commit 58d60b367d7f811a61ee26684d8edf67093aaefa
parent 15c44af028bea6e4306b8d937d1fd6bcc364e7f5
Author: CarlSouthall <[email protected]>
Date: Thu, 18 May 2017 13:18:38 +0100
Merge pull request #3 from Python3pkg/master
Convert to Python3
Diffstat:
8 files changed, 22 insertions(+), 22 deletions(-)
diff --git a/ADTLib/__init__.py b/ADTLib/__init__.py
@@ -5,6 +5,6 @@
"""
-from __future__ import absolute_import, division, print_function
+
from . import utils, nn, models
diff --git a/ADTLib/__init__.py b/ADTLib/__init__.py.bak
diff --git a/ADTLib/models/__init__.py b/ADTLib/models/__init__.py
@@ -6,7 +6,7 @@ ADTBDRNN
"""
-from __future__ import absolute_import, division, print_function
+
import madmom
import tensorflow as tf
@@ -37,24 +37,24 @@ def ADTBDRNN(TrackNames, out_sort='time',ret='yes', out_text='no', savedir='curr
names=list(np.zeros(len(TrackNames)))
Track=list(np.zeros(len(TrackNames)))
- for i in xrange(len(TrackNames)):
+ for i in range(len(TrackNames)):
Track[i]=Wavread(TrackNames[i])
name=TrackNames[i].split('.wav')
names[i]=name[0]
Frames=list(np.zeros(len(Track)))
Train=list(np.zeros(len(Track)))
- for j in xrange(len(Track)):
+ for j in range(len(Track)):
NFrames=int(np.ceil(len(Track[j])/float(HS)))
Frames[j]=np.zeros((NFrames,WL))
- for i in xrange(NFrames):
+ for i in range(NFrames):
Frames[j][i]=np.squeeze(madmom.audio.signal.signal_frame(Track[j],i,WL,HS,origin=-HS))
Spectrogram=madmom.audio.spectrogram.spec(madmom.audio.stft.stft(Frames[j],np.hanning(WL), fft_size=WL))
Train[j]=np.zeros((len(Spectrogram),Time_Steps,len(Spectrogram[0])))
- for i in xrange(len(Spectrogram)):
- for k in xrange(Time_Steps):
+ for i in range(len(Spectrogram)):
+ for k in range(Time_Steps):
if i-k >= 0:
Train[j][i][Time_Steps-k-1]=Spectrogram[i-k,:]
@@ -68,17 +68,17 @@ def ADTBDRNN(TrackNames, out_sort='time',ret='yes', out_text='no', savedir='curr
AF=list(np.zeros(len(Track)))
P=list(np.zeros(len(Track)))
- for j in xrange(len(Track)):
+ for j in range(len(Track)):
AF[j]=list([Kout[j][:,0],Sout[j][:,0],Hout[j][:,0]])
P[j]=list(np.zeros(3))
- for i in xrange(len(AF[j])):
+ for i in range(len(AF[j])):
P[j][i]=MeanPP(AF[j][i],lambd[i])
x=np.sort(P[j][i])
peak=[]
if len(x) > 0:
peak=np.append(peak,x[0])
- for k in xrange(len(x)-1):
+ for k in range(len(x)-1):
if (x[k+1]-peak[len(peak)-1]) >= close_error:
peak=np.append(peak,x[k+1])
@@ -88,7 +88,7 @@ def ADTBDRNN(TrackNames, out_sort='time',ret='yes', out_text='no', savedir='curr
if out_text == 'yes':
write_text(P,names,save_dir=savedir)
- for i in xrange(len(P)):
+ for i in range(len(P)):
Pnew=list(np.zeros(2))
Pnew[0]=np.array(P[i][:,0],dtype=float)
Pnew[1]=np.array(P[i][:,1],dtype=str)
diff --git a/ADTLib/models/__init__.py b/ADTLib/models/__init__.py.bak
diff --git a/ADTLib/nn/__init__.py b/ADTLib/nn/__init__.py
@@ -2,7 +2,7 @@
"""
@author: CarlSouthall
"""
-from __future__ import absolute_import, division, print_function
+
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
@@ -11,7 +11,7 @@ def BDRNNRestoreAll(X, RestoreFileName, num_layers=3,Truncated=1,n_hidden=50,n_c
tf.reset_default_graph()
batch_size=0;
- for i in xrange(len(X)):
+ for i in range(len(X)):
if len(X[i]) > batch_size:
batch_size=len(X[i])
@@ -57,7 +57,7 @@ def BDRNNRestoreAll(X, RestoreFileName, num_layers=3,Truncated=1,n_hidden=50,n_c
sess.run(init)
saver.restore(sess,RestoreFileName)
- for i in xrange (len(Test)):
+ for i in range (len(Test)):
test_len = len(Test[i])
if test_len != batch_size:
e=np.zeros((batch_size-test_len,1,len(Test[i][0,0])))
diff --git a/ADTLib/nn/__init__.py b/ADTLib/nn/__init__.py.bak
diff --git a/ADTLib/utils/__init__.py b/ADTLib/utils/__init__.py
@@ -2,7 +2,7 @@
"""
@author: CarlSouthall
"""
-from __future__ import absolute_import, division, print_function
+
import scipy.io.wavfile as wav
import numpy as np
import os
@@ -13,7 +13,7 @@ def MeanPP(Track,Lambda):
m=np.mean(Track)*Lambda
onsets=[]
Track=np.append(Track,0)
- for i in xrange(len(Track)):
+ for i in range(len(Track)):
if Track[i]>Track[i-1] and Track[i]>Track[i+1] and Track[i]> m:
onsets=np.append(onsets,i)
@@ -36,13 +36,13 @@ def arrange_output(Inputs,output_sort='time'):
Out=list(np.zeros(len(Inputs)))
Out1=list(np.zeros(len(Inputs)))
- for i in xrange(len(Inputs)):
+ for i in range(len(Inputs)):
Out[i]=list(np.zeros(len(Inputs[i])))
Out1[i]=list(np.zeros((1,2)))
- for j in xrange(len(Inputs[i])):
+ for j in range(len(Inputs[i])):
Out[i][j]=list(np.zeros((len(Inputs[i][j]))))
- for k in xrange(len(Inputs[i][j])):
+ for k in range(len(Inputs[i][j])):
Out[i][j][k]=list(np.zeros(2))
Out[i][j][k][0]=Inputs[i][j][k]
Out[i][j][k][1]=Names[j]
@@ -58,7 +58,7 @@ def arrange_output(Inputs,output_sort='time'):
Out[i][:,0]=np.array(np.sort(Out1[i]),dtype=str)
indexs=np.argsort(Out1[i])
out_names=list(Out[i][:,1])
- for j in xrange(len(indexs)):
+ for j in range(len(indexs)):
Out[i][j,1]=out_names[indexs[j]]
@@ -70,9 +70,9 @@ def write_text(X,names,suffix='.ADT.txt',save_dir='current'):
current_dir=os.getcwd()
os.chdir(save_dir)
- for i in xrange(len(names)):
+ for i in range(len(names)):
file = open(names[i]+suffix, "w")
- for j in xrange(len(X[i])):
+ for j in range(len(X[i])):
X[i][j][0]=X[i][j][0][0:8]
item=" ".join(X[i][j])
file.write("%s\n" % item)
diff --git a/ADTLib/utils/__init__.py b/ADTLib/utils/__init__.py.bak
