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README.md

@@ -24,7 +24,9 @@ Download the code. The code is based on signal processing package in Python call
 Dependencies:
 Run these lines in a terminal to install everything necessary for feature extraction.
 ```
-sudo apt-get install python3-numpy python3-scipy python3-nose
+sudo apt-get install python-numpy python-scipy python-nose python-pip
+
+sudo pip install scikits.talkbox 
 ```
 Next for the installation of Torch for loading the models run this.
 ```
@@ -35,31 +37,30 @@ cd ~/torch; bash install-deps;
 ./install.sh
 ```
 ```
-git clone https://github.com/Element-Research/rnn.git old-rnn
+luarocks install rnn
-cd old-rnn; luarocks make rocks/rnn-scm-1.rockspec
 ```
-The Estimation model can be downloaded here and because of size constraints the Tracking model can be obtained by download from this link:
+The Estimation model can be downloaded here and because of size constraints the Tracking model can be abtained by download from this link
-[tracking_model.mat](https://drive.google.com/open?id=0Bxkc5_D0JjpiZWx4eTU1d0hsVXc)
+[tracking_model.mat] (https://drive.google.com/open?id=0Bxkc5_D0JjpiZWx4eTU1d0hsVXc)
 
 ## How to use:
 
 For vowel formant estimation, call the main script in a terminal with the following inputs: wav file, formant output filename, and the vowel begin and end times:
 
 ```
-python3 formants.py data/Example.wav data/ExamplePredictions.csv --begin 1.2 --end 1.3
+python formants.py data/Example.wav data/ExamplePredictions.csv --begin 1.2 --end 1.3
 ```
 
 or the vowel begin and end times can be taken from a TextGrid file (here the name of the TextGrid is Example.TextGrid and the vowel is taken from a tier called "VOWEL"):
 
 ```
-python3 formants.py data/Example.wav data/examplePredictions.csv --textgrid_filename data/Example.TextGrid \
+python formants.py data/Example.wav data/examplePredictions.csv --textgrid_filename data/Example.TextGrid \
           --textgrid_tier VOWEL
 ```
 
 For formant tracking, just call the script with the wav file and output filename:
 
 ```
-python3 formants.py data/Example.wav data/ExamplePredictions.csv
+python formants.py data/Example.wav data/ExamplePredictions.csv
 ```
 
 

VTR_Results.py

@@ -1,142 +0,0 @@
-from keras.models import model_from_json
-import numpy as np
-import csv
-import math
-
-model = model_from_json(open('model.json').read())
-model.load_weights('weights.h5')
-data_dir = ""
-X_test = np.load(data_dir+'VTR_test_X.npy')
-Y = np.load(data_dir+'VTR_test_Y.npy')
-
-names = Y[:, :1]
-Y_test = Y[:,1:]
-predictions = []
-
-loss1 = 0.0
-loss2 = 0.0
-loss3 = 0.0
-loss4 = 0.0
-max_1 = 0.0
-max_2 = 0.0
-max_3 = 0.0
-max_4 = 0.0
-list_1 = []
-list_2 = []
-list_3 = []
-list_4 = []
-male = [0.0, 0.0, 0.0, 0.0, 0.0, [], [], [], []]
-female = [0.0, 0.0, 0.0, 0.0, 0.0, [], [], [], []]
-karma_list = [0, 0.0, 0.0, 0.0, 0.0]
-AVG_list = [0, 0.0, 0.0, 0.0, 0.0]
-
-y_hat = model.predict(X_test)
-for i in range(0,len(Y_test)):
-    l1 = np.abs(float(Y_test[i, 0]) - y_hat[i, 0])
-    l2 = np.abs(float(Y_test[i, 1]) - y_hat[i, 1])
-    l3 = np.abs(float(Y_test[i, 2]) - y_hat[i, 2])
-    l4 = np.abs(float(Y_test[i, 3]) - y_hat[i, 3])
-    pred = [names[i][0], float(Y_test[i, 0]), float(Y_test[i, 1]), float(Y_test[i, 2]), float(Y_test[i, 3])]
-
-    AVG_list[0] += 1
-    AVG_list[1] += float(Y_test[i, 0]) - y_hat[i, 0]
-    AVG_list[2] += float(Y_test[i, 1]) - y_hat[i, 1]
-    AVG_list[3] += float(Y_test[i, 2]) - y_hat[i, 2]
-    AVG_list[4] += float(Y_test[i, 3]) - y_hat[i, 3]
-
-    pred.extend([y_hat[i, 0], y_hat[i, 1], y_hat[i, 2], y_hat[i, 3]])
-
-    if names[i][0].split('_')[3][0] == 'f':
-        female[0] += 1
-        female[1] += l1
-        female[2] += l2
-        female[3] += l3
-        female[4] += l4
-        female[5].append(l1)
-        female[6].append(l2)
-        female[7].append(l3)
-        female[8].append(l4)
-    elif names[i][0].split('_')[3][0] == 'm':
-        male[0] += 1
-        male[1] += l1
-        male[2] += l2
-        male[3] += l3
-        male[4] += l4
-        male[5].append(l1)
-        male[6].append(l2)
-        male[7].append(l3)
-        male[8].append(l4)
-
-    predictions.append(pred)
-
-    list_1.append(l1)
-    list_2.append(l2)
-    list_3.append(l3)
-    list_4.append(l4)
-    max_1 = max(max_1,l1)
-    max_2 = max(max_2,l2)
-    max_3 = max(max_3,l3)
-    max_4 = max(max_4,l4)
-
-    loss1 += l1
-    loss2 += l2
-    loss3 += l3
-    loss4 += l4
-
-    karma_list[0] += 1
-    karma_list[1] += l1 * l1
-    karma_list[2] += l2 * l2
-    karma_list[3] += l3 * l3
-    karma_list[4] += l4 * l4
-loss1 /= len(Y_test)
-loss2 /= len(Y_test)
-loss3 /= len(Y_test)
-loss4 /= len(Y_test)
-total_loss = loss1+loss2+loss3+loss4
-total_loss /= 4.0
-print('standard deviation', round(np.std(list_1)*1000, 2), round(np.std(list_2)*1000, 2), round(np.std(list_3)*1000, 2), round(np.std(list_4)*1000, 2))
-print('median', round(np.median(list_1)*1000, 2), round(np.median(list_2)*1000, 2), round(np.median(list_3)*1000, 2), round(np.median(list_4)*1000, 2))
-print('max loss ', round(max_1*1000, 2), round(max_2*1000, 2), round(max_3*1000, 2), round(max_4*1000, 2))
-print('total loss ', round(total_loss*1000, 2))
-print('Real test score:', round(loss1*1000, 2), round(loss2*1000, 2), round(loss3*1000, 2), round(loss4*1000, 2))
-
-female[1] = round((female[1] / female[0])*1000, 2)
-female[2] = round((female[2] / female[0])*1000, 2)
-female[3] = round((female[3] / female[0])*1000, 2)
-female[4] = round((female[4] / female[0])*1000, 2)
-female[5] = round(np.std(female[5])*1000, 2)
-female[6] = round(np.std(female[6])*1000, 2)
-female[7] = round(np.std(female[7])*1000, 2)
-female[8] = round(np.std(female[8])*1000, 2)
-
-male[1] = round((male[1] / male[0])*1000, 2)
-male[2] = round((male[2] / male[0])*1000, 2)
-male[3] = round((male[3] / male[0])*1000, 2)
-male[4] = round((male[4] / male[0])*1000, 2)
-male[5] = round(np.std(male[5])*1000, 2)
-male[6] = round(np.std(male[6])*1000, 2)
-male[7] = round(np.std(male[7])*1000, 2)
-male[8] = round(np.std(male[8])*1000, 2)
-
-print("male: ", male)
-print("female: ", female)
-
-# karma
-
-karma_list[1] /= karma_list[0]
-karma_list[2] /= karma_list[0]
-karma_list[3] /= karma_list[0]
-karma_list[4] /= karma_list[0]
-print('root mean squared error ', round(math.sqrt(karma_list[1]) * 1000, 2), round(math.sqrt(karma_list[2]) * 1000, 2),
-                            round(math.sqrt(karma_list[3]) * 1000, 2), round(math.sqrt(karma_list[4]) * 1000, 2))
-
-AVG_list[1] /= AVG_list[0]
-AVG_list[2] /= AVG_list[0]
-AVG_list[3] /= AVG_list[0]
-AVG_list[4] /= AVG_list[0]
-print('AVG ', round(AVG_list[1] * 1000, 2), round(AVG_list[2] * 1000, 2), round(AVG_list[3] * 1000, 2), round(AVG_list[4] * 1000, 2))
-
-
-with open("results/VTR.csv", "wb") as f:
-    writer = csv.writer(f)
-    writer.writerows(predictions)

extract_features.py

@@ -9,9 +9,9 @@ from os.path import isfile, join
 import math
 from scipy.fftpack.realtransforms import dct
 from scipy.signal import lfilter, hamming
+from copy import deepcopy
 from scipy.fftpack import fft, ifft
-#from scikits.talkbox.linpred import lpc  # obsolete
+from scikits.talkbox.linpred import lpc
-from helpers.conch_lpc import lpc
 import shutil
 from helpers.utilities import *
 
@@ -26,8 +26,7 @@ def build_data(wav,begin=None,end=None):
     dstr = wav_in_file.readframes(N)
     data = np.fromstring(dstr, np.int16)
     if begin is not None and end is not None:
-        #return data[begin*16000:end*16000] #numpy 1.11.0
+        return data[begin*16000:end*16000]
-        return data[np.int(begin*16000):np.int(end*16000)] #numpy 1.14.0
     X = []
     l = len(data)
     for i in range(0, l-100, 160):
@@ -88,9 +87,9 @@ def periodogram(x, nfft=None, fs=1):
 
     pxx = np.abs(fft(x, nfft)) ** 2
     if nfft % 2 == 0:
-        pn = nfft // 2 + 1
+        pn = nfft / 2 + 1
     else:
-        pn = (nfft + 1) // 2
+        pn = (nfft + 1 )/ 2
 
     fgrid = np.linspace(0, fs * 0.5, pn)
     return pxx[:pn] / (n * fs), fgrid
@@ -137,9 +136,9 @@ def arspec(x, order, nfft=None, fs=1):
 
     # This is not enough to deal correctly with even/odd size
     if nfft % 2 == 0:
-        pn = nfft // 2 + 1
+        pn = nfft / 2 + 1
     else:
-        pn = (nfft + 1) // 2
+        pn = (nfft + 1 )/ 2
 
     px = 1 / np.fft.fft(a, nfft)[:pn]
     pxx = np.real(np.conj(px) * px)
@@ -200,6 +199,7 @@ def preemp(input, p):
 
 
 def arspecs(input_wav,order,Atal=False):
+    epsilon = 0.0000000001
     data = input_wav
     if Atal:
         ar = atal(data, order, 30)
@@ -210,10 +210,8 @@ def arspecs(input_wav,order,Atal=False):
         for k, l in zip(ars[0], ars[1]):
             ar.append(math.log(math.sqrt((k**2)+(l**2))))
         for val in range(0,len(ar)):
-            if ar[val] < 0.0:
+            if ar[val] == 0.0:
-                ar[val] = np.nan
+                ar[val] = deepcopy(epsilon)
-            elif ar[val] == 0.0:
-                ar[val] = epsilon
         mspec1 = np.log10(ar)
         # Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)
         ar = dct(mspec1, type=2, norm='ortho', axis=-1)
@@ -222,10 +220,10 @@ def arspecs(input_wav,order,Atal=False):
 
 def specPS(input_wav,pitch):
         N = len(input_wav)
-        samps = N // pitch
+        samps = N/pitch
         if samps == 0:
             samps = 1
-        frames = N // samps
+        frames = N/samps
         data = input_wav[0:frames]
         specs = periodogram(data,nfft=4096)
         for i in range(1,int(samps)):
@@ -237,11 +235,10 @@ def specPS(input_wav,pitch):
             specs[0][s] /= float(samps)
         peri = []
         for k, l in zip(specs[0], specs[1]):
-            m = math.sqrt((k ** 2) + (l ** 2))
+            if k == 0 and l == 0:
-            if m > 0: m = math.log(m)
+                peri.append(epsilon)
-            if m == 0: m = epsilon
+            else:
-            elif m < 0: m = np.nan
+                peri.append(math.log(math.sqrt((k ** 2) + (l ** 2))))
-            peri.append(m)
         # Filter the spectrum through the triangle filterbank
         mspec = np.log10(peri)
         # Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)

formants.py

@@ -9,19 +9,19 @@ import shutil
 
 def predict_from_times(wav_filename, preds_filename, begin, end):
     tmp_features_filename = tempfile._get_default_tempdir() + "/" + next(tempfile._get_candidate_names()) + ".txt"
-    print(tmp_features_filename)
+    print tmp_features_filename
 
     if begin > 0.0 or end > 0.0:
         features.create_features(wav_filename, tmp_features_filename, begin, end)
-        easy_call("luajit load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
+        easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
     else:
         features.create_features(wav_filename, tmp_features_filename)
-        easy_call("luajit load_tracking_model.lua " + tmp_features_filename + ' ' + preds_filename)
+        easy_call("th load_tracking_model.lua " + tmp_features_filename + ' ' + preds_filename)
 
 
 def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textgrid_tier):
 
-    print(wav_filename)
+    print wav_filename
 
     if os.path.exists(preds_filename):
         os.remove(preds_filename)

formants.sh

@@ -4,12 +4,12 @@ if [ $# -eq 2 ]
 then
     tempfile=`mktemp -t txt`
     python extract_features.py $1 $tempfile
-    luajit load_estimation_model.lua $tempfile $2
+    th load_estimation_model.lua $tempfile $2
 elif [ $# -eq 4 ]
 then
     tempfile=`mktemp -t txt`
     python extract_features.py $1 $tempfile --begin $3 --end $4
-    luajit load_estimation_model.lua $tempfile $2
+    th load_estimation_model.lua $tempfile $2
 else
     echo "$0 wav_filename pred_csv_filename [begin_time end_time]"
 fi

helpers/conch_lpc.py

@@ -1,286 +0,0 @@
-# This file has been copied (with minor changes) from Michael
-# McAuliffe's Conch project, to provide a compatible replacement
-# implementation of the lpc() function from the obsolete Python-2-only
-# scikits.talkbox library.
-#
-# Conch repository: https://github.com/mmcauliffe/Conch-sounds
-# Source: https://github.com/mmcauliffe/Conch-sounds/blob/master/conch/analysis/formants/lpc.py
-
-# Copyright (c) 2015 Michael McAuliffe
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-# THE SOFTWARE.
-
-#import librosa
-import numpy as np
-import scipy as sp
-from scipy.signal import lfilter
-
-from scipy.fftpack import fft, ifft
-from scipy.signal import gaussian
-
-#from ..helper import nextpow2
-#from ..functions import BaseAnalysisFunction
-
-# Source: https://github.com/mmcauliffe/Conch-sounds/blob/master/conch/analysis/helper.py
-def nextpow2(x):
-    """Return the first integer N such that 2**N >= abs(x)"""
-    return np.ceil(np.log2(np.abs(x)))
-
-def lpc_ref(signal, order):
-    """Compute the Linear Prediction Coefficients.
-
-    Return the order + 1 LPC coefficients for the signal. c = lpc(x, k) will
-    find the k+1 coefficients of a k order linear filter:
-
-      xp[n] = -c[1] * x[n-2] - ... - c[k-1] * x[n-k-1]
-
-    Such as the sum of the squared-error e[i] = xp[i] - x[i] is minimized.
-
-    Parameters
-    ----------
-    signal: array_like
-        input signal
-    order : int
-        LPC order (the output will have order + 1 items)
-
-    Notes
-    ----
-    This is just for reference, as it is using the direct inversion of the
-    toeplitz matrix, which is really slow"""
-    if signal.ndim > 1:
-        raise ValueError("Array of rank > 1 not supported yet")
-    if order > signal.size:
-        raise ValueError("Input signal must have a lenght >= lpc order")
-
-    if order > 0:
-        p = order + 1
-        r = np.zeros(p, 'float32')
-        # Number of non zero values in autocorrelation one needs for p LPC
-        # coefficients
-        nx = np.min([p, signal.size])
-        x = np.correlate(signal, signal, 'full')
-        r[:nx] = x[signal.size - 1:signal.size + order]
-        phi = np.dot(sp.linalg.inv(sp.linalg.toeplitz(r[:-1])), -r[1:])
-        return np.concatenate(([1.], phi))
-    else:
-        return np.ones(1, dtype='float32')
-
-
-# @jit
-def levinson_1d(r, order):
-    """Levinson-Durbin recursion, to efficiently solve symmetric linear systems
-    with toeplitz structure.
-
-    Parameters
-    ---------
-    r : array-like
-        input array to invert (since the matrix is symmetric Toeplitz, the
-        corresponding pxp matrix is defined by p items only). Generally the
-        autocorrelation of the signal for linear prediction coefficients
-        estimation. The first item must be a non zero real.
-
-    Notes
-    ----
-    This implementation is in python, hence unsuitable for any serious
-    computation. Use it as educational and reference purpose only.
-
-    Levinson is a well-known algorithm to solve the Hermitian toeplitz
-    equation:
-
-                       _          _
-        -R[1] = R[0]   R[1]   ... R[p-1]    a[1]
-         :      :      :          :      *  :
-         :      :      :          _      *  :
-        -R[p] = R[p-1] R[p-2] ... R[0]      a[p]
-                       _
-    with respect to a (  is the complex conjugate). Using the special symmetry
-    in the matrix, the inversion can be done in O(p^2) instead of O(p^3).
-    """
-    r = np.atleast_1d(r)
-    if r.ndim > 1:
-        raise ValueError("Only rank 1 are supported for now.")
-
-    n = r.size
-    if n < 1:
-        raise ValueError("Cannot operate on empty array !")
-    elif order > n - 1:
-        raise ValueError("Order should be <= size-1")
-
-    if not np.isreal(r[0]):
-        raise ValueError("First item of input must be real.")
-    elif not np.isfinite(1 / r[0]):
-        raise ValueError("First item should be != 0")
-
-    # Estimated coefficients
-    a = np.empty(order + 1, 'float32')
-    # temporary array
-    t = np.empty(order + 1, 'float32')
-    # Reflection coefficients
-    k = np.empty(order, 'float32')
-
-    a[0] = 1.
-    e = r[0]
-
-    for i in range(1, order + 1):
-        acc = r[i]
-        for j in range(1, i):
-            acc += a[j] * r[i - j]
-        k[i - 1] = -acc / e
-        a[i] = k[i - 1]
-
-        for j in range(order):
-            t[j] = a[j]
-
-        for j in range(1, i):
-            a[j] += k[i - 1] * np.conj(t[i - j])
-
-        e *= 1 - k[i - 1] * np.conj(k[i - 1])
-
-    return a, e, k
-
-
-# @jit
-def _acorr_last_axis(x, nfft, maxlag):
-    a = np.real(ifft(np.abs(fft(x, n=nfft) ** 2)))
-    return a[..., :maxlag + 1] / x.shape[-1]
-
-
-# @jit
-def acorr_lpc(x, axis=-1):
-    """Compute autocorrelation of x along the given axis.
-
-    This compute the biased autocorrelation estimator (divided by the size of
-    input signal)
-
-    Notes
-    -----
-        The reason why we do not use acorr directly is for speed issue."""
-    if not np.isrealobj(x):
-        raise ValueError("Complex input not supported yet")
-
-    maxlag = x.shape[axis]
-    nfft = int(2 ** nextpow2(2 * maxlag - 1))
-
-    if axis != -1:
-        x = np.swapaxes(x, -1, axis)
-    a = _acorr_last_axis(x, nfft, maxlag)
-    if axis != -1:
-        a = np.swapaxes(a, -1, axis)
-    return a
-
-
-# @jit
-def lpc(signal, order, axis=-1):
-    """Compute the Linear Prediction Coefficients.
-
-    Return the order + 1 LPC coefficients for the signal. c = lpc(x, k) will
-    find the k+1 coefficients of a k order linear filter:
-
-      xp[n] = -c[1] * x[n-2] - ... - c[k-1] * x[n-k-1]
-
-    Such as the sum of the squared-error e[i] = xp[i] - x[i] is minimized.
-
-    Parameters
-    ----------
-    signal: array_like
-        input signal
-    order : int
-        LPC order (the output will have order + 1 items)
-
-    Returns
-    -------
-    a : array-like
-        the solution of the inversion.
-    e : array-like
-        the prediction error.
-    k : array-like
-        reflection coefficients.
-
-    Notes
-    -----
-    This uses Levinson-Durbin recursion for the autocorrelation matrix
-    inversion, and fft for the autocorrelation computation.
-
-    For small order, particularly if order << signal size, direct computation
-    of the autocorrelation is faster: use levinson and correlate in this case."""
-    n = signal.shape[axis]
-    if order > n:
-        raise ValueError("Input signal must have length >= order")
-
-    r = acorr_lpc(signal, axis)
-    return levinson_1d(r, order)
-
-
-def process_frame(X, window, num_formants, new_sr):
-    X = X * window
-    A, e, k = lpc(X, num_formants * 2)
-
-    rts = np.roots(A)
-    rts = rts[np.where(np.imag(rts) >= 0)]
-    angz = np.arctan2(np.imag(rts), np.real(rts))
-    frqs = angz * (new_sr / (2 * np.pi))
-    frq_inds = np.argsort(frqs)
-    frqs = frqs[frq_inds]
-    bw = -1 / 2 * (new_sr / (2 * np.pi)) * np.log(np.abs(rts[frq_inds]))
-    return frqs, bw
-
-
-def lpc_formants(signal, sr, num_formants, max_freq, time_step,
-                 win_len, window_shape='gaussian'):
-    output = {}
-    new_sr = 2 * max_freq
-    alpha = np.exp(-2 * np.pi * 50 * (1 / new_sr))
-    proc = lfilter([1., -alpha], 1, signal)
-    if sr > new_sr:
-        proc = librosa.resample(proc, sr, new_sr)
-    nperseg = int(win_len * new_sr)
-    nperstep = int(time_step * new_sr)
-    if window_shape == 'gaussian':
-        window = gaussian(nperseg + 2, 0.45 * (nperseg - 1) / 2)[1:nperseg + 1]
-    else:
-        window = np.hanning(nperseg + 2)[1:nperseg + 1]
-    indices = np.arange(int(nperseg / 2), proc.shape[0] - int(nperseg / 2) + 1, nperstep)
-    num_frames = len(indices)
-    for i in range(num_frames):
-        if nperseg % 2 != 0:
-            X = proc[indices[i] - int(nperseg / 2):indices[i] + int(nperseg / 2) + 1]
-        else:
-            X = proc[indices[i] - int(nperseg / 2):indices[i] + int(nperseg / 2)]
-        frqs, bw = process_frame(X, window, num_formants, new_sr)
-        formants = []
-        for j, f in enumerate(frqs):
-            if f < 50:
-                continue
-            if f > max_freq - 50:
-                continue
-            formants.append((np.asscalar(f), np.asscalar(bw[j])))
-        missing = num_formants - len(formants)
-        if missing:
-            formants += [(None, None)] * missing
-        output[indices[i] / new_sr] = formants
-    return output
-
-
-#class FormantTrackFunction(BaseAnalysisFunction):
-#    def __init__(self, num_formants=5, max_frequency=5000,
-#                 time_step=0.01, window_length=0.025, window_shape='gaussian'):
-#        super(FormantTrackFunction, self).__init__()
-#        self.arguments = [num_formants, max_frequency, time_step, window_length, window_shape]
-#        self._function = lpc_formants
-#        self.requires_file = False

training_for_git/batch.lua

@@ -0,0 +1,104 @@
+require 'torch'   -- torch
+require 'optim'
+require 'nn'      -- provides a normalization operator
+local train_file_path = 'train.th7' 
+local test_file_path = 'test.th7'
+local train_data = torch.load(train_file_path)
+local test_data = torch.load(test_file_path)
+local Y = train_data[{{},{2,5}}]
+local X = train_data[{{},{6,-1}}]
+local test_labels = test_data[{{},{2,5}}]
+local test_X = test_data[{{},{6,-1}}]
+local batch_size = 30
+epochs = 3
+
+model = nn.Sequential()                 -- define the container
+ninputs = 350; noutputs = 4 ; nhiddens1 = 1024; nhiddens2 = 512; nhiddens3 = 256
+model:add(nn.Linear(ninputs,nhiddens1))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens1,nhiddens2))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens2,nhiddens3))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens3,noutputs))
+criterion = nn.AbsCriterion()--MSECriterion()
+x, dl_dx = model:getParameters()
+sgd_params = {
+   learningRate = 0.01,
+   learningRateDecay = 1e-08,
+   weightDecay = 0,
+   momentum = 0
+}
+
+function train(X,Y)
+	   
+   current_loss = 0
+   for batch = 1,(#train_data)[1], batch_size do
+
+      local inputs = {}
+      local targets = {} 
+      local x_start = batch
+      local x_end = math.min(batch + batch_size-1, (#train_data)[1])
+      for i = x_start,x_end do    
+      local target = Y[i]    
+      local input = X[i]  
+      table.insert(inputs, input)
+      table.insert(targets, target)
+      end
+	local feval = function(x_new)
+	   if x ~= x_new then
+	      x:copy(x_new)
+	   end
+	   dl_dx:zero()
+           local f=0
+	for i = 1, #inputs do
+	   local loss_x = criterion:forward(model:forward(inputs[i]), targets[i])
+	   model:backward(inputs[i], criterion:backward(model.output, targets[i]))
+	f = f+loss_x
+	end
+	   return f/#inputs, dl_dx:div(#inputs)
+	end
+      _,fs = optim.adagrad(feval,x,sgd_params)
+      current_loss = current_loss + fs[1]
+   end
+   current_loss = current_loss/( (#train_data)[1]/batch_size)
+   print('train loss = ' .. current_loss)
+   return current_loss
+end
+
+time = sys.clock()
+local cumm_loss = 0.
+for j = 1, epochs do
+    print(j)
+    cumm_loss = train( X, Y )
+print( 'Final loss = ' .. cumm_loss )
+if j%10 == 0 then
+print('id  approx   text')
+local loss1 = 0.0
+local loss2 = 0.0
+local loss3 = 0.0
+local loss4 = 0.0
+for i = 1,(#test_data)[1] do
+   local myPrediction = model:forward(test_X[i])
+   loss1 = loss1+math.abs(myPrediction[1] - test_labels[i][1])
+   loss2 = loss2+math.abs(myPrediction[2] - test_labels[i][2])
+   loss3 = loss3+math.abs(myPrediction[3] - test_labels[i][3])
+   loss4 = loss4+math.abs(myPrediction[4] - test_labels[i][4])
+end
+
+loss1 = loss1/(#test_data)[1]
+loss2 = loss2/(#test_data)[1]
+loss3 = loss3/(#test_data)[1]
+loss4 = loss4/(#test_data)[1]
+end
+end
+
+
+-- time taken
+time = sys.clock() - time
+print( "Time per epoch = " .. (time / epochs) .. '[s]')
+
+
+
+print(loss1,loss2,loss3,loss4)
+torch.save('estimation_model.dat',model)

training_for_git/gpu_rec.lua

@@ -0,0 +1,130 @@
+require 'rnn'
+require 'optim'
+
+batchSize = 30
+rho = 10
+hiddenSize = 512
+hiddenSize1 = 256
+inputSize = 400
+outputSize = 3
+epochs = 100
+xStart = 6
+yStart = 2
+yEnd = 4
+
+
+local train_file_path = 'recurrent_train.th7' 
+local train_data = torch.load(train_file_path)
+local Y = train_data[{{},{yStart,yEnd}}]
+local X = train_data[{{},{xStart,-1}}]
+seriesSize = (#train_data)[1]
+print(seriesSize)
+local test_file_path = 'recurrent_test.th7'
+local test_data = torch.load(test_file_path)
+local test_labels = test_data[{{},{yStart,yEnd}}]
+local test_X = test_data[{{},{xStart,-1}}]
+
+model = nn.Sequential()
+model:add(nn.Sequencer(nn.FastLSTM(inputSize, hiddenSize, rho)))
+model:add(nn.Sequencer(nn.FastLSTM(hiddenSize, hiddenSize1, rho)))
+model:add(nn.Sequencer(nn.Linear(hiddenSize1, outputSize)))
+
+criterion = nn.SequencerCriterion(nn.AbsCriterion())
+
+-- dummy dataset (task predict the next item)
+--dataset = torch.randn(seriesSize, inputSize)
+
+-- define the index of the batch elements
+offsets = {}
+for i= 1, batchSize do
+   table.insert(offsets, i)--math.ceil(math.random() * batchSize))
+end
+offsets = torch.LongTensor(offsets)
+
+function nextBatch()
+   local inputs, targets = {}, {}
+   for step = 1, rho do
+      --get a batch of inputs
+      table.insert(inputs, X:index(1, offsets))
+      -- shift of one batch indexes
+      offsets:add(1)
+      for j=1,batchSize do
+         if offsets[j] > seriesSize then
+            offsets[j] = 1
+         end
+      end
+      -- a batch of targets
+      table.insert(targets, Y[{{},{1,3}}]:index(1,offsets))
+   end
+   return inputs, targets
+end
+
+-- get weights and loss wrt weights from the model
+x, dl_dx = model:getParameters()
+
+feval = function(x_new)
+	-- copy the weight if are changed
+	if x ~= x_new then
+		x:copy(x_new)
+	end
+
+	-- select a training batch
+	local inputs, targets = nextBatch()
+
+	-- reset gradients (gradients are always accumulated, to accommodate
+	-- batch methods)
+	dl_dx:zero()
+
+	-- evaluate the loss function and its derivative wrt x, given mini batch
+	local prediction = model:forward(inputs)
+	local loss_x = criterion:forward(prediction, targets)
+	model:backward(inputs, criterion:backward(prediction, targets))
+	
+	return loss_x, dl_dx
+end
+
+sgd_params = {
+   learningRate = 0.01,
+   learningRateDecay = 1e-08,
+   weightDecay = 0,
+   momentum = 0
+}
+
+time = sys.clock()
+for j = 1, epochs do
+   -- train a mini_batch of batchSize in parallel
+	 _, fs = optim.adagrad(feval,x, sgd_params)
+	print('error for iteration ' .. sgd_params.evalCounter  .. ' is ' .. fs[1])
+
+end
+
+
+print('id  approx   text')
+local loss1 = 0.0
+local loss2 = 0.0
+local loss3 = 0.0
+local loss4 = 0.0
+for i = 1,(#test_data)[1], 1 do
+   local inputs = {}
+   for step = 1, 1 do
+      --get a batch of inputs
+      table.insert(inputs, test_X[i])
+   end
+   local myPrediction = model:forward(inputs)
+   loss1 = loss1+math.abs(myPrediction[1][1] - test_labels[i][1])
+   loss2 = loss2+math.abs(myPrediction[1][2] - test_labels[i][2])
+   loss3 = loss3+math.abs(myPrediction[1][3] - test_labels[i][3])
+   --loss4 = loss4+math.abs(myPrediction[4] - test_labels[i][4])
+end
+
+loss1 = loss1/(#test_data)[1]
+loss2 = loss2/(#test_data)[1]
+loss3 = loss3/(#test_data)[1]
+--loss4 = loss4/(#test_data)[1]
+
+-- time taken
+time = sys.clock() - time
+print( "Time per epoch = " .. (time / epochs) .. '[s]')
+
+print(loss1,loss2,loss3,loss4)
+torch.save('recurrent.dat',model)

training_for_git/no_validation.lua

@@ -0,0 +1,129 @@
+require 'torch'   -- torch
+require 'optim'
+require 'nn'      -- provides a normalization operator
+local train_file_path = 'train.th7' 
+local test_file_path = 'test.th7'
+local train_data = torch.load(train_file_path)
+local test_data = torch.load(test_file_path)
+local train_labels = train_data[{{},{2,5}}]
+local train_X = train_data[{{},{6,-1}}]
+local test_labels = test_data[{{},{2,5}}]
+local test_X = test_data[{{},{6,-1}}]
+local batch_size = 30
+model = nn.Sequential()                 -- define the container
+ninputs = 350; noutputs = 4 ; nhiddens1 = 1024; nhiddens2 = 512; nhiddens3 = 256
+--model:add(nn.Linear(ninputs, noutputs)) -- define the only module
+model:add(nn.Linear(ninputs,nhiddens1))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens1,nhiddens2))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens2,nhiddens3))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens3,noutputs))
+criterion = nn.AbsCriterion()--MSECriterion()
+x, dl_dx = model:getParameters()
+
+feval = function(x_new)
+   if x ~= x_new then
+      x:copy(x_new)
+   end
+   -- select a new training sample
+   _nidx_ = (_nidx_ or 0) + 1
+   if _nidx_ > (#train_data)[1] then _nidx_ = 1 end
+   --local sample = data[_nidx_]
+   local target = train_labels[_nidx_]      -- this funny looking syntax allows
+   local inputs = train_X[_nidx_]    -- slicing of arrays.
+   -- reset gradients (gradients are always accumulated, to accommodate 
+   -- batch methods)
+   dl_dx:zero()
+   -- evaluate the loss function and its derivative wrt x, for that sample
+   --print(inputs)
+   --print(target)
+   for i=1, 350 do
+  if type(inputs[i]) ~= 'number' then
+  print(i)
+  print(inputs[i])
+  print(type(inputs[i])) end
+  end
+   --io.write("continue with this operation (y/n)?")
+   --answer=io.read()
+   local loss_x = criterion:forward(model:forward(inputs), target)
+   model:backward(inputs, criterion:backward(model.output, target))
+   -- return loss(x) and dloss/dx
+   return loss_x, dl_dx
+end
+-- Given the function above, we can now easily train the model using SGD.
+-- For that, we need to define four key parameters:
+--   + a learning rate: the size of the step taken at each stochastic 
+--     estimate of the gradient
+--   + a weight decay, to regularize the solution (L2 regularization)
+--   + a momentum term, to average steps over time
+--   + a learning rate decay, to let the algorithm converge more precisely
+sgd_params = {
+   learningRate = 0.01,
+   learningRateDecay = 1e-08,
+   weightDecay = 0,
+   momentum = 0
+}
+-- We're now good to go... all we have left to do is run over the dataset
+-- for a certain number of iterations, and perform a stochastic update 
+-- at each iteration. The number of iterations is found empirically here,
+-- but should typically be determinined using cross-validation.
+-- we cycle 1e4 times over our training data
+for i = 1,1 do
+   print(i)
+   -- this variable is used to estimate the average loss
+   current_loss = 0
+   -- an epoch is a full loop over our training data
+   for i = 1,(#train_data)[1] do
+      -- optim contains several optimization algorithms. 
+      -- All of these algorithms assume the same parameters:
+      --   + a closure that computes the loss, and its gradient wrt to x, 
+      --     given a point x
+      --   + a point x
+      --   + some parameters, which are algorithm-specific      
+      _,fs = optim.adagrad(feval,x,sgd_params)
+      -- Functions in optim all return two things:
+      --   + the new x, found by the optimization method (here SGD)
+      --   + the value of the loss functions at all points that were used by
+      --     the algorithm. SGD only estimates the function once, so
+      --     that list just contains one value.
+      current_loss = current_loss + fs[1]
+   end
+   -- report average error on epoch
+   current_loss = current_loss / (#train_data)[1]
+   print('train loss = ' .. current_loss)
+   
+end
+----------------------------------------------------------------------
+-- 5. Test the trained model.
+
+-- Now that the model is trained, one can test it by evaluating it
+-- on new samples.
+
+-- The text solves the model exactly using matrix techniques and determines
+-- that 
+--   corn = 31.98 + 0.65 * fertilizer + 1.11 * insecticides
+
+-- We compare our approximate results with the text's results.
+
+print('id  approx   text')
+local loss1 = 0.0
+local loss2 = 0.0
+local loss3 = 0.0
+local loss4 = 0.0
+for i = 1,(#test_data)[1] do
+   local myPrediction = model:forward(test_X[i])
+   loss1 = loss1+math.abs(myPrediction[1] - test_labels[i][1])
+   loss2 = loss2+math.abs(myPrediction[2] - test_labels[i][2])
+   loss3 = loss3+math.abs(myPrediction[3] - test_labels[i][3])
+   loss4 = loss4+math.abs(myPrediction[4] - test_labels[i][4])
+end
+
+loss1 = loss1/(#test_data)[1]
+loss2 = loss2/(#test_data)[1]
+loss3 = loss3/(#test_data)[1]
+loss4 = loss4/(#test_data)[1]
+
+print(loss1,loss2,loss3,loss4)
+torch.save('save.dat',model)

training_for_git/recurrent.lua

@@ -0,0 +1,109 @@
+require 'rnn'
+require 'optim'
+
+function range(from, to, step)
+  step = step or 1
+  return function(_, lastvalue)
+    local nextvalue = lastvalue + step
+    if step > 0 and nextvalue <= to or step < 0 and nextvalue >= to or
+       step == 0
+    then
+      return nextvalue
+    end
+  end, nil, from - step
+end
+
+local train_file_path = 'recurrent_train.th7' 
+local test_file_path = 'recurrent_test.th7'
+local train_data = torch.load(train_file_path)
+local test_data = torch.load(test_file_path)
+local Y = train_data[{{},{2,5}}]
+local X = train_data[{{},{6,-1}}]
+local test_labels = test_data[{{},{2,5}}]
+local test_X = test_data[{{},{6,-1}}]
+
+batchSize = 5
+rho = 10
+hiddenSize1 = 1024
+hiddenSize2 = 512
+hiddenSize3 = 256
+inputSize = 1
+outputSize = 1
+seriesSize = 100
+
+model = nn.Sequential()
+model:add(nn.Sequencer(nn.FastLSTM(inputSize, hiddenSize2, rho)))
+model:add(nn.Sequencer(nn.FastLSTM(hiddenSize2, hiddenSize3, rho)))
+--model:add(nn.Sequencer(nn.Linear(hiddenSize2, hiddenSize3, rho)))
+--model:add(nn.Sequencer(nn.Sigmoid()))
+model:add(nn.Sequencer(nn.Linear(hiddenSize3, outputSize)))
+
+criterion = nn.SequencerCriterion(nn.MSECriterion())
+
+-- dummy dataset (task predict the next item)
+--dataset = torch.randn(seriesSize, inputSize)
+
+-- define the index of the batch elements
+offsets = {}
+for i= 1, batchSize do
+   table.insert(offsets,i)
+end
+offsets = torch.LongTensor(offsets)
+print(offsets)
+function nextBatch()
+   local inputs, targets = {}, {}
+   for step = 1, rho do
+      --get a batch of inputs
+      table.insert(inputs, X:index(1, offsets))
+      -- shift of one batch indexes
+      offsets:add(1)
+      for j=1,batchSize do
+         if offsets[j] > seriesSize then
+            offsets[j] = 1
+         end
+      end
+      -- a batch of targets
+      table.insert(targets, Y:index(1,offsets))
+   end
+   return inputs, targets
+end
+
+-- get weights and loss wrt weights from the model
+x, dl_dx = model:getParameters()
+
+feval = function(x_new)
+  -- copy the weight if are changed
+  if x ~= x_new then
+    x:copy(x_new)
+  end
+
+  -- select a training batch
+  local inputs, targets = nextBatch()
+
+  -- reset gradients (gradients are always accumulated, to accommodate
+  -- batch methods)
+  dl_dx:zero()
+
+  -- evaluate the loss function and its derivative wrt x, given mini batch
+  local prediction = model:forward(inputs)
+  local loss_x = criterion:forward(prediction, targets)
+  model:backward(inputs, criterion:backward(prediction, targets))
+
+  return loss_x, dl_dx
+end
+
+sgd_params = {
+   learningRate = 0.01,
+   learningRateDecay = 1e-08,
+   weightDecay = 0,
+   momentum = 0
+}
+
+for i = 1, 2 do
+   -- train a mini_batch of batchSize in parallel
+   _, fs = optim.adagrad(feval,x, sgd_params)
+
+  if sgd_params.evalCounter % 100 == 0 then
+    print('error for iteration ' .. sgd_params.evalCounter  .. ' is ' .. fs[1] / rho)
+  end
+end

training_for_git/recurrent_training.lua

@@ -0,0 +1,144 @@
+require 'rnn'
+require 'optim'
+
+batchSize = 30
+rho = 20
+hiddenSize = 512
+hiddenSize1 = 256
+inputSize = 400
+outputSize = 4
+epochs = 10000
+xStart = 6
+yStart = 2
+yEnd = 5
+
+
+local train_file_path = 'recurrent_train.th7' 
+local train_data = torch.load(train_file_path)
+local Y = train_data[{{},{yStart,yEnd}}]
+local X = train_data[{{},{xStart,-1}}]
+local place = train_data[{{},{1}}]
+seriesSize = (#train_data)[1]
+print(seriesSize)
+local test_file_path = 'recurrent_test.th7'
+local test_data = torch.load(test_file_path)
+local test_labels = test_data[{{},{yStart,yEnd}}]
+local test_X = test_data[{{},{xStart,-1}}]
+
+model = nn.Sequential()
+model:add(nn.Sequencer(nn.FastLSTM(inputSize, hiddenSize, rho)))
+model:add(nn.Sequencer(nn.FastLSTM(hiddenSize, hiddenSize1, rho)))
+model:add(nn.Sequencer(nn.Linear(hiddenSize1, outputSize)))
+
+criterion = nn.SequencerCriterion(nn.AbsCriterion())
+--local method = 'xavier'
+--local model_new = require('weight-init')(model, method)
+
+-- define the index of the batch elements
+offsets = {}
+function offset_(seed)
+offsets = {}
+math.randomseed(seed)
+for i= 1, batchSize do
+   table.insert(offsets, math.ceil(math.random() * batchSize))
+end
+offsets = torch.LongTensor(offsets)
+end
+function nextBatch()
+   local inputs, targets = {}, {}
+   local nums = {}
+   for step = 1, rho do
+      --get a batch of inputs
+      table.insert(inputs, X:index(1, offsets))
+      -- shift of one batch indexes
+      offsets:add(1)
+      for j=1,batchSize do
+         if offsets[j] > seriesSize then
+            offsets[j] = 1
+         end
+      end
+      -- a batch of targets
+      table.insert(targets, Y[{{},{1,4}}]:index(1,offsets))
+      table.insert(nums,place:index(1,offsets))
+   end
+   return inputs, targets
+end
+
+-- get weights and loss wrt weights from the model
+x, dl_dx = model:getParameters()
+
+feval = function(x_new)
+	-- copy the weight if are changed
+	if x ~= x_new then
+		x:copy(x_new)
+	end
+
+	-- select a training batch
+	local inputs, targets = nextBatch()
+
+	-- reset gradients (gradients are always accumulated, to accommodate
+	-- batch methods)
+	dl_dx:zero()
+
+	-- evaluate the loss function and its derivative wrt x, given mini batch
+	local prediction = model:forward(inputs)
+	local loss_x = criterion:forward(prediction, targets)
+	model:backward(inputs, criterion:backward(prediction, targets))
+	
+	return loss_x, dl_dx
+end
+
+adagrad_params = {
+   learningRate = 0.01,
+   learningRateDecay = 1e-08,
+   weightDecay = 0,
+   momentum = 0
+}
+seed = 1
+offset_(seed)
+time = sys.clock()
+for j = 1, epochs do
+	if j%1000 == 0 then 
+		seed = seed + 1
+		offset_(seed)
+	end
+   -- train a mini_batch of batchSize in parallel
+	 _, fs = optim.adagrad(feval,x, adagrad_params)
+	print('error for iteration ' .. adagrad_params.evalCounter  .. ' is ' .. fs[1]/rho)
+
+end
+
+
+print('id  approx   text')
+local loss1 = 0.0
+local loss2 = 0.0
+local loss3 = 0.0
+local loss4 = 0.0
+predict_batch = 100
+for i = 1,(#test_data)[1], predict_batch do
+   local inputs = {}
+   for step = 0, predict_batch-1 do
+      --get a batch of inputs
+      table.insert(inputs, test_X[i+step])
+   end
+   local myPrediction = model:forward(inputs)
+   for step = 1, predict_batch do
+   loss1 = loss1+math.abs(myPrediction[step][1] - test_labels[i+step-1][1])
+   loss2 = loss2+math.abs(myPrediction[step][2] - test_labels[i+step-1][2])
+   loss3 = loss3+math.abs(myPrediction[step][3] - test_labels[i+step-1][3])
+   loss4 = loss4+math.abs(myPrediction[4] - test_labels[i][4])
+   end
+
+end
+
+loss1 = loss1/(#test_data)[1]
+loss2 = loss2/(#test_data)[1]
+loss3 = loss3/(#test_data)[1]
+loss4 = loss4/(#test_data)[1]
+
+-- time taken
+time = sys.clock() - time
+print( "Time per epoch = " .. (time / epochs) .. '[s]')
+
+print(loss1,loss2,loss3,loss4)
+torch.save('recurrent3.dat',model)