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import os
import pickle
import torch
import madmom
import numpy as np
from utils import AverageMeter
from torch.utils.data import DataLoader
from DilatedTransformer import Demixed_DilatedTransformerModel
from spectrogram_dataset import audioDataset
from tqdm import tqdm
import shutil
import warnings
warnings.filterwarnings('ignore')
FPS = 44100 / 1024
NUM_FOLDS = 8
DEVICE='cuda:0'
#model
NORM_FIRST=True
ATTN_LEN=5
INSTR=5
NTOKEN=2
DMODEL=256
NHEAD=8
DHID=1024
NLAYER=9
#directories
DATASET_PATH = "./data/demix_spectrogram_data.npz"
ANNOTATION_PATH = './data/full_beat_annotation.npz'
DATA_TO_LOAD = ['ballroom'] #'ballroom', 'hainsworth', 'smc', 'carnetic', 'harmonix'
TEST_ONLY = []
DEMO_SAVE_ROOT = './save/inference'
if not os.path.exists(DEMO_SAVE_ROOT):
os.makedirs(DEMO_SAVE_ROOT)
PARAM_PATH = {
0: "./checkpoints/fold_0_trf_param.pt",
1: "./checkpoints/fold_1_trf_param.pt",
2: "./checkpoints/fold_2_trf_param.pt",
3: "./checkpoints/fold_3_trf_param.pt",
4: "./checkpoints/fold_4_trf_param.pt",
5: "./checkpoints/fold_5_trf_param.pt",
6: "./checkpoints/fold_6_trf_param.pt",
7: "./checkpoints/fold_7_trf_param.pt"
}
def infer_activation():
"""
run the model to predict (down-)beat activations
"""
dataset = audioDataset(data_to_load=DATA_TO_LOAD,
test_only_data = TEST_ONLY,
data_path = DATASET_PATH,
annotation_path = ANNOTATION_PATH,
fps = FPS,
sample_size = None,
num_folds = NUM_FOLDS)
inference_pred = {}
beat_gt = {}
downbeat_gt = {}
for FOLD in range(NUM_FOLDS):
print(f'\nFold {FOLD}')
train_set, val_set, test_set = dataset.get_fold(fold=FOLD)
#loader = DataLoader(val_set, batch_size=1, shuffle=False)
loader = DataLoader(test_set, batch_size=1, shuffle=False)
model = Demixed_DilatedTransformerModel(attn_len=ATTN_LEN,
instr=INSTR,
ntoken=NTOKEN,
dmodel=DMODEL,
nhead=NHEAD,
d_hid=DHID,
nlayers=NLAYER,
norm_first=NORM_FIRST
)
#model.load_state_dict(torch.load(os.path.join(MODEL_PATH, f'Fold_{FOLD}', 'model', 'trf_param_012.pt'), map_location=torch.device('cpu')))
model.load_state_dict(torch.load(PARAM_PATH[FOLD], map_location=torch.device('cpu'))['state_dict'])
model.to(DEVICE)
model.eval()
with torch.no_grad():
for idx, (dataset_key, data, beat, downbeat, tempo, root) in tqdm(enumerate(loader), total=len(loader)):
#data
data = data.float().to(DEVICE)
pred, _ = model(data)
beat_pred = torch.sigmoid(pred[0, :, 0]).detach().cpu().numpy()
downbeat_pred = torch.sigmoid(pred[0, :, 1]).detach().cpu().numpy()
beat = torch.nonzero(beat[0]>.5)[:, 0].detach().numpy() / (FPS)
downbeat = torch.nonzero(downbeat[0]>.5)[:, 0].detach().numpy() / (FPS)
dataset_key = dataset_key[0]
root = root[0]
if not dataset_key in inference_pred:
inference_pred[dataset_key] = []
beat_gt[dataset_key] = []
downbeat_gt[dataset_key] = []
inference_pred[dataset_key].append(np.stack((beat_pred, downbeat_pred), axis=0))
beat_gt[dataset_key].append(beat)
downbeat_gt[dataset_key].append(downbeat)
#uncomment to save new activation inference
#print('saving prediction ...')
#with open(os.path.join(DEMO_SAVE_ROOT, 'inference_pred.pkl'), 'wb') as f:
# pickle.dump( inference_pred, f)
#print('saving gt ...')
#with open(os.path.join(DEMO_SAVE_ROOT, 'beat_gt.pkl'), 'wb') as f:
# pickle.dump(beat_gt, f)
#with open(os.path.join(DEMO_SAVE_ROOT, 'down_gt.pkl'), 'wb') as f:
# pickle.dump(downbeat_gt, f)
def inference_dbn():
"""
run DBN to decode activations (saved by the last function) to (down-)beat timesteps and calculate accuracy w.r.t. groundtruth
"""
beat_DBN_meter = AverageMeter()
downbeat_DBN_meter = AverageMeter()
beat_tracker = madmom.features.beats.DBNBeatTrackingProcessor(min_bpm=55.0, max_bpm=215.0, fps=FPS,
transition_lambda=100,
observation_lambda=6,
num_tempi=None,
threshold=0.2)
downbeat_tracker = madmom.features.downbeats.DBNDownBeatTrackingProcessor(beats_per_bar=[3, 4], min_bpm=55.0, max_bpm=215.0, fps=FPS,
transition_lambda=100,
observation_lambda=6,
num_tempi=None,
threshold=0.2)
print('loading activations ...')
with open(os.path.join(DEMO_SAVE_ROOT, 'inference_pred.pkl'), 'rb') as f:
activations = pickle.load(f)
with open(os.path.join(DEMO_SAVE_ROOT, 'beat_gt.pkl'), 'rb') as f:
beat_gt = pickle.load(f)
with open(os.path.join(DEMO_SAVE_ROOT, 'down_gt.pkl'), 'rb') as f:
downbeat_gt = pickle.load(f)
for dataset_key in activations:
print(f'inferencing on {dataset_key} ...')
beat_error = 0
downbeat_error = 0
for i in tqdm(range(len(activations[dataset_key]))):
pred = activations[dataset_key][i]
#print(pred.shape)
beat = beat_gt[dataset_key][i]
downbeat = downbeat_gt[dataset_key][i]
try:
dbn_beat_pred = beat_tracker(pred[0])
beat_score_DBN = madmom.evaluation.beats.BeatEvaluation(dbn_beat_pred, beat)
beat_DBN_meter.update(f'{dataset_key}-fmeasure', beat_score_DBN.fmeasure)
beat_DBN_meter.update(f'{dataset_key}-cmlt', beat_score_DBN.cmlt)
beat_DBN_meter.update(f'{dataset_key}-amlt', beat_score_DBN.amlt)
except Exception as e:
#print(f'beat inference encounter exception {e}')
beat_error += 1
try:
combined_act = np.concatenate((np.maximum(pred[0] - pred[1], np.zeros(pred[0].shape))[:, np.newaxis], pred[1][:, np.newaxis]), axis=-1) #(T, 2)
#print(combined_act.shape)
dbn_downbeat_pred = downbeat_tracker(combined_act)
dbn_downbeat_pred = dbn_downbeat_pred[dbn_downbeat_pred[:, 1]==1][:, 0]
downbeat_score_DBN = madmom.evaluation.beats.BeatEvaluation(dbn_downbeat_pred, downbeat)
downbeat_DBN_meter.update(f'{dataset_key}-fmeasure', downbeat_score_DBN.fmeasure)
downbeat_DBN_meter.update(f'{dataset_key}-cmlt', downbeat_score_DBN.cmlt)
downbeat_DBN_meter.update(f'{dataset_key}-amlt', downbeat_score_DBN.amlt)
except Exception as e:
#print(f'downbeat inference encounter exception {e}')
downbeat_error += 1
print(f'beat error: {beat_error}; downbeat error: {downbeat_error}')
print('DBN beat detection')
for key in beat_DBN_meter.avg.keys():
print('\t', key, beat_DBN_meter.avg[key])
print('DBN downbeat detection')
for key in downbeat_DBN_meter.avg.keys():
print('\t', key, downbeat_DBN_meter.avg[key])
def demo_inference_dbn():
"""
calculate accuracy and save the predicted (down-)beat timesteps into txt files
"""
beat_DBN_meter = AverageMeter()
downbeat_DBN_meter = AverageMeter()
beat_tracker = madmom.features.beats.DBNBeatTrackingProcessor(min_bpm=55.0, max_bpm=215.0, fps=FPS,
transition_lambda=100,
observation_lambda=6,
num_tempi=None,
threshold=0.2)
downbeat_tracker = madmom.features.downbeats.DBNDownBeatTrackingProcessor(beats_per_bar=[3, 4], min_bpm=55.0, max_bpm=215.0, fps=FPS,
transition_lambda=100,
observation_lambda=6,
num_tempi=None,
threshold=0.2)
for dataset in DATA_TO_LOAD:
save_dir = os.path.join(DEMO_SAVE_ROOT, dataset)
print(f'Inferencing on {dataset} dataset ...')
for song in tqdm(os.listdir(save_dir)):
song_dir = os.path.join(save_dir, song)
beat_pred = np.loadtxt(os.path.join(song_dir, 'beat_activation.txt'))
downbeat_pred = np.loadtxt(os.path.join(song_dir, 'downbeat_activation.txt'))
beat_gt = np.loadtxt(os.path.join(song_dir, 'gt_beat.txt'))
dbn_beat_pred = beat_tracker(beat_pred)
np.savetxt(os.path.join(song_dir, 'dbn_beat_pred.txt'), dbn_beat_pred[:, np.newaxis])
beat_score_DBN = madmom.evaluation.beats.BeatEvaluation(dbn_beat_pred, beat_gt)
accuracy = [f'fmeasure\t{beat_score_DBN.fmeasure}\n', \
f'cmlt\t{beat_score_DBN.cmlt}\n', \
f'amlt\t{beat_score_DBN.amlt}\n']
with open(os.path.join(song_dir, 'accuracy.txt'), 'w') as f:
f.writelines(accuracy)
beat_DBN_meter.update(f'{dataset}-fmeasure', beat_score_DBN.fmeasure)
beat_DBN_meter.update(f'{dataset}-cmlt', beat_score_DBN.cmlt)
beat_DBN_meter.update(f'{dataset}-amlt', beat_score_DBN.amlt)
combined_act = np.concatenate((np.maximum(beat_pred - downbeat_pred, np.zeros(beat_pred.shape))[:, np.newaxis], downbeat_pred[:, np.newaxis]), axis=-1) #(T, 2)
#print(combined_act.shape)
dbn_downbeat_pred = downbeat_tracker(combined_act)
dbn_downbeat_pred = dbn_downbeat_pred[dbn_downbeat_pred[:, 1]==1][:, 0]
np.savetxt(os.path.join(song_dir, 'dbn_downbeat_pred.txt'), dbn_downbeat_pred[:, np.newaxis])
#downbeat_score_DBN = madmom.evaluation.beats.BeatEvaluation(dbn_downbeat_pred, downbeat)
#downbeat_DBN_meter.update(f'{dataset}-fmeasure', downbeat_score_DBN.fmeasure)
#downbeat_DBN_meter.update(f'{dataset}-cmlt', downbeat_score_DBN.cmlt)
#downbeat_DBN_meter.update(f'{dataset}-amlt', downbeat_score_DBN.amlt)
print('DBN beat detection')
for key in beat_DBN_meter.avg.keys():
print('\t', key, beat_DBN_meter.avg[key])
print('DBN downbeat detection')
for key in downbeat_DBN_meter.avg.keys():
print('\t', key, downbeat_DBN_meter.avg[key])
def infer_gtzan_activation():
"""
predict (down-)beat activations for the test-only GTZAN dataset
"""
dataset = audioDataset(data_to_load=['gtzan'],
test_only_data = ['gtzan'],
data_path = DATASET_PATH,
annotation_path = ANNOTATION_PATH,
fps = FPS,
sample_size = None,
num_folds = NUM_FOLDS)
inference_pred = {}
beat_gt = {}
downbeat_gt = {}
FOLD = 7
train_set, val_set, test_set = dataset.get_fold(fold=FOLD)
#loader = DataLoader(val_set, batch_size=1, shuffle=False)
loader = DataLoader(test_set, batch_size=1, shuffle=False)
model = Demixed_DilatedTransformerModel(attn_len=ATTN_LEN,
instr=INSTR,
ntoken=NTOKEN,
dmodel=DMODEL,
nhead=NHEAD,
d_hid=DHID,
nlayers=NLAYER,
norm_first=NORM_FIRST
)
#model.load_state_dict(torch.load(os.path.join(MODEL_PATH, f'Fold_{FOLD}', 'model', 'trf_param_012.pt'), map_location=torch.device('cpu')))
model.load_state_dict(torch.load(PARAM_PATH[FOLD], map_location=torch.device('cpu'))['state_dict'])
model.to(DEVICE)
model.eval()
with torch.no_grad():
for idx, (dataset_key, data, beat, downbeat, tempo, root) in tqdm(enumerate(loader), total=len(loader)):
#data
data = data.float().to(DEVICE)
pred, _ = model(data)
beat_pred = torch.sigmoid(pred[0, :, 0]).detach().cpu().numpy()
downbeat_pred = torch.sigmoid(pred[0, :, 1]).detach().cpu().numpy()
beat = torch.nonzero(beat[0]>.5)[:, 0].detach().numpy() / (FPS)
downbeat = torch.nonzero(downbeat[0]>.5)[:, 0].detach().numpy() / (FPS)
dataset_key = dataset_key[0]
if not dataset_key in inference_pred[FOLD]:
inference_pred[FOLD][dataset_key] = []
beat_gt[dataset_key] = []
downbeat_gt[dataset_key] = []
inference_pred[FOLD][dataset_key].append(np.stack((beat_pred, downbeat_pred), axis=0))
beat_gt[dataset_key].append(beat)
downbeat_gt[dataset_key].append(downbeat)
#uncomment to save new activation inference
#print('saving prediction ...')
#with open(os.path.join(DEMO_SAVE_ROOT, 'inference_gtzan_pred.pkl'), 'wb') as f:
# pickle.dump( inference_pred, f)
#print('saving gt ...')
#with open(os.path.join(DEMO_SAVE_ROOT, 'beat_gtzan_gt.pkl'), 'wb') as f:
# pickle.dump(beat_gt, f)
#with open(os.path.join(DEMO_SAVE_ROOT, 'down_gtzan_gt.pkl'), 'wb') as f:
# pickle.dump(downbeat_gt, f)
def inference_gtzan_dbn():
"""
locate (down-)beat timesteps from activations for the test-only GTZAN dataset
"""
beat_tracker = madmom.features.beats.DBNBeatTrackingProcessor(min_bpm=55.0, max_bpm=215.0, fps=FPS,
transition_lambda=100,
observation_lambda=6,
num_tempi=None,
threshold=0.2)
downbeat_tracker = madmom.features.downbeats.DBNDownBeatTrackingProcessor(beats_per_bar=[3, 4], min_bpm=55.0, max_bpm=215.0, fps=FPS,
transition_lambda=100,
observation_lambda=6,
num_tempi=None,
threshold=0.2)
print('loading activations ...')
with open(os.path.join(DEMO_SAVE_ROOT, 'inference_gtzan_pred.pkl'), 'rb') as f:
activations = pickle.load(f)
with open(os.path.join(DEMO_SAVE_ROOT, 'beat_gtzan_gt.pkl'), 'rb') as f:
beat_gt = pickle.load(f)
with open(os.path.join(DEMO_SAVE_ROOT, 'down_gtzan_gt.pkl'), 'rb') as f:
downbeat_gt = pickle.load(f)
dataset_key ='gtzan'
print(f'inferencing on {dataset_key} ...')
FOLD = 7
beat_DBN_meter = AverageMeter()
downbeat_DBN_meter = AverageMeter()
beat_error = 0
downbeat_error = 0
for i in tqdm(range(len(activations[FOLD][dataset_key]))):
pred = activations[FOLD][dataset_key][i]
#print(pred.shape)
beat = beat_gt[dataset_key][i]
downbeat = downbeat_gt[dataset_key][i]
try:
dbn_beat_pred = beat_tracker(pred[0])
beat_score_DBN = madmom.evaluation.beats.BeatEvaluation(dbn_beat_pred, beat)
beat_DBN_meter.update(f'{dataset_key}-fmeasure', beat_score_DBN.fmeasure)
beat_DBN_meter.update(f'{dataset_key}-cmlt', beat_score_DBN.cmlt)
beat_DBN_meter.update(f'{dataset_key}-amlt', beat_score_DBN.amlt)
except Exception as e:
#print(f'beat inference encounter exception {e}')
beat_error += 1
try:
combined_act = np.concatenate((np.maximum(pred[0] - pred[1], np.zeros(pred[0].shape))[:, np.newaxis], pred[1][:, np.newaxis]), axis=-1) #(T, 2)
#print(combined_act.shape)
dbn_downbeat_pred = downbeat_tracker(combined_act)
dbn_downbeat_pred = dbn_downbeat_pred[dbn_downbeat_pred[:, 1]==1][:, 0]
downbeat_score_DBN = madmom.evaluation.beats.BeatEvaluation(dbn_downbeat_pred, downbeat)
downbeat_DBN_meter.update(f'{dataset_key}-fmeasure', downbeat_score_DBN.fmeasure)
downbeat_DBN_meter.update(f'{dataset_key}-cmlt', downbeat_score_DBN.cmlt)
downbeat_DBN_meter.update(f'{dataset_key}-amlt', downbeat_score_DBN.amlt)
except Exception as e:
#print(f'downbeat inference encounter exception {e}')
downbeat_error += 1
print(f'beat error: {beat_error}; downbeat error: {downbeat_error}')
print('DBN beat detection')
for key in beat_DBN_meter.avg.keys():
print('\t', key, beat_DBN_meter.avg[key])
print('DBN downbeat detection')
for key in downbeat_DBN_meter.avg.keys():
print('\t', key, downbeat_DBN_meter.avg[key])
if __name__ == '__main__':
#infer_activation()
inference_dbn()
#infer_gtzan_activation
inference_gtzan_dbn()
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