (p==0)): | |
| ALL_NOISE.append((s, p)) | |
| ALL_NOISE = np.array(ALL_NOISE) | |
| # music transformer params | |
| REP_MODEL_NAME = REPO_PATH + "checkpoints/music_representation/sentence_embedding/smallbert_b256_r128_1/best_model" | |
| MUSIC_REP_PATH = REPO_PATH + "checkpoints/b256_r128_represented/" | |
| MUSIC_NN_PATH = REPO_PATH + "checkpoints/music_representation/b256_r128_represented/nn_model.pickle" | |
| TRANSLATION_VAE_CHKP_PATH = REPO_PATH + "checkpoints/music2cocktails/music2flavor/b256_r128_classif001_ld40_meanstd_regground2.5_egg_bubbles/" | |
| # piano solo evaluation | |
| # META_DATA_PIANO_EVAL_PATH = REPO_PATH + 'data/music/audio/is_piano.csv' | |
| # CHKPT_PATH_PIANO_EVAL = REPO_PATH + 'data/checkpoints/piano_detection/piano_solo_model_32k.pth' |