def makechunks_for_predict(x, window_len):
chunk_total = int((x.shape[1] - window_len) / window_len)
contextlen, duration, window_len = get_config()
trainX = np.zeros([chunk_total, x.shape[0], window_len])
start = 0
for i_frame in range(0, chunk_total):
trainX[i_frame] = x[:, start:start + window_len]
start += window_len
return trainX
def get_data_from_npy(data_path, label_path):
# data_path = 'F:/项目/花城音乐项目/音符起始点检测/onset_db/data_pt_single_file/melgram1All.npy'
# 梅尔频谱图数据
melgram1All = np.load(data_path, allow_pickle=True)
means, stds = get_mean_and_std()
# 标签数据
labels = np.load(label_path, allow_pickle=True).astype(int)
# onset_times = [i*10e-3 for i in range(len(labels)) if labels[i] == 1]
# librosa.display.specshow(melgram1All, x_axis='time', y_axis='mel', sr=44100, hop_length=441, fmax=16000)
# plt.vlines(onset_times, 0, 4000, colors="c", linestyles="dashed")
# plt.show()
true_labels = [i for i, x in enumerate(labels) if x == 1] # 正样本的下标
# print(true_labels)
false_labels = [i for i, x in enumerate(labels) if x == 0] # 负样本的下标
random_indexs = int_random_v2(0, len(false_labels),
len(labels) - len(false_labels))
selected_false_labels = [false_labels[i]
for i in random_indexs] # 欠采样后的负样本下标
selected_labels = true_labels + selected_false_labels #正负样本下标
selected_labels = sorted(selected_labels)
np.random.shuffle(selected_labels)
# print(selected_labels)
contextlen, duration, window_len = get_config()
trainX = makechunks(melgram1All, duration)
# start =90
# lens = 15
# display_pic(trainX[start:start + lens], labels[start:start + lens])
# print(trainX.shape)
# trainX.transpose((2,1))
trainX = np.transpose(trainX, (0, 2, 1))
trainY = labels.reshape(len(labels), )
# print(trainX.shape)
# print(trainY.shape)
# start = 100
# length = 25
# print(trainY[start:start + length])
# display_pic_T(trainX[start:start + length], None)
selected_trainX = np.zeros(
(len(selected_labels), trainX.shape[1], trainX.shape[2]), dtype=float)
selected_trainY = np.zeros((len(selected_labels)), dtype=int)
for i, x in enumerate(selected_labels):
selected_trainX[i, :, :] = trainX[x, :, :]
selected_trainY[i] = trainY[x]
# print(selected_trainX.shape)
# print(selected_trainY.shape)
trainy_tmp = np.squeeze(selected_trainY)
# print(Counter(trainy_tmp))
return selected_trainX, selected_trainY
def get_chunks_from_file_v2(filename):
stats = np.load('./data/means_stds_jj.npy')
means = stats[0]
stds = stats[1]
contextlen, duration, window_len = get_config()
x, fs = librosa.load(filename, sr=44100)
melgram1 = librosa.feature.melspectrogram(x,
sr=fs,
n_fft=1024,
hop_length=441,
n_mels=80,
fmin=27.5,
fmax=16000)
melgram1 = 10 * np.log10(1e-10 + melgram1)
melgram1 = (melgram1 - np.atleast_2d(means[0]).T) / np.atleast_2d(
stds[0]).T #normalize
melgram1 = zeropad2d(melgram1, contextlen) #zero pad ends
contextlen, duration, window_len = get_config()
trainX = makechunks_for_predict(melgram1, window_len)
return trainX
def makechunks_v2(x, y, window_len):
hop_len = int(window_len * 0.5)
chunk_total = int((x.shape[1] - window_len) / hop_len)
contextlen, duration, window_len = get_config()
trainX = np.zeros([chunk_total, x.shape[0], window_len])
start = 0
trainY = np.zeros([chunk_total])
for i_frame in range(0, chunk_total):
trainX[i_frame] = x[:, start:start + window_len]
tmp = y[start:start + window_len]
if np.max(tmp) > 0:
trainY[i_frame] = 1
start += hop_len
return trainX, trainY
def get_cqt_chunks_from_file(filename):
stats = np.load('./data/cqt_means_stds_ourself.npy', allow_pickle=True)
means = stats[0]
stds = stats[1]
contextlen, duration, window_len = get_config()
x, fs = librosa.load(filename, sr=44100)
melgram1 = librosa.amplitude_to_db(librosa.cqt(x,
sr=fs,
hop_length=512,
n_bins=80),
ref=np.max)
melgram1 = (melgram1 - np.atleast_2d(means[0]).T) / np.atleast_2d(
stds[0]).T #normalize
melgram1 = zeropad2d(melgram1, contextlen) #zero pad ends
trainX = makechunks(melgram1, duration)
return trainX
def get_data_from_npy_v2(data_path, label_path):
# data_path = 'F:/项目/花城音乐项目/音符起始点检测/onset_db/data_pt_single_file/melgram1All.npy'
# 梅尔频谱图数据
melgram1All = np.load(data_path, allow_pickle=True)
# 标签数据
labels = np.load(label_path, allow_pickle=True).astype(int)
contextlen, duration, window_len = get_config()
trainX, trainY = makechunks_v2(melgram1All, labels, window_len)
print(trainX.shape, trainY.shape)
labels = trainY
true_labels = [i for i, x in enumerate(labels) if x == 1] # 正样本的下标
false_labels = [i for i, x in enumerate(labels) if x == 0] # 负样本的下标
random_indexs = int_random_v2(0, len(false_labels),
len(labels) - len(false_labels))
selected_false_labels = [false_labels[i]
for i in random_indexs] # 欠采样后的负样本下标
selected_labels = true_labels + selected_false_labels #正负样本下标
sorted(selected_labels)
np.random.shuffle(selected_labels)
print(selected_labels)
# trainX.transpose((2,1))
trainX = np.transpose(trainX, (0, 2, 1))
trainY = labels.reshape(len(labels), )
print(trainX.shape)
print(trainY.shape)
selected_trainX = np.zeros(
(len(selected_labels), trainX.shape[1], trainX.shape[2]), dtype=float)
selected_trainY = np.zeros((len(selected_labels)), dtype=int)
for i, x in enumerate(selected_labels):
selected_trainX[i, :, :] = trainX[x, :, :]
selected_trainY[i] = trainY[x]
print(selected_trainX.shape)
print(selected_trainY.shape)
trainy_tmp = np.squeeze(selected_trainY)
print(Counter(trainy_tmp))
return selected_trainX, selected_trainY
def get_chunks_from_file(filename):
root_path = os.path.abspath(os.path.dirname(__file__))
data_path = os.path.join(root_path, './data/means_stds_ourself.npy')
stats = np.load(data_path)
means = stats[0]
stds = stats[1]
contextlen, duration, window_len = get_config()
x, fs = librosa.load(filename, sr=44100)
melgram1 = librosa.feature.melspectrogram(x,
sr=fs,
n_fft=1024,
hop_length=441,
n_mels=80,
fmin=27.5,
fmax=16000)
melgram1 = 10 * np.log10(1e-10 + melgram1)
melgram1 = (melgram1 - np.atleast_2d(means[0]).T) / np.atleast_2d(
stds[0]).T #normalize
melgram1 = zeropad2d(melgram1, contextlen) #zero pad ends
trainX = makechunks(melgram1, duration)
return trainX