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GUI_ASR.py
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GUI_ASR.py
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from tkinter import *
from tkinter import ttk
from tkinter import filedialog
from scipy.io.wavfile import write, read
import pyaudio, wave
import threading
import pandas as pd
import matplotlib as plt
plt.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
import numpy as np
#import librosa
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
from matplotlib.figure import Figure
########################## Config DataBase
def config():
fonema = ['A', 'B', 'THETA', 'CH', 'D', 'E', 'F', 'G', 'I', 'J', 'K', 'L', 'LL', 'M', 'N', 'Ñ', 'O', 'P', 'R', 'RR',
'S', 'T', 'U', 'KS', 'Y']
gen = ['H', 'M']
edad = ['N', 'P', 'A']
######################################################################################
# Base de datos generada
newDBHeader = ['CODIGO', 'FORMANTE 1', 'FORMANTE 2', 'FORMANTE 3', 'FORMANTE 4', 'FORMANTE 5', 'FORMANTE 6',
'FORMANTE 7']
global distance_columns
distance_columns = newDBHeader[1:4]
global data1
data1 = pd.read_csv('BD_Formantes.xlsx')
new_header = data1.iloc[0]
data1 = data1[1:]
data1.columns = new_header
######################################################################################
# Base de datos sólo vocales
global dataVocals
dataVocals = pd.read_csv('BD_Formantes2.xlsx')
new_header = dataVocals.iloc[0]
dataVocals = dataVocals[1:]
dataVocals.columns = new_header
######################################################################################
# Creamos base de datos estadística localmente solo Fonemas
global statisticDBA
statisticDBA = []
for letter in fonema:
dataAux = data1[((data1['FONEMA'] == letter))]
dic = {}
for j in range(1, 8):
nums = np.array(dataAux['FORMANTE ' + str(j)])
res = []
for i in range(len(nums)):
if (nums[i] != '0'):
res.append(float(nums[i]))
if (len(res) != 0):
res = np.array(res)
mean = np.mean(res)
dic['FORMANTE ' + str(j)] = mean
restuple = []
restuple.append(letter)
for i in dic.values():
restuple.append(i)
statisticDBA.append(restuple)
statisticDBA = pd.DataFrame(statisticDBA)
statisticDBA.columns = newDBHeader
######################################################################################
# Creamos base de datos estadística localmente con TODO
statisticDB = []
for letter in fonema:
for g in gen:
for age in edad:
dataAux = data1[((data1['FONEMA'] == letter) & (data1['EDAD'] == age) & (data1['GENERO'] == g))]
dic = {}
for j in range(1, 8):
nums = np.array(dataAux['FORMANTE ' + str(j)])
res = []
for i in range(len(nums)):
if (nums[i] != '0'):
res.append(float(nums[i]))
if (len(res) != 0):
res = np.array(res)
mean = np.mean(res)
dic['FORMANTE ' + str(j)] = mean
restuple = []
restuple.append(letter + '_' + age + '_' + g)
for i in dic.values():
restuple.append(i)
statisticDB.append(restuple)
statisticDB = pd.DataFrame(statisticDB)
statisticDB.columns = newDBHeader
######################################################################################
# Creacion de base de datos estadística local con TODO
global statisticDBVocals
statisticDBVocals = []
for letter in ['A', 'E', 'I', 'O', 'U']:
for g in gen:
for age in edad:
dataAux = data1[
((dataVocals['FONEMA'] == letter) & (dataVocals['EDAD'] == age) & (dataVocals['GENERO'] == g))]
dic = {}
for j in range(1, 8):
nums = np.array(dataAux['FORMANTE ' + str(j)])
res = []
for i in range(len(nums)):
if (nums[i] != '0'):
res.append(float(nums[i]))
if (len(res) != 0):
res = np.array(res)
mean = np.mean(res)
dic['FORMANTE ' + str(j)] = mean
restuple = []
restuple.append(letter)
for i in dic.values():
restuple.append(i)
statisticDBVocals.append(restuple)
statisticDBVocals = pd.DataFrame(statisticDBVocals)
statisticDBVocals.columns = newDBHeader
######################################################################################
# Creacion de base de datos estadística local sólo Vocales
global statisticDBVocalsA
statisticDBVocalsA = []
for letter in ['A', 'E', 'I', 'O', 'U']:
dataAux = dataVocals[((dataVocals['FONEMA'] == letter))]
dic = {}
for j in range(1, 8):
nums = np.array(dataAux['FORMANTE ' + str(j)])
res = []
for i in range(len(nums)):
if (nums[i] != '0'):
res.append(float(nums[i]))
if (len(res) != 0):
res = np.array(res)
mean = np.mean(res)
dic['FORMANTE ' + str(j)] = mean
restuple = []
restuple.append(letter)
for i in dic.values():
restuple.append(i)
statisticDBVocalsA.append(restuple)
statisticDBVocalsA = pd.DataFrame(statisticDBVocalsA)
statisticDBVocalsA.columns = newDBHeader
########################## Read File
def fileDialog():
global rate, audio1Chan
filename = filedialog.askopenfile(initialdir="/", title="Select a File", filetype=(("wav", "*.wav"), ("All Files", "*.*")))
#label = Label(window, text="")
#label.grid( row=8, column=0)
#label.configure(text=filename.name)
text = filename.name
e1.delete(0, "end")
e1.insert(0, text)
rate, audio1Chan = read(filename.name)
########################## Record audio
chunk = 1024
sample_format = pyaudio.paInt16
channels = 1
fs = 44100
frames = []
def startrecording():
global p
global stream
global isrecording
isrecording = False
p = pyaudio.PyAudio()
stream = p.open(format=sample_format, channels=channels, rate=fs,
frames_per_buffer=chunk, input=True)
isrecording = True
text2= 'Recording'
e2.delete(0, "end")
e2.insert(0, text2)
t = threading.Thread(target=record)
t.start()
def stoprecording():
isrecording = False
text2 = 'recording complete'
e2.delete(0, "end")
e2.insert(0, text2)
filename = e1.get()
filename = filename + ".wav"
e1.delete(0, "end")
wf = wave.open(filename, 'wb')
wf.setnchannels(channels)
wf.setsampwidth(p.get_sample_size(sample_format))
wf.setframerate(fs)
wf.writeframes(b''.join(frames))
wf.close()
def record():
while isrecording:
data = stream.read(chunk)
frames.append(data)
########################## PLOT
def plot():
x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
v = np.array([16, 16.31925, 17.6394, 16.003, 17.2861, 17.3131, 19.1259, 18.9694, 22.0003, 22.81226])
p = np.array([16.23697, 17.31653, 17.22094, 17.68631, 17.73641, 18.6368,
19.32125, 19.31756, 21.20247, 22.41444, 22.11718, 22.12453])
fig = Figure(figsize=(6, 6))
a = fig.add_subplot(111)
a.scatter(v, x, color='red')
a.plot(p, range(2 + max(x)), color='blue')
a.invert_yaxis()
a.set_title("Estimation Grid", fontsize=16)
a.set_ylabel("Y", fontsize=14)
a.set_xlabel("X", fontsize=14)
canvas = FigureCanvasTkAgg(fig, master=window)
canvas.get_tk_widget().pack()
canvas.draw()
########################### ASR
def findFormantes(datos):
datos = np.asfortranarray(datos)
A = librosa.lpc(datos, 16)
raices = np.roots(A) # formantes!
formantes = []
for k in raices:
if (k.imag > 0):
w = np.arctan2(k.imag, k.real)
Fk = w * (rate / (2 * np.pi))
Bw = (-1 / 2) * (rate / (2 * np.pi)) * np.log((k.real ** 2 + k.imag ** 2) ** (1 / 2))
if (Fk > 90 and Bw < 450):
formantes.append(Fk)
return np.sort(formantes)
def findLetra(newFonema, data, distance_columns, distance):
#################################################
# Encuentra letra más cercana
data_Formants = data[distance_columns].astype(float)
data_Formants.fillna(0, inplace=True)
data_F_norm = (data_Formants - data_Formants.mean()) / data_Formants.std()
letraNorm = (newFonema - data_Formants.mean()) / data_Formants.std()
euclidian_distances = data_F_norm.apply(lambda row: scipy.spatial.distance.euclidean(row, letraNorm), axis=1)
indices = euclidian_distances.sort_values().index
cont = 0
# print(indices)
res = []
for i in indices[:5]:
if (euclidian_distances.sort_values().iloc[cont] > distance):
break
try:
res.append(data.iloc[i - 2][['FONEMA', 'EDAD', 'GENERO', 'ID']],
euclidian_distances.sort_values().iloc[cont]) # global
except:
res.append(data.iloc[i - 2]['CODIGO'], euclidian_distances.sort_values().iloc[cont]) # statisticDB
cont += 1
return res
def aproxWord(matrixOfFormants,data,distance_columns,distance,res):
#################################################
res=[]
#Divide el espectrograma en sus partes
for i in matrixOfFormants:
j=i[1][0:3]
res.append("Ventana: "+str(i[0]))
for j in (findLetra(j,data,distance_columns,distance)):
res.append(j)
return res
def runASR(audio, rate, muestras, distance_columns, distance):
# Plot audio
rate, audio = read(audioName)
try:
audio = audio[:, 1]
except:
audio = audio
plt.plot(audio)
print(len(audio))
############################################################################
# Plot Spectrogram and characteristics
plt.figure()
audioFiltrado = lfilter([1], [1, 0.63], audio)
# muestras= 1024 # 21 ms
s, w, t, im = plt.specgram(audioFiltrado, Fs=rate, NFFT=muestras, window= scipy.signal.blackman(muestras),
noverlap=100)
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [sec]')
plt.show()
print("Vector que van a poblar nuestras frecuencias es de tamaño:")
print(len(s))
print("Número de ventanas:")
print(len(s[0]))
print("Tenemos la siguiente cantidad de frecuencia")
print(len(w))
# print(s)
#############################################################################
# Creamos una matriz con las formantes de todo el audio arrojadas por el espectro
i = 0
matrixFormantsSpectro = []
for i in range(len(s[0])):
try:
valor = findFormantes(s[:, i])
if (len(valor) > 2):
matrixFormantsSpectro.append([i, valor])
except:
a = 1
#############################################################################
# Llamamos función para calcular la palabra aproximada
array1 = aproxWord(matrixFormantsSpectro, statisticDBVocalsA, distance_columns, distance)
array2 = aproxWord(matrixFormantsSpectro, statisticDBVocals, distance_columns, distance)
array3 = aproxWord(matrixFormantsSpectro, statisticDB, distance_columns, distance)
array4 = aproxWord(matrixFormantsSpectro, statisticDBA, distance_columns, distance)
for l1 in array1:
list1.insert(END, l1)
for l2 in array2:
list2.insert(END, l2)
for l3 in array3:
list3.insert(END, l3)
for l4 in array4:
list4.insert(END, l4)
def runASR2():
runASR(audio, rate, 1024, distance_columns, 0.25)
########################## WINDOW
def window():
global window
global isrecording
window = Tk()
window.minsize(700, 700)
l1 = Label(window,text='File')
l1.grid(row=0, column=0)
# Define Entries
global e1
file_text = StringVar()
e1 = Entry(window, textvariable = file_text)
e1.grid(row=0,column=1)
l6 = Label(window, text='Status')
l6.grid(row=0, column=2)
# Define Entries
global e2
status_text = StringVar()
e2 = Entry(window, textvariable=status_text)
e2.grid(row=0, column=3)
l2 = Label(window, text='Vocals')
l2.grid(row=2, column=0)
#Define list box
global list1
list1 = Listbox(window, height=6, width=35 )
list1.grid(row=3, column=0,rowspan=6, columnspan=2)
# Atach scrollbarr to the list
sb1 = Scrollbar(window)
sb1.grid(row=3, column=2, rowspan=6)
list1.configure(yscrollcommand =sb1.set)
sb1.configure(command=list1.yview)
# Define list box
global list2
list2 = Listbox(window, height=6, width=35)
list2.grid(row=3, column=3, rowspan=6, columnspan=2)
# Atach scrollbarr to the list
sb2 = Scrollbar(window)
sb2.grid(row=3, column=5, rowspan=6)
list2.configure(yscrollcommand=sb2.set)
sb2.configure(command=list2.yview)
l3 = Label(window, text='All Fonemas')
l3.grid(row=10, column=0)
# Define list box
global list3
list3 = Listbox(window, height=6, width=35)
list3.grid(row=11, column=0, rowspan=6, columnspan=2)
# Atach scrollbarr to the list
sb3 = Scrollbar(window)
sb3.grid(row=11, column=2, rowspan=6)
list3.configure(yscrollcommand=sb3.set)
sb3.configure(command=list3.yview)
# Define list box
global list4
list4 = Listbox(window, height=6, width=35)
list4.grid(row=11, column=3, rowspan=6, columnspan=2)
# Atach scrollbarr to the list
sb4 = Scrollbar(window)
sb4.grid(row=11, column=5, rowspan=6)
list4.configure(yscrollcommand=sb4.set)
sb4.configure(command=list4.yview)
l4 = Label(window, text='Posible Word')
l4.grid(row=17, column=0)
# Define list box
global list5
list5 = Listbox(window, height=6, width=35)
list5.grid(row=18, column=0, rowspan=6, columnspan=2)
# Atach scrollbarr to the list
sb5 = Scrollbar(window)
sb5.grid(row=18, column=2, rowspan=6)
list5.configure(yscrollcommand=sb5.set)
sb5.configure(command=list5.yview)
#Define buttons
b1 = Button(window, text="File",width=12, command=fileDialog)
b1.grid(row=5, column=10)
b1 = Button(window, text="Record", width=12, command=startrecording)
b1.grid(row=6, column=10)
b1 = Button(window, text="Stop", width=12, command=stoprecording)
b1.grid(row=7, column=10)
b1 = Button(window, text="Run ASR", width=12, command=runASR2)
b1.grid(row=8, column=10)
b1 = Button(window, text="Reset", width=12)
b1.grid(row=9, column=10)
plot()
if __name__ == '__main__':
try:
window()
except:
pass
window.title('Sistema de reconocimiento de voz automático')
window.mainloop()