def EstimateChord(idx, dnnmodel, todir=False):
#dnn = networks.FeatureDNN()
#dnn = networks.ConvnetFeatExtractor()
dnn = networks.FullCNNFeatExtractor()
#dnn = networks.NoOperation()
dnn.load(dnnmodel)
dnn.to_gpu(0)
decoder = networks.NBLSTMCRF()
decoder.load()
decoder.to_gpu(0)
cqtfilelist = np.array(find_files(const.PATH_HCQT, ext="npy"))[idx]
i = 0
chainer.config.train = False
chainer.config.enable_backprop = False
for cqtfile in cqtfilelist:
cqt = utils.Embed(utils.PreprocessSpec(np.load(cqtfile)[:, :, :]), 1)
chroma = dnn.GetFeature(cp.asarray(cqt)).data
path = decoder.argmax(chroma)
feat = cp.asnumpy(chroma)
if todir:
fname = cqtfile.split("/")[-1] + ".lab"
alb = cqtfile.split("/")[-2]
utils.SaveEstimatedLabelsFramewise(
path, const.PATH_ESTIMATE_CROSS + alb + "/" + fname, feat)
else:
utils.SaveEstimatedLabelsFramewise(
path, const.PATH_ESTIMATE + "%03d.lab" % i, feat)
i += 1
def TrainConvnetExtractor(trainidx, epoch=20, saveas="convnet.model"):
cqtfilelist = np.array(find_files(const.PATH_MIDIHCQT,
ext="npz"))[trainidx]
#midifilelist = find_files(const.PATH_MIDI,ext="mid")[:filecnt]
config.train = True
config.enable_backprop = True
convnet = networks.FullCNNFeatExtractor()
model = networks.ConvnetPredictor(convnet)
model.to_gpu(0)
opt = optimizers.AdaDelta()
opt.setup(model)
print("train set length: %d" % trainidx.size)
print("start epochs...")
S = []
T = []
for cqtfile in cqtfilelist:
dat = np.load(cqtfile)
spec = utils.PreprocessSpec(dat["spec"])[:const.CQT_H, :, :]
targ = GetConvnetTargetFromPianoroll(dat["target"]).astype(np.int32)
assert (spec.shape[1] == targ.shape[0])
S.append(spec)
T.append(targ)
S = np.concatenate(S, axis=1)
T = np.concatenate(T, axis=0)
for ep in range(epoch):
sum_loss = 0
assert (S.shape[1] == T.shape[0])
randidx = np.random.randint(0,
S.shape[1] - const.CONV_TRAIN_SEQLEN - 1,
S.shape[1] // const.CONV_TRAIN_SEQLEN * 4)
for i in range(0, randidx.size - const.CONV_TRAIN_BATCH,
const.CONV_TRAIN_BATCH):
x_batch = np.stack([
S[:, randidx[j]:randidx[j] + const.CONV_TRAIN_SEQLEN, :]
for j in range(i, i + const.CONV_TRAIN_BATCH)
])
t_batch = np.stack([
T[randidx[j]:randidx[j] + const.CONV_TRAIN_SEQLEN, :]
for j in range(i, i + const.CONV_TRAIN_BATCH)
])
x_in = cp.asarray(x_batch)
t_in = cp.asarray(t_batch)
model.cleargrads()
loss = model(x_in, t_in)
loss.backward()
opt.update()
sum_loss += loss.data
convnet.save(saveas)
print("epoch: %d/%d loss:%.04f" %
(ep + 1, epoch, sum_loss / const.CONV_TRAIN_BATCH))
convnet.save(saveas)
type=str, default="nblstm_crf.model", action="store")
args = parser.parse_args()
audio_list = find_files("Datas/audios_estimation")
for audiofile in audio_list:
fname = audiofile.split("/")[-1]
print("Processing: %s" % fname)
#load audio
y,sr = load(audiofile,sr=C.SR)
#extract Harmonic-CQT from audio
fmin = note_to_hz("C1")
hcqt = np.stack([np.abs(cqt(y,sr=C.SR,hop_length=C.H,n_bins=C.BIN_CNT,bins_per_octave=C.OCT_BIN,fmin=fmin*(h+1),filter_scale=2,tuning=None)).T.astype(np.float32) for h in range(C.CQT_H)])
#extract feature using trained CNN extractor
cnn_feat_extractor = N.FullCNNFeatExtractor()
cnn_feat_extractor.load(args.f)
feat = cnn_feat_extractor.GetFeature(U.PreprocessSpec(hcqt)).data
#decode label sequence
decoder = N.NBLSTMCRF()
decoder.load(args.d)
labels = decoder.argmax(feat)
#convert into .lab file
labfile = os.path.join("Datas/labs_estimated",fname+".lab")
U.SaveEstimatedLabelsFramewise(labels,labfile,feat)
def TrainNStepCRF(idx,
epoch=20,
augment=0,
featmodel=const.DEFAULT_CONVNETFILE,
path_blstm="blstm.model",
savefile="nblstm_crf.model"):
cqtfilelist = np.array(find_files(const.PATH_HCQT, ext="npy"))
chordlablist = np.array(
find_files(const.PATH_CHORDLAB, ext=["lab", "chords"]))
if idx is not None:
cqtfilelist = cqtfilelist[idx]
chordlablist = chordlablist[idx]
chainer.config.train = False
chainer.config.enable_backprop = False
#dnn = networks.TripleDNNExtractor()
#dnn = networks.FeatureDNN()
dnn = networks.FullCNNFeatExtractor()
#dnn = networks.NoOperation()
#dnn = networks.ConvnetFeatExtractor()
dnn.load(featmodel)
dnn.to_gpu(0)
rnn = networks.NBLSTMCRF()
rnn.blstm.load(path_blstm)
rnn.to_gpu(0)
opt = optimizers.MomentumSGD()
opt.setup(rnn)
#opt.add_hook(optimizer.WeightDecay(0.001))
X = []
T = []
for cqtfile, labfile in zip(cqtfilelist, chordlablist):
cqt = utils.Embed(utils.PreprocessSpec(np.load(cqtfile)[:, :, :]), 1)
feature = cp.asnumpy(dnn.GetFeature(cp.asarray(cqt)).data)
lab = utils.LoadLabelArr(labfile)
min_sz = min([feature.shape[0], lab.shape[0]])
X.append(feature[:min_sz, :])
T.append(lab[:min_sz])
sizes = np.array([x.shape[0] for x in X], dtype="int32")
print("start epoch:")
chainer.config.train = False
chainer.config.enable_backprop = True
last_loss = np.inf
for ep in range(epoch):
sum_loss = 0.0
rand_songid = np.random.randint(len(X),
size=np.sum(sizes) //
const.DECODER_TRAIN_SEQLEN * 8)
for i in range(0, rand_songid.size, const.DECODER_TRAIN_BATCH):
xbatch = []
tbatch = []
for songid in rand_songid[i:i + const.DECODER_TRAIN_BATCH]:
seq_len = sizes[songid]
idx = np.random.randint(seq_len - const.DECODER_TRAIN_SEQLEN -
1)
x_snip = X[songid][idx:idx + const.DECODER_TRAIN_SEQLEN, :]
t_snip = T[songid][idx:idx + const.DECODER_TRAIN_SEQLEN]
if augment > 0:
shift = np.random.randint(augment)
x_snip, t_snip = shift_data(x_snip, t_snip, shift)
xbatch.append(Variable(cp.asarray(x_snip)))
tbatch.append(Variable(cp.asarray(t_snip)))
rnn.cleargrads()
opt.update(rnn, xbatch, tbatch)
sum_loss += rnn.loss.data
print("epoch %d/%d loss=%.3f" % (ep + 1, epoch, sum_loss / 12800.0))
rnn.save(savefile)
cqt(wav,
sr=C.SR,
hop_length=C.H,
n_bins=144,
bins_per_octave=24,
filter_scale=2,
tuning=None)).T.astype(np.float32)),
size=1)
#dat = np.load("/media/wuyiming/TOSHIBA EXT/midihcqt_12/000005.npy")
#dat_24 = np.load("/media/wuyiming/TOSHIBA EXT/midihcqt_24/000005.npz")
#spec_dnn = U.Embed(U.PreprocessSpec(dat_24["spec"]),size=7)
spec = spec[:, :250, :]
spec_dnn = spec_dnn[:250, :]
cnn = networks.FullCNNFeatExtractor()
cnn.load("fullcnn_crossentropy_6000.model")
deepchroma = networks.FeatureDNN()
deepchroma.load(
"/home/wuyiming/Projects/TranscriptionChordRecognition/dnn3500.model")
chroma_cnn = cnn.GetFeature(spec).data[:, 12:24].T
chroma_dnn = deepchroma.GetFeature(spec_dnn).data[:, 12:24].T
chroma = np.log(
1 + chroma_cqt(wav, sr=C.SR, hop_length=C.H, bins_per_octave=24)[:, :250])
target = chromatemplate.GetConvnetTargetFromPianoroll(
U.GetPianoroll(
"/media/wuyiming/TOSHIBA EXT/AIST.RWC-MDB-P-2001.SMF_SYNC/RM-P051.SMF_SYNC.MID"
))
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 6 13:13:37 2018
@author: wuyiming
"""
import networks as N
from librosa.core import cqt, load, note_to_hz
import const as C
import numpy as np
cnn = N.FullCNNFeatExtractor()
cnn.load("fullcnn_crossentropy_6000.model")
y, sr = load("audio.wav", sr=C.SR)
fmin = fmin = note_to_hz("C1")
spec = np.stack([
np.abs(
cqt(y,
sr=C.SR,
hop_length=C.H,
n_bins=C.BIN_CNT,
bins_per_octave=C.OCT_BIN,
fmin=fmin * (h + 1),
filter_scale=2,
tuning=None)).T.astype(np.float32) for h in range(C.CQT_H)
])
