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CapsNet @ 7d884474
1 +Subproject commit 7d8844740c119ae66576be9510474a791240a745
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This diff is collapsed. Click to expand it.
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 3,
6 + "metadata": {},
7 + "outputs": [],
8 + "source": [
9 + "import matplotlib.pyplot as plt\n",
10 + "from scipy.fftpack import fft\n",
11 + "from scipy.io import wavfile # get the api"
12 + ]
13 + },
14 + {
15 + "cell_type": "code",
16 + "execution_count": 4,
17 + "metadata": {
18 + "scrolled": false
19 + },
20 + "outputs": [
21 + {
22 + "ename": "TypeError",
23 + "evalue": "'numpy.int16' object is not iterable",
24 + "output_type": "error",
25 + "traceback": [
26 + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
27 + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
28 + "\u001b[0;32m<ipython-input-4-c176e6e452f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwavfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1.wav'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# load the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# this is a two channel soundtrack, I get the first track\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mele\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m8.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mele\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# this is 8-bit track, b is now normalized on [-1,1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# calculate fourier transform (complex numbers list)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# you only need half of the fft list (real signal symmetry)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
29 + "\u001b[0;31mTypeError\u001b[0m: 'numpy.int16' object is not iterable"
30 + ]
31 + }
32 + ],
33 + "source": [
34 + "fs, data = wavfile.read('1.wav') # load the data\n",
35 + "a = data.T[0] # this is a two channel soundtrack, I get the first track\n",
36 + "b=[(ele/2**8.)*2-1 for ele in a] # this is 8-bit track, b is now normalized on [-1,1)\n",
37 + "c = fft(b) # calculate fourier transform (complex numbers list)\n",
38 + "d = int(len(c)/2) # you only need half of the fft list (real signal symmetry)\n",
39 + "plt.plot(abs(c[:(d-1)]),'r') \n",
40 + "plt.show()"
41 + ]
42 + },
43 + {
44 + "cell_type": "code",
45 + "execution_count": 16,
46 + "metadata": {},
47 + "outputs": [],
48 + "source": [
49 + "#wav파일 프린트 할줄알아야함 \n",
50 + "#(채널 : 모노라면 1, 스테레오라면 2\n",
51 + "\n"
52 + ]
53 + },
54 + {
55 + "cell_type": "code",
56 + "execution_count": null,
57 + "metadata": {},
58 + "outputs": [],
59 + "source": []
60 + }
61 + ],
62 + "metadata": {
63 + "kernelspec": {
64 + "display_name": "Python 3",
65 + "language": "python",
66 + "name": "python3"
67 + },
68 + "language_info": {
69 + "codemirror_mode": {
70 + "name": "ipython",
71 + "version": 3
72 + },
73 + "file_extension": ".py",
74 + "mimetype": "text/x-python",
75 + "name": "python",
76 + "nbconvert_exporter": "python",
77 + "pygments_lexer": "ipython3",
78 + "version": "3.6.5"
79 + }
80 + },
81 + "nbformat": 4,
82 + "nbformat_minor": 2
83 +}
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 2,
6 + "metadata": {},
7 + "outputs": [],
8 + "source": [
9 + "import math\n",
10 + "import wave\n",
11 + "import sys\n",
12 + "import struct\n"
13 + ]
14 + },
15 + {
16 + "cell_type": "code",
17 + "execution_count": 3,
18 + "metadata": {},
19 + "outputs": [
20 + {
21 + "ename": "ValueError",
22 + "evalue": "invalid literal for int() with base 10: '/run/user/1000/jupyter/kernel-6454a929-4509-4b51-949d-f1c910f7ce09.json'",
23 + "output_type": "error",
24 + "traceback": [
25 + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
26 + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
27 + "\u001b[0;32m<ipython-input-3-cc8beb2556b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0msample_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetframerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtotal_num_samps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetnframes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mfft_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mnum_fft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtotal_num_samps\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfft_length\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
28 + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '/run/user/1000/jupyter/kernel-6454a929-4509-4b51-949d-f1c910f7ce09.json'"
29 + ]
30 + }
31 + ],
32 + "source": [
33 + "# 웨이브 파일을 열어라\n",
34 + "fp = wave.open('birdsound.wav',\"rb\")\n",
35 + "sample_rate = fp.getframerate()\n",
36 + "total_num_samps = fp.getnframes()\n",
37 + "fft_length = int(sys.argv[2])\n",
38 + "num_fft = (total_num_samps / fft_length ) - 2"
39 + ]
40 + },
41 + {
42 + "cell_type": "code",
43 + "execution_count": null,
44 + "metadata": {},
45 + "outputs": [],
46 + "source": [
47 + "# 임시로 사용할 작업 배열을 만들어라\n",
48 + "temp = zeros((num_fft,fft_length),Float)"
49 + ]
50 + },
51 + {
52 + "cell_type": "code",
53 + "execution_count": null,
54 + "metadata": {},
55 + "outputs": [],
56 + "source": [
57 + "# 파일로부터 데이터를 읽어 들여라\n",
58 + "for i in range(num_fft):\n",
59 + " tempb = fp.readframes(fft_length);\n",
60 + " temp[i,:] = array(struct.unpack(\"%dB\"%(fft_length), \\\n",
61 + " tempb),Float) - 128.0\n",
62 + "fp.close()"
63 + ]
64 + },
65 + {
66 + "cell_type": "code",
67 + "execution_count": null,
68 + "metadata": {},
69 + "outputs": [],
70 + "source": [
71 + "# 데이터를 창틀화하라\n",
72 + "temp = temp * hamming(fft_length)\n",
73 + "\n",
74 + "# FFT를 사용하여 변환하라, 파워를 반환하라\n",
75 + "freq_pwr = 10*log10(1e-20+abs(real_fft(temp,fft_length))"
76 + ]
77 + },
78 + {
79 + "cell_type": "code",
80 + "execution_count": null,
81 + "metadata": {},
82 + "outputs": [],
83 + "source": [
84 + "# 결과를 도표하라\n",
85 + "n_out_pts = (fft_length / 2) + 1\n",
86 + "y_axis = 0.5*float(sample_rate) / n_out_pts * \\\n",
87 + " arange(n_out_pts)\n",
88 + "x_axis = (total_num_samps / float(sample_rate)) / \\\n",
89 + " num_fft * arange(num_fft)\n",
90 + "setvar(\"X\",\"Time (sec)\")\n",
91 + "setvar(\"Y\",\"Frequency (Hertz)\")\n",
92 + "conshade(freq_pwr,x_axis,y_axis)\n",
93 + "disfin()"
94 + ]
95 + }
96 + ],
97 + "metadata": {
98 + "kernelspec": {
99 + "display_name": "Python 3",
100 + "language": "python",
101 + "name": "python3"
102 + },
103 + "language_info": {
104 + "codemirror_mode": {
105 + "name": "ipython",
106 + "version": 3
107 + },
108 + "file_extension": ".py",
109 + "mimetype": "text/x-python",
110 + "name": "python",
111 + "nbconvert_exporter": "python",
112 + "pygments_lexer": "ipython3",
113 + "version": "3.6.5"
114 + }
115 + },
116 + "nbformat": 4,
117 + "nbformat_minor": 2
118 +}
This diff could not be displayed because it is too large.
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 2,
6 + "metadata": {},
7 + "outputs": [],
8 + "source": [
9 + "import wave\n",
10 + "import pyaudio"
11 + ]
12 + },
13 + {
14 + "cell_type": "code",
15 + "execution_count": 3,
16 + "metadata": {},
17 + "outputs": [],
18 + "source": [
19 + "def play_file(fname):\n",
20 + " #오디오 객체 생성\n",
21 + " wf = wave.open('output.wav','rb') # wave파일 할당해준 객체\n",
22 + " p = pyaudio.PyAudio() #파이오디오 할당해준 객체\n",
23 + " chunk = 1024\n",
24 + " \n",
25 + " #stream = pyaudio로 open 하는것\n",
26 + " stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),\n",
27 + " channels=wf.getnchannels(),\n",
28 + " rate=wf.getframerate(),\n",
29 + " output=True)\n",
30 + " \n",
31 + " #데이터 wav파일에서 읽기\n",
32 + " data = wf.readframes(chunk)\n",
33 + " \n",
34 + " #읽은 데이터 있는동안 \n",
35 + " while data !='':\n",
36 + " stream.write(data) #스트림에 데이터 쓰기 \n",
37 + " data = wf.readframes(chunk) #데이터 wav파일에서 다시읽기\n",
38 + " \n",
39 + " #객체 닫아주기\n",
40 + " stream.close()\n",
41 + " p.terminate()\n",
42 + " "
43 + ]
44 + },
45 + {
46 + "cell_type": "code",
47 + "execution_count": null,
48 + "metadata": {},
49 + "outputs": [],
50 + "source": [
51 + "play_file('output.wav')"
52 + ]
53 + },
54 + {
55 + "cell_type": "code",
56 + "execution_count": null,
57 + "metadata": {},
58 + "outputs": [],
59 + "source": []
60 + }
61 + ],
62 + "metadata": {
63 + "kernelspec": {
64 + "display_name": "Python 3",
65 + "language": "python",
66 + "name": "python3"
67 + },
68 + "language_info": {
69 + "codemirror_mode": {
70 + "name": "ipython",
71 + "version": 3
72 + },
73 + "file_extension": ".py",
74 + "mimetype": "text/x-python",
75 + "name": "python",
76 + "nbconvert_exporter": "python",
77 + "pygments_lexer": "ipython3",
78 + "version": "3.6.5"
79 + }
80 + },
81 + "nbformat": 4,
82 + "nbformat_minor": 2
83 +}
This diff is collapsed. Click to expand it.
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 1,
6 + "metadata": {},
7 + "outputs": [],
8 + "source": [
9 + " import numpy as np\n",
10 + " import pylab\n",
11 + " import matplotlib.pyplot as plt\n",
12 + " from scipy.io import wavfile\n",
13 + " import time\n",
14 + " import sys\n",
15 + " import seaborn as sns\n",
16 + " import pyaudio"
17 + ]
18 + },
19 + {
20 + "cell_type": "code",
21 + "execution_count": null,
22 + "metadata": {},
23 + "outputs": [],
24 + "source": [
25 + "\n",
26 + "i=0\n",
27 + "f,ax = plt.subplots(2)\n",
28 + "\n",
29 + "# Prepare the Plotting Environment with random starting values\n",
30 + "x = np.arange(10000)\n",
31 + "y = np.random.randn(10000)\n",
32 + "\n",
33 + "# Plot 0 is for raw audio data\n",
34 + "li, = ax[0].plot(x, y)\n",
35 + "ax[0].set_xlim(0,1000)\n",
36 + "ax[0].set_ylim(-5000,5000)\n",
37 + "ax[0].set_title(\"Raw Audio Signal\")\n",
38 + "# Plot 1 is for the FFT of the audio\n",
39 + "li2, = ax[1].plot(x, y)\n",
40 + "ax[1].set_xlim(0,5000)\n",
41 + "ax[1].set_ylim(-100,100)\n",
42 + "ax[1].set_title(\"Fast Fourier Transform\")\n",
43 + "# Show the plot, but without blocking updates\n",
44 + "plt.pause(0.01)\n",
45 + "plt.tight_layout()\n"
46 + ]
47 + },
48 + {
49 + "cell_type": "code",
50 + "execution_count": null,
51 + "metadata": {},
52 + "outputs": [],
53 + "source": [
54 + "\n",
55 + "FORMAT = pyaudio.paInt16 # We use 16bit format per sample\n",
56 + "CHANNELS = 1\n",
57 + "RATE = 44100\n",
58 + "CHUNK = 1024 # 1024bytes of data red from a buffer\n",
59 + "RECORD_SECONDS = 0.1\n",
60 + "WAVE_OUTPUT_FILENAME = \"file.wav\"\n",
61 + "\n",
62 + "audio = pyaudio.PyAudio()\n",
63 + "\n",
64 + "# start Recording\n",
65 + "stream = audio.open(format=FORMAT,\n",
66 + " channels=CHANNELS,\n",
67 + " rate=RATE,\n",
68 + " input=True)#,\n",
69 + " #frames_per_buffer=CHUNK)\n",
70 + "\n",
71 + "global keep_going\n",
72 + "keep_going = True\n",
73 + "\n",
74 + "def plot_data(in_data):\n",
75 + " # get and convert the data to float\n",
76 + " audio_data = np.fromstring(in_data, np.int16)\n",
77 + " # Fast Fourier Transform, 10*log10(abs) is to scale it to dB\n",
78 + " # and make sure it's not imaginary\n",
79 + " dfft = 10.*np.log10(abs(np.fft.rfft(audio_data)))\n",
80 + "\n",
81 + " # Force the new data into the plot, but without redrawing axes.\n",
82 + " # If uses plt.draw(), axes are re-drawn every time\n",
83 + " #print audio_data[0:10]\n",
84 + " #print dfft[0:10]\n",
85 + " #print\n",
86 + " li.set_xdata(np.arange(len(audio_data)))\n",
87 + " li.set_ydata(audio_data)\n",
88 + " li2.set_xdata(np.arange(len(dfft))*10.)\n",
89 + " li2.set_ydata(dfft)\n",
90 + "\n",
91 + " # Show the updated plot, but without blocking\n",
92 + " plt.pause(0.01)\n",
93 + " if keep_going:\n",
94 + " return True\n",
95 + " else:\n",
96 + " return False\n"
97 + ]
98 + },
99 + {
100 + "cell_type": "code",
101 + "execution_count": null,
102 + "metadata": {},
103 + "outputs": [],
104 + "source": [
105 + "\n",
106 + "# Open the connection and start streaming the data\n",
107 + "stream.start_stream()\n",
108 + "print (\"\\n+---------------------------------+\")\n",
109 + "print (\"| Press Ctrl+C to Break Recording |\")\n",
110 + "print (\"+---------------------------------+\\n\")\n",
111 + "\n",
112 + "# Loop so program doesn't end while the stream callback's\n",
113 + "# itself for new data\n",
114 + "while keep_going:\n",
115 + " try:\n",
116 + " plot_data(stream.read(CHUNK))\n",
117 + " except KeyboardInterrupt:\n",
118 + " keep_going=False\n",
119 + " except:\n",
120 + " pass\n",
121 + "\n",
122 + "# Close up shop (currently not used because KeyboardInterrupt\n",
123 + "# is the only way to close)\n",
124 + "stream.stop_stream()\n",
125 + "stream.close()\n",
126 + "\n",
127 + "audio.terminate()\n"
128 + ]
129 + },
130 + {
131 + "cell_type": "code",
132 + "execution_count": null,
133 + "metadata": {},
134 + "outputs": [],
135 + "source": []
136 + }
137 + ],
138 + "metadata": {
139 + "kernelspec": {
140 + "display_name": "Python 3",
141 + "language": "python",
142 + "name": "python3"
143 + },
144 + "language_info": {
145 + "codemirror_mode": {
146 + "name": "ipython",
147 + "version": 3
148 + },
149 + "file_extension": ".py",
150 + "mimetype": "text/x-python",
151 + "name": "python",
152 + "nbconvert_exporter": "python",
153 + "pygments_lexer": "ipython3",
154 + "version": "3.6.5"
155 + }
156 + },
157 + "nbformat": 4,
158 + "nbformat_minor": 2
159 +}
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This diff is collapsed. Click to expand it.
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "markdown",
5 + "metadata": {},
6 + "source": [
7 + "TEST할것\n",
8 + "<입력값>\n",
9 + "1. 일정 데시벨파워 이하는 다 지운 멜스펙토그램 <-아... 원래 데이터셋 새소리 어느정도 작은 소리까지 잡는겨\n",
10 + "2. mel filter 80 & axis=1평균을 뺀 멜스펙토그램\n",
11 + "3. 새소리 주파수 이하는 주파수대는 짜름\n",
12 + "5. 커널씩 보면서 차이나는것만 뽑아내기 (그주변만 비교하는게 있을거같은데)\n",
13 + "6. 멜필터 안씌운거 보고 새소리 주파수 이하 짤라버릴때등.. 안씌운거 보기\n",
14 + "\n",
15 + "<모델>\n",
16 + "1. 일반 뛰어난 CNN모델. 첫번째 모델이 왜 더 좋은지 보자\n",
17 + "3. C+RNN <- RNN 부분 잘 된건가?\n",
18 + "4. Capsul network\n",
19 + "\n",
20 + "<추가적>\n",
21 + "1. 라벨링 : 확실히 귀에 잘 들리고 눈에 잘 보이는 애들만 1로 라벨링, 희미한건 0으로 라벨링 \n",
22 + " -> 필드테스트 한 애들만 맞춘거 비율이 높도록 보이기. 머신이 새라고 한거중에 0인애들만 또 보여주기\n",
23 + "2. 필드테스트랑 원래하던거랑 왜안될까? -> 짹짹이가 데이터셋에 별로없거나 모델이 안좋거나.. (먼저 컴한테 분류시켜보고 판단?)\n",
24 + "3. 그경우, 찌르레기 소리에 초점을 맞춰서 저 패턴을 학습시키고 아예 그걸 찾도록 하는것도 나쁘지 않을듯\n",
25 + "4. 아이폰녹음이랑 뭐가다른지, 실제로 차이가 난건지도 봐야함. "
26 + ]
27 + },
28 + {
29 + "cell_type": "markdown",
30 + "metadata": {},
31 + "source": [
32 + "1. CRNN - RNN 코드 다시 보고 돌리기\n",
33 + "2. CNN - 논문1네 모델 돌리기\n",
34 + "3. 캡슐 네트워크 돌리기\n",
35 + "\n",
36 + "#### scipy / librosa 둘다로 mel spectogram 짜봤는데 librosa가 더 좋았음."
37 + ]
38 + }
39 + ],
40 + "metadata": {
41 + "kernelspec": {
42 + "display_name": "Python 3",
43 + "language": "python",
44 + "name": "python3"
45 + },
46 + "language_info": {
47 + "codemirror_mode": {
48 + "name": "ipython",
49 + "version": 3
50 + },
51 + "file_extension": ".py",
52 + "mimetype": "text/x-python",
53 + "name": "python",
54 + "nbconvert_exporter": "python",
55 + "pygments_lexer": "ipython3",
56 + "version": "3.6.5"
57 + }
58 + },
59 + "nbformat": 4,
60 + "nbformat_minor": 2
61 +}
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1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 2,
6 + "metadata": {},
7 + "outputs": [],
8 + "source": [
9 + "import pyaudio\n",
10 + "import wave"
11 + ]
12 + },
13 + {
14 + "cell_type": "code",
15 + "execution_count": 6,
16 + "metadata": {},
17 + "outputs": [
18 + {
19 + "name": "stdout",
20 + "output_type": "stream",
21 + "text": [
22 + "* recording\n",
23 + "* done recording\n"
24 + ]
25 + }
26 + ],
27 + "source": [
28 + "CHUNK = 1024\n",
29 + "FORMAT = pyaudio.paInt16\n",
30 + "CHANNELS = 1\n",
31 + "RATE = 25600\n",
32 + "RECORD_SECONDS = 10\n",
33 + "WAVE_OUTPUT_FILENAME = \"test.wav\"\n",
34 + "\n",
35 + "p = pyaudio.PyAudio()\n",
36 + "\n",
37 + "stream = p.open(format=FORMAT,\n",
38 + " channels=CHANNELS,\n",
39 + " rate=RATE,\n",
40 + " input=True,\n",
41 + " frames_per_buffer=CHUNK)\n",
42 + "\n",
43 + "print(\"* recording\")\n",
44 + "\n",
45 + "frames = []\n",
46 + "\n",
47 + "for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n",
48 + " data = stream.read(CHUNK)\n",
49 + " frames.append(data)\n",
50 + "\n",
51 + "print(\"* done recording\")\n",
52 + "\n",
53 + "stream.stop_stream()\n",
54 + "stream.close()\n",
55 + "p.terminate()\n",
56 + "\n",
57 + "wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')\n",
58 + "wf.setnchannels(CHANNELS)\n",
59 + "wf.setsampwidth(p.get_sample_size(FORMAT))\n",
60 + "wf.setframerate(RATE)\n",
61 + "wf.writeframes(b''.join(frames))\n",
62 + "wf.close()"
63 + ]
64 + },
65 + {
66 + "cell_type": "code",
67 + "execution_count": null,
68 + "metadata": {},
69 + "outputs": [],
70 + "source": []
71 + },
72 + {
73 + "cell_type": "code",
74 + "execution_count": null,
75 + "metadata": {},
76 + "outputs": [],
77 + "source": []
78 + }
79 + ],
80 + "metadata": {
81 + "kernelspec": {
82 + "display_name": "Python 3",
83 + "language": "python",
84 + "name": "python3"
85 + },
86 + "language_info": {
87 + "codemirror_mode": {
88 + "name": "ipython",
89 + "version": 3
90 + },
91 + "file_extension": ".py",
92 + "mimetype": "text/x-python",
93 + "name": "python",
94 + "nbconvert_exporter": "python",
95 + "pygments_lexer": "ipython3",
96 + "version": "3.6.5"
97 + }
98 + },
99 + "nbformat": 4,
100 + "nbformat_minor": 2
101 +}
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 2,
6 + "metadata": {},
7 + "outputs": [],
8 + "source": [
9 + "import pydub"
10 + ]
11 + },
12 + {
13 + "cell_type": "code",
14 + "execution_count": 4,
15 + "metadata": {},
16 + "outputs": [
17 + {
18 + "name": "stdout",
19 + "output_type": "stream",
20 + "text": [
21 + "done\n"
22 + ]
23 + }
24 + ],
25 + "source": [
26 + "from pydub import AudioSegment\n",
27 + "\n",
28 + "for i in range(0,45):\n",
29 + " t1 = i * 10000 #Works in milliseconds\n",
30 + " t2 = (i+1) * 10000\n",
31 + " \n",
32 + " newAudio = AudioSegment.from_wav(\"./New/IMG_3867.wav\")\n",
33 + " newAudio = newAudio[t1:t2]\n",
34 + " newAudio.export('./field/IMG_3867%d.wav'%(i), format=\"wav\")\n",
35 + "\n",
36 + "print('done')"
37 + ]
38 + },
39 + {
40 + "cell_type": "code",
41 + "execution_count": null,
42 + "metadata": {},
43 + "outputs": [],
44 + "source": []
45 + }
46 + ],
47 + "metadata": {
48 + "kernelspec": {
49 + "display_name": "Python 3",
50 + "language": "python",
51 + "name": "python3"
52 + },
53 + "language_info": {
54 + "codemirror_mode": {
55 + "name": "ipython",
56 + "version": 3
57 + },
58 + "file_extension": ".py",
59 + "mimetype": "text/x-python",
60 + "name": "python",
61 + "nbconvert_exporter": "python",
62 + "pygments_lexer": "ipython3",
63 + "version": "3.6.5"
64 + }
65 + },
66 + "nbformat": 4,
67 + "nbformat_minor": 2
68 +}