先来说下二进制读写文件，这需要struct库
#二进制文件读写 import struct a=1 b=-1 # print(struct.pack("h",b)) # print(struct.pack("i",b)) f=open("bbb.bin","wb") f.write(struct.pack("h",a))#对a装包，并写入 f.write(struct.pack("h",b)) f.close() f=open("bbb.bin",'rb') b1=f.read(2) a=struct.unpack("h",b1)#解包操作 b2=f.read(2) b=struct.unpack("h",b2) print(a,b)
然后是通过python创造一段声音
import wave import math import struct f=wave.open("v0.wav","w") f.setframerate(8000)#声音频率 f.setnchannels(1)#声道数 f.setsampwidth(2)#声音宽度 t=0#时间 v=0.5#音量 dt=1/8000.0#录入声音的时间 while t<5: s=math.sin(t*math.pi*2*800)*v*32768#设置声音频率为-32768到32768，并且为正弦变化曲线 s=int(s) fd=struct.pack("h",s)#二进制写入 f.writeframes(fd) t+=dt f.close()
运用这个原理可以写一个简单的声音文件，比如说两只老虎
import wave import math import struct ff=wave.open("v1.wav","w") ff.setframerate(8000) ff.setnchannels(1) ff.setsampwidth(2) def wv(t=0,f=0,v=0.5,wf=ff,sr=8000): ''' t:写入时长 f:声音频率 v：音量 wf：一个可以写入的音频文件 sr：采样率 ''' tt=0 dt=1.0/sr while tt<=t: s=math.sin(tt*math.pi*2*f)*v*32768#采样，调节音量，映射到[-2^15,2^15) s=int(s) fd=struct.pack("h",s)#转换成8bit二进制数据 wf.writeframes(fd)#写入音频文件 tt+=dt#时间流逝 note={"1":262,"2":294,"3":330,"4":349,"5":392,"6":440,"7":494,"6-":220,"0":0} n=[ "1","2","3","1","1","2","3","1","0", "3","4","5","0","3","4","5","0", "5","6","5","4","3","1","0","5","6","5","4","3","1","0", "2","6-","1","0","2","6-","1" ] tm=[ 2,2,2,2,2,2,2,2,1, 2,2,2,1.5,2,2,2,2, 1,1,1,1,2,2,1,1,1,1,1,1,2,1, 2,2,2,2,2,2,2 ] for i in range(len(n)): wv(tm[i]/4.0,note[n[i]]) ff.close()
#河大校歌
import wave import math import struct ff=wave.open("henu.wav","w") ff.setframerate(8000) ff.setnchannels(1) ff.setsampwidth(2) ff.getsampwidth def wv(t=0,f=0,v=0.5,wf=ff,sr=8000): ''' t:写入时长 f:声音频率 v：音量 wf：一个可以写入的音频文件 sr：采样率 ''' tt=0 dt=1.0/sr while tt<=t: s=math.sin(tt*math.pi*2*f)*v*math.pow(math.e,-2.5*tt)*32768#采样，调节音量，映射到[-2^15,2^15) s=int(s) fd=struct.pack("h",s)#转换成8bit二进制数据 wf.writeframes(fd)#写入音频文件 tt+=dt#时间流逝 note={"-":0,"0":0,"1---":49,"1#---":52,"2b---":52,"2---":55,"2#---":58,"3b---":58,"3---":62,"4---":65,"4#---":69,"5b---":69,"5---":73,"5#---":78,"6b---":78,"6---":82,"6#---":87,"7b---":87,"7---":92,"1--":98,"1#--":104,"2b--":104,"2--":110,"2#--":117,"3b--":117,"3--":123,"4--":131,"4#--":139,"5b--":139,"5--":147,"5#--":156,"6b--":156,"6--":165,"6#--":175,"7b--":175,"7--":185,"1-":196,"1#-":208,"2b-":208,"2-":220,"2#-":233,"3b-":233,"3-":247,"4-":262,"4#-":277,"5b-":277,"5-":294,"5#-":311,"6b-":311,"6-":330,"6#-":349,"7b-":349,"7-":370,"1":392,"1#":415,"2b":415,"2":440,"2#":466,"3b":466,"3":494,"4":523,"4#":554,"5b":554,"5":587,"5#":622,"6b":622,"6":659,"6#":698,"7b":698,"7":740,"1+":784,"1#+":831,"2b+":831,"2+":880,"2#+":932,"3b+":932,"3+":988,"4+":1047,"4#+":1109,"5b+":1109,"5+":1175,"5#+":1245,"6b+":1245,"6+":1319,"6#+":1397,"7b+":1397,"7+":1480,"1++":1568,"1#++":1661,"2b++":1661,"2++":1760,"2#++":1865,"3b++":1865,"3++":1976,"4++":2093,"4#++":2217,"5b++":2217,"5++":2349,"5#++":2489,"6b++":2489} n=[ "5-","1","1","1","7-", "1","3","3","3","2", "5","5","6","5", "4","3","2","-", "3","2","1","7-","6-", "7-","1","2","3","4", "6","5","4","3", "2","3","1","5","-", "2","3","4","4", "6","6","5","4","3", "4","2","3","5", "2","3","2","7-","5-", "1","3","5","6","5","-", "6","4","2","-", "2","3","4","2","3","-", "6-","7-","1","-" ] tm=[ 2,2,2,1,1, 2,2,2,1,1, 3,1,2,2, 2,2,2,2, 3,1,1,1,2, 3,1,1,1,2, 2,2,2,2, 2,1,1,2,2, 3,1,2,2, 3,1,1,1,2, 2,2,2,2, 3,1,1,1,2, 1,1,1,1,2,2, 2,2,2,2, 1,1,1,1,2,2, 3,1,3,4 ] for i in range(len(n)): wv(tm[i]/2.0,note[n[i]]) ff.close()
接下来是比较高级的了，显示声音的音波
import matplotlib.pyplot as plt
import wave
import struct
fig=plt.figure()
ax=fig.add_subplot(1,1,1)#如果参数是349的意思是：将画布分割成3行4列，图像画在从左到右从上到下的第9块
plt.ion()#使matplotlib的显示模式转换为交互（interactive）模式。即使在脚本中遇到plt.show()，代码还是会继续执行。
read_size=200#每次绘画的帧数
pass_f=0#跳过前pass的帧数
draw_channel=0
stay_time=1.0
file=wave.open("henu.wav","r")
frame_rate=file.getframerate()
frames = file.getnframes()#得到频率
channels=file.getnchannels()#得到声道数
draw_channel=draw_channel %channels
i=0
x_data=[x / read_size for x in range(read_size)]
fmt="h" * (read_size * channels)
while i<frames:
fs=file.readframes(read_size)
i+=read_size
if pass_f > 0:
pass_f-=1
continue
f_data = struct .unpack(fmt,fs) # f_data中包含多个声道的声音
y_data =[]
for j in range(0,len(f_data)):
if j%channels==draw_channel:
y_data.append(f_data[j]/32768.0)
lines = ax.plot(x_data, y_data, 'g-', lw=1)
plt.pause(stay_time)
ax.lines.remove(lines[0])
file.close()
这个是拟合一个图像的曲线，机器学习？？？？
#训练一个多项式，来拟合一条曲线 from PIL import Image import matplotlib.pyplot as plt import numpy as np import random import math import tensorflow as tf pix2=math.pi*2 img=np.array(Image.open("./p2.bmp"),dtype=np.int32) #(249, 277, 3)图像的 高度，宽度，通道rgb #Y中点125 data=[] a=img[:,:,0]+img[:,:,1]+img[:,:,2] h,w,_=np.shape(img) nw=-1 for j in range(w): for i in range(h): if a[i,j]<200: data.append([j*1.0/w,(125.0-i)*2.0/w]) if j%13==0 and nw!=j: nw=j data_len=len(data) #y=a1*sin(2pix*b1)+a2*sin(2pix*b2)+a3*sin(2pix*b3)... #画图 fig=plt.figure()#生成图像框 ax=fig.add_subplot(1,1,1)#编号 ax.scatter([ w[0] for w in data],[w[1] for w in data],linewidths=1) plt.ion() ####################################################### #模型 w_size=8 batch_size=64 x=tf.placeholder(dtype=tf.float32, shape=[None,1]) y=tf.placeholder(dtype=tf.float32, shape=[None,1]) w=tf.Variable(tf.truncated_normal(shape=[w_size,1],mean=0.0,stddev=2/w_size,dtype=tf.float32)) #c=tf.constant([[(i+1)*2*math.pi for i in range(w_size)]],tf.float32) c=tf.Variable(tf.constant([[(i+1)*2*math.pi for i in range(w_size)]],tf.float32)) res=tf.matmul(tf.sin(tf.matmul(x,c)),w) loss=tf.reduce_mean(tf.abs((res-y))) train_step=tf.train.AdamOptimizer(0.02).minimize(loss) init=tf.global_variables_initializer() sess=tf.Session() sess.run(init) for i in range(1,10000): rnddata=[random.randint(1,data_len-1) for w in range(batch_size)] x_d=[[data[p][0]] for p in rnddata] y_d=[[data[p][1]] for p in rnddata] ls,st=sess.run([loss,train_step],feed_dict={x:x_d,y:y_d}) print(i,":",ls) if i%100==0: x_data=[[j/500.0] for j in range(500)] y_data,ww,cc=sess.run([res,w,c],feed_dict={x:x_data}) print(ww) print(cc) lines=ax.plot([j[0] for j in x_data],[j[0] for j in y_data],'r-',lw=1) plt.pause(1) ax.lines.remove(lines[0])
最后是你输入文字然后让它发音，前提是你要有汉字表和发音表
t2pinyin={}
yunmu="āáǎàōóǒòēéěèīíǐìūúǔùǖǘǚǜ"
yindiao="123412341234123412341234"
rp="aaaaooooeeeeiiiiuuuuvvvv"
for i in range(1,10):#总共有十个文件，循环打开
fn="./pinyin1/h"+str(i)+".txt"
py=open(fn,"r",encoding="utf-8")#打开文件名 只读 utf-8
line1=py.readline() #读取一行，再次调用就是下一行，先把第一行文字读进来
line2="" #读拼音
while len(line2)<=10:#查找哪一行是拼音
line2=py.readline()
line1=line1.replace("
","")#替换换行
line2=line2.replace("
","").replace("ü","v")
py.close()
pinyin=line2.split(" ")#变换成列表
for j in range(len(line1)):
word=line1[j]#word和pyj照应
pyj=pinyin[j]
flag=0
for k in range(24):
if pyj.find(yunmu[k])>=0:#查找拼音里的韵母
pyj=pyj.replace(yunmu[k],rp[k])+yindiao[k]#替换，并标记声调
flag=1
break
if flag==0:#没找到就是轻声
pyj=pyj+"0"
t2pinyin[word] = pyj
print(t2pinyin["大"],t2pinyin["家"],t2pinyin["好"])
s="我在学拍森"
import wave
wf=wave.open("py.wav","w")
wf.setframerate(16000)#设置频率的最大值
wf.setnchannels(2)#设置声道数
wf.setsampwidth(2)#设置声音宽度
for w in s:
if w in t2pinyin:#找s中的每个字的读音
vv=t2pinyin[w]
file_name = "./pinyin/" + vv + ".wav"
speak_file=wave.open(file_name,"r")
data=speak_file.readframes(100000000)#将该文件下的前100000000个频率赋值给data
wf.writeframes(data)
speak_file.close()
wf.close()